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Building Intelligent Conversational Tutors and Mentors for Team Collaborative Problem Solving: Guidance from the 2015 Program for International Student Assessment: What Matters

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BUILDING INTELLIGENT
CONVERSATIONAL TUTORS
AND MENTORS FOR TEAM
COLLABORATIVE PROBLEM
SOLVING: GUIDANCE FROM
THE 2015 PROGRAM FOR
INTERNATIONAL STUDENT
ASSESSMENT
Arthur C. Graesser, Nia Dowell, Andrew J. Hampton,
Anne M. Lippert, Haiying Li and
David Williamson Shaffer
ABSTRACT
This chapter describes how conversational computer agents have been used in
collaborative problem-solving environments. These agent-based systems are
designed to (a) assess the students’ knowledge, skills, actions, and various
other psychological states on the basis of the students’ actions and the con-
versational interactions, (b) generate discourse moves that are sensitive to
the psychological states and the problem states, and (c) advance a solution
Building Intelligent Tutoring Systems for Teams: What Matters
Research on Managing Groups and Teams, Volume 19, 173211
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All rights of reproduction in any form reserved
ISSN: 1534-0856/doi:10.1108/S1534-085620180000019012
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to the problem. We describe how this was accomplished in the Programme
for International Student Assessment (PISA) for Collaborative Problem
Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human
test taker (15-year-old student) interacts with one, two, or three agents that
stage a series of assessment episodes. This chapter proposes that this PISA
framework could be extended to accommodate more open-ended natural lan-
guage interaction for those languages that have developed technologies for
automated computational linguistics and discourse. Two examples support
this suggestion, with associated relevant empirical support. First, there is
AutoTutor, an agent that collaboratively helps the student answer difficult
questions and solve problems. Second, there is CPS in the context of a multi-
party simulation called Land Science in which the system tracks progress
and knowledge states of small groups of 34 students. Human mentors or
computer agents prompt them to perform actions and exchange open-ended
chat in a collaborative learning and problem-solving environment.
Keywords: AutoTutor; collaboration; collaborative problem solving;
conversational agents; PISA; problem solving
This chapter describes how conversational computer agents can be used in
collaborative problem-solving environments. These agent-based systems are
designed to (1) assess the team members’ knowledge, skills, actions, and various
other psychological states on the basis of their actions and conversation,
(2) generate discourse moves that are sensitive to their psychological states and
the problem states, and (3) advance a solution to the problem. The develop-
ment of agent-based systems has traditionally focused on tutorial dialogue
between a single human and computer tutor. There are also a number of sys-
tems that have a single human interact with two or more agents to help them
learn subject matter in various domains (such as literacy, numeracy, science,
engineering, and technology). At the horizon is the use of agents to facilitate
team learning, problem solving, and work. This chapter describes how conver-
sational agents have been used in tutorial dialogue and how they are starting to
be used in team collaborative problem solving.
A broad theoretical framework is of course desired for guiding the design of
agents in collaborative problem solving. This chapter adopts the framework
that was articulated in the Programme for International Student Assessment
(PISA) for Collaborative Problem Solving (CPS) in 2015 (OECD, 2013). In the
PISA CPS 2015 assessment, a single human test taker (15-year-old student)
interacts with one, two, or three agents that stage a series of assessment
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episodes. The human’s responses in these assessment episodes were used to
scale their CPS proficiency. However, their responses consisted of selecting
alternatives on action pallets or chat menus rather than natural language
because it was not feasible for the computer to interpret natural language in
several dozen languages and cultures. This chapter proposes that this PISA
framework could be extended to accommodate more open-ended natural lan-
guage interaction for those languages that have developed technologies for
automated computational linguistics and discourse. This can be accomplished
by combining the advances of automated analyses of tutorial dialogue in
natural language with the theoretical framework provided by PISA CPS 2015.
The chapter begins by justifying the need to better understand collaborative
problem solving and describing the theoretical framework of PISA CPS 2015.
We subsequently describe AutoTutor, an agent that collaboratively helps the
student answer difficult questions and solve problems by holding a conversation
in natural language. AutoTutor illustrates how computers can semantically
interpret dialogue in natural language, assess the quality of human contribu-
tions, and guide the agent in adaptively responding to help students learn. Our
contention is that these automated discourse mechanisms can be transferred to
team learning and problem solving environments. We show how this has been
attempted in the context of a multi-party simulation called Land Science,in
which the system tracks progress and knowledge states of small groups of 34
students in computer-mediated chat. Human mentors or computer agents
prompt them to perform actions and exchange open-ended chat in a collabora-
tive learning and problem solving environment.
This chapter has the primary lens on communication in natural language,
computer agents, and collaborative problem solving. Nevertheless, we assume
this work can generalize to broader contexts and applications. There are
channels of communication other than natural language, such as facial expres-
sions, gesture, and physical action. Investigations of computer agents can pre-
sumably be extended to communication among humans and all sorts of hybrid
humanagent combinations among team members. Collaborative problem
solving has many similarities to other team efforts, such as collaborative learn-
ing and coordinated work. Communication is essential in all of these team
efforts, so the present focus on natural language is far reaching.
WHY FOCUS ON COLLABORATIVE PROBLEM
SOLVING?
Much of the planning, problem solving, and decision making in the modern
world is performed by teams. Many problems are so complex that it takes a
group of experts with diverse perspectives and talents to collaborate in finding
optimal solutions. The success of a team can be threatened by a social loafer,
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a saboteur, an uncooperative unskilled member, or a counterproductive
alliance, whereas solutions can be facilitated by a strong leader that draws out
different perspectives, helps negotiate conflicts, assigns roles, and promotes
team communication (Cesareni, Cacciamani, & Fujita, 2016; Fiore, Wiltshire,
Oglesby, O’Keefe, & Salas, 2014; Salas, Cooke, & Rosen, 2008). To understand
these dynamics, many advocate discussions in national assessments and the
development of educational curricula that designate collaborative problem
solving (CPS) as an important twenty-first-century skill (Care, Scoular, &
Griffin, 2016; Griffin & Care, 2015; Hesse, Care, Buder, Sassenberg, & Griffin,
2015; National Research Council, 2011; Von Davier, Zhu, & Kyllonen, 2017).
At the international level, CPS was selected by the Organisation for
Economic Co-operation and Development (OECD) as a new development for
the Programme for International Student Assessment (PISA) in the 2015 inter-
national survey of student skills and knowledge (Graesser, Forsyth, & Foltz,
2017; Graesser, Foltz, et al., 2017; OECD, 2013). Fifteen-year-old students
from over 50 countries completed this PISA CPS 2015 assessment in addition
to assessments of mathematics, science, literacy, and other proficiencies.
It is important to acknowledge that CPS is a category of team interaction
that is different from other categories. There normally are objective criteria on
whether the problem is solved so we can assess whether, or the extent to which,
the team solves the problem successfully. There can also be analyses on the
extent to which different team members contribute to the solution. Team mem-
bers play different roles in guiding the team (teamwork) or solving the problem
(taskwork) in route to a group solution, which can be tracked with automated
measures. This is different than collaborative learning, which involves an
assessment of whether each team member and the group as a whole have
learned a subject matter according to measured criteria (such as an achievement
test, as opposed to the quality of a solution to a problem). CPS is also different
than coordinated work, as in the case of a team that produces artifacts accord-
ing to a well-established plan. CPS is believed to be a more difficult and practi-
cally useful proficiency than collaborative learning and coordinated work in the
twenty-first century.
Conversational agents were used in the PISA CPS 2015 assessment that
followed the definition in the assessment framework:
Collaborative problem solving competency is the capacity of an individual to effectively
engage in a process whereby two or more agents attempt to solve a problem by sharing the
understanding and effort required to come to a solution and pooling their knowledge, skills
and efforts to reach that solution. (OECD, 2013; p. 6)
An agent could be either a human or a computer agent that interacts with the
student according to this definition. The final decision was to have computer
agents in the final assessment. That is, a single student interacted with one
to three computer agents in each problem rather than interacting with other
humans.
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Although conversational agents have been used to assess and to facilitate
collaborative interactions (Graesser, 2016; Graesser, Forsyth, et al., 2017;
Tegos, Demetriadis, Papadopoulos, & Weinberger, 2016), the decision to have
the students interact with computer agents during the PISA CPS 2015 assess-
ment was motivated entirely by logistical and assessment constraints. PISA
required a computer-based assessment that would measure CPS skills of indi-
vidual students in a short time window (two 30-minute sessions) and 23
problem solving scenarios per session. It was believed that the computer agents
could systematically control the interaction and thereby provide reliable and
valid assessments within the time constraints. A student could be assessed in
multiple teams, multiple tasks, different characteristics of team members, and
multiple phases of a problem in a controlled interaction. This would be logisti-
cally impossible with humanhuman interaction. It often takes a few minutes
for a new group of humans to get acquainted in computer-mediated conversa-
tion before important problem solving processes begin. There is no guarantee
that a student would be paired with several groups of students with the ideal
combination of characteristics and assessment episodes. In contrast, assess-
ments with computer agents handle the challenges of (a) assembling groups of
humans (via computer mediated communication) in an expedient manner
within rigid time constraints, (b) the necessity of having multiple teams per stu-
dent to obtain reliable and valid assessments, and (c) measurement uncertainty
and error when a student is paired with humans who are unresponsive or
defiant. A systematic design of computer agents was ultimately created that
provided control, many activities and interactions per unit time, and multiple
groups of agents.
Another serious logistical constraint was that each assessment had to be
translated into several dozen languages and cultures. OECD (the Organisation
for Economic Co-operation and Development) has always had an English and
a French version of each assessment scenario, which the home language is com-
pared against. Country representatives examine and ultimately sign that the
translation is adequate. OECD manages to achieve successful approvals from
the participating countries but there is always the persistent possibility that dif-
ferences in language and cultures impact scores for particular items (El Masri,
Baird, & Graesser, 2016). The main repercussion of this constraint is that it is
practically and financially impossible to score open-ended responses in natural
language. The expense and time to have human experts’ grade responses was
not feasible. Automated natural language is sufficiently reliable for only a
handful of languages so the different countries would not be treated equally.
Therefore, assessments could only be inferred from actions performed on a
computer interface or options selected from alternatives in a chat menu. Quite
clearly, this is very different from open-ended chat interactions and incited
discussion throughout OECD, countries, and researchers on how to evaluate
the use of computer agents in PISA CPS 2015.
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The computer agents solved many of the logistical and assessment problems,
but questions emerged on this approach to assessing CPS. How similar are
these CPS environments with agents to bona fide environments among
humans? How well can these agents simulate actual humans? How similar are
the assessments between humans and agents (HA) to the assessments among
humans (HH)? Some researchers are in the process of answering these ques-
tions, particularly the last question (Rosen, 2014).
The traditional conception of “computer-support” in collaborative learning
consists of stationary technology, such as structured interfaces, prompts, and
assignment of students to scripted roles (Fischer, Kollar, Stegmann, & Wecker,
2013). However, more recent research efforts highlight the benefit of interactive
and context-sensitive assessment and support in group learning interactions
(Erkens, Bodemer, & Hoppe, 2016; Gilbert et al., 2017; Liu, Von Davier, Hao,
Kyllonen, & Zapata-Rivera, 2015; Rose
´& Ferschke, 2016; Tegos et al., 2016).
These same trends would apply to CPS per se. The computer agents help solve
assessment concerns in PISA CPS 2015, but they can also provide adaptive and
interactive computer support technologies, particularly when coupled with
open-ended natural language processing.
Once again, this chapter shows how interactive computer supports, namely
intelligent conversational agents and open-ended natural language processing,
can enhance collaborative interactions and assessment of CPS proficiencies.
Toward this goal, we next clarify how PISA CPS 2015 is assessing CPS profi-
ciency with the agent-based approach. We later examine how the components
of the PISA CPS 2015 framework can be assessed with open-ended natural lan-
guage for languages that have sufficient advances in computational linguistics
and discourse (such as English). We also discuss the possibilities and benefits of
integrating advances within the context of AutoTutor tutorial dialogues
(Graesser, 2016) with automated analyses and facilitation of chat interactions
among groups of 34 students who collaboratively learn and solve problems.
