The Andes Physics Tutoring System: Five Years of Evaluations.
ABSTRACT Andes is a mature intelligent tutoring system that has helped hundreds of students improve their learning of university physics. It replaces pencil and paper problem solving homework. Students continue to attend the same lectures, labs and recitations. Five years of experimentation at the United States Naval Academy indicates that it significantly improves student learning. This report describes the evaluations and what was learned from them.
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ABSTRACT: ITSs (Intelligent Tutoring Systems) provide a way of addressing some of the issues that the more traditional CAI (Computer Aided Instruction) systems do not address - the individual learning needs and individual learning abilities and levels of users - so that the user is in control of their learning experience. An ITS needs to be able to provide an explanation, for a real world situation, that successfully meets the needs of the user. To ensure relevant explanation content requires the ITS be based on sound planning principles and tutoring knowledge as well as knowledge of the domain and the user. To ensure a coherent explanation structure requires that the tutoring knowledge be applied with full recognition of the knowledge of the domain and the user. For a model of the user's knowledge to be effective, the system should be able to use it to enhance the flexibility and responsiveness of explanations generated. A user model should guide the generation of explanations so they are pitched at the correct level of the user's existing knowledge; models should be able to actively support the needs of the user so that the user's efforts in seeking out information are minimised. The aim of this research is to generate effective, flexible and responsive explanations, in educational software systems, through developing better explanation facilities than exist in currently available ITS software. In achieving this aim, I am advancing research into dialogue planning and user modelling. The explanation facilities described meet the requirements of an explanation that is tailored to the user's needs, a sound theory from which particular explanations are constructed, and a user model that can accurately represent the behaviour and beliefs of the user. My research contributions include explicitly and formally representing discourse planning / reasoning, from both the user's view and the tutor's view so that they can be clearly understood and represented in the ITS. More recent planners have adopted approaches that can be characterised as using adaptations of the classical planning approach, with informally specified planning algorithms and planning languages. Without clear, explicit and full descriptions of actions and the planning algorithm we can not be certain of the plans that such planners produce. I adopt a theoretically rigorous approach based on classical planning theory - the actions available to the planner, the planning language and algorithm should be explicitly represented to ensure that plans are complete and consistent. Classical regression planning uses dynamic planning thus enabling the system to be flexible in a variety of situations and providing the responsiveness required for an ITS. I take a theoretically rigorous approach in constructing a well specified model of discourse, building upon existing research in the area. I present a tutoring module that is able to find a way to motivate the user to take a recommended action, by relating the action to the user's goals, and that is able to reason about the text structure to generate an effective explanation - putting together several clauses of text whilst maintaining coherency. As part of developing such constructs for motivating, enabling and recommending, as well as constructs for structuring text, I use a pedagogic model based on the principled approach of (i) advising the user to take an action (ii) motivating the user to want to take the action and (iii) ensuring the user knows how to do the action. I take a clear and realistic approach to user modelling, making explicit models of the user's behaviour and beliefs. I adopt a theoretically rigorous approach, formally distinguishing between the user's reasoning and their actions, so they can be focused on separately. Formally making this distinction, more easily enables models of the user's reasoning to be tailored to the individual user. To enable the tutor to consider the full impact on the user, of the information to be delivered to the user, I use different plan spaces. I explicitly identify the different perspectives of the user and the tutor so that they can be focused on separately to generate an explanation that is tailored to the user. In my approach, reasoning about the user's skills, rules and knowledge is independent from reasoning about those of the tutor.
