A Conceptual Framework for Task and Tool Personalisation in IS Education

Conference Paper (PDF Available) · December 2015with335 Reads

Conference: International Conference on Information Systems, At Fort Worth, Texas, USA
Learner-centred, self-regulated learning approaches such as flipped classrooms or personalised learning environments are popular. This paper analyses personalised learning in collaborative, self-regulated e-learning approaches applying the theory of cognitive fit to explain the personalisation of learning tasks and learning tools. The personalised learning framework (PLF) is presented defining the core constructs of such learning processes as well as a method of personalisation. The feasibility of the framework is demonstrated using a thought experiment describing its possible application to a university course on electronic negotiations as part of an IS curriculum. Current learning methods used in the course and new learning methods matching the PLF are compared and discussed critically, identifying potentials to improve personalised learning as well as avenues for personalised learning research.


A Framework for Personalised Learning in IS-Education
Thirty Sixth International Conference on Information Systems, Fort Worth 2015 1
A Conceptual Framework for Task and Tool
Personalisation in IS Education
Completed Research Paper
Philipp Melzer
University of Hohenheim
Stuttgart, Germany
Mareike Schoop
University of Hohenheim
Stuttgart, Germany
Learner-centred, self-regulated learning approaches such as flipped classrooms or
personalised learning environments are popular. This paper analyses personalised
learning in collaborative, self-regulated e-learning approaches applying the theory
of cognitive fit to explain the personalisation of learning tasks and learning tools.
The personalised learning framework (PLF) is presented defining the core
constructs of such learning processes as well as a method of personalisation. The
feasibility of the framework is demonstrated using a thought experiment describing
its possible application to a university course on electronic negotiations as part of
an IS curriculum. Current learning methods used in the course and new learning
methods matching the PLF are compared and discussed critically, identifying
potentials to improve personalised learning as well as avenues for personalised
learning research.
Keywords: personalisation, e-learning, IS education, cognitive fit
In recent years, the importance of e-learning has increased leading to a convergence of technological
and pedagogical innovation aiming for educational goals supported by technology (Garrison 2011).
Conforming to Dewey (1997, p.46) who noted that teachers are concerned with providing conditions
so adapted to individual needs and powers as to make for the permanent improvement of observation,
suggestion, and investigation”, the importance of personalised learning has been recognised in
research and practice. Personalisation by a teacher, however, is only possible in small classes mostly
relying on face-to-face learning. To enable automatic personalisation, new methods using expert
systems or data mining approaches are employed leading to high investments in start-ups developing
and applying such technologies (Emerson 2013). According to the learning paradigm of
constructivism (Kafai 2006), only learners themselves are truly able to regulate their learning
processes. Such learner-centred, self-regulated approaches (such as learning in informal settings
directly at the workplace or flipped classrooms) are getting more and more popular shifting
responsibilities for organising the learning process from teachers to learners (Tsai et al. 2013). Self-
regulated personalisation not only includes time and pace but the definition of learning objectives and
even learning tasks to achieve these objectives. Such personalisation, however, requires a certain
awareness based on a profound evaluation of one’s own skills and learning preferences (Zimmerman
Personalised learning environments (PLEs) strive to support personalisation in self-regulated
learning. In contrast to virtual learning environments (VLEs), PLEs are not single systems but user-
configured sets of interchangeable social media (formerly Web 2.0) tools such as blogs, wikis, media
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sharing services, podcasts, social networks, or social bookmarking services (Attwell 2007). Due to
their ubiquitous availability, conjunction to private use, and independence of learning institutions,
PLEs are easy to set up and to use for individuals as well as for groups of learners. However,
configuration, usage, and evaluation of social media tools in the context of PLEs requires digital
literacy and awareness (McLoughlin and Lee 2010): (1) Although there is an increasing expectation
that learners as digital natives already possess digital literacy through the permanent engagement
with social media, there is also a strong need for explicit scaffolding as learners might not know how
to use such technologies for learning or see their relevance for learning (Katz and Macklin 2007); (2)
Constant private use of social media might also affect their behaviour adversely leading to impatience
or an overly casual approach to learning (CLEX 2009).
Both problems, i.e. the matching of learning preferences to learning tasks as well as to learning tools,
can be generalised to the class of matching problems which has been the topic of numerous studies in
the IS domain (e.g. Gupta and Anson 2014; Robey and Taggart 1981) and the learning sciences (e.g.
Kolb and Kolb 2005; Vermunt 1996). Although different kinds of cognitive styles or learning styles
have been analysed with different kinds of learning methods or IS designs, matches have rarely been
found. Until today, there is no consistent theory that is able to explain such matching processes
(Coffield et al. 2004; Pashler et al. 2009).
The research goal of this work is thus to explain and support self-regulated personalisation,
matching learning preferences to learning tasks and PLE tools. In contrast to previous attempts to
demonstrate specific matches between learning styles and learning methods or contents, this paper
focuses on learning tasks as the construct of personalisation which is defined by the learners
themselves providing an alternative method to define such matches. Therefore, this paper aims to
provide an overview of the heterogeneous theories of learning and cognitive fit (Vessey 1991) in
section 2 and integrate them into the PLF showing the main influence factors for collaborative, self-
regulated personalised learning in section 3. In section 4, the feasibility of the PLF will be
demonstrated by a thought experiment, applying it to an example university course which is part of an
IS curriculum. The paper concludes with a discussion and an outlook to future work.