This is one path on a roadmap for incorporating these technologies in team
learning and problem solving.
PISA 2015 COLLABORATIVE PROBLEM SOLVING
The problem solving dimension of CPS directly incorporated the PISA 2012
problem solving framework for individuals (Funke, 2010; Greiff et al., 2014;
OECD, 2010). This draws on influential theoretical frameworks for analyzing
CPS, such as the teamwork processing model of O’Neil, Chuang, and Baker
(2010), teamwork models of Fiore and colleagues (Fiore et al., 2010; Salas
et al., 2008), and the Assessment and Teaching of 21st Century Skills findings
(Griffin & Care, 2015; Hesse et al., 2015). All of these include both a cognitive
and a collaborative dimension, as well as a differentiation between teamwork
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and taskwork. The four cognitive processes (focusing on taskwork) of the prob-
lem solving assessment in both PISA 2012 and 2015 were: (A) exploring and
understanding, (B) representing and formulating, (C) planning and executing,
and (D) monitoring and reflecting. It should be noted that the A and B
processes were difficult to differentiate in both PISA 2012 and 2015. The collab-
oration processes (focusing on teamwork) of CPS 2015 had three competencies:
(1) establishing and maintaining shared understanding, (2) taking appropriate
action, and (3) establishing and maintaining team organization. When the four
problem solving processes are crossed with the three collaboration competen-
cies, there are 12 skills in the resulting matrix representing the competencies of
CPS. The 4 ×3 matrix appears in Table 1. A satisfactory assessment of CPS
would assess the skill levels of students for each of these 12 cells. These skill
levels contributed to a student’s overall CPS proficiency score.
Problem solving scenarios needed to be carefully composed to allow scores
to be computed on each of the 12 cells. All of the problems had instructions on
what needed to be accomplished, a work area for the problem to be solved, and
a chat facility for the human to interact with the agents. Agents and humans
often differed on what information they could see or have access to (following
the hidden profile problem in collaboration), so they needed to have a
Table 1. Matrix of Collaborative Problem Solving Skills for PISA CPS 2015.
(1) Establishing and
Maintaining Shared
Understanding
(2) Taking Appropriate
Action to Solve the
Problem
(3) Establishing and
Maintaining Team
Organization
(A) Exploring and
understanding
(A1) Discovering
perspectives and
abilities of team
members
(A2) Discovering the
type of collaborative
interaction to solve the
problem, along with
goals
(A3) Understanding
roles to solve problem
(B) Representing and
formulating
(B1) Building a shared
representation and
negotiating the
meaning of the problem
(common ground)
(B2) Identifying and
describing tasks to be
completed
(B3) Describe roles and
team organization
(communication
protocol/rules of
engagement)
(C) Planning and
executing
(C1) Communicating
with team members
about the actions to be/
being performed
(C2) Enacting plans (C3) Following rules of
engagement, (e.g.,
prompting other team
members to perform
their tasks)
(D) Monitoring and
reflecting
(D1) Monitoring and
repairing the shared
understanding
(D2) Monitoring results
of actions and
evaluating success in
solving the problem
(D3) Monitoring,
providing feedback, and
adapting the team
organization and roles
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conversation to achieve common ground (Clark, 1996; Dillenbourg, 1999).
Conversations were also necessary for task assignments among team members,
task progress, and task achievements. The agents sometimes made errors, were
slackers, or disagreed so the system measured whether the human test taker
took steps to handle these problems. These capabilities and manipulations
created a broad set of situations to enable assessment of all 12 cells in the
Table 1 matrix.
One advantage of computer agent assessment is the degree of control over
the conversation. The discourse contributions of the two agents (a1, b2) and
the digital media (m) can be coordinated so that each [a1, b2, m] sequential dis-
play is functionally a single episodic unit (U) to which the human responds
through language, action, or silence in a particular human turn (HT). Thus,
there is an orchestrated finite-state transition network that alternates between
episodic units (U) and human turns (HT), which is formally isomorphic to a
dialogue. This is different from a collaboration in which many people can speak
simultaneously and overlap in time (Dascalu, Trausan-Matu, McNamara, &
Dessus, 2015). There can be conditional branching in the state transition net-
work so that the computer’s generation of U
nþ1
at turn nþ1 is contingent on
the state of the human turn HT
n
at turn n. There is a finite number of states
associated with each human turn (HT
n
) in PISA CPS 2015, with two to five
options at each turn (i.e., either chat options or alternative actions to be per-
formed). The complexity of the branching depends on the number of finite
states at each turn and the length of exchanges. In the PISA assessment, the
number of options is small at each turn and the length of the branching is short
for each episodic unit. To foreground what will come later, it would be feasible
to accommodate open-ended natural language within these constraints. There
could be a semantic match score between the student’s verbal contributions and
the correct answer of the episodic unit, with a small number of branching
options, for example, correct, incomplete, incorrect, no response, bad answer
(Cai, Graesser, & Hu, 2015; Zapata-Rivera, Jackson, & Katz, 2015); these lim-
ited options would allow extended but manageable branching options in the
state transition network.
In the PISA assessment, there is only one score associated with each episodic
unit and each episodic unit is aligned with one and only one cell in the Table 1
matrix. These constraints are compatible with conventional psychometric
modeling, which requires a fixed set of items (i.e., episodic units). Consequently,
PISA CPS 2015 had a fixed sequence of episodic units (U
1
,U
2
,U
m
) that were
distributed throughout the problem solving scenario. The decisions of the
human for each episodic unit determined the score for that unit. Moreover, the
conversations were finessed so they would naturally close at the end of each
episodic unit by either an agent’s speech acts (e.g., “We should do X, let’s go
on”) or an event in the scenario (such as an announcement that a train station
was closed in a transportation problem). After one episodic unit closed, the next
unit would systematically appear. Assessment scores were collected for each
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student for the M episodic units that were distributed among the cells in the
Table 1 matrix.
Students are assessed on a diverse set of situations in PISA CPS 2015. Those
who respond randomly to the response options would obviously score low on
CPS proficiency as well as on the collaboration and problem solving dimen-
sions. A student may be a good team player but not take the initiative when
problems arise (e.g., an agent fails to respond or gives an incorrect answer). A
student may take on some initiative when breakdowns occur, but fail to handle
complex cognitive problems. A student who scores high in CPS proficiency
leads the team in achieving group goals during difficult times (conflicts,
incorrect actions, unresponsive team members) and can also handle complex
problems with many cognitive components that burden working memory and
require reasoning. An adequate CPS assessment would require episodic units
for all of these situations. Assessment with agents (HA) can guarantee com-
plete coverage assessments with other humans (HH) cannot.
Once again, the construction of problem scenarios and episodic units were
critical in PISA CPS 2015. It was important to select problems that had high
interdependency, such that team members could not solve the problems alone
and needed to communicate, formulate plans together, assign roles, and track
each other’s progress. Many problems were hidden profile problems in which
the team members did not have access to the same information and needed to
establish shared understanding through conversational exchanges. Team mem-
bers needed equal status so that they did not fear penalty if their culture stigma-
tizes questions or requests from a low status person to a high status person. In
some problems, unresponsive, low ability, or uncooperative agents required
a high ability student to monitor team members, troubleshoot problems, and
sometimes be pushy.
There are many impressive characteristics of the PISA CPS 2015 assessment.
The framework is theoretically and empirically grounded (OECD, 2013). The
assessment covers a broad range of important CPS situations and aligns with
traditional psychometric methodology. Students can be scaled on different
levels of CPS proficiency, with the reliability and validity of the proficiency
scale and levels currently undergoing analysis.
Nevertheless, both advocates and critics of the PISA CPS 2015 assessment
have raised important questions about potential liabilities (Graesser, Foltz,
et al., 2017). Do scenarios with agents reflect bona fide CPS mechanisms among
humans? To what extent does the assessment with agents match an assessment
that could be accomplished among humans? Does the limited set of response
options prevent an adequate assessment of CPS compared with open-ended
chat responses? It is beyond the scope of this chapter to resolve these important
questions. Instead, we explore whether it is feasible to assess CPS and the
Table 1 cells with open-ended verbal responses in the chat scenarios. We also
clarify the important role of agents in these assessments.
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Our research builds on a community of researchers in the learning sciences,
team science, computer supported collaborative learning, and computational
linguistics who have investigated successful versus unsuccessful conversation
patterns among team members in small groups by analyzing computer-
mediated interactions in chat, discussion forums, and other digital environments AU:1
(Cen, Ruta, Powell, Hirsch, & Ng, 2016; Dascalu et al., 2015; Dowell et al.,
2015; Foltz & Martin, 2008; Liu et al., 2015; Morgan, Keshtkar, Duan, &
Graesser, 2012; Mu, Stegmann, Mayfield, Rose
´, & Fischer, 2012; Nash &
Shaffer, 2013; Rose
´et al., 2008; Shaffer et al., 2009; Tausczik & Pennebaker,
2013; Von Davier & Halpin, 2013). We have investigated the conversations
using a variety of automated text analysis tools, such as Linguistic Inquiry and
Word Count (Pennebaker, Booth, & Francis, 2007), Coh-Metrix (Graesser
et al., 2014; McNamara, Graesser, McCarthy, & Cai, 2014), latent semantic
analysis (Foltz, Kintsch, & Landauer, 1998), epistemic network analysis
(Shaffer, 2017; Shaffer et al., 2009), and state-transition networks that track
speech acts of team players (Morgan et al., 2012). These automated tools have
been applied to conversations in their entirety, to subsets of the conversation at
a particular window size (e.g., five consecutive turns), to single conversational
turns, to adjacent conversational turns, and to turns of specific team members.
The conversation profile includes measures of team cohesion, percentage of
on-topic versus off-topic contributions, amount of new information, character-
istics of team members (e.g., leader, organizer, follower, social loafer), alliances
between team members, and presence of specific conversation patterns.
It is conceivable that open-ended student responses with other humans or
with computer agents could cover all of the cells in the Table 1 matrix. If so,
there is hope of assessing CPS proficiencies with open-ended student responses.
The feasibility of this possibility is explored in the next section. If not, it is
prudent to pursue the PISA CPS 2015 approach with scripted agents, a fixed
sequence of episodic units, decisions on a limited number of alternatives at
each human turn, and a small number of conversation paths within each epi-
sodic unit.
SCORING OPEN-ENDED STUDENT RESPONSES
WITH AUTOTUTOR
The simplest agent collaboration is a dialogue in which the human interacts
with only one agent. In this chapter, we use AutoTutor (Graesser, 2016;
Nye, Graesser, & Hu, 2014) as an example. In this system, a tutor agent collab-
oratively interacts with the human student to solve a problem or answer a
difficult question that requires reasoning. AutoTutor presents problems to
solve (reflected in difficult questions that require reasoning) that cover one to
seven sentence-like ideas (i.e., propositions, claims, clauses) in an ideal answer.
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The human student and tutor agent co-construct a solution by multiple conver-
sational turns. It may take up to a 100 conversational turns back and forth to
solve a problem.
Automatic Evaluation of Student Contributions in AutoTutor
AutoTutor evaluates the meaning of student contributions during the course
of the tutorial interaction. Consider a typical example problem with AutoTutor
in physics:
PHYSICS PROBLEM: If a lightweight car and a massive truck have a head-on collision,
upon which vehicle is the impact force greater? Which vehicle undergoes the greater change
in its motion, and why?
A conversation would have many turns between the student and agent in
answering these questions. As the dialogue evolves, AutoTutor compares the
student’s verbal contributions within a single turn and also the previous student
turns in the conversation against (a) a set of good answers (called expectations)
and (b) a set of bad answers (slips and misconceptions). For example, E1 is an
example expectation and M1 is an example misconception for this problem.
E1: The magnitudes of the forces exerted by the two objects on each other are equal.
M1: A lighter object exerts no force on a heavier object.
AutoTutor has a semantic matcher that matches the verbal contributions of the
student to the expectations and misconceptions in order to assess how well the
student is performing on the physics problem. Performance increases with high
matches to good answers and low matches to bad answers.