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ABSTRACT: Prior research has shown that tutored problem solving with intelligent software tutors is an effective instructional method, and that worked examples are an effective complement to this kind of tutored problem solving. The work on the expertise reversal effect suggests that it is desirable to tailor the fading of worked examples to individual students’ growing expertise levels. One lab and one classroom experiment were conducted to investigate whether adaptively fading worked examples in a tutored problem-solving environment can lead to higher learning gains. Both studies compared a standard Cognitive Tutor with two example-enhanced versions, in which the fading of worked examples occurred either in a fixed manner or in a manner adaptive to individual students’ understanding of the examples. Both experiments provide evidence of improved learning results from adaptive fading over fixed fading over problem solving. We discuss how to further optimize the fading procedure matching each individual student’s changing knowledge level. KeywordsCognitive tutor-Worked examples-Adaptive fading-Expertise reversal effectInstructional Science 01/2010; 38(3):289-307. · 1.83 Impact Factor
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ABSTRACT: We developed and evaluated a multimodal affect detector that combines conversational cues, gross body language, and facial features. The multimodal affect detector uses feature-level fusion to combine the sensory channels and linear discriminant analyses to discriminate between naturally occurring experiences of boredom, engagement/flow, confusion, frustration, delight, and neutral. Training and validation data for the affect detector were collected in a study where 28 learners completed a 32-min. tutorial session with AutoTutor, an intelligent tutoring system with conversational dialogue. Classification results supported a channel × judgment type interaction, where the face was the most diagnostic channel for spontaneous affect judgments (i.e., at any time in the tutorial session), while conversational cues were superior for fixed judgments (i.e., every 20 s in the session). The analyses also indicated that the accuracy of the multichannel model (face, dialogue, and posture) was statistically higher than the best single-channel model for the fixed but not spontaneous affect expressions. However, multichannel models reduced the discrepancy (i.e., variance in the precision of the different emotions) of the discriminant models for both judgment types. The results also indicated that the combination of channels yielded superadditive effects for some affective states, but additive, redundant, and inhibitory effects for others. We explore the structure of the multimodal linear discriminant models and discuss the implications of some of our major findings. KeywordsMultimodal affect detection-Conversational cues-Gross body language-Facial features-Superadditivity-AutoTutor-Affective computing-Human-computer interactionUser Modeling and User-Adapted Interaction 01/2010; 20:147-187. · 1.60 Impact Factor
The Andes Physics Tutoring System:
Five Years of Evaluations
Kurt VANLEHN1, Collin Lynch1, Kay Schulze2, Joel A. Shapiro3, Robert Shelby4,
Linwood Taylor1, Don Treacy4, Anders Weinstein1, and Mary Wintersgill4
1 LRDC, University of Pittsburgh, Pittsburgh, PA, USA
2 Computer Science Dept., US Naval Academy, Annapolis, MD, USA
3 Dept. of Physics and Astronomy, Rutgers University, Piscataway, NJ, USA
4 Physics Department, US Naval Academy, Annapolis, MD, USA
Abstract. Andes is a mature intelligent tutoring system that has helped hundreds of
students improve their learning of university physics. It replaces pencil and paper
problem solving homework. Students continue to attend the same lectures, labs and
recitations. Five years of experimentation at the United States Naval Academy
indicates that it significantly improves student learning. This report describes the
evaluations and what was learned from them.
Although many students have personal computers now and many effective tutoring
systems have been developed, few academic courses include tutoring systems. A major
point of resistance seems to be that instructors care deeply about the content of their
courses, even down to the finest details. Most instructors are not completely happy with
their textbooks; adopting a tutoring system means accommodating even more details that
they cannot change.
Three solutions to this problem have been pursued. One is to include instructors in the
development process. This lets them get the details exactly how they want them, but this
solution does not scale well. A second solution is to include the tutoring system as part of a
broader reform with significant appeal to instructors. For instance, the well-know
Cognitive Tutors (www.carnegielearning.com) are packaged with an empirically grounded,
NCTM-compliant mathematics curriculum, textbook and professional development
program. A third solution is to replace grading, a task that many instructors would rather
delegate anyway. This is the solution discussed here.
The rapid growth in web-based homework (WBH) grading services, especially for
college courses, indicates that instructors are quite willing to delegate grading to
technology. In physics, the task domain discussed here, popular WBH services include
WebAssign (www.webassign.com), CAPA (www.lon-capa.org/index.html) and Mastering
Physics (www.masteringphysics.com). Ideally, instructors still chose their favorite
problems from their favorite textbooks, and they may still use innovative interactive
instruction during classes and labs.  All that changes is that students enter their
homework answers on-line, and the system provides immediate feedback on the answer. If
the answer is incorrect, the student may receive a hint and may get another chance to derive
the answer. Student homework scores are reported electronically to the instructor.