Theoretical Foundations
The following section presents a literature review of the theories shaping the personalised learning
framework, integrating collaborative e-learning, personalised learning and cognitive fit.
Collaborative Electronic Learning
Several learning paradigms existing in the learning sciences are applied to e-learning, defining how
learners acquire knowledge (figure 1). Instructivism focuses on a teacher standing in front of the class
transmitting knowledge to the learners. Whilst Behaviourism (Skinner 1958) follows a stimuli-
response model where the human mind is modelled as a black box, Cognitivism (Tennyson 1992)
particularly investigates this black box modelling human memory. Cognitivism thereby focuses on the
information processing taking place along the transmission of knowledge. In contrast to instructivism,
constructivism (Jonassen 1990) defines learning as the construction of knowledge by the learners
using observation and reflective thinking. There are two major streams within constructivism, namely
situated learning in communities of practice (Lave and Wenger 1991), (aiming to explore authentic
problems) and constructionism (Kafai 2006) (which explicitly emphasises social aspects such as
learning in groups describing learning as an inseparable relationship between personal meaning
making and social influences) (Garrison 2011). Through social interaction between teachers and
learners as well as among learners, ideas are communicated and knowledge is constructed and
confirmed. Learners, therefore, have an important responsibility to manage the learning process and
achieve their learning goals while teachers merely assist this process.
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Figure 1: Taxonomy of E-Learning Paradigms (adapted from Melzer and Schoop 2014b,
To reflect the inherent connection of e-learning and constructionism, this paper follows the definition
of Garrison describing e-learning as “electronically mediated asynchronous and synchronous
communication for the purpose of constructing and confirming knowledge(Garrison 2011, p.2). This
is performed in Communities of Inquiry (CoIs). The CoI framework (figure 2) defines cognitive
presence, social presence, and teaching presence as key dimensions providing guidelines for
implementing and evaluating constructionist e-learning courses. Cognitive presence describes the
individual perception and acquisition of new knowledge, skills and abilities through critical discourse
and application to a problem domain. Social presence represents the transfer of these individual
efforts to a group of learners. CoIs focus on asynchronous exchange of text messages to enable
collaboration. This type of electronically mediated communication is described to be particularly
effective in facilitating critical discourse providing users with more time to think through their
utterances systematically and to document all statements making them public to the CoI. Sustainable
and cohesive groups of learners are particularly important to facilitate discourse providing each
individual with the opportunity to discuss and confirm individual knowledge as well as to help other
learners. Teaching presence represents the influence of the teacher moderating discourse ensuring an
open climate assisting the learning process. At the same time, the teacher is responsible for selecting
and preparing the learning contents according to the course goals to facilitate information processing
adhering to the learners’ preferences. Thereby learners need to be enabled to regulate and personalise
their learning experience themselves. Overall, these three heavily intertwined dimensions represent
the core of constructionist e-learning.
Figure 2: Community of Inquiry Theoretical Research Framework (Garrison 2011, p.23)
Personalised Learning
Personalised learning can be structured into two dimensions: (1) Who is responsible for the
personalisation a teacher or learning system on the one hand or the learners themselves on the
other hand; (2) What is going to be personalised - learning methods or learning content. Following
constructionism, a learner-centred approach to personalisation is pursued. Thereby, the paper focuses
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on personalisation of the learning method in a self-regulated fashion, keeping the learning contents
Personalised learning is usually related to individual characteristics and abilities of the learners. The
work of Jung on personality types (Jung 1923) has led to numerous theories and instruments on
learning styles (Coffield et al. 2004). They can be structured from largely constitutionally-based
factors to concrete learning approaches, strategies, orientations, and conceptions. Each learning style
is supposed to fit certain learning environments, methods, or scenarios. Personality-based factors
have been a topic in IS research, analysing cognitive styles in IS usage patterns (e.g. Robey and
Taggart 1981; Taggart et al. 1982) or learning styles in end-user trainings (e.g. Davis and Bostrom 199;
Melzer and Schoop 2014a). Several matches between learning styles and learning methods have been
proposed. However, many learning style instruments lack validation and findings are seldom
reproduced due to small effect sizes and numerous confounding variables. Thus, the value of using
personality traits in the design and usage of IS has been questioned (e.g. Gupta and Anson 2014;
Huber 1983).
Cognitive Fit
The theory of cognitive fit (Vessey 1991) emerged from the debate whether graphical or tabular
problem-solving tasks fit specific mental representations of how to solve these tasks. Emphasising
information processing theory, it created the theoretical foundations to match mental representations
for a task-solution to problem-solving tasks, proposing a consistent mental representation in human
memory to decrease complexity leading to a better problem-solving performance. Over the years, the
model of cognitive fit has been extended (figure 3) to grasp more detail including an internal
representation of the problem domain as well as an external problem representation (Shaft and
Vessey 2006). While the internal representation refers to knowledge about the meaning of symbols or
mathematical procedures which has to be retrieved from memory, the external representation refers
to shapes and positions of symbols on paper or other media which can be retrieved from the
environment. Both the internal and external representation influence each other leading to a mental
representation for task-solution. Cognitive fit has already been applied to interdependent tasks in the
domain of software engineering (Shaft and Vessey 2006). An analysis of the interwoven software
maintenance tasks of code comprehension and code modification showed how cognitive fit can be
used to explain and integrate effects on the overall problem.