Advances in computational linguistics and semantics have made impressive
gains in the accuracy of semantic matches between one short text (i.e., a sen-
tence or two) and another short text (Rus, Lintean, Graesser, & McNamara,
2012; Rus & Stefanescu, 2016). The AutoTutor research team has evaluated
many computational semantic matchers over the years in AutoTutor and other
intelligent tutoring systems with conversational agents (Cai et al., 2011;
Graesser, Penumatsa, Ventura, Cai, & Hu, 2007; Rus et al., 2012). Semantic
matchers automatically compute the semantic similarity between a student’s
verbal contribution and an expectation (or misconception), with a similarity
score that varies from 0 to 1. These semantic match algorithms have included
keyword overlap scores, word overlap scores that place higher weight on lower
frequency words in the English language, scores that consider the order of
words, latent semantic analysis cosine values, comparisons to regular expres-
sions, and procedures that compute semantic logical entailment; some of these
algorithms are defined in this chapter. Excellent results can be achieved by
a combination of latent semantic analysis (Landauer, Foltz, & Laham, 1998;
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Landauer, McNamara, Dennis, & Kintsch, 2007), frequency-weighted word
overlap (rarer words and negations have higher weight), regular expressions
(Jurafsky & Martin, 2008), and semantic entailment (Rus et al., 2012). For
example, in one analysis of AutoTutor in the area of research methods, latent
semantic analysis together with regular expressions had high agreement scores
in direct comparisons with human experts (Cai et al., 2011). Two human
experts along with a computational model using latent semantic analyses and
regular expressions both evaluated a sample of 892 student answers to
AutoTutor questions. The correlation of similarity scores between AutoTutor
and human expert judges was r¼0.67, which was about the same as between
two experts (r¼0.69). Interestingly, syntactic parsers did not prove useful in
these analyses because a high percentage of the students’ contributions are tele-
graphic, elliptical, and ungrammatical. At the time of this writing, the best
automated semantic matcher is the SEMILAR system developed by Rus and
Stefanescu (2016). SEMILAR won the semantic textual similarity competition
at SemEval-2015, the premier international forum for semantic evaluation.
It is beyond the scope of this chapter to provide a technical specification of
the components in these automated semantic matchers. However, it is impor-
tant to briefly clarify both latent semantic analyses and regular expressions
because they together have proven adequate in AutoTutor on several topics,
such as computer literacy, physics, and electronics. They can also be used in
tracking performance in CPS assessments.
Latent Semantic Analysis (LSA)
LSA (Foltz et al., 1998; Landauer et al., 2007) computes the conceptual similar-
ity between words, sentences, paragraphs, or texts by considering implicit world
knowledge in addition to the explicit words. It is a mathematical, statistical
technique for representing world knowledge, based on a large corpus of texts.
The central assumption is that the meaning of a word is captured by the com-
pany of other words that surround it in naturalistic documents. Two words are
similar in meaning to the extent that they share similar surrounding words in
documents. For example, the word glass will be highly associated with words of
the same functional context, such as cup,liquid, and pour.
LSA is different than a dictionary or a thesaurus because the highly associ-
ated words may be in different syntactic classes and not follow structured
definition frames. Instead, LSA considers how words are used in naturalistic
documents. LSA starts with a very large word by document matrix that counts
how often each word appears in each document. This forms a largely sparse
matrix (lots of 0s); if there are 100,000 words and 50,000 documents (e.g., para-
graphs), there would be 5 billion cells in the matrix. LSA uses a statistical tech-
nique called singular value decomposition to condense the matrix of the large
corpus of texts to 100500 statistical dimensions. Each word is represented as
a vector of values on the K dimensions. The conceptual similarity between any
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two text excerpts (e.g., word, clause, sentence, text) is computed as the geomet-
ric cosine between the values of the words (on the K dimensions) in one text
excerpt versus the other. The value of the cosine typically varies from 0 (not at
all similar) to 1 (perfectly similar). Many other classes of high dimensional
semantic spaces do as well or slightly better than LSA, but these nuances are
beyond the scope of this chapter.
LSA-based semantic similarity can be used in a number of ways when evalu-
ating student contributions. There can be comparisons of the student contribu-
tions to the expectations and misconceptions associated with the problem. For
example, if there are four expectations, the first student turn in the conversation
would have four semantic match scores, one for each expectation. As the con-
versation progresses, these four scores would be updated with each conversa-
tional turn of the student. An expectation would be considered covered when
the match score for an expectation meets or exceeds some threshold value.
When the conversation ends, the student’s overall performance on a problem
can be computed as the mean match score of the four expectations; alterna-
tively, it could be the mean of the expectations minus the mean of the
misconceptions.
A second use of LSA-based similarity is to evaluate different types of dis-
course coherence between the student and tutor (Graesser, Jeon, Yang, & Cai,
2007). That is, to what extent is the content of a student’s turn T
n
related
conceptually to the content of the tutor agent’s previous turn T
n1
or any of
the turns in the previous conversation (T
1
,T
2,
T
n1
)? These would be com-
puted as sim(T
n1
,T
n
) and max{sim(T
nm
,T
n
)}, where n>m. Alternatively,
we could also compute whether the tutor’s turns are coherently related to the
student’s contributions. The two of these together would yield coherence scores
for the entire conversation.
A third use of LSA-based similarity is to evaluate the newness, givenness, and
relevance of each conversational turn. The distinction between given (old) infor-
mation versus new information in discourse is a foundational distinction in the-
ories of discourse processing (Haviland & Clark, 1974; Prince, 1981) and
assessment (Von Davier & Halpin, 2013). Given information includes words,
concepts, and ideas that have already been mentioned in the discourse, in this
case a conversation on a particular tutoring problem. New information builds
on the given information or launches a new thread of ideas. Relevance is
another foundational construct in discourse processing theories (Sperber &
Wilson, 1995). Discourse contributions are expected to be relevant to the topic
at hand and discourse goals. There are LSA-based metrics that compute the
newness (N), givenness (G), and relevance (R) of each turn in the conversation,
with values that vary from 0 to 1 (Hempelmann et al., 2005; Hu et al., 2003;
McCarthy et al., 2012). The statistical method is called span in the computation
of G and N. The LSA vector of an incoming turn, V(T), is compared with the
existing vector of the preceding discourse, V(P); the existing vector determines
the span of the preceding discourse. The portion of V(T) that is parallel with
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the V(P) is the computation for G (given) whereas the component of the vector
that is perpendicular is the computation of N (new). McCarthy et al. (2012)
reported that there was a high correlation between the G and N values and the
decisions of experts who annotated discourse samples with Prince’s given-new
theory. Regarding relevance, it is possible to compute semantic similarity scores
between the vector V(T) and the subject matter being tutored, as reflected in
comparisons to excerpts from a textbook for example.
All of these LSA-based metrics can be directly applied to CPS among stu-
dents. This includes semantic matches to expectations and misconceptions, as
well as measures of coherence, newness, givenness, and relevance. However, the
pragmatic ground rules are quite different in tutorial dialogues than in CPS dia-
logues among humans. Good tutors do not merely lecture but instead attempt
to get the student to be active learners and articulate the expectations during
problem solving (Chi, Siler, Jeong, Yamauchi, & Hausmann, 2001; Dzikovska,
Steinhauser, Farrow, Moore, & Campbell, 2014; Graesser, Person, & Magliano,
1995). This is accomplished through a variety of dialogue moves (such as hinting
and evaluative feedback) that are described later. The tutor often withholds
information and waits for the student to contribute. In contrast, students who
interact in CPS are not prone to withhold information, generate hints, and
quiz team members. Instead, the students are all doing what they can to contrib-
ute useful information to solve the problem. In summary, the ground rules of
the discourse constitute a central component in the design of team learning
environments.
Regular Expressions
Each expectation and misconception in AutoTutor is expressed in both natural
language and regular expressions. The natural language format is used in the
LSA analyses whereas regular expressions are needed to accommodate (a) mis-
spellings, (b) words that are functionally equivalent in the context of a particu-
lar problem to be solved, and (c) structure-sensitive semantic matches. Regular
expressions are structured symbolic expressions that can accommodate a large
number of alternative verbalizations (Jurafsky & Martin, 2008). In the context
of the example physics problem involving vehicle collision, the notion of a
“collision” could be captured with a number of words in different word classes:
collision, collisions, collide, collides, colliding, and so on. The regular expres-
sion col. (the period allows any other letters in the word) can accommodate all
of these words plus misspellings (colision, colishun). It is important to have
enough letters in the regular expression for a word to distinguish it from other
words that might be expressed in that particular discourse context. So co.
would probably not work because alternative words in that context might be
come or counterforce.
Regular expressions allow functionally equivalent terms with “or” operators
designated as a |. So a car might be referred to as a car, an automobile, a
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vehicle, or simply a pronoun (it). Regarding structure-sensitive semantic
matches, regular expressions can group constituents, order elements within a
constituent, and embed constituents. This allows these expressions to distin-
guish, for example, “the truck moves a shorter distance than the car” from “the
car moves a shorter distance than the truck.”
Regular expressions are needed to tune the semantic matcher component in
ways that cannot be provided by LSA. LSA cannot reliably handle any of the
above problems (misspellings, context-functional synonyms, and structure-
sensitive semantic analysis). As already discussed, data consistently show that
there is substantial added value when adding regular expressions to LSA in
evaluations that compare the semantic matching models to expert human
judgments (Cai et al., 2011).
Some aspects of the subject matter and skills are not routinely measured by
this semantic matching approach with AutoTutor. AutoTutor does not directly
measure deductive, inductive, and other types of logical reasoning unless
the expectations capture the relevant semantic islands underlying reasoning.
AutoTutor does not directly measure particular steps, procedures, and phases
of problem solving unless those are anticipated ahead of time. Instead,
AutoTutor tracks the content through pattern matching processes that compare
the student’s verbal responses to the expectations and misconceptions.
Automatic Generation of Dialogue Moves in AutoTutor
AutoTutor needs to generate dialogue moves to advance conversation. AutoTutor
incorporates dialogue generation mechanism of human tutors as well as more
ideal tutoring strategies that even expert tutors rarely exhibit (Cade, Copeland,
Person, & D’Mello, 2008; Graesser, 2016; Graesser, D’Mello, & Person, 2009).
These analyses of tutoring have revealed that students essentially never give a
satisfactory answer on the first turn after receiving a problem. It takes a conversa-
tion to assess what they know, to build on their knowledge, and converge on a
satisfactory answer.
AutoTutor has a conversational mechanism that includes follow-up
exchanges that draws out more of what the student might know. A pump is a
generic expression to get the student to provide more information, such as
“What else?” or “Tell me more.” Hints and prompts are selected by the tutor to
get the student to articulate missing content words, phrases, and propositions.
A hint tries to get the student to express a complex idea (e.g., proposition,
clause, sentence) whereas a prompt is a question that tries to get the student to
express a single word or phrase. For example, a hint to get the student to artic-
ulate expectation E1 above might be “What about the forces exerted by the
vehicles on each other?” This hint would ideally elicit the answer “The magni-
tudes of the forces are equal.” A prompt to get the student to say “equal”
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would be “What are the magnitudes of the forces of the two vehicles on each
other?” The tutor generates an assertion if the student fails to express the expec-
tation after multiple hints and prompts. AutoTutor provides cycles of pump
hint prompt assertion for each expectation after the student’s initial
response to the main question; the cycle ends as soon as the student articulates
the expectation or the assertion is expressed. As the student and tutor express
information over many turns, the list of expectations is eventually covered and
the main task is completed. The pump hint prompt assertion cycles
have been validated by correlations between the students’ prior knowledge
about physics and the proportion of AutoTutor dialogue moves that are
pumps, hints, prompts, or assertions (Jackson & Graesser, 2006). Correlations
between pretest scores on the Force Concept Inventory (FCI) and the propor-
tion of AutoTutor dialogue moves in each category show the predicted trend
(pump hint prompt assertion), with correlations varying monotonically
from 0.5 to 0.4.