Although WBH saves instructors time, the impact on student learning is unclear.
WBH’s immediate feedback might increases learning relative to paper-and-pencil
homework, or it might increase guessing and thus hurt learning. Although most studies
merely report correlations between WBH usage and learning gains, 3 studies of physics
instruction have compared learning gains of WBH to those of paper-and-pencil homework
(PPH). In the first study,  one of 3 classes showed more learning with WBH than PPH.
Unfortunately, PPH homework was not collected and graded, but WBH was. It could be
that the WBH students did more homework, which in turn caused more learning. In the
other studies, [3, 4] PPH problem solutions were submitted and graded, so students in the
two conditions solved the roughly the same problems for the same stakes. Despite a large
number of students and an impressive battery of assessments, none of the measures showed
a difference between PPH students and WBH students. In short, WBH appears to neither
benefit nor harm students’ learning compared to PPH.
The main goal of the Andes project is to develop a system that is like WBH in that it
replaces only the PPH of a course, and yet it increases student learning. Given the null
results of the WBH studies, this appears to be a tall challenge. This paper discusses Andes
only briefly—see  for details. It focuses on the evaluations that test whether Andes
increases student learning compared to PPH.
The function and behavior of Andes
In order to make Andes’ user interface easy to learn, it is as much like pencil and paper
as possible. A typical physics problem and its solution on the Andes screen are shown in
Figure 1. Students read the problem (top of the upper left window), draw vectors and
coordinate axes (bottom of the upper left window), define variables (upper right window)
and enter equations (lower right window). These are actions that they do when solving
physics problems with pencil and paper.
Unlike PPH, as soon as an action is done, Andes gives immediate feedback. Entries
are colored green if they are correct and red if they are incorrect. In Figure 1, all the entries
are green except for equation 3, which is red.
Also unlike PPH, variables and vectors must be defined before being used. Vectors
and other graphical objects are first drawn by clicking on the tool bar on the left edge of
Figure 1, then drawing the object using the mouse, then filling out a dialogue box. Filling
out these dialogue boxes forces students to precisely define the semantics of variables and
vectors. For instance, when defining a force, the student uses menus to select two objects:
the object that the force acts on and the object the force is due to.
Andes includes a mathematics package. When students click on the button labeled
“x=?” Andes asks them what variable they want to solve for, then it tries to solve the
system of equations that the student has entered. If it succeeds, it enters an equation of the
form <variable> = <value>. Although physics students routinely use powerful hand
calculators, Andes’ built-in solver is more convenient and avoids calculator typos.
Andes provides three kinds of help:
• Andes pops up an error messages whenever the error is probably due to lack of
attention rather than lack of knowledge. Typical slips are leaving a blank entry
in a dialogue box, using an undefined variable in an equation (which is usually
caused by a typo), or leaving off the units of a dimensional number. When an
error is not recognized as a slip, Andes merely colors the entry red.
• Students can request help on a red entry by selecting it and clicking on a help
button. Since the student is essentially asking, “what’s wrong with that?” we
call this What’s Wrong Help.
• If students are not sure what to do next, they can click on a button that will give
them a hint. This is called Next Step Help.
What’s Wrong Help and Next Step Help generate a hint sequence that usually has three
hints: a pointing hint, a teaching hint and a bottom-out hint. As an illustration, suppose a
student who is solving Figure 1 has asked for What’s Wrong Help on the incorrect equation
Fw_x = -Fw*cos(20 deg). The first hint, which is a pointing hint, is “Check your
trigonometry.” It directs the students’ attention to the location of the error, facilitating self-
repair and learning. [6, 7] If the student clicks on “Explain more”, Andes gives a teaching
If you are trying to calculate the component of a vector along an axis, here is a general
formula that will always work: Let θV be the angle as you move counterclockwise
from the horizontal to the vector. Let θx be the rotation of the x-axis from the
horizontal. (θV and θx appear in the Variables window.) Then: V_x = V*cos(θV-θx)
and V_y = V*sin(θV-θx).
