Adaptive Learning Opportunities
Gary Natriello
Teachers College, Columbia University
Technical Report
Draft for Comment
September 2010
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Abstract
This report reviews a wide range of work to create adaptive learning applications
both within the education sector and the research community and beyond. The
review is organized according to a model of the processes leading to adaptive
learning opportunities that calls attention to seven categories of work. One type of
work focuses on what we term the “natural domain,” or the broad set of tasks in
which individuals may be involved as they encounter adaptive systems in
everyday interactions. A second type focuses on what we refer to as the “learning
domain,” or tasks that had a deliberate learning goal. Applications growing out of
knowledge of the learning process constitute a third type of work based on what
we call the “learner model” tradition. We identify adaptive testing efforts as the
fourth type of work and refer to these in the assessment tradition. The fifth type of
work is based on knowledge of the relationships among content elements or
curriculum components and constitutes what we term work according to a content
model. Development activities rooted in an understanding of effective
pedagogical activities form a sixth type of work that we label those based on a
teacher model. The seventh type of development work which we refer to as
network oriented draws on the possibilities for adaptive learning opportunities
growing out of new social networking options. We identify research and
development activities in each of these traditions, note some promising directions
for additional work, and conclude with a discussion of the structures that might be
used to connect work both across different development communities and between
these communities and practicing educators interested in applying adaptive
learning opportunities in their own educational settings.
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Introduction
Personalizing education by adapting learning opportunities and instructional
practices to individual abilities and dispositions has been a long-standing objective
among educators and indeed, among all who seek more powerful learning
experiences. Indeed, anyone who has been in the role of a student understands the
need for attention to specific forms of knowledge and skill and particular learning
orientations. Corno (1995) cites Snow (1982) who found references to the need to
adapt education to learner differences in Chinese, Hebrew, and Roman texts as
early as the first century BC. Snow also identified a specific fifth century BC
passage from Quintilian discussing the different patterns of student learning,
noting that some students work best with a free rein while others work best under
some threat and that some make progress over time while others achieve more
through a single burst of energy.
Interest in adapting learning opportunities to meet the needs of learners has
continued up to the present time. This enduring interest has appeared in multiple
stands of thought reflected in diverse writing ranging from formal scholarly papers
to popular commentaries. Although the strands take quite different forms, they
each signal a continuing quest for powerful learning experiences rooted in
addressing distinctly individual states and characteristics. The strands can be
characterized as comprising five different foci we can define as: relationships,
institutional settings, communities, and mechanical affordances. Brief
explanations of each strand provide background for the model of adaptive learning
opportunities
The Relationship Strand
One strand of thinking has focused attention on the unique relationship between
two individuals, one in a teaching role and the other in a learning role. The interest
is in the relationship between individual students and their tutors or mentors.
Examples of students and their mentors run throughout history, e.g., Plato and
Socrates (Plato, 2003), Arthur and Merlin (Lupack, 2007). The primacy of the
one-to-one relationship between learner and teacher has been captured perhaps
most graphically in James Garfield’s image of the ideal college as a log hut with a
bench and student at one end and Mark Hopkins at the other (Mark Hopkins,
2009). Examples in this strand of thinking contemplate the possibilities for
enhancing learning through a masterful teacher providing undivided attention to
am individual student.
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The Institutional Strand
A second strand of thinking has sought to address limitations on adaptive learning
posed by the increasingly institutional forms of education in the modern era. The
organization of modern educational systems aims to configure classes of students
who must meet common goals; their individual needs must be addressed by a
single teacher presenting a standard curriculum. Thus, there continues to be an
interest in the power of pedagogies that can be adapted to individual needs. In the
latter part of the twentieth century, this interest in adaptive learning opportunities
has taken form in the movement toward individualize instruction (Glaser, 1977;
Tomlinson, 1999; Betrus, 2002) even in schools in which students are organized
by age-grades and classrooms.
The Community Strand
A third strand of thought has focused on addressing the strengths and weaknesses
of individual learners by connecting them to broad networks of resources that
extend beyond the boundaries of particular relationships and particular institutions.