Figure 3: Extended Cognitive Fit Model (Shaft and Vessey 2006, p.32)
Vessey and Galleta (Vessey and Galletta 1991, p.69) emphasise the importance of tasks as the unit of
analysis referring to the debate on cognitive styles: “Rather than seeking measures of cognitive style in
an attempt to explain the incremental effects of individual differences on performance, we suggest
seeking information processing skills that support a particular task […]”. We, therefore, use
cognitive fit as a new approach to personalised learning arguing that the self-regulated personalisation
of learning tasks and PLE tools are two parallel but interdependent processes of cognitive fit, where
the learners have to match their representations of the respective learning problem to specific learning
tasks and learning tools. Achieving such a fit in one or both matching processes should increase
learning performance. Following the idea of cognitive fit, personalised learning can be analysed
focusing on the configuration, management, and evaluation of learning tasks as well as learning tools
to infer preferences and predict learning performance. However, a clear-cut taxonomy of learning
tasks and learning tools is necessary to define possible matches.
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Taxonomy of Learning Tasks
Bloom’s taxonomy of learning objectives (Bloom et al. 1984), one of the most prominent taxonomies
in the learning sciences, defines learning tasks together with specific levels of knowledge as a two-
dimensional allocation of learning objectives in its revised version (Anderson and Krathwohl 2001).
The knowledge dimension differentiates knowledge on facts, concepts, or procedures from
metacognitive knowledge (i.e. knowing about one’s own knowledge). In self-regulated learning
through web-based systems, such meta-cognitive knowledge is particularly important because it is
used to organise and personalise the learning experience (Narciss et al. 2007). Knowledge can be
acquired performing different cognitive processes grouped in ascending order of complexity from
lower order thinking skills (i.e. remembering, understanding, and applying) to higher order thinking
skills (i.e. analysing, evaluating, and creating). Courses typically encompass several learning objectives
combining cognitive processes and knowledge levels. The taxonomy, furthermore, defines specific
learning tasks, which can be used to achieve these learning objectives for every cognitive process
(table 1). Bloom’s taxonomy shows its cognitivist roots as a tool for teachers to structure their classes
only describing knowledge acquisition omitting constructionist learning tasks focusing on situated
learning or collaboration.
Digital Learning Tasks
Lower Order
Bullet pointing, Highlighting, Bookmarking,
Social networking, Social bookmarking,
Favouriting/Local bookmarking, Searching,
Advanced Searches, Boolean searches, Blog
journaling, Twittering, Categorising, Tagging,
Commenting, Annotating, Subscribing
Running, Loading, Playing, Operating,
Uploading, Sharing, Editing
Higher Order
Mashing, Linking, Validating, Reverse
engineering, Cracking, Media Clipping
Blog commenting, Reviewing, Pos
Moderating, Collaborating, Networking,
Refactoring, Testing
Programming, Filming, Animating, Bloggin
Video blogging, Mixing, Wikiing, Publishing,
Videocasting, Podcasting, Directing
Table 1: Cognitive Process and Learning Tasks (adapted from Anderson and Krathwohl
2001; Churches 2009)
Churches (2009) applied Bloom’s taxonomy to digital learning extending it by learning tasks
performed in digital environments using social media tools as well as including the notion of
collaboration inherent to social media. Remembering can, therefore, be supported digitally by
highlighting words in a text, building a social network to ask experts, or searching and bookmarking
resources on the web, while understanding is facilitated by advanced searches using complex
expressions, journaling contents in (micro)-blogs, categorising or tagging it. Application tasks
represent lower as well as higher order thinking skills including running a software and especially
sharing content over media sharing services. Higher order thinking skills such as analysis and
evaluation include the mashing up, reverse engineering, commenting, or refactoring of content in
blogs focusing, for example, on reports and their assessment. Finally, the creation of content, as a
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main goal of social media, includes the complete generation and publishing of programs, videos,
wikis, podcasts etc. on the web (table 1).
Taxonomy of Learning Tools
Promoting openness, interoperability, and user control (Siemens 2007), PLEs reflect the idea of social
media. In contrast to VLEs, they represent an approach rather than a specific application where
learners can create, share, mash-up, and discuss content using the tools they prefer (Downes 2005).
Since PLEs by no means restrict the social media tools which can be used, and technological evolution
still produces numerous new kinds of tools, the definition of an exhaustive taxonomy of tools is
impossible. Thus, we focus on the most prominent types of tools which are used within PLEs, namely
microblogging services, social bookmarking services, podcasts, blogs, wikis, mindmaps, video sharing
platforms, and image creation services (Attwell 2007; Siemens 2007).
Such tools are configured and used within PLEs for two reasons: (1) customisation of the learning
environment providing ownership, control, and literacy and (2) social support through collaboration
with a learning group or across boundaries with practitioners facilitating the learning process
(Buchem et al. 2011). Supporting the individual dimensions ownership and control, learners will be
enabled to design and manage their learning processes breaking down learning objectives into
learning tasks based on individual learning preferences. Personalisation of tools thus is guided by the
learning tasks required to achieve the learning objectives (Bower et al. 2010; Churches 2009). Bower
et al (2010), consequently, propose a framework of social media learning designs assigning social
media learning tasks to Bloom’s taxonomy of learning objectives defining how specific social media
tools can be used to achieve certain learning objectives following a constructionist perspective (table
2). It must be noted that table 2 only shows a reduced version of social media tools for the sake of
clarity, omitting the concrete learning tasks that have to be defined w.r.t. a specific learning content.