We try to keep teaching hints as short as possible, because students tend not to read long
hints. [8, 9] In other work, we have tried replacing the teaching hints with either
multimedia [10, 11]or natural language dialogues.  These more elaborate teaching hints
significantly increased learning, albeit in laboratory settings.
If the student again clicks on “Explain more,” Andes gives the bottom-out hint,
“Replace cos(20 deg) with sin(20 deg).” This tells the student exactly what to do.
Andes sometimes cannot infer what the student is trying to do, so it must ask before it
can give help. An example is shown in Figure 1. The student has just asked for Next Step
Help and Andes has asked, “What quantity is the problem seeking?” Andes pops up a
Figure 1: The Andes screen (truncated on the right)
menu or a dialogue box for students to supply answers to such questions. The students’
answer is echoed in the lower left window.
In most other respects, Andes is like WBH. Instructors assign problems via email.
Students submit their solutions via the web. Instructors access student solutions via a
spreadsheet-like gradebook. They can accept Andes’ scores for the problems or do their
own scoring, and so on.
Andes was evaluated in the U.S. Naval Academy’s introductory physics class every fall
semester from 1999 to 2003. This section describes the 5 evaluations and their results.
Andes was used as part of the normal Academy physics course. The course has
multiple sections, each taught by a different instructor. Students in all sections take the
same final exam and use the same textbook but different instructors assign different
homework problems and give different hour exams, where hour exams are in-class exams
given approximately monthly. In sections taught by the authors (Shelby, Treacy and
Wintersgill), students were encouraged to do their homework on Andes. Each year, the
Andes instructors recruited some of their colleagues’ sections as Controls. Students in the
Control sections did the same hour exams as students in the Andes section.
Control sections did homework problems that were similar but not identical to the ones
solved by Andes students. The Control instructors reported that they required students to
hand in their homework, and credit was given based on effort displayed. Early in the
semester, instructors marked the homework carefully in order to stress that the students
should write proper derivations, including drawing coordinate systems, vectors, etc. Later
in the semester, homework was graded lightly, but instructors’ marks continued the
emphasis on proper derivations. In some classes, instructors gave a weekly quiz consisting
of one of the problems from the preceding homework assignment. All these practices
encouraged Control students to both do the assignments carefully and to study the solutions
that the instructor handed out.
The same final exams were given to all students in all sections. The final exams
comprised approximately 50 multiple choice problems to be solved in 3 hours. The hour
exams had approximately 4 problems to be solved in 1 hour. Thus, the final exam
questions tended to be less complex (3 or 4 minutes each) than the hour exam questions (15
minutes each). On the final exam, students just entered the answer, while on the hour
exams, students showed all their work to derive an answer. The hour exam results will be
3.1 Hour exam results
Table 1 shows the hour exam results for all 5 years. It presents the mean score (out of
100) over all problems on one or more exams per year. In all years, the Andes students
scored reliably higher than the Control students with moderately high effect sizes, where
effect size defined as (Andes_mean – Control_mean)/Control_standard_deviation. The
Table 1: Hour exam results
70.0 (13.6) 71.8 (14.3)
57.1 (19.0) 64.4 (13.1)
< .0001 .003
Andes mean (SD)
Control mean (SD) 70.4 (15.6)
1999 evaluation had a lower effect size, probably because Andes had few physics problems
and some bugs, thus discouraging students from using it. It should probably not be
considered representative of Andes’ effects, and will be excluded from other analyses in
In order to calculate overall results (rightmost column of Table 1), it was necessary to
normalize the exam scores because the exams had different grand means in different years
(the grand mean includes all students who took the exam). Each student’s exam score was
converted to a z-score, where z_score = (score – grand_mean) ÷ grand_standard_deviation.