Such learning opportunities have been seen as abundant in the study groups of
colonial America (Cremin, 1972), in the envisioning of elaborate society-wide
learning webs in a post-schooling era (Illich, 1972), and more recently as elements
of communities of practice (Wenger, 1999). In each of these cases the power of
adaptive learning opportunities grows out of the assemblage of vast and broad
human resources and the highly refined informed connections between those
resources and individual learners.
The Mechanical Strand
A particularly provocative strand of thinking has dealt with the shortage of human
resources to support individualized learning by imagining some type of
mechanical or non-human resource that would be responsive to learner capabilities
and dispositions. This line of thinking is part of a broader quest for non-human
resources to provide assistance of various kinds (Petrina 2008), where learning is
but one. An early form of such a desired resource is seen in the Mechanical Turk,
ostensibly a machine that could play chess, a skill demonstrated across Europe in
the late 18th and early 19th centuries by besting a string of human opponents
(Levitt, 2006; Standage, 2002). Later revealed as a hoax, this machine
nevertheless evidenced a continuing desire on the part of human beings to believe
in the power of mechanisms that would support or supplant intellectual work. In
the mid-twentieth century this same desire for tools that would extend intellectual
capacities was manifested in Vannevar Bush’s (1945) vision of the memex. The
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memex was an early version of a hypertext system that would allow individuals to
create and share paths through libraries of materials. The spirit of the memex was
carried forward though multiple designs and iterations in the early days of
computers as devices to expand the capacity of the human mind. It then moved
into the era of computer networks linking individuals to machines, and eventually
to other human beings, again as a way to address enhance capacities (Markoff,
2005). A parallel path motivated successive generations of technologies to be
used in schools, including film-strips, films, radio, television, programmed
materials, computers, interactive games, etc. In each case one goal was to assist
learning through non-human resources.
The Current Press for Adaptive Learning Opportunities
The goal of developing means for responding to the states and learning
orientations of individuals has received renewed emphasis in recent years both as a
result of new demands and the prospect of enhanced possibilities for meeting such
demands. These two forces are converging to make the present a particularly
propitious time to realize significant progress in the development of adaptive
learning opportunities.
New demands for learning are driven by the growing realization that advanced
economies and their societies require greater proportions of their citizenry to
achieve high levels of learning and the capacity to continue learning throughout
the life course. The growth of “knowledge work,” i.e., those tasks that entail the
symbolic analysis or the manipulation of information, means that it is increasingly
important for individuals to have access to opportunities to acquire the intellectual
capacities necessary to perform higher level tasks in all sectors, eg., health,
education, government, high tech manufacturing, agriculture.
If the new post-industrial economies in nations throughout the world represent the
pull for adaptive learning opportunities that facilitate the development of higher
order skills essential to knowledge work, then it is developments in the
technologies of computing and communications that represent the push for such
opportunities. Progress over the past twenty years in computing and
communications technologies has set the stage for the development of a new
infrastructure that promises to support adaptive learning in ways never before
possible (Computer Research Association, 2005; NSF Task Force on Cyberlearning,
2008). The Information Age has created opportunities for a generation of students to
experience web-based learning environments (Strayhorn, 2006). Students, especially
those in secondary and post-secondary education, are growing increasingly
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knowledgeable in computer sciences and technology (Strayhorn, 2006). Many use email,
word processing, search engines, and web resources to facilitate research and supplement
their learning. This situation has served to further aid the creation of personalized
learning experiences for individual learners (Oh & Lim, 2005). Particularly in recent
years, the World Wide Web has become a popular platform for creating and hosting
adaptive learning environments (Weber, 1999). This new infrastructure, anchored by the
internet and growing upon it, promises to support the kind of individually responsive
learning environments that have only been a dream to earlier generations (Maeroff, 2003;
Gardner, 2009).
Method of this Report
Work to develop what we are calling personally adaptive learning opportunities
has evolved in a variety of contexts, and progress has been achieved through a
variety of methods and approaches. Moreover, work related to adaptive learning
opportunities has been performed by scholars, publishers, software engineers,
practicing educators, and others who work in widely disparate professional
communities. Because our longer-term goal is to foster the development of
linkages among these communities, in this report we take a deliberately expansive
perspective on adaptive learning reaching far beyond formal intentional teaching
and learning efforts to include those that have evolved for a range of other
purposes, at times quite incidentally.