The allocation of tools shows that social media facilitates the idea of constructionism by numerous
possibilities to create contents collaboratively. Regarding the knowledge dimension, microblogging
and social bookmarking services match the acquisition of factual knowledge while wikis provide
conceptual knowledge. Video-related tools such as recording software, podcasts, and media sharing
are especially suitable to acquire procedural knowledge. Finally, mindmaps and blogs focus on meta-
cognitive knowledge. The more constructive a tool is, the better it facilitates higher order thinking
skills (Bower et al. 2010).
The Cognitive Process
The Knowledge
Remember Understand Apply Analyse Evaluate Create
Blog, Wiki,
Wiki, Blog Mindmap
Video Blog, Video
Mindmap Mindmap Blog Blog Blog Mindmap
Table 2: Framework of Social Media Learning Designs (adapted from Bower et al. 2010,
pp. 190-191)
The Personalised Learning Framework
This section aims to integrate the heterogeneous theories described in the previous sections into the
personalised learning framework (PLF) to explain the process of personalised learning. Reflecting
constructionism inherently involving collaborative learning, the source of the PLF is not an individual
learner, but a Community of Inquiry. Although an inherent property of personalisation is its focus on
individuality, personalisation of tasks and tools in constructionist learning occurs in groups
considering the process of learning equally important than the learning outcomes. Therefore,
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individuals have to negotiate their preferred tasks and tools with their peers and teachers to find a
consensus. The core framework (figure 4) thus contains the CoI personalising learning tasks and
learning tools. Matching learning preferences of the learners to respective tasks and tools are
modelled as cognitive fit processes.
The analysis of personality traits as learning styles typically treats such styles as fairly stable.
Literature on personality-based learning styles, however, shows that there are numerous contextual
variables that often outshine personality traits and thus have to be considered in the framework
(Pashler et al. 2009). Classroom contextual factors such as learning styles, for example, are criticised
for their often normative nature. Defined and assessed by a teacher, a non-preferred style might lead
to disadvantages for the learner (Pintrich et al. 1993). Looking at informal learning scenarios, learning
motivation differs greatly. Learning goals need to be balanced between personal life, work life, and
other interests. A part-time learner’s motivation is often non-comparable to that of a full-time learner
(Haggis 2003).
Most of the time, PLEs are taken to be completely learner-driven environments, exceeding the
learning goals of a single course being available for further learning. However, the PLF adheres to the
narrow definition of PLEs adhering to a learning institution to “enable self-direction, knowledge
building, and autonomy by providing options and choice while still supplying the necessary structure
and scaffolding.” (McLoughlin and Lee 2010, p.33). If applied to a real university course, the learning
institution’s strategy and culture as well as its infrastructure will affect learning. A university’s strategy
is transferred to the staff and eventually to the students reflecting the country’s culture as well as a
learning culture.
Cognitive Fit and Personalised Learning
The PLF shows that the personalisation of learning tasks and learning tools are two interdependent
processes of cognitive fit. Learners personalise their learning experience throughout the learning
process configuring, managing, and evaluating tasks and tools to achieve their desired learning
objectives, at the same time acquiring awareness and digital literacy for further learning processes
(Narciss et al. 2007).
Learning awareness is an important prerequisite to personalisation in self-regulated learning. In the
model of cognitive fit, learning awareness is represented as internal representation of the learning
domain as well as external representation of the learning domain. The internal representation
contains experiences, feelings, and thoughts (i.e. which tools do I like to use; how do I want to break
down a learning objective into learning tasks?). The internal representation can be guided by
personality traits or learning preferences. The problem here is to retrieve and explicate such
information to make it accessible and understandable, which requires experience. The external
representation encompasses material such as written text or guidance by peers that present
information increasing the learning awareness (i.e. what tasks are available; which tools provide
which features). Complexity lies in finding such information e.g. on the internet. Both internal and
Figure 4: Conceptual Framework of Requirements for Personalised Learning
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external representation together form the mental representation of the task/tool solution, defining
how learners want to achieve learning objectives.
Cognitive Fit and the Personalisation of Learning Tasks
Regarding the personalisation between the learners’ mental representation of a learning task
solution and the respective learning task, there are three important factors reflecting the three
presences of the CoI framework, namely (1) task complexity, (2) individual experience, both reflecting
cognitive presence , and (3) external support reflecting social and teaching presence.
The concrete learning tasks complexity must match the complexity of the learner’s problem
representation (i.e. the mental representation of a learning task solution). Cognitive fit demonstrated
that achieving a fit between a task and a mental representation of a problem reduces mental
complexity and thus increases problem-solving performance. The presented taxonomy of learning
tasks distinguishes learning task complexity into lower order and higher order thinking skills.
Performing overly complex tasks leads to overburdened learners who are unable to execute the
learning task, while performing overly easy tasks leads to ineffective learning. To break down learning
objectives into matching learning tasks regarding their complexity, individual experience is an
important factor. In the domain of cognitive fit, higher information processing skills (e.g. through
experience) for a specific decision-making task as well as task and problem combination have been
demonstrated to increase decision-making performance (Vessey and Galletta 1991). Such meta-
cognitive knowledge about previously performed learning tasks, contents, or individual preferences
demonstrates the internal representation. The social constructionist notion of PLEs can also help to
create such knowledge by engaging in discussions with peers or teachers to confirm or dismiss
knowledge collaboratively fostering the exchange between internal and external knowledge of the
learning domain. Achieving a cognitive fit between this mental representation of the task-solution
and the learning task represents an optimally personalised learning task.