Discourse Structure of AutoTutor Dialogues
The conversational structure of AutoTutor is based on systematic qualitative
and quantitative analyses of human-to-human expert tutoring sessions
(Graesser & Person, 1994; Graesser et al., 1995). The following frequent con-
versation patterns in human tutoring have been simulated in AutoTutor.
Tutoring sessions are organized AU:2around problems, challenging questions, and
tasks. The outer loop of tutoring consists of the selection of major tasks for
the tutor and student to work on (VanLehn, 2006) whereas the inner loop
consists of the steps and dialogue interactions to manage the interaction
within these major tasks. After the tutor and student settle on a major task
(outer loop), the tutor guides the specific agenda within the task (inner loop).
A 5-step tutoring frame guides the major task. Once a problem is selected to
work on, a 5-step tutoring frame is launched (Graesser & Person, 1994).
(1) TUTOR asks a difficult question or presents a problem.
(2) STUDENT gives an initial answer.
(3) TUTOR gives short feedback on the quality of the answer.
(4) TUTOR and STUDENT have a multi-turn collaborative dialogue to
improve the answer.
(5) TUTOR assesses whether the student understands the correct answer.
Step 4 in this 5-step tutoring frame involves collaborative discussion and
joint action. Step 4 is the heart of the collaborative interaction.
Expectation and misconception tailored (EMT) dialogue guides micro-
adaptation (the inner loop). This structure has already been described with
the pump hint prompt assertion cycles to draw out what the student
knows as a solution to the problem is collaboratively constructed. When
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students express misconceptions (incorrect beliefs, errors, bugs) they are
immediately corrected in AutoTutor.
Tutor turns are well structured. Most turns of the tutor have three informa-
tional components during the inner loop of step 4 of the 5-step frame:
Tutor Turn Short Feedback þDialogue Advancer þFloor Shift
The first component is short feedback (positive, neutral, negative) on the
quality of the student’s last turn. The second component is a dialogue
advancer that moves the tutoring agenda forward with either pumps, hints,
prompts, assertions with correct information, corrections of misconceptions,
or answers to student questions. The third component shifts the conversa-
tional floor with cues from the tutor to the student. For example, the human
ends each turn with a question or a gesture to cue the student to do the
talking.
Tutors are sensitive to the speech act categories of the student’s last turn.
AutoTutor segments the content of each student turn into speech act units
and assigns each unit to a category. The natural language of the students is
often fragmentary, ungrammatical, and not semantically well formed so there
are limits to the accuracy of segmentation and classification. However, the
vast majority of student turns are short, typically one or two speech acts,
allowing reasonable performance on speech act classification (Olney et al.,
2003; Samei, Li, Keshtkar, Rus, & Graesser, 2014). The primary speech act
categories of students that AutoTutor accommodates are: short responses
(e.g., yes, no, okay), statement contributions, questions, and metacognitive
expressions (e.g., “I don’t know,” “I’m lost”). Analyses of multi-party chat
conversations among humans have included these four categories plus three
others: expressive evaluations (e.g., “this is ridiculous”), greetings, and
requests (Samei et al., 2014).
AutoTutor has a dialogue advancer network with production rules that
respond appropriately to the four main speech act categories of students that
frequently occur in tutorial dialogue (Cai, Feng, Baer, & Graesser, 2014;
Graesser, Person, Harter, & Tutoring Research Group, 2001). The generation
of the content in a tutor turn is sensitive to both the speech act categories of
the student’s previous turn and the semantic match scores for the expectations
and misconceptions at that point in the conversation. The expectation and
misconception tailored dialogue attempts to get the student to articulate the
expectations through the pump hint prompt assertion cycles until the
semantic matches reach some threshold for the expectations; when a match
score is sub-threshold, an expectation is not covered, so the tutor needs to pres-
ent scaffolding dialogue moves (pumps, hints, prompts) in an attempt to
achieve successful pattern completion. That is, the hints and prompts are strate-
gically selected to achieve the pattern completion for an expectation. Suppose
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an expectation has constituents A, B, C, and D and that the student has
expressed A and B, but not C and D. The tutor would generate hints and
prompts to attempt to get the student to cover C and D so that the threshold
for the expectation would be reached. The tutor gives up covering the expecta-
tion after a few hints and prompts, ultimately generating an assertion to cover
the expectation in the conversation.
It is important to emphasize that the collaborative dialogue manifests much
more of what a student knows than what is revealed after the main question is
asked, that is, step 2 in the 5-step tutoring frame. The semantic match scores
are small or modest for the set of expectations when step 2 is completed
because the responses are short, typically only one or two sentences. The match
scores for the expectations are much higher after the step 4 collaborative inter-
action is completed. This underscores the importance of the AutoTutor agent
in providing a more detailed assessment of the student’s knowledge. Without
the AutoTutor interaction, an assessment would underestimate what the
student knows.
Tutoring versus Collaborative Problem Solving among Peers
Our coverage of AutoTutor illustrates how CPS is automated in a tutoring
environment and how the system assesses the student’s knowledge and mastery
of the material. These same approaches can be applied to automated assess-
ment of CPS. Chat conversations among humans on a team can be assessed on
coherence, newness, givenness, and relevance using precisely the same algo-
rithms as are articulated for LSA-based measures. There can be semantic
matches to the expectations and misconceptions associated with the episodic
units associated with the cells in Table 1 for PISA CPS 2015. This can be imple-
mented with LSA-based measures, regular expressions, and more advanced
semantic matching evaluators such as SEMILAR (Rus, Lintean, Banjade,
Niraula, & Stefanescu, 2013; Rus & Stefanescu, 2016). Conversational agents
can generate dialogue moves that elicit or verify the human’s mastery of the
skills in these assessments. The agents can take on different roles, such as peers
on the team or mentors who help the team accomplish their task of solving the
problem.
As discussed earlier, the ground rules of the conversation differ between
tutoring and CPS among peers when there is no tutor or mentor present. The
pedagogical goal of AutoTutor is to have dialogue moves that encourage the
student to supply the answer information to solve the problem. CPS instead
involves team members contributing information and actions that lead to a
team solution. Nevertheless, in the context of CPS assessment, AutoTutor is
quite relevant even though the pragmatic context diverges. In assessment, the
agent can express dialogue moves that give the test taker a second chance with
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a follow-up question or request, just to make sure the test taker has every
opportunity to contribute. For example, one important dimension of CPS is
whether the test tasker takes the initiative in correcting problems or advancing
solutions (row C in Table 1), as opposed to merely responding to questions and
requests. A student with initiative would communicate and act in ways that
solve a problem, without being prompted by a peer. A student who is respon-
sive but has little initiative would only contribute when another entity nudges
the student with a question or request. A low ability student would be silent
when nudged or would give random responses. The episodic units and agents in
the PISA CPS 2015 assessment incorporated these different levels of assessment
when many of the cells in Table 1 were assessed, but did not accommodate nat-
ural language input of the test taker. The feasibility of natural language human
input is a central underlying question posed in this chapter.
The ground rules of the conversation are more similar to AutoTutor when a
tutor agent or mentor is added to the team in order to enhance team CPS
(Gilbert et al., 2017). The tutor or mentor agent tracks the conversation among
the team members and generates discourse moves that advance the conversa-
tion to cover expectations, correct misconceptions, encourage silent team mem-
bers to contribute, redirect the conversation when the discussion is off topic,
and so on. More will be said about the discourse moves of tutor/mentor agents
in team conversations later in this chapter.
AUTOMATED ASSESSMENT OF COLLABORATIVE
PROBLEM SOLVING WITHOUT AND WITH AGENTS
Automated assessments of CPS and collaborative learning have been developed
in previous research projects that analyze computer-mediated communication
among team members (Cen et al., 2016; Dowell, Graesser, & Cai, 2016; Foltz &
Martin, 2008; Gress, Fior, Hadwin, & Winne, 2010; Liu et al., 2015; Mu et al.,
2012; Nash & Shaffer, 2013; Rose
´et al., 2008; Shaffer, 2017; Shaffer et al.,
2009; Tausczik & Pennebaker, 2013; Von Davier & Halpin, 2013). These sys-
tems analyze the conversations that transpire for the conversation as a whole as
well as the language of individual team members. These research efforts have
automatically analyzed constructs such as group cohesion, responsivity of
individual team members to a group, emotions of team members, and the
personality of team members.
Our contention is that CPS assessment of a team and team members will be
limited without outside involvement of either a human mentor or an agent in
the role of a mentor, tutor, or fellow student. Students will take the easy road
of being polite and the team will take the easy road of “groupthink” (getting a
quick solution that makes most people happy, Janis, 1982) unless there is a
human, agent, or task that shakes up that sanguine world with some challenges,
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contradictions, skepticism, follow-ups on task completion, and modeling of
good behavior (Dillenbourg, 1999). Moreover, such assessments and interven-
tions need to be accomplished in real time so that the agents or humans can
quickly respond to the team or particular team members.
This section describes how CPS assessment has been applied to a particular
computer-mediated communication environment called the Land Sciences simu-
lation (Bagley & Shaffer, 2015; Shaffer, 2017). We illustrate a number of meth-
ods that automatically analyze the language and discourse of chat interactions
between students and a human mentor. We point out how these approaches
have alignments to the PISA CPS 2015 assessment that is captured by the cells
in the Table 1 matrix. The section ends with the proposal that CPS assessment
would benefit from a team member agent that produces conversation moves
that contribute to CPS skills of team members and the team as a whole.
Land Science: Collaborative Learning and Problem Solving of Small Teams
with a Mentor
In the virtual internship Land Science (Bagley & Shaffer, 2015; Shaffer, 2017),
students play the role of interns at Regional Design Associates, a fictional
urban and regional planning firm. Their problem solving task is to prepare a
rezoning plan for the city of Lowell, Massachusetts, that addresses the requests
of various stakeholder groups (business, environment, industry, or housing)
that have views on socioeconomic and ecological issues, some of which are
incompatible. The students read about the different viewpoints and preferences
of stakeholders and eventually prepare individual reports on how to handle
competing concerns. During the course of making these decisions, students dis-
cuss options with their project teams through online chat. They also use profes-
sional tools, such as a geographic information system model of Lowell and
preference surveys, to model the effects of land-use changes and obtain
stakeholder feedback. At the end of the internship, students write a proposal
in which they present and justify their rezoning plans. During this process,
there is a mentor who keeps the small group of three to four students moving
forward, but does not encourage any particular solution to the problem solving
tasks.
The 10-hour game is divided into 14 rooms with different goals and objec-
tives. These rooms and descriptions are presented in Table 2. There is a
sequence of rooms, each of which involves chat interaction among team mem-
bers (plus the mentor) and a product (e.g., survey, recommendation, justifica-
tion) provided by each student independently. As the mentor watches over the
collaboration among students, the mentor has access to an AutoSuggester tool
that analyses the current conversation and provides pre-scripted suggestions
that the human mentor may choose to execute. The mentor also has the ability
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to type freely to students as a group or individually. The first 10 rooms have
students assigned to a group with the same stakeholder; the final four rooms
have representatives from each stakeholder group in a new group that has to
negotiate solutions that consider multiple perspectives and compromises among
the stakeholders.
Table 2. Description of Rooms in Land Science Simulation.
Room # and Name Description
(1) Entrance interview Players introduce themselves to their designated group and
complete the entrance survey. Groups consist of 34 players and
one mentor.
(2) Request for proposals Players read the request for proposals which gives a broad
overview of the game and then groups hold team meetings to
discuss the request for proposals.
(3) Virtual site visit and site
assessment
Players read and take notes on designated stakeholder
requirements, navigate the necessary note taking tools within the
game environment, and write a personal reflection. Stakeholders
have one key interest (business, environment, industry, or
housing). Groups do not take notes on stakeholders from each
interest.
(4) iPlan practice Players learn to use the iPlan tool through changing land use codes
and discussing how the changes impact the virtual environment.
(5) Target identification
matrix 1
Individual players indicate a value taking into consideration
stakeholder input. Then the team as a whole recommends an
agreed upon value.
(6) Preference survey 1 Players create an iPlan map designed to hit the targets determined
in the target identification matrix 1 and write justifications for the
decisions made.