Our activities to assemble a rudimentary base of information on these efforts have
included three prominent efforts: 1) surveying the available literature, particularly
in areas where the agenda has been driven by researchers; 2) examining a broad
set of formal adaptive learning programs of disparate origin; and 3) gathering a
wide range of technology mediated or supported experiences that are adaptive in
nature and only secondarily involve intentions to support learning.
To facilitate understanding of this broad and disparate base of information,
activities, and actors, we propose a model of the set of processes and interactions
that can lead to adaptive learning opportunities. This model includes elements
common to cybernetic feedback processes (Weiner, 1948) and general systems
thinking (Bertalanffy, 1968) while also drawing on work on evaluation and control
systems (Dornbusch & Scott, 1975) and adaptive technologies (Shute & ZapataRivera, 2007).
Figure 1 highlights the major features of the model. The learner is shown in the
inner-most circle as the focal point of activity within the model. Three arrows
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going to or from the learner represent the interactions between the learner and the
adaptive system in which s/he is embedded. These interactions are anchored by
three defining dimensions of the system shown on the outer circle. Beyond these
are diverse resources that may be invoked by the system in operation. As will
become clear, these seven classes of resources correspond to areas of development
and research that serve to advance the state of the art of adaptive learning
opportunities.
Figure 1: A Model of the Processes Leading to Adaptive Learning Opportunities
The elements of the model describe the major features of adaptive learning
opportunities as follows.
1. Natural Domain
The term natural domain is used to refer to the broad set of tasks in which
individuals may be involved as they encounter adaptive systems. Those concerned
with learning deliberately are handled in the learning domain; all others are
handled in the natural domain.
2. Learning Domain
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Because we are concerned with adaptive learning opportunities it is reasonable to
devote special attention to those efforts deliberately or intentionally focused on the
promotion of learning. Thus, the domain of tasks deliberately concerned with the
promotion of learning receives specific consideration.
3. Goals
The adaptive learning opportunity is initiated in the context of a goal or set of
goals. These goals can be learning focused or more general in nature, i.e., pertain
to the learning domain or the natural domain. In addition to variation in the
substance of the goals, there can also be variation in the origin of the goals; in
some cases goals are generated by learners or performers themselves, in other
cases goals are generated by other individuals or systems. So, for example,
learners can seek to locate themselves within adaptive learning opportunities to
pursue goals (learning or otherwise) of their own choosing, or as prescribed by a
curriculum.
4. Assign Task
Tasks within the model are important orienting devices that serve to evoke learner
cognitive behavior or performance. Tasks are “assigned” (i.e., attached to
learners) either by learners themselves or by other individuals or systems. So, for
example, learners taking charge of their own learning opportunities can embrace a
task thought to advance their learning by affiliating with an adaptive system.
5. Learner
The learner as the focal point of the model is the subject of all action within the
system. More generally, the learner can be any individual performer targeted by
the action of the system, whether or not s/he conceives of her/him self as a learner.
6. Capture Performance Sample
An essential element in any system intended to adapt to the individual learner is
some way to capture a relevant sample of the performance of that learner. Further
action within the adaptive system is based on this sample.
7. Appraisal
Once a sample of performance is taken, the information contained within the
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sample must be analyzed or appraised from some perspective or according to some
criterion or criteria, i.e., it must be made meaningful to guide further action.
8. Learner Model
A model or models of the learner or learning are drawn on as the basis of the
appraisal of the learner performance. Both the nature of the learner models and
the extent to which they are used to drive further action can vary substantially.
Elaborate models can be tied closely to fine-grained action while simple models
can trigger system actions of a rudimentary nature.
9. Selection
The process of selecting the next action of the adaptive system is governed both by
the appraisal and by the resources that the system can draw upon. Selecting
involves matching the state of the learner indicated in the appraisal with the
appropriate resource or resources.
10. Assessment
One resource that can be presented to learners based on a sample of performance
appraised with reference to a learner model is an assessment. Such assessments
can be tailored based on the prior performance to achieve more refined
understanding of the learner state.