Cognitive Fit and the Personalisation of Learning Tools
A similar process takes place regarding the PLE tools used to achieve learning objectives. However,
these tools cannot achieve learning objectives alone, but support specific learning tasks. Therefore, the
learners have to match their mental representation of learning tool solution to a specific learning
task supported by a learning tool. There are several matches of tasks (e.g. discussion) to tools (e.g.
social networks) leading to a task-technology-fit while other combinations do not match. Predictors of
task-technology either reside within the tasks’ or technologies’ characteristics (Goodhue and
Thompson 1995). Task-related predictors facilitating fit are performing routine tasks, few task
interdependences, and power to define and orchestrate the tasks themselves. While the PLF fosters
the hand-over of responsibilities to the learners to create such openness, learning is seldom focusing
on easy routine tasks. Technology-related predictors are the experience of the user with a specific
software and the departmental background, both pointing out the necessity of digital literacy.
However, it is assumed that achieving a cognitive fit in the personalisation of learning tools implicitly
leads to a task-technology-fit, since the learning tasks influence both processes. Investigating
cognitive fit, analyses have been conducted w.r.t. tools supporting the decision process (e.g. structured
English, decision tables or decision trees) in programming tasks. Cognitive fit could show specific
matching conditions that increased performance (Vessey and Weber 1986).
We will complement these findings from a learning perspective, analysing the PLE-tool-selection-
process, which depends on (1) the overarching learning objectives and outcomes, (2) the respective
dimensions of knowledge and cognitive processes expected, (3) the type of pedagogy applied, and (4)
the preferred modalities of representation (Bower et al. 2010). This confirms the importance of a clear
communication of learning objectives and the freedom and awareness to deconstruct them to concrete
learning tasks to achieve learning outcomes. Digital literacy is also important, referring to the internal
representation, to know which PLE tools enable which learning outcomes. Regarding the type of
pedagogy, however, social media tools particularly support higher order thinking skills such as the
creation of contents in blogs or wikis. Finally, learners can influence the preferred mode of
presentation choosing for example blogs over image creation. Achieving a cognitive fit between this
mental representation of the learning tool-solution and the learning task supported by a learning
tool represents an optimally personalised learning tool.
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Synthesis of the Personalisation of Tasks and Tools
The analysis of cognitive fit in interdependent processes proposes that both personalisation of
learning tasks and personalisation of learning tools run in parallel for each sub-task (Shaft and Vessey
2006). The resulting mental representations of the learning task-solution and mental representation
of the learning tool solution are then integrated into one mental representation for personalised
learning again requiring a fit, consequently leading to improved learning performance. Increasing
learning awareness via the facilitation of constant (re-)evaluation of the internal representation as
well as external representation enables the learners to achieve cognitive fit regarding their mental
representation of the learning task-solution, mental representation of the learning tool solution, and,
consequently, the mental representation of personalised learning increasing learning performance
(figure 5). Learning performance is thus defined as the degree to which the learning outcomes fulfil
the learning objectives. In a constructionist learning experience, learning outcomes can be divided
into cognitive, affective, and psychomotor outcomes (Bloom et al. 1984). However, this paper focuses
primarily on the cognitive outcomes.
Figure 5: Cognitive Fit in Personalised Learning (adapted from Shaft and Vessey 2006,
An Example Application of the Personalised Learning Framework
The feasibility of the PLF is demonstrated by applying it to an actual university course called
Advanced Negotiation Management (ANM) in a thought experiment. First, the status quo of teaching
in ANM is described leading to a detailed description of learning methods used and contents taught.
We will then present the application of the PLF to ANM, resulting in a new course with the identical
content and learning objectives but with different learning methods facilitating collaborative learning
and self-regulated personalisation.
Teaching Electronic Negotiations in Information Systems
Negotiations represent complex management tasks comprising of interdependent communication and
decision making processes (Bichler et al. 2003). As such, they are often included in IS or business
administration curricula in higher education preparing students for their jobs. Electronic
communication media such as e-mail are increasingly used for negotiations, although they possess
certain obstacles which inhibit optimal negotiation performance (Schoop et al. 2008). For example,
communication is unstructured; archiving of messages is left to the negotiators; and decision-making
in multi-attributive negotiations is challenging. Electronic negotiations are defined as negotiations
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supported by electronic means providing additional support features (Ströbel and Weinhardt 2003).
Negotiation support systems (NSSs), as archetypes of information systems, aim to support human
negotiators providing communication support, decision support, document management, and further
support functionalities (Schoop et al. 2003; Schoop 2010).
Negotiation pedagogy in management education largely focuses on instructivist face-to-face courses
(Lewicki 1997). E-learning courses on negotiations are scarce, providing web-based trainings that
mainly follow instructivism sometimes including simulations (Eliashberg et al. 1992; Kaufman 1998).
Nevertheless the necessity of combining conceptual and procedural knowledge is acknowledged by
employing explicit examples, case studies, negotiation experts, or negotiation simulations
(Loewenstein and Thompson 2006). Practicing the use of NSSs additionally requires e-negotiation-
related content such as electronic communication media, specific support features, and experience in
using NSSs. In electronic negotiation courses, learner motivation is usually very high facilitating self-
regulated learning approaches (Köszegi and Kersten 2003). Because of the collaborative nature of
negotiations, the process of negotiation itself is often seen as a collaborative learning task (Andriessen
Advanced Negotiation Management: Status Quo
The current ANM represents a typical half-year university course involving around 100 full-time
graduate students from management-related subjects such as Information Systems, Management, or
International Business and Economics. The course consists of weekly lectures and a negotiation
journal. The journal is graded and provides half of the final grade. The other half comes from the end-
of-course exam. ANM is designed to afford a total of 180 hours of work per student and semester.