(7) Stakeholder assessment 1 Players review feedback from stakeholders on the preference
surveys, make additional changes to the iPlan map, and justify the
changes made.
(8) Target identification
matrix 2
Steps from target identification matrix 1 are repeated with more
specific targets.
(9) Preference survey 2 Steps from preference survey 1 are repeated based on the target
identification matrix 2.
(10) Stakeholder assessment 2 Steps from stakeholder assessment 1 are repeated.
(11) Final plan Players are grouped into new teams with players who worked
previously with different stakeholders. They collaborate to create
an iPlan map that meets all the stakeholders’ needs. When players
are re-grouped, they have to deal with and negotiate with
stakeholder groups they had not encountered yet.
(12) Final proposal Players write a formal final proposal and justification.
(13) Reflection Players write personal reflections on the game process.
(14) Exit interview Players complete an exit survey.
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A Qualitative Analysis of Mentor Speech Acts in Land Science
What do mentors do during these Land Science chat interactions? In order to
answer this question, we analyzed 200 mentor turns, randomly sampled from
the mentors’ free-type moves (as opposed to those from the AutoSuggester).
The student participants were 91 students in middle school and high school
in the United States who were assembled in groups of 34 students (noting
that groups shift after room 10). There were 50,100 chat turns of students and
mentors in the corpus, with 4,399 unique mentor turns. The 200 mentor turns
in this analysis were randomly sampled from that set.
We qualitatively annotated the five turns before and the five turns after each
unique mentor turn, based on the timestamp of the conversations. These turns
before and after the mentor turn could be expressed by any team member,
including the mentor. This chat window of five turns has frequently been
adopted to analyze the context of particular turns (Collier, Ruis, & Shaffer,
2016; Samei et al., 2014). The before and after turns were extracted in order to
define the context of the unique mentor turn and to help identify patterns
within these conversations.
All turns were assigned to one of the following speech act categories:
Statement, Question, Request, Reaction, MetaStatement, Expressive Evaluation,
and Greeting. The percentages of observations in these categories were 20%,
20%, 15%, 35%, 4%, 4%, and 1%, respectively. Table 3 contains an example
of each speech act category.
For each of the mentor turns, the speech act content and speech act catego-
ries were examined for the student and mentor turns that occurred five turns
in the past and five turns in the future. For each of these sequences of 11 turns,
the annotators induced what higher level goal the mentor was trying to achieve.
The coding scheme developed for the purposes of this investigation include the
following categories: Seed Planting, Explanation, Elaboration, Game Status,
Table 3. Speech Act Category Examples.
Speech Act Category Example
Expressive evaluation That’s a really great observation
Greeting Goodmorning, Play112
Metastatement ooops overtyped, sorry
Statement There should be a message waiting for you from our supervisor
Question in order to make a plan that everyone can live with, what do
we need to know about what they want?
Reaction ok, thanks Player105
Request Please remember to send Maggie an email letting her know
that you finished it
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Spoon Feeding, Technical, and Verification. Seed Planting refers to instances in
which the mentor expresses a hint that prompts players to respond in somewhat
elaborate ways. Explanation refers to instances in which a mentor explains or
talks about something that was previously stated. Often these explanations
come in the form of clarifications of definitions, correcting misconceptions, and
providing verbal feedback to players. Elaborations are instances in which a
mentor’s chat references the behavior of the player or players. Examples of
elaborations include asking players to help other players, giving behavioral
feedback, and giving directions outside of standard game play. Game Status is
a mentor chat that indicates where players are, where they should be, what is
happening, or what will be happening during game play. These utterances
include informing players of emails, deadlines, and instructions. Spoon Feeding
refers to utterances in which a mentor asks players for information that can be
procured with very little effort, or a mentor gives players an answer to a
question without prompting them to answer it themselves. Technical refers to
mentor chats that pertain to some technical aspect of the game, such as game
functionality, instructions, saving work, and connection issues. Verification
includes utterances in which a mentor corrects or acknowledges a minor spelling
error and slip. The following percentages reflect the occurrence of these higher
level goals in this sample: Seed Planting (10%), Explanation (34%), Elaboration
(13%), Game Status (19%), Spoon Feeding (5%), Technical (17%), and
Verification (4%).
These annotations illuminate the nature of the mentors’ moves but are
limited in two fundamental ways. First, the higher level goals were annotated
by human judges (no automation). Machine learning methods might extract
diagnostic features that recognize the higher level goals, just as what has been
accomplished for speech act classification, but that would require a corpus that
is two orders of magnitude beyond what is available in 200 mentor turns.
Second, these higher level goals are not aligned with the cells in the PISA CPS
2015 framework of Table 1. That is, it is not clear how these goal categories
map onto any theoretical framework. The human annotations contribute to
academic science but do not advance automated assessment during CPS unless
there is a large enough corpus.
State Transition Networks of Speech Acts
In the AutoTutor section, we claimed that speech act classifiers offered reason-
able accuracy in assigning speech acts to the categories when compared to
expert classifications (Olney et al., 2003; Rus et al., 2012; Samei et al., 2014).
We conducted automated analyses of the speech act categories in the chat
conversations in Land Sciences (and an earlier similar system called Urban
Science). These analyses adopted the Table 3 speech act categories and the
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speech act classifier of Samei et al. (2014). Some of these speech acts pressure
the audience to respond, such as questions and requests, where it is impolite
not to respond (Sacks, Schegloff, & Jefferson, 1974). That is, questions call for
an answer and requests call for an action to satisfy politeness norms, but the
recipient has the option of whether or not to respond. However, other speech
act categories grant considerable options to the recipients as to whether they
choose to respond.
The PISA CPS 2015 assessment often tracks whether the student (a) initiates
progress in the various Table 1 cells and serves as a leader, as opposed to
(b) responding to questions and requests, or, at the worst, (c) not responding or
randomly responding. The selection of many of the chat options in the assess-
ment reflects these alternatives. A cooperative CPS partner takes charge when
needed or responds to others when that is essential.
State-transition-networks can play a role in automatically assessing the
extent to which a team member takes initiative, responds to questions/requests,
versus responds randomly. The speech act categories of a team member can be
a reasonable assessment of a team member taking initiative. A team member
leader would have a high proportion of questions, requests, and statements
whereas a non-leader who is nevertheless involved in the group would have a
distribution of speech act categories that drift toward reactions. However, the
assessment can go further with state transition networks. We can compute the
probabilities of adjacency pairs in a corpus of chat sequences, which is the tran-
sition probability between two adjacent “participant-speech-act-category”
nodes, [P-SAC
n
P-SAC
nþ1
]. For example, what is the probability that a
team member responds to a question or request by another team member? If
that probability is low, the team member in question is not responsive to other
team members.
State transition networks have been created in Land Science and the related
Urban Science simulation by calculating the conditional probability of each
adjacency transition between categorized speech acts, as well as the overall rela-
tive frequency of each speech act in the corpus (Morgan et al., 2012). Fig. 1
depicts a network in the Urban Science domain for chat interactions between
34 students and a mentor. The speech act categories are self-explanatory
whereas the symbols consist of M ¼Mentor, S ¼Student versus O ¼other
student responding to a previous student. There are three speaker categories
(M, S, O) and eight speech act categories (including junctures that signify
the beginning or end of an exchange). This results in a 24 ×24 matrix of transi-
tion probabilities. Fig. 1 shows only those links with a transition probability
that is 0.15 or higher. These values average over all of the students and conver-
sations. Analyses of conversations in Land Science and Urban Science support
the claim that the transition probabilities are quantitatively stable across
(a) spoken interactions versus computer-mediated communication with chat
facilities (Morgan, Burkett, Bagley, & Graesser, 2011) and (b) rooms that are
discussion-oriented versus action-oriented (Morgan et al., 2012). However, the
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relative frequency distribution of P-SAC nodes is quite different across rooms
and between spoken and computer-mediated communication.
Some measures of CPS can theoretically be derived from the distribution of
participant-speech-act-category nodes and the transition probabilities between
these node categories. As mentioned already, students who take initiative would
be expected to have a high proportion of questions, requests, and statements,
whereas responsive team members (but not leaders) would have a relatively
high proportion of reactions. A disruptive team member would have a high
proportion of negative expressive evaluations and a social loafer would have a
low number of contributions compared with other team members. Regarding
the state transitions, responsive team members would be expected to have a
relatively high transition probability between questions and requests of others
and their reactions or statements; these transition probabilities would be low
for unresponsive team members. Thus, these probabilistic metrics have rele-
vance to many of the cells in the framework for PISA CPS 2015.
Nevertheless, there are some limitations of these metrics extracted from state
transition networks when assessing the CPS proficiency of team members and
the team as a whole. First, the network analysis specifies the category of the
speech acts but not the content of the speech acts. Therefore, this network
approach does not indicate which of the cells, rows, or columns in Table 1 are
being referenced. Second, the state transition networks only consider adjacent
speech acts, so it misses more global conversation patterns. Adjacencies are of
course important units of conversation (Sacks et al., 1974), as in the case of
questionanswer or greetinggreeting. However, conversation patterns with
Fig. 1. State Transition Network for Discussion-oriented Rooms in Land Sciences.
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three or more speech acts are also important in negotiations and collaborative
planning, but are not captured by mere adjacencies.
Latent Semantic Analyses (LSA)
LSA (Landauer et al., 2007) can be used to analyze the content of the team
member’s contributions, as described in the section on AutoTutor. Foltz and
Martin (2008) have successfully used LSA to analyze the coherence of teams
and characteristics of individual team members. Similarly, unpacking temporal
patterns in group interactions and understanding how these patterns relate to
group and individual student performance is acknowledged as high priority for
research in team science (Kapur, 2011; Reimann, 2009; Sawyer, 2014; Stahl,
2005; Suthers, 2006; Von Davier et al., 2017). However, the use of automated
approaches for identifying the dynamics of interactions between group mem-
bers has rarely been investigated until recently (Reimann, 2009).
LSA-based statistical metrics could potentially provide an assessment of
establishing a shared understanding (Clark, 1996) and building on what each
other knows, which theoretically are important in CPS (see column 1 in
Table 1). The relevance of a contribution to the topic at hand is important for
distinguishing turns that are on-topic versus off-topic. As discussed in the
AutoTutor section, it is possible to statistically measure relevance (R), given-
ness (G), and newness (N) of individual turns that are expressed by individual
team members and the team as a whole. A good collaborative team member
contributes relevant information that is new and also builds on other team
member’s topic-relevant ideas. Scores for R, G, and N can be automatically
computed by LSA, as discussed earlier. A team member who productively leads
the conversation would have a vector of RGN measures such as (0.9, 0.4, 0.6).
Team members who echo ideas of others in a conversation would have a (0.9,
0.5, 0.0) vector if they stay on topic, but a (0.0, 0.5, 0.0) vector on off-topic
talk. A team member with a (0.0, 0.0, 0.9) vector would be in their own irrele-
vant worlds and not helpful to collaboration.
These LSA-based vector predictions were recently tested by Dowell (2017) in
collaborative learning and problem solving environments. One of her corpora
was a Land Science chat corpus that attempted to assess characteristics of team
members. A sample of N¼38 participants interacted in 19 CPS simulations
with Land Sciences. Each game had multiple rooms (Table 2) and each room
had multiple chat sessions. There were a total of 630 distinct chat sessions, with
a reasonable distribution among student players and mentors. As previously
mentioned, players in the simulation game communicated both with other
players and with mentors using a chat feature embedded in the interface.
For the purposes of detecting the social roles of players, we analyzed all of
the players’ chats as well as the mentors who periodically entered into
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the conversations. There were also teachers and non-player characters who did
not participate in the chat interactions but played roles in other dimensions of
the simulation.
A number of measures were collected on each move of a student and mentor
on the Land Sciences corpus. Participation is the relative proportion of a parti-
cipant’s contributions (turns) out of the total number of group contributions.