11. Content Model
Content or curriculum can be used to foster learning, and the selection of content
that is most appropriate in response to a sample of prior learner performance can
be guided by a more or less elaborate content model. More elaborate content
models can generate more fine-grained content decisions.
12. Teacher
Teacher is used to signify both the availability of a human teacher or an intelligent
tutor or teaching agent and the specific behaviors evinced by either. A key
defining feature of this resource is that a single individual or single intelligent
agent is made available to the learner.
13. Network
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In contrast to the single resource implied by the teacher category, the network
category refers to a multi-actor set of interactions. The network can be bounded in
a deliberate way as defined by the system or it can be open. In either case,
information on the actors in the set drive the selection process to address learner
needs.
14. Present
The third action impacting learners is the act of presenting the resource selected
(assessment, content, teacher, network set) to foster learning. The presentation act
concludes a single adaptive cycle. The system can be configured to continue with
another cycle as it repeats the stages already reviewed.
Areas of Development and Research on Adaptive Learning Opportunities
The model of adaptive learning opportunities highlights the kinds of resources that
shape and define different approaches to the construction of adaptive learning
systems. The seven classes of resources in the model and our review of existing
development and research on adaptive systems suggests seven major types of
work that provide perspectives on contemporary adaptive learning opportunities.
Natural Tasks - A growing variety of everyday tasks that require or benefit
from enhanced knowledge or skills on the part of participants have come to
rely upon adaptive learning strategies to secure higher quality individual
efforts. In these settings learning is embedded in the broader goal of client
engagement (e.g., TurboTax and other programs to guide tax return
preparation).
Constructed (Learning) Tasks - Learning opportunities that are deliberately
organized around tasks or similar application structures constructed for the
purpose of promoting learning. With the growth in computing and
communications capabilities, approaches that have traditionally relied on
more deliberately constructed tasks such as problem-based learning,
project-based learning, and scenario-based learning, are increasingly
including personally adaptive elements.
Learning Research - Research efforts driven by a learning process
perspective have been influenced by the base of knowledge on the patterns
of human learning and strategies to apply technologies to leverage those
patterns. (e.g., hypermedia and self-regulated learning)
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Assessments - In the realm of assessment a considerable amount of work
has been devoted to creating adaptive dimensions within single assessments
or programs of assessments (e.g., computer adaptive testing)
Curriculum Development - Efforts growing out of a curriculum perspective
have been rooted in an understanding of how content can be organized to
facilitate mastery of material, whether based on traditional expert-driven
approaches to generate curriculum scope and sequence plans or based on
empirical investigations of sequences and their impact on learning as
represented in learning progressions.
Teaching Research - Work oriented by the teaching perspective has focused
on developing insight into differences in student learning that can be
matched to a broadened repertoire of instructional strategies to achieve
optimal learning. (e.g., adaptive teaching)
Social Networks – Technology mediated or supported social networks have
offered the capacity to connect learners with other individuals who can
support and enhance their learning (e.g., online professional learning
communities).
Any parceling of the broad set of activities that may contribute to the evolution of
adaptive learning opportunities is necessarily somewhat arbitrary. The seven
perspectives identified are at times overlapping, and at least some adaptive
learning activities draw upon multiple perspectives. Nevertheless, the seven
perspectives identified provide a starting point for our effort to scour the terrain of
activities that might be brought into a conversation to advance thinking about
adaptive learning possibilities.
The remainder of this report is organized around these seven perspectives, and
reviews the diverse landscape within which developments in adaptive learning
opportunities are taking place. We taken into account the uneven theoretical and
empirical progress in different areas, and assess the potential contributions of the
various efforts for strengthening understanding of personally adaptive learning
opportunities. We conclude by identifying strategies for facilitating convergence
in the field.
A. The Natural Task Perspective
Perhaps the broadest of the seven resource areas specified in the model of adaptive
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learning systems is what we have termed the natural domain or natural tasks. We
use the term “natural” to distinguish this domain of tasks in our model from those
that are “constructed” with a deliberate learning goal or focus. “Natural” thus
refers to the fact that these tasks (which may facilitate learning incidentally) are
not designed deliberately or primarily to promote learning.