The z-scores from years 2000 through 2003 were aggregated. The overall effect size was
The physics instructors recognize that the point of solving physics problems is not to
get the right answers but to understand the reasoning involved, so they used a grading
rubric for the hour exams that scored the students’ work in addition to their answers. In
particular, 4 subscores were defined (weights in the total score are shown in parentheses):
• Drawings: Did the student draw the appropriate vectors, axes and bodies? (30%)
• Variable definitions: Did the student use standard variable names or provide
definitions for non-standard names? (20%)
• Equations: Did the student display major principle applications by writing their
equations without algebraic substitutions and otherwise using symbolic equations
• Answers: Did the student calculate the correct number with proper units? (10%)
Andes was designed to increase student conceptual understanding, so we would expect it to
have more impact on the more conceptual subscores, namely the first 3. Table 2 shows the
effect sizes, with p-values from two-tailed t-tests shown in parentheses. Results are not
available for 2001. Two hour exams are available for 2002, so their results are shown
There is a clear pattern: The skills that Andes addressed most directly were the ones on
which the Andes students scored higher than the Control students. For two subscores,
Drawing and Variable definitions, the Andes students scored significantly higher then the
Control students in every year. These are the problem solving practices that Andes requires
students to follow.
The third subscore, Equations, can also be considered a measure of conceptual
understanding. However, prior to 2003, Andes was incapable of discriminating between
good and poor usage of equations, so it is not surprising that the Andes and Control
students tied on the Equations subscore in years 2000 and 2002. In 2003, Andes gave
students warnings and points off on their problem scores if their first use of a major
principle was combined algebraically with other equations. Although Andes could have
required students to obey this problem solving practice, it only suggested it. This may
explain why the Andes students still did no better than the Control students on the
Equations subscore in 2003.
The Answers subscore was the same for both groups of students for all years even
though the Andes students produced better drawings and variable definitions on those tests.
This suggests that the probability of getting a correct answer depends strongly on other
skills, such as algebraic manipulation, that are not measured by the more conceptual
subscores and not emphasized by Andes. The tied Answer subscores suggest that the
Table 2: Hour exam effect sizes broken down by subscore
1.82 (<.001) 0.49 (.003)
Variable definitions 0.88 (<.001) 0.42 (.009)
Equations 0.20 (.136) 0.12 (.475)
Answers -0.10 (.461) -0.09 (.585)
Andes students’ use of the equation solving tool did not seem to hurt their algebraic
manipulation on the hour exams.
3.2 Final Exam scores
A final exam covers the whole course, but Andes does not. However, its coverage has
steadily increased over the years. In 2003, Andes covered 70% of the homework problems
in the course. This section reports an analysis of the 2003 final exam data.
In this physics course, engineering and science majors tend to score higher on the final
exam than other majors. Unfortunately, there were reliably more engineers among the
Andes students than the non-Andes students (p < .0001, 3x2 Chi-squared test). Thus, for
each group of majors, we regressed the final exam scores against the students’ GPAs. (Of
the 931 students, we discarded scores from 19 students with unclassifiable majors or
extremely low scores). This yielded three statistically reliable linear models, one for each
type of major. For each student, we subtracted the exam score predicted by the linear
model from the student’s actual score. This residual score represents how much better or
worse this student scored compared to the score predicted solely on the basis of their GPA
and their major. That is, the residual score factors out the students’ general competence.
The logic is the same as that used with an ANCOVA, with GPA and major serving as
covariates instead of pre-test scores. (This kind of statistical compensation was
unnecessary in our analysis of the hour exams, because the distributions of majors and
student GPAs did not differ across conditions in any year.)
Using these residual scores, we evaluated Andes’ impact on students in each of the 3
groups of majors. As Table 3 indicates, the residual scores of the engineering and science
majors were not statistically different with Andes than with paper homework. However,
the other majors did learn more with Andes than with paper homework (p=0.013; effect
size = 0.52). Over all students, the mean residual scores for Andes students was higher than
for non-Andes students (p=0.028; effect size = 0.25).
As though we were gratified to see that Andes students learned more than non-Andes
students, we were not surprised that that Andes had little effect on the learning of the
engineering and science majors, for two reasons. (1) In many studies, instructional
manipulations tend to affect only the less competent students’ learning, because highly
competent students can usually learn equally well from the experimental and the control
instruction . (2) The engineering majors were concurrently taking a course on Statics,
which has very similar content to the physics courses. This dilutes the effect of Andes,
since it affected only their physics homework and not their Statics homework.