Responsiveness (analogous to G for givenness) is an LSA-based measure that
assesses how responsive a student’s contributions are to all other group mem-
bers’ previous contributions in the conversation. Conversely, social impact is an
LSA-based measure of how turn contributions of a student have a semantic
similarity to other student contributions in the future conversation. Student
cohesion is an LSA-based measure of how semantically similar a student’s con-
tribution is to that student’s previous conversational turns (i.e., is a student say-
ing the same thing over and over?). Newness is the amount of new information
in a contribution, as defined earlier. Communication density is an LSA-based
measure that assesses how much information in a turn is distinctive to the
topic, compared with everyday topics of conversation.
A communication matrix among these measures had some interesting pat-
terns. Those individuals with high participation also had high newness, commu-
nication density, and internal cohesion (rcorrelations between 0.35 and 0.56),
but not a comparatively high social impact and responsivity (r¼0.03 to 0.06).
So the highest information contributors were not necessarily the most sensitive
and impactful members on the team. Another finding is that those with the
highest social impact were also most responsive (r¼0.40). Finally, those with
new information had the highest communication density (r¼0.79). It appears
that there are differences between the individuals with new information and
those who manage the social interaction. Interestingly, Dowell et al. (2015)
documented that those who learn most are content-centered rather that social-
centered (keeping the group going) when analyzing the discourse of team mem-
bers with Coh-Metrix, an automated tool that analyses natural language at
multiple levels of language and discourse (Graesser et al., 2014; McNamara
et al., 2014). There does seem to be a differentiation between team members
who focus on substantive content of the subject matter and team members who
keep the conversation alive and moving forward. The former may learn more
(i.e., taskwork, the rows in Table 1) but the latter may contribute more to CPS
progress (i.e., teamwork, the columns in Table 1).
The LSA-based assessments have the advantages of assessing content, track-
ing substantive communication between team members, and providing semantic
links to the content of episodic units associated with the Table 1 cells of PISA
CPS 2105. We can assess how well the content of individual team members
semantically resonate with the expectations and misconceptions of Table 1 cells
when aggregated over many turns. We can determine whether team members
respond to other team members, whether the turns spawn conversations among
other team members, and whether the content is relevant to the subject matter.
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Nevertheless, these LSA measures also have limitations. First, LSA scores
ignore syntax, semantic precision, and structure-sensitive computation. This
motivates the need for regular expressions in any comparison to a Table 1 con-
tent rubric. Second, these assessments are most reliable when they aggregate
scores over many turns, as opposed to individual turns. Thus, the grain size of
reliable assessment specification is global rather than local, even though LSA-
based scores can be collected on each turn (with limited reliability and validity).
It is an open question what a good window size of chat turns should be in these
analyses, but some of our results suggest that a moving window of five chat
turns is reasonable.
Epistemic Network Analyses
Epistemic network analysis (ENA) attempts to assess the complex thinking,
discourse, reasoning, and topics addressed in professional disciplines and com-
munities (Nash & Shaffer, 2011; Shaffer, 2017; Shaffer, Collier, & Ruis, 2016;
Shaffer et al., 2009). There does not need to be a golden standard on what is
said in ENA, but there does need to be a disciplinary style of thinking and
talking that resonates with the expertise of the community of stakeholders.
Do stakeholders give evidence for claims? Do they track causal chains of
reasoning? Do they talk about important topics of the discipline? Do stake-
holders minimize vernacular small talk? Do team members eventually learn
disciplinary discourse over time as they interact with good professional role
models?
ENA has four features in its assessment of the chat discourse in CPS. First,
ENA models complex thinking by representing it as a network of connections
among critical knowledge, skills, values, and epistemic moves in the profes-
sional domain. ENA measures the strength of association among these cogni-
tive elements that characterize complex thinking and quantifies changes in the
composition and strength of those connections over time. Second, ENA models
collaboration by accounting for the cognitive connections that each individual
student contributes to the group conversation. That is, ENA models connec-
tions among concepts when considering how team members interact rather
than merely quantifying who talks to whom. Third, ENA constructs a metric
space that enables comparison of individual or group networks through (a) dif-
ference graphs, which visualize the differences in weighted connections between
two networks, and (b) summary statistics that reflect the weighted structure
of connections in the networks, allowing comparison of large numbers of
networks. Fourth, model parameters can initially be prepared theoretically and
then modified through statistical machine learning algorithms as more data are
collected. ENA attempts to preserve correspondences between qualitative data
and quantitative models.
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It is beyond the scope of this chapter to precisely specify the algorithms that
underlie ENA and the process of applying ENA to CPS data (see Shaffer et al.,
2016, for the ENA Toolkit). The initial steps consist of (a) annotating chat turn
sequences (i.e., stanzas, sliding turn windows of about length five) on important
cognitive categories (i.e., expressions of skills, knowledge, identity, values, and
epistemic content), based on the words expressed in those turns, (b) computing
a matrix with the co-occurrence of these cognitive categories within these turn
sequences, and (c) reducing the large set of co-occurrence matrices to a small
number of dimensions through singular value decomposition. When there are
only two or three dimensions, it is possible to plot each cognitive category in a
two- or three-dimensional metric space. The size of the cognitive category in
the space reflects its relative frequency, and thickness of the links between the
concept categories reflects the co-occurrence frequency. The node and link
patterns in these networks can be compared for different team members, the
team as a whole, and different chat contexts associated with the profession.
Comparison of different networks is accomplished through summary statistics
and standard inferential statistics after the ENA spaces have been suitably nor-
malized in a way that allows direct comparisons.
ENA has been applied to the Land Science chat corpora (Collier et al.,
2016). The logfile contained team chat conversations (41,332 lines of chat in
total) from 265 students who used Land Science. There were novices on the
team that included high school students (N¼110) and relative experts that
included college students enrolled in an introductory urban science course
(N¼155). The chat utterances were coded for 17 cognitive categories associ-
ated with the epistemic frame of urban planning, including:
Knowledge of stakeholder representation. Knowledge of stakeholders, whose
requests pertain to social, economic, and environmental issues.
Skills and practices urban planning using tools of the domain. Discussion or
actions involving the tools of the urban planning domain, such as a virtual
site visit to key regions in the city, a stakeholder preference survey, and
iPlan, a geographic information system-enabled zoning model.
Data-based justifications. Justifications using data such as graphs, results
tables, numerical values, or research papers.
Fig. 2 depicts how frequently the 17 cognitive categories (larger points) are
represented in the ENA network as well as the connections between the cogni-
tive categories (thicker lines). This graph is associated with the relatively expert
students, as opposed to the novice students. The strongest nodes referred to
knowledge of social issues, environmental issues, data, stakeholders, and urban
planning tools in addition to the skill of using urban planning tools. Regarding
connections between nodes, the relative experts were found to connect “data-
based justifications” with knowledge elements as well as with skills and other
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justifications. This distribution of connections was not prevalent among the
novice students when we inspected their graphs.
The similarity of these graphic diagrams have been analyzed for different
students, teams, contexts of conversation, and time phases of a learning envi-
ronment. Large samples of networks can be examined in order to discover
trends in discourse thinking in addition to CPS mechanisms. Team members
can be compared on the similarity of their ENA profiles. All of this can be
accomplished without a golden standard and the methodology can be applied
to both ill-structured and well-structured domains of knowledge. These are all
encouraging characteristics of this approach.
This approach does have its liabilities from the standpoint of automation
and assessment of CPS. First, the researchers need to declare a priori or dis-
cover through data mining the set of cognitive categories and the words associ-
ated with each category before the system can be automated by computer.
Fortunately, the process of completing this development is being reduced
by authoring tools available to professional experts and designers of virtual
Fig. 2. Mean ENA Network Diagrams that Show the Connections Made by the
Relatively Expert Students.
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internships (Shaffer, Ruis, & Graesser, 2015). Second, it is unclear how the
ENA diagrams and cognitive categories are aligned with the 12 cells in the
Table 1 matrix for PISA CPS 2015. Perhaps the discourse thinking parameters
are particularly relevant to row D (monitoring and reflecting) and the identity
concept categories are relevant to column 3 (establishing and maintaining team
organization). These are all open questions for further research.
Matches to Expectations and Misconceptions in Episodic Units
The automated measures of open-ended conversations and the discourse of
professional talk are important accomplishments, but they do not go the
distance in revealing how well the talk matches good solutions and important
collaboration milestones in CPS. We argue that an adequate CPS assessment
with natural language requires mechanisms that include semantic matching
components to ideal expectations and likely misconceptions in the context of
particular problems to be solved by the team. If our argument is correct, then
the computational architecture of AutoTutor is directly applicable to the use of
agents in collaborative learning and problem solving.
One important first step in open-ended natural language assessments of CPS
would be to piggyback on the episodic units in the existing PISA CPS 2015
materials, as discussed in the first section. For each episodic unit there is an
ideal answer in the set of multiple choice options in a chat move. The ideal
answer (the correct response in the MC test) would be the expectation to assess
semantic matches whereas the incorrect options would constitute a few of the
misconceptions and team members may express. Moreover, each episodic unit
for evaluation could have a set of expectations that are deemed as good
answers instead of only one golden answer. As the conversation for an episodic
unit is discussed in chat, the test taker’s chat content would be compared to the
set of good answers and the set of bad answers through the semantic similarity
models discussed in the AutoTutor section of this chapter. This approach
would require a set of expectations and a set of misconceptions for each
episodic unit. It would also require that the episodic units cover the 12 cells in
Table 1, with content in each cell that is readily distinguishable from other cells.
Otherwise, it would be impossible to know which of the verbal content in the
chat is associated with each of the 12 cells.
PISA CPS 2015 currently has each episodic unit assigned to one and only
one cell in the Table 1 matrix. Thus, at each multiple choice point, there can be
a set of expectations associated with the correct answer and a set of misconcep-
tions associated with each distractor item. The chat contributions of the student
(in the relevant speech act categories) would be semantically matched to each
option (a match score varying from 0 to 1) and that would be the score for
that cell in the Table 1 matrix. Suppose that the match score was 0.6 with the
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expectation and 0.1, 0.1 and 0.0 for three wrong answers in the multiple choice;
the score for that cell could be computed as [0.6/(0.6 þ0.1 þ0.1 þ0.0)] ¼0.75.
In contrast, a 0.2 match to an expectation and a distractor vector of (0.6, 0.1,
0.1) would have a score of 0.2 and a 0.75 misconception to follow up and
verify. Consequently, the existing PISA CPS 2015 items could be evaluated
with open-ended conversation through this approach and compared with the
existing multiple choice interface.
It should be acknowledged that it takes substantial effort to design the epi-
sodic units, family of expectations and misconceptions, and set of discourse
moves in this proposed approach to automated assessment and facilitation of
CPS. The time and expense of this effort can to some extent be mitigated with
authoring tools for developing the scripted content (Sottilare, Graesser, Hu, &
Brawner, 2015) and tool kits for evaluating semantic similarity (Rus et al.,
2013). The feasibility of this approach is one direction for future research.
DESIGNING AGENTS TO FACILITATE CPS
The above analysis of CPS is incomplete because it does not specify the
discourse moves generated by a fellow student agent, tutor, or mentor agent.
The system’s “comprehension” of the students’ verbal contributions is only half
of the mechanism to worry about, the other half being the “production” of the
agent’s conversation moves.
In CPS assessment, a computer agent expresses conversation moves that
give the test taker a second chance with a more direct question or request. For
example, an important construct in CPS is whether the team member takes the
initiative during difficult challenges, merely responds to questions/requests, or
is entirely unresponsive and unhelpful. Many of the cells in Table 1 have chat
options in the episodic units and follow-up conversation paths that attempt to
try to get at this construct. Without intelligent agents, these follow-ups would
not systematically occur and an adequate assessment of CPS proficiency would
be compromised. Our contention is that computer agents will provide a more
accurate assessment of CPS proficiency because they can address many subtle-
ties of collaboration.