Teaching is supported using the VLE ILIAS (Graf and List 2005) to share learning material, upload
and evaluate assignments, and facilitate communication between students as well as with teachers.
Learning tools are completely pre-defined, requiring assignments to be turned in as Microsoft Office
documents prescribing a minimum word count. Besides the official bulletin board and e-mail for
questions and answers, other communication channels are not actively supported.
The ANM lecture covers face-to-face and electronic negotiations in a holistic manner, beginning with
basic definitions and characteristics, then outlining the negotiation process. Preparation, execution,
and evaluation of negotiations are taught applying them to electronic negotiations focusing on
communication, decision making and mediation aspects. Finally, selected topics from negotiation
research (e.g. intercultural aspects) are discussed. The lecture involves numerous interactive
individual and group tasks to enable students to experience negotiation aspects first-hand. For
example, to illustrate negotiator profiling, students have to judge their fellow learners without talking
to each other and report about their interests. To experience different negotiation styles (Kilmann and
Thomas 1992), students engage in negotiation role plays with each other portraying specific styles,
eventually evaluating each other’s performance. Besides these interactive elements performed during
lectures, the negotiation journal complements teaching providing several assignments to be
completed outside the lecture to facilitate practical experience and reflection. All of these assignments
have to be handed in in textual form or as a presentation for grading as well as feedback. The first
assignment is a summary of individual expectations regarding the course and previous negotiation
experience. Later on, students have to make requests in real-life contexts to experience and analyse
when a person is not willing to fulfil a request and thus not willing to enter into a negotiation. The
major assignment is to engage in an electronic negotiation simulation with fellow students or
practitioners conforming to a predefined case study lasting from one to two weeks. This includes
preparation, execution, and evaluation of this negotiation and of the negotiation partner, thereby
applying the knowledge learned. Specific aspects of ex-post negotiation analysis are also practiced
analysing negotiation scenes in movies (Kunkel et al. 2006).
Table 3 assigns the learning methods described above to their respective learning objectives according
to Bloom’s taxonomy. Although there is no real separation between passive lecture and interactive
lecture as both are intertwined, they represent different methods leading to different objectives. While
the passive part of the lecture focuses on lower order thinking skills regarding negotiation knowledge
using slides and readings presented by the teacher for explanations, the interactive parts, including
discussions, role plays and case studies focus on higher order thinking skills e.g. by portraying specific
negotiation styles in role plays. The assignments of the negotiation journal especially focus on higher
order thinking skills and conceptual negotiation knowledge (e.g. evaluating methods for negotiation
analysis applying them to movie scenes) as well as procedural negotiation knowledge (e.g. adoption of
a negotiation process model in the negotiation simulation) being intertwined with the interactive
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Thirty Sixth International Conference on Information Systems, Fort Worth 2015 11
lecture. Metacognitive knowledge is not explicitly addressed in the course, as it is very much
prescribed by the teacher.
The Cognitive Process
The Knowledge
Remember Understand Apply Analyse Evaluate Create
Passive Lecture
Interactive Lecture (Q&A)
Passive Lecture
Interactive Lecture
(Role Plays, Case
Journal (Expectations,
Passive Lecture
Journal (Movie Analysis, Simulation)
Not Addressed
Table 3 Status Quo of Learning Methods according to Learning Objectives
Advanced Negotiation Management: Introducing the Personalised Learning
Implementing the PLF to ANM means: (1) facilitating the construction of CoI to enable learning in
groups, providing an open climate (cf. components of figure 4), and (2) supporting self-regulated
personalisation following cognitive fit regarding learning tasks and tools (cf. relationships in figure 4).
The learning method of the flipped classrooms neither facilitates personalisation per se, nor is it the
only learning method being able to support self-regulated personalisation, however, it matches the
learning objectives of ANM as well as provides enough openness for the PLF combining passive and
interactive parts (Bishop and Verleger 2013). Therefore, we decided to follow the four-step cyclic
model of the flipped classroom by Oeste et al (2014) which is iteratively processed. One example
iteration of this process will be described in the following to show how the PLF can be implemented.
The negotiation journal runs in parallel to the online and co-presence sessions, providing more
complex assignments following a self-regulated approach at the same time fostering diversity of tasks
and tools compared to the status quo. Thus, PLEs can be introduced to a large scale, providing
benefits such as collaborative self-regulated exploration and easy access to authentic tasks facilitating
higher order thinking skills, consequently transforming journal entries to public blogs or wikis
combining videos, images, or podcasts commented and assessed by peers and teachers.
In the first step (Objectives), an outline of the course is provided defining learning objectives and
constraints regarding learning tasks, tools and collaboration. In the online learning phase, access to a
course-related knowledge base is provided, containing learning units, videos, and readings to acquire
basic factual and conceptual knowledge about negotiation basics, definitions, and seminal theories e.g.
regarding negotiation process models and underlying phases (Adair and Brett 2005). The negotiation
journal complements the iterations of the flipped classroom providing practical assignments. Similar
to the status quo of ANM, a negotiation simulation can be used to illustrate the negotiation process,
however, being executed in groups in a larger context requiring exploration of important concepts
beforehand and evaluation afterwards. The focus of step one is to organise the learning process
negotiating deconstruction of learning objectives into tasks and tools. Therefore, the learning groups
have to gather knowledge regarding the relevant topics (i.e. negotiation basics) as well as regarding
the learning process (i.e. learning tools in the domain of negotiations) referring to experiences and the
knowledge base (i.e. internal and external representation of the learning domain/tool in figure 5) to
achieve a cognitive fit.