3.3 Comparing Andes to the “benchmark” system
Next we compare our results to results from one of the few large-scaled, controlled
field studies of intelligent tutoring systems in the open literature, namely, the evaluation of
a combination of an intelligent tutoring system (PAT) and a novel curriculum (PUMP),
which is now distributed by Carnegie Learning as the Algebra I Cognitive Tutor. The
evaluation was conducted by Koedinger et al. . It is arguably the benchmark against
Table 3: Residual scores on the 2003 final exam
Andes students 55
Non-Andes students 278
Andes students mean (SD) 0.74 (5.51)
Non-Andes students mean (SD) 0.00 (5.39)
Effect size 0.223
which all other tutoring systems should be compared.
Koedinger et al. used both experimenter-defined and standardized tests. Using the
experimenter-designed tests, they found effect sizes of 1.2 and 0.7. In our evaluation, the
closest matching measures are the Diagram and Variables components of the hour exams,
which tap the conceptual skills most directly taught by Andes. Surprisingly, these
assessments had exactly the same effect sizes as the Koedinger et al. tests: Diagrams: effect
size 1.21; Variables: effect size 0.69.
Koedinger et al. found smaller effect sizes, 0.3, when using multiple-choice
standardized tests. The standardized tests most closely match our multiple-choice final
exam, where Andes students scored marginally higher than non-Andes students with an
effect size of 0.25.
Thus, the Andes evaluations and the Koedinger et al. evaluations have remarkably
similar tests and effect sizes. They both have impressive 1.2 and 0.7 effect sizes for
conceptual, experimenter-designed tests, and lower effect sizes on standardized, answer-
The Andes evaluations differed from the Koedinger et al. evaluation in a crucial way.
The Andes evaluations manipulated only the way that students did their homework—on
Andes vs. on paper. The evaluation of the Pittsburgh Algebra Tutor (PAT) was also an
evaluation of the Pittsburgh Urban Mathematics Project curriculum (PUMP), which
focused on analysis of real world situations and the use of computational tools such as
spreadsheets and graphers. It is not clear how much gain was due to the tutoring system
and how much was due to the new curriculum. In our evaluation, the curriculum was not
reformed. The gains in our evaluation are a better measure of the power of intelligent
tutoring systems per se. This is good news for the whole field of intelligent tutoring
Conclusions and future work
It appears that we have succeeded in finding a way to use intelligent tutoring systems
to help students learn while replacing only their paper-and-pencil homework. Moreover,
Andes is probably more effective than existing WBH services, such as WebAssign, CAPA
and Mastering Physics. The existing evaluations, which were reviewed in the introduction,
suggest that WBH is no more effective than paper-and-pencil homework (PPH), whereas
Andes is significantly more effective than PPH. The effect sizes for the open response and
multiple choice exams are 0.61 and 0.25, respectively. To be certain that Andes is more
effective than WBH, however, one should compare it directly to one of these systems.
We have also shown that Andes’ benefits are similar in size to those of the
“benchmark” intelligent tutoring system developed by Anderson, Corbett and Koedinger
and now distributed by Carnegie Learning. However, Andes’ benefits were achieved
without attempting to reform the content of the course.
For the immediate future, we have three goals. The first is to help people all over the
world use Andes as the U.S. Naval Academy has done, as a homework helper for their
courses. Please see www.andes.pitt.edu if you are interested, and please view the training
video before trying to use the system.
The second goal is to develop a self-paced, open physics course based on Andes based
on mastery learning. We are currently looking for instructors who are interested in
developing such a self-paced physics course with us. Please write us if you are interested.
Lastly, the Pittsburgh Science of Learning Center (www.learnlab.org) uses Andes in its
physics LearnLab course. A LearnLab course is a regular course that has been heavily
instrumented so that investigators can test hypotheses with the same rigor as they would
obtain in the laboratory, but with the added ecological validity of a field setting.
This research was supported by the Cognitive Sciences Program of the Office of Naval
Research under grants N00019-03-1-0017 and ONR N00014-96-1-0260, and by NSF under
grant SBE-0354420. We gratefully acknowledge the Andes Alumni: Drs. Patricia
Albacete, Cristina Conati, Abigail Gertner, Zhendong Niu, Charles Murray, Stephanie Siler,
and Ms. Ellen Dugan
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