CPS assessment is important, but the goal of the tutor or mentor agent is to
facilitate CPS by well-constructed, well-timed, and adaptively intelligent dis-
course moves (Gilbert et al., 2017). There are a number of reasons that few
intelligent tutoring systems have been developed to train teams. One important
reason is that it is difficult to create systems that are able to assess complex
interaction patterns among team members and the quality of the team as a
unit. Adequate assessment of team performance is necessary for effective train-
ing of team learning and problem solving, as elaborated in a recent meta-
analysis of the impact of teams on learning and the resulting implications for
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team training (Sottilare et al., 2017). As discussed in the previous section, it is
important to design CPS environments so that informative assessments can be
collected. That requires the capacity to design tasks that have the affordances
for informative CPS assessments. Gilbert and colleagues (2017) propose a
framework to guide the authoring process for team tutors, and demonstrate the
framework using a case study about a team tutor that was developed using a
military surveillance scenario for teams of two. Their work offers conceptual
scaffolding for authors of intelligent tutoring systems that are designed for
CPS. Consequently, another direction for future research is to design and test
intelligent tutoring systems that facilitate CPS.
Besides helping overcome difficulty in assessing complex team interactions,
computer agents can yield assessments in comparatively short amounts of time.
In PISA, there is only about an hour of assessment per student. The likelihood
is near zero of encountering many subtle but important situations in which
particular components of CPS can be assessed. For example, it may take a 100
hours of chat before the test taker encounters a situation in which (a) the entire
group wants to make a particular decision or take an action and (b) the test
taker has the option to disagree and propose a different solution that is more
on target. Some test takers would never encounter this option because of the
particular ensemble of team members who are resistant to group think; if so,
that aspect of CPS would be indeterminate (missing data). However, all of these
situations can be manufactured with a smartly designed construction of epi-
sodic units and thereby have a complete and theoretically mature assessment of
CPS. This approach to intelligent conversation-based assessments with agents
is currently being pursued with open-ended responses at Educational Testing
Service (Liu et al., 2015; Zapata-Rivera et al., 2015). Without the agents push-
ing the envelope of assessment, the experience with meaningful episodic units
would be extremely rare.
There are more generic ways of having agents drum up content from team
members that may or may not be constrained by the expectations and miscon-
ceptions of episodic units. Some of these generic production rules have been
articulated in Graesser, Cai, et al. (2017), as summarized below.
If the team is stuck and not producing contributions on the relevant topic,
then the agent says “What’s the goal here?” or “Let’s get back on track.”
If the team meanders from topic to topic without much coherence, then the
agent says “I’m lost!” or “What are we doing now?”
If the team is saying pretty much the same thing over and over, then the
agent says “So what’s new?” or “Can we move on?”
If a particular team member (Harry) is loafing, the agent says “What do you
think, Harry?”
If a particular team member (Sally) is dominating the conversation exces-
sively, the agent says “I wonder what other people think about this?”
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If one or more team members express unprofessional language, the agent
says “Let’s get serious now. I don’t have all day.”
If someone asks the agent a question or makes a request, the agent says
“Sorry, but I’m busy now.”
An important unanswered question from the standpoint of assessment is
whether a system with agents that express generic dialogue moves like those
above would result in more reliable and valid assessments than a system with-
out agents. Rules 13 might unveil the potential of a team that would not be
manifested without the agent. Rules 46 might expose the potential of individ-
ual team members that otherwise would not be exhibited. At this point in the
science, we lack systematic empirical research that addresses these possibilities.
Meanwhile, we believe that CPS assessment from open-ended chat with no
agents and no expectation and misconception tailored conversation is substan-
tially limited. We also suspect that intelligent tutoring systems will be needed
for more nuanced and impactful improvements on CPS performance of teams.
Again, these views require empirical testing in future research.
ACKNOWLEDGMENTS
The research was supported by the National Science Foundation (DRK-12-
0918409, DRK-12 1418288), the Institute of Education Sciences (R305C
120001), Army Research Lab (W911INF-12-2-0030), and the Office of Naval
Research (N00014-12-C-0643; N00014-16-C-3027). Any opinions, findings,
and conclusions or recommendations expressed in this material are those of the
authors and do not necessarily reflect the views of NSF, IES, or DoD. The
Tutoring Research Group (TRG) is an interdisciplinary research team com-
prised of researchers from psychology, computer science, and other depart-
ments at University of Memphis (visit http://www.autotutor.org).
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... Coh-Metrix incorporates automated computational methods of NLP, such as syntactic parsing and cohesion computation, to capture language characteristics at the word-level, sentence-level, and deeper levels of discourse. Coh-Metrix provides useful insights into learners' affective, social, and cognitive processes in a variety of digital learning environments (Choi et al., 2018;D'Mello & Graesser, 2012;Dowell et al., 2014;Graesser et al., 2011;Graesser et al., 2018;McNamara & Graesser, 2012). Coh-Metrix has been extensively validated through more than 150 published studies, which have demonstrated that Coh-Metrix indices can be used to detect subtle differences in text and discourse (Graesser, 2011;Graesser et al., 2011;McNamara et al., 2006;McNamara et al., 2014). ...
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Over the last decade, psychological interventions, such as the values affirmation intervention, have been shown to alleviate the male-female performance difference when delivered in the classroom, however, attempts to scale the intervention are less successful. This study provides unique evidence on this issue by reporting the observed differences between two randomized controlled implementations of the values affirmation intervention: (a) successful in-class and (b) unsuccessful online implementation at scale. Specifically, we use natural language processing to explore the discourse features that characterize successful female students’ values affirmation essays to gain insight on the underlying mechanisms that contribute to the beneficial effects of the intervention. Our results revealed that linguistic dimensions related to aspects of cohesion, affective, cognitive, temporal, and social orientation, independently distinguished between males and females, as well as more and less effective essays. We discuss implications for the pipeline from theory to practice and for psychological interventions.
... This concept foregrounds the importance of designing for specific actors, with particular tasks and levels of expertise, rather than a vague "user." This concept also enables us to more explicitly differentiate between collaboration analytics and those previous dataintensive AIED, CSCL, and ITS research works in which the purpose is often for the system to automatically make decisions to provide adapted actions (i.e., as in intelligent tutoring, recommender systems, and conversational agents) (Graesser et al., 2018), or to support researchers to gain new understanding of group communication processes (Dowell, Nixon, & Graesser, 2019). Although some of that work can also be considered within the umbrella of collaboration analytics, here we provide a conceptual model of collaboration analytics being strongly linked to the notion of actionable group insights. ...
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Using data to generate a deeper understanding of collaborative learning is not new, but automatically analyzing log data has enabled new means of identifying key indicators of effective collaboration and teamwork that can be used to predict outcomes and personalize feedback. Collaboration analytics is emerging as a new term to refer to computational methods for identifying salient aspects of collaboration from multiple group data sources for learners, educators, or other stakeholders to gain and act upon insights. Yet, it remains unclear how collaboration analytics go beyond previous work focused on modelling group interactions for the purpose of adapting instruction. This paper provides a conceptual model of collaboration analytics to help researchers and designers identify the opportunities enabled by such innovations to advance knowledge in, and provide enhanced support for, collaborative learning and teamwork. We argue that mapping from low-level data to higher-order constructs that are educationally meaningful, and that can be understood by educators and learners, is essential to assessing the validity of collaboration analytics. Through four cases, the paper illustrates the critical role of theory, task design, and human factors in the design of interfaces that inform actionable insights for improving collaboration and group learning.
... We argue, therefore, that CPS training needs to be curricular rather than extracurricular. There is a need to better understand how to develop and adopt methods for learning CPS processes Gilbert et al., 2018;Graesser, Dowell, et al., 2018;Graesser, Foltz, et al., 2018;Hesse et al., 2015;Scoular, Care, & Hesse, 2017). The PISA report points to the kinds of educational initiatives that help develop collaborative competencies for all students. ...
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Collaborative problem solving (CPS) has been receiving increasing international attention because much of the complex work in the modern world is performed by teams. However, systematic education and training on CPS is lacking for those entering and participating in the workforce. In 2015, the Programme for International Student Assessment (PISA), a global test of educational progress, documented the low levels of proficiency in CPS. This result not only underscores a significant societal need but also presents an important opportunity for psychological scientists to develop, adopt, and implement theory and empirical research on CPS and to work with educators and policy experts to improve training in CPS. This article offers some directions for psychological science to participate in the growing attention to CPS throughout the world. First, it identifies the existing theoretical frameworks and empirical research that focus on CPS. Second, it provides examples of how recent technologies can automate analyses of CPS processes and assessments so that substantially larger data sets can be analyzed and so students can receive immediate feedback on their CPS performance. Third, it identifies some challenges, debates, and uncertainties in creating an infrastructure for research, education, and training in CPS. CPS education and assessment are expected to improve when supported by larger data sets and theoretical frameworks that are informed by psychological science. This will require interdisciplinary efforts that include expertise in psychological science, education, assessment, intelligent digital technologies, and policy.
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Metacognitive experiences are the feelings and judgments that emerge in relation to an ongoing learning task. Much of the work on metacognitive experiences has studied these constructs piecemeal and mostly in individual learning settings. Thus, little is known about how metacognitive experiences co-occur in social learning settings. In light of this, we investigated the relationships between metacognitive experiences and their impact on perceived and objective task performance in a collaborative problem solving (CPS) simulation. Seventy-seven higher education students participated in the study. Metacognitive experiences (judgment of confidence, mental effort, task difficulty, task interest, and emotional valence) were measured with self-reports at multiple time points during CPS. A path analysis was conducted to investigate the relationship between metacognitive experiences and perceived performance. A generalized estimating equation was used to observe the relationships between metacognitive experiences and objective group CPS performance. Overall, the findings indicate complex relationships among the metacognitive experiences and performance in CPS and further highlight the social characteristics of metacognition.
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Learning‐in‐action depends on interactions with learning content, peers and real world problems. However, effective learning‐in‐action also depends on the extent to which students are active‐in‐thinking, making meaning of their learning experience. A critical component of any technology to support active thinking is the ability to ascertain whether (or to what extent) students have succeeded in internalizing the disciplinary strategies, norms of thinking, discourse practices and habits of mind that characterize deep understanding in a domain. This presents what we call a dilemma of modeling‐in‐context: teachers routinely analyze this kind of thinking for small numbers of students in activities they create or customize for the needs of their students; however, doing so at scale and in real‐time requires some automated processes for modeling student work. Current techniques for developing models that reflect specific pedagogical activities and learning objectives that a teacher might create require either more expertise or more time than teachers have. In this paper, we examine a theoretical approach to addressing the problem of modeling active thinking in its pedagogical context that uses teacher‐created rubrics to generate models of student work. The results of this examination show how appropriately constructed learning technologies can enable teachers to develop custom automated rubrics for modeling active thinking and meaning‐making from the records of students' dialogic work. Practitioner Notes What is already known about this topic Many immersive educational technologies, such as digital games and simulations, enable students to take consequential action in a realistic context and to interact with peers, mentors and pedagogical agents. Such technologies help students to be active‐in‐thinking: engaging deeply with, reflecting on and otherwise making meaning of their learning experience. There are now many immersive educational technologies with integrated authoring tools that enable teachers to customize the learning experience with relative ease, reducing barriers to adoption and improving student learning. Educational technologies that support learning‐in‐action typically contain student models that operate in real‐time to control the behavior of pedagogical agents, deliver just‐in‐time interventions, select an appropriate content or otherwise measure and promote active thinking, but these student models may not work appropriately if teachers customize the learning experience. Much as there are authoring tools that allow teachers to customize the curriculum of a given learning technology, there is a need for authoring tools that allow teachers to customize the associated student models as well. What this paper adds This paper presents a novel, rubric‐based approach to develop automated student models for new activities that teachers develop in digital learning environments that promote active thinking. Our approach combines machine learning techniques with teacher expertise, allowing teachers to participate in the design of automated student models of active thinking that with further development could be scaled by leveraging their skills in rubric development. Our results show that a rubric‐based approach can outperform a machine learning approach in this context. More importantly, in some cases, the rubric‐based approach can produce reliable automated models based on the information that a teacher can easily provide. Implications for practice and/or policy If integrated into authoring tools, the rubric‐based approach could allow teachers to participate in the design of automated models for educational technologies customized to their instructional needs. Through this design process, teachers could develop a better understanding of how the automated modeling system works, which in turn could increase the adoption of educational technologies that promote active thinking. Because the rubric‐based approach enables teachers to identify key connections among concepts relevant to the pedagogical context, rather than general concepts or linguistic features, it is more likely to facilitate targeted feedback to help promote the development of active thinking. What is already known about this topic Many immersive educational technologies, such as digital games and simulations, enable students to take consequential action in a realistic context and to interact with peers, mentors and pedagogical agents. Such technologies help students to be active‐in‐thinking: engaging deeply with, reflecting on and otherwise making meaning of their learning experience. There are now many immersive educational technologies with integrated authoring tools that enable teachers to customize the learning experience with relative ease, reducing barriers to adoption and improving student learning. Educational technologies that support learning‐in‐action typically contain student models that operate in real‐time to control the behavior of pedagogical agents, deliver just‐in‐time interventions, select an appropriate content or otherwise measure and promote active thinking, but these student models may not work appropriately if teachers customize the learning experience. Much as there are authoring tools that allow teachers to customize the curriculum of a given learning technology, there is a need for authoring tools that allow teachers to customize the associated student models as well. What this paper adds This paper presents a novel, rubric‐based approach to develop automated student models for new activities that teachers develop in digital learning environments that promote active thinking. Our approach combines machine learning techniques with teacher expertise, allowing teachers to participate in the design of automated student models of active thinking that with further development could be scaled by leveraging their skills in rubric development. Our results show that a rubric‐based approach can outperform a machine learning approach in this context. More importantly, in some cases, the rubric‐based approach can produce reliable automated models based on the information that a teacher can easily provide. Implications for practice and/or policy If integrated into authoring tools, the rubric‐based approach could allow teachers to participate in the design of automated models for educational technologies customized to their instructional needs. Through this design process, teachers could develop a better understanding of how the automated modeling system works, which in turn could increase the adoption of educational technologies that promote active thinking. Because the rubric‐based approach enables teachers to identify key connections among concepts relevant to the pedagogical context, rather than general concepts or linguistic features, it is more likely to facilitate targeted feedback to help promote the development of active thinking.