In step two (Exploration), students engage in learning gathering knowledge in a self-regulated,
authentic way. For example to achieve the learning objective of being able to conduct electronic
negotiations, students gather information (e.g. on characteristics of electronic communication media
relevant for negotiations) on the internet, in papers, or in books. Conforming to the PLF, students are
free to choose learning tools (e.g. mindmaps or wikis) to paraphrase and rearrange relevant concepts.
Training materials and access to negotiation support systems is provided, including it in the PLE, to
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Thirty Sixth International Conference on Information Systems, Fort Worth 2015 12
get the students familiar with such a system and prepare possible negotiation scenarios. As part of the
negotiation journal, the simulation is conducted during this step. Conducting an electronic
negotiation conforming to a case study, the students can apply, analyse and evaluate their knowledge
acquired in the previous steps. A first form of re-evaluation and assessment is conducted within the
learning groups aiming to achieve a satisfying result for all members. Further reflection will be
encouraged as the student groups have to keep an electronic diary about the negotiation facilitating
evaluation and creation of knowledge. Such a blog entry could link video clips to textual explanations
of the negotiation process. Again the students are able to choose for example the mode of
representation using different social media tools increasing ownership and control, which
consequently benefits satisfaction and learning outcomes. The focus of step two however, is on the
management of these tools during execution of the learning tasks.
Step three (Evaluation) represents the first face-to-face session focusing on the interactive discussion
of the previous steps to clarify and consolidate knowledge acquisition. Student groups present their
negotiation diaries to each other and discuss their negotiation with their partnering groups. Students
thus can present their expert knowledge regarding their individual learning objectives and their
fulfilment, spreading this knowledge and thereby educating their peers, while the teacher moderates
this process. Additionally the learning process should be evaluated, providing assessments of the tool
selection, management and achievement of learning outcomes to the peers.
Finally, step four (Immersion) focuses on the immersion of the knowledge acquired, employing
further interactive presence learning by working with peer instruction, role plays, case studies, and
readings exercised and discussed in class. Peer instruction (Mazur 1997) aims to deepen knowledge
acquisition by posing realistic questions to the students integrating several of the learnt concepts.
These questions can be answered anonymously via electronic voting systems or traditional methods
requiring students to persuade their peers of their answer. Thus, peer instruction supports the
integration of knowledge learnt in the self-regulated parts of the flipped classroom avoiding to
embarrass students who opted for a wrong answer.
The concept of the flipped classroom presents a learning method, which fits the requirements of the
PLF. Table 4 shows how the learning objectives of ANM can be addressed with implementing these
methods as described above making them comparable to the current approach (cf. table 3). The online
parts of the flipped classroom (Objectives and Exploration) improve the passive lecture focusing on
lower order thinking skills, the co-presence parts replace the interactive lecture focusing on higher
order thinking skills, they are much more intertwined with the negotiation journal integrating higher
order thinking skills in early phases. In total, the focus on the negotiation journal is increased
fostering its self-regulated and collaborative character. In contrast to the current approach, meta-
cognitive knowledge is now explicitly addressed communicating objectives in the beginning to scaffold
the students choosing tasks and tools and facilitating peer assessment during the evaluation.
The Cognitive Process
The Knowledge
Remember Understand Apply Analyse Evaluate Create
Flipped Classroom (Objectives & Exploration & Evaluation)
Flipped Classroom (Exploration & Immersion)
Self-regulated Journal
Flipped Classroom
(Exploration & Immersion)
Self-regulated Journal (Exploration/Simulation)
Self-regulated Journal (Objectives, Evaluation)
Table 4 Learning Methods according to Learning Objectives applying the PLF
The following section compares the status quo of ANM with its modified version applying the PLF.
Advantages and disadvantages of the framework are discussed from a learner’s perspective as well as
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Thirty Sixth International Conference on Information Systems, Fort Worth 2015 13
from a teacher’s perspective also integrating external influence factors guiding the implementation of
self-regulated personalisation in university courses.
The main advantage of the personalised learning framework is that it enables the learners themselves
to personalise their learning experience in a self-regulated way. By handing over the responsibility for
personalisation to the learner (who is then able to deconstruct learning objectives into tasks and
tools), teacher-driven personalisation using learning styles becomes obsolete. Results of previous
studies on learning styles and personalisation show its relevance; however, individual learning styles
are too coarse a measure to define reliable matches between learning styles and learning methods
(Gupta and Anson 2014; Melzer and Schoop 2014a). Personalisation is thus not imposed by the
teacher anymore, but by the learners being scaffolded by the teacher. Furthermore, the PLF can be
used as an alternative way to enable personalised learning and to explain its underlying relationships,
deriving possible support capabilities regarding the personalisation of learning tasks and learning
Self-regulated personalisation also improves the alignment of tasks within a course towards a central
theme, which plays a pivotal role for learner satisfaction (Chan et al. 2014). However, the self-
regulated alignment requires additional effort in negotiating tasks and tools in the learning group
before engaging in the learning itself. In these negotiations, network effects (Shapiro and Varian 1999)
play a vital role reducing the number of possible tools considerably, often inhibiting cognitive fit. Such
a negotiation, however, is part of the learning process itself enforcing digital literacy and facilitating
personal development (Hirshon 2005).