Chapter
This chapter considers the essential elements and processes in designing and building a computer-based tutor to instruct teams. In this chapter, the choices of authoring tools, the instructional context, the goal of the instruction, and the characteristics of the domain were evaluated in terms of their influence on the Intelligent Tutoring System (ITS) design in support of team learning and performance. While each team tutor may be unique in terms of its learning objectives, measures, selections of learning strategies and tutor interventions, there are some identified design decisions that need to be made. Considering the best decision for the specific tutor’s design is intended to ease the authoring burden and make computer-based team tutoring more ubiquitous. © US Army Research Laboratory, 2018 All rights of reproduction in any form reserved.
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With the movement in education towards collaborative learning, it is becoming more important that learners be able to work together in groups and teams. Intelligent tutoring systems (ITSs) have been used successfully to teach individuals, but so far only a few ITSs have been used for the purpose of training teams. This is due to the difficulty of creating such systems. An ITS for teams must be able to assess complex interactions between team members (team skills) as well as the way they interact with the system itself (task skills). Assessing team skills can be difficult because they contain social components such as communication and coordination that are not readily quantifiable. This article addresses these difficulties by developing a framework to guide the authoring process for team tutors. The framework is demonstrated using a case study about a particular team tutor that was developed using a military surveillance scenario for teams of two. The Generalized Intelligent Framework for Tutoring (GIFT) software provided the team tutoring infrastructure for this task. A new software architecture required to support the team tutor is described. This theoretical framework and the lessons learned from its implementation offer conceptual scaffolding for future authors of ITSs.
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Human one-to-one tutoring has been shown to be a very effective form of instruction. Three contrasting hypotheses, a tutor-centered one, a student-centered one, and an interactive one could all potentially explain the effectiveness of tutoring. To test these hypotheses, analyses focused not only on the effectiveness of the tutors' moves, but also on the effectiveness of the students' construction on learning, as well as their interaction. The interaction hypothesis is further tested in the second study by manipulating the kind of tutoring tactics tutors were permitted to use. In order to promote a more interactive style of dialogue, rather than a didactic style, tutors were suppressed from giving explanations and feedback. Instead, tutors were encouraged to prompt the students. Surprisingly, students learned just as effectively even when tutors were suppressed from giving explanations and feedback. Their learning in the interactive style of tutoring is attributed to construction from deeper and a greater amount of scaffolding episodes, as well as their greater effort to take control of their own learning by reading more. What they learned from reading was limited, however, by their reading abilities.
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The goal of this research was the development of a practical architecture for the computer-based tutoring of teams. This article examines the relationship of team behaviors as antecedents to successful team performance and learning during adaptive instruction guided by Intelligent Tutoring Systems (ITSs). Adaptive instruction is a training or educational experience tailored by artificially-intelligent, computer-based tutors with the goal of optimizing learner outcomes (e.g., knowledge and skill acquisition, performance, enhanced retention, accelerated learning, or transfer of skills from instructional environments to work environments). The core contribution of this research was the identification of behavioral markers associated with the antecedents of team performance and learning thus enabling the development and refinement of teamwork models in ITS architectures. Teamwork focuses on the coordination, cooperation, and communication among individuals to achieve a shared goal. For ITSs to optimally tailor team instruction, tutors must have key insights about both the team and the learners on that team. To aid the modeling of teams, we examined the literature to evaluate the relationship of teamwork behaviors (e.g., communication, cooperation, coordination, cognition, leadership/coaching, and conflict) with team outcomes (learning, performance, satisfaction, and viability) as part of a large-scale meta-analysis of the ITS, team training, and team performance literature. While ITSs have been used infrequently to instruct teams, the goal of this meta-analysis make team tutoring more ubiquitous by: identifying significant relationships between team behaviors and effective performance and learning outcomes; developing instructional guidelines for team tutoring based on these relationships; and applying these team tutoring guidelines to the Generalized Intelligent Framework for Tutoring (GIFT), an open source architecture for authoring, delivering, managing, and evaluating adaptive instructional tools and methods. In doing this, we have designed a domain-independent framework for the adaptive instruction of teams.
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This paper provides a tutorial on epistemic network analysis (ENA), a novel method for identifying and quantifying connections among elements in coded data and representing them in dynamic network models. Such models illustrate the structure of connections and measure the strength of association among elements in a network, and they quantify changes in the composition and strength of connections over time. Importantly, ENA enables comparison of networks both directly and via summary statistics, so the method can be used to explore a wide range of qualitative and quantitative research questions in situations where patterns of association in data are hypothesized to be meaningful. While ENA was originally developed to model cognitive networks—the patterns of association between knowledge, skills, values, habits of mind, and other elements that characterize complex thinking—ENA is a robust method that can be used to model patterns of association in any system characterized by a complex network of dynamic relationships among a relatively small, fixed set of elements.
Book
This second volume of papers from the ATC21STM project deals with the development of an assessment and teaching system of 21st century skills. Readers are guided through a detailed description of the methods used in this process. The first volume was published by Springer in 2012 ( Griffin, P., McGaw, B. & Care, E., Eds., Assessment and Teaching of 21st Century Skills, Dordrecht: Springer). The major elements of this new volume are the identification and description of two 21st century skills that are amenable to teaching and learning: collaborative problem solving, and learning in digital networks. Features of the skills that need to be mirrored in their assessment are identified so that they can be reflected in assessment tasks. The tasks are formulated so that reporting of student performance can guide implementation in the classroom for use in teaching and learning. How simple tasks can act as platforms for development of 21st century skills is demonstrated, with the concurrent technical infrastructure required for its support. How countries with different languages and cultures participated and contributed to the development process is described. The psychometric qualities of the online tasks developed are reported, in the context of the robustness of the automated scoring processes. Finally, technical and educational issues to be resolved in global projects of this nature are outlined.
Book
This edited volume provides a platform for experts from various fields to introduce and discuss their different perspectives on the topic of teamwork and collaborative problem solving. It brings together researchers in organizational teaming, educational collaboration, tutoring, simulation, and gaming as well as those involved in statistical and psychometric process modelling. This book seeks to channel this expertise towards advances in the measurement and assessment of cognitive and non-cognitive skills of individuals and teams. “ The ability to understand the states, traits, and habits of individual learners, collaborative groups, or team is a necessary prerequisite to guiding, adapting, and optimizing instructional experiences. The modeling and assessment of learners interacting with peers, human instructors, or computer-based tutors provides a window into the effectiveness of instructional tools and methods that is needed to continuously improve their learning experiences. Any action taken by the tutor/teacher/instructor without knowledge of those being taught is a shot in the dark. We applaud those who dedicate their lives to helping us solve the hard problems that will turn on the light and allow us to easily tailor learning experiences for every person.” (Robert Sottilare, Ph.D., US Army Research Laboratory, Adaptive Training Scientist)
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The interdisciplinary field of the learning sciences encompasses educational psychology, cognitive science, computer science, and anthropology, among other disciplines. The Cambridge Handbook of the Learning Sciences, first published in 2006, is the definitive introduction to this innovative approach to teaching, learning, and educational technology. In this dramatically revised second edition, leading scholars incorporate the latest research to provide practical advice on a wide range of issues. The authors address the best ways to write textbooks, design educational software, prepare effective teachers, organize classrooms, and use the Internet to enhance student learning. They illustrate the importance of creating productive learning environments both inside and outside school, including after school clubs, libraries, and museums. Accessible and engaging, the Handbook has proven to be an essential resource for graduate students, researchers, teachers, administrators, consultants, software designers, and policy makers on a global scale.
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How can we make sense of make sense of the deluge of information in the digital age? The new science of Quantitative Ethnography dissolves the boundaries between quantitative and qualitative research to give researchers tools for studying the human side of big data: to understand not just what data says, but what it tells us about the people who created it. Thoughtful, literate, and humane, Quantitative Ethnography integrates data-mining, discourse analysis, psychology, statistics, and ethnography into a brand-new science for understanding what people do and why they do it. Packed with anecdotes, stories, and clear explanations of complex ideas, Quantitative Ethnography is an engaging introduction to research methods for students, an introduction to data science for qualitative researchers, and an introduction to the humanities for statisticians-but also a compelling philosophical and intellectual journey for anyone who wants to understand learning, culture and behavior in the age of big data.
Chapter
Conversational agents have been developed to help students learn in computerlearning environments with collaborative reasoning and problem solving. Conversational agents were used in the 2015 Programme for International Student Assessment (PISA) collaborative problem-solving assessments, where a human interacted with one, two or three agents. This chapter reviews advances in conversational agents and how they can help students learn by engaging in collaborative reasoning and problem solving. Using the example of AutoTutor it demonstrates how dialogues can mimic the approaches of expert human tutors. Conversations with intelligent systems are quite different depending on the number of agents involved. A human interacting with only one computer agent during a dialogue needs to continuously participate in the exchange. In trialogues there are two agents, so there are more options available to the human (including social loafing and vicarious observation) and the conversation patterns can be more complex, illustrated by Operation ARIES!, which uses a number of patterns in teaching students the basics of research methodology. Conversational agent systems use online, continuous, formative assessment of human abilities, achievements, and psychological states, tracked during the course of the conversations. Some of these formative assessment approaches are incorporated in the PISA 2015 assessment of collaborative problem solving. However, this chapter focuses on formative assessment in learning environments rather than on summative assessments.
Chapter
The ATC21STM project followed a research and development plan that consisted of five phases: conceptualisation, hypothesis formulation, development, calibration and dissemination. (The acronym ATC21STM has been globally trademarked. For purposes of simplicity the acronym is presented throughout the chapter as ATC21S.) Within the conceptualisation phase, the project focused on the definition of twenty-first century skills. This chapter outlines the selection and conceptualisation of the skills to be assessed. It describes how this led to the development of hypothesised learning progressions which portrayed how the skills might vary across more and less adept individuals. Assessment tasks were commissioned to be developed from a mixture of commercial agencies and universities. The tasks were then subjected to concept checking, cognitive laboratories, pilot studies and calibration trials. The final stage of the process is dissemination, which includes the development of scoring, reporting and teaching in the Assessment and Teaching of 21st Century Skills system.