Collaborative, self-regulated learning heavily shifts responsibilities from the teacher to the learners
providing ownership and control (Buchem et al. 2011) requiring extensive scaffolding (Tsai et al.
2013). Pedagogy in self-regulated courses must enable learners to make informed educational
decisions providing metacognitive knowledge such as learning awareness and digital literacy. At the
same time, open learning environments must be created encouraging application of diverse skills and
knowledge with learner-centred forms of feedback and assessment (Green et al. 2005). As a
consequence, self-regulated courses shift the focus towards learning processes instead of learning
outcomes (Azevedo et al. 2008). Clear instructions, timely feedback, and competent staff - being
relevant factors for learners’ satisfaction according to Chan et al. (2014) - are thus particularly
important in such personalised learning scenarios. Personalised learning is usually only implemented
in rather small courses. ANM exhibits a considerable number of participants usually leading to
anonymity and limited pedagogical opportunities for collaboration and interaction, which might
decrease learning outcomes and satisfaction (Lehmann and Söllner 2014). However, personalised
learning has been shown to counter these effects (Alonso et al. 2009), albeit requiring a suitable
pedagogical integration, which is provided by the PLF framework. An integration as described in the
previous section enables large numbers of learners to engage in real and practical exercises exploring
the topic of negotiations in contemporary examples making the future value of the course easily
recognisable for the learners (Chan et al. 2014).
There are also detrimental factors which must be considered planning and conducting self-regulated
personalised learning. Besides the alignment of learning objectives within a course, the alignment of
learning objectives and effort within a study program is also important to the learners. Attending
traditional courses and collaborative courses at the same time can be problematic as the latter require
more effort distributed over the semester, while the former are mainly laborious at the end of the
semester preparing for the exams. Increasing the number of collaborative and self-regulated courses
in curricula may lead to a large scale shift in the distribution of work. Seen from a staff perspective,
the change in learning methods means a huge one-off effort developing and implementing a new
course. At the same time, teaching becomes more efficient with the teacher being able to reuse
learning units and videos for several classes and also using lecture time more efficiently focusing on
interactive learning (Garrison and Vaughan 2011). However, teachers need to be comfortable handing
over responsibilities to the learners. From a technological perspective, the teachers also need to be
open and proficient to work together with learners using different software. Also successful online
learning material exhibits high quality, which requires a large amount of time to create and support.
Matters of data security and copyright regarding such media on public platforms also have to be dealt
The evaluation by thought experiment to demonstrate feasibility presents the main limitation of this
work. Regarding the literature, numerous concepts used in the PLF such as e-learning (Andersson et
A Framework for Personalised Learning in IS-Education
Thirty Sixth International Conference on Information Systems, Fort Worth 2015 14
al. 2009), blended learning (Garrison and Vaughan 2011), flipped classrooms (Strayer 2012), and self-
regulated learning (Azevedo et al. 2008) have proven their beneficial effects. However, the
combination of all of these heterogeneous ideas has to be evaluated again analysing their interplay.
Thus, our next steps will be to extend and implement ANM applying the personalised learning
framework based on the thought experiment above. This instantiation of the course will then be
evaluated combining design science research in information systems (Hevner et al. 2004) with design-
based research in the learning sciences (Brown 1992) aiming for a naturalistic ex-post evaluation
focusing on quality, utility, and efficacy. Both methodologies require building and evaluating artefacts
aiming to emphasise the connection between research rigour and practical relevance (Collins et al.
2004; Gregor and Hevner 2013).
From a theoretical point of view, the PLF is aimed to be generalizable to a broad range of courses and
contents in IS education. However, it is very much nested into the constructivist learning theories.
Thus, besides pursuing a practical evaluation, the framework should be applied to other courses
varying content, learning methods or method of evaluation to improve its generalisability.
Finally, the definition of the PLF implies several directions for future research. Firstly, the PLF
proposes a cognitive fit between learning preferences and tasks or tools as well as a task-technology fit
between tasks and tools. The relationship between those processes needs further investigation. Also
such a cognitive fit is not always possible in learning groups with different preferences making
analyses on group level necessary analysing the detrimental effects of missing fit. Secondly, the
framework proposes two interdependent processes of cognitive fit, namely personalisation of tasks
and personalisation of tools. Both processes are interdependent and are thus integrated into an overall
cognitive fit for personalised learning. Whilst achieving cognitive fit reduces complexity and thus
increases learning performance, the process of integrating both separate processes of personalisation
might lead to interferences that increase complexity and thus decrease learning outcomes (Shaft and
Vessey 2006).
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  • [Show abstract] [Hide abstract] ABSTRACT: Universities often blend traditional learning and e-learning by providing software licenses, electronic learning materials, and access to Learning Management Systems. Following the idea of personalised learning in higher education, students are free to choose between a wide range of learning tools constructing their Personalised Learning Environment. However, the characteristics of the chosen tools need to match the characteristics of the learning tasks to support students adequately. In the present paper, a mixed-method approach is used to analyse which types of tools are used in practice and which types of learning tasks are performed using these learning tools. Furthermore, important factors influencing the decision to select learning tools are identified. This study shows that a wide array of learning tools is used in practice. Although students consider individual factors (such as perceived ease of use and task-technology fit) to be most important when selecting their tools, several exogenous factors such as the lecturers' targeted pedagogy, social norm and the occurrence of higher order thinking skills limit the range of adequate learning tools.
    Full-text · Conference Paper · Apr 2016