A wide range and growing number of other tasks in contemporary life are being
developed in ways that make them personally adaptive. These naturally occurring
tasks make use of learning (and are sometimes based on learning principles), but
they are not explicitly conceived as educational tasks. Brusilovsky and colleagues
(Brusilovsky, 1996; Brusilovsky, 1998; Brusilovsky, 2001) provide a key
orientation to work in adaptive hypertext and hypermedia. Since the mid-nineties
this work has largely concentrated on web-based media. Stand-alone hypermedia
systems are still being introduced, but the growing capacity of web interfaces and
the distributional advantages of the Internet make web systems an increasingly
attractive avenue for development. Accompanying the shift to web-based adaptive
systems has been the coming together of the user modeling and hypermedia
community to allow for the development of systems that adapt in response to
individual users.
Brusilovsky and Maybury (2002) distinguish between adaptable systems and
adaptive systems. Adaptable systems require the user to specify the ways in which
the system should be different for his or her use. For example, in an adaptable
news source, the user would specify his or her location to add local news.
Adaptive systems rely on an explicit model of the individual user that includes
information on the user (e.g., knowledge, goals, interests, recent online behavior)
that is then used to distinguish among different users. Brusilovsky and Maybury
(2002) note that user data can be gathered by observing user interaction and by
making direct requests for user input. In our model of adaptive learning
opportunities, these data sources are organized through the task assignment
process.
Brusilovsky (1996) identified five types of information on individual users that
might be used in an adaptive system: goals, knowledge, background, hyperspace
experience, and preferences. Brusilosvky (2001) identified two additional types of
information about the user; namely, interests and individual traits. He adds
environmental dimensions as another kind of information to drive adaptation.
Consideration of these eight types of information that might be employed in a user
model illustrates the scope of individualization that is possible.
Goals refer to the objectives the user is trying to accomplish in the context of the
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environment in question. Adaptive systems typically support a set of possible user
goals. Simple systems may support one or a small number of goals or tasks, while
more complex systems may support a hierarchy of related tasks leading to goal
accomplishment.
User knowledge usually refers to the knowledge that a user possesses in relation to
a model of the structure of a bounded knowledge domain (Brusilovsky, 1996).
The knowledge domain is represented by a structure of concepts and their
relationships, and the knowledge of the user is represented in an overlay model
that seeks to assess the state of user knowledge in reference to the domain
structure. The assessment can be binary, a qualitative measure, or a quantitative
measure used to represent the knowledge state of the user. In some cases a
simpler stereotype model with several typical or stereotype users is specified as
the reference for assessing user knowledge
Background refers to all of the available information related to the user’s previous
experience outside the subject of the adaptive system. Included under background
are factors such as the user’s profession, work history, and point of view
(Brusilovsky, 1996).
Hyperspace experience refers to the user’s familiarity with the structure of the
hyperspace system as reflected in the ease of navigation. Such familiarity with the
structure of the system is distinct from the user’s knowledge of the subject, and
has direct bearing on the most appropriate adaptive navigation techniques.
Experienced users of systems that are not adaptive may encounter the effects of
this kind of hyperspace experience as they become less patient with the very
navigation prompts that they found useful as novice users.
Preferences refer to the fact that a user can prefer some areas of a system over
others and some parts of an individual page over others. Brusilovsky (1996)
points out that the user has to inform the system about these preferences and that
adaptive systems can then generalize such preferences in new contexts within the
system. He also suggests that when user preferences are represented numerically
they can lead to the development of group user models that accumulate the
preferences of a particular group and lead to systems optimized for collaboration.
Interests refer to the long-term or enduring interests of the user (as opposed to the
short-term task or user goal). Interests are an increasingly important dimension of
information retrieval systems as the systems combine interests with short-term
goals or tasks to develop models to improve information filtering and
recommendations. As Brusilovsky (2001) notes, user interests are a part of
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adaptive bookmarking systems and some other systems that rely on interests for
making recommendations.
Traits, as conceptualized Brusilovsky (2001) refer to user features such as
personality factors, cognitive factors, and learning styles that are stable or only
change over long periods of time. There is controversy among psychologists over
use of this term; some reserve the term trait for personal qualities that cannot be
changed, and prefer to use propensity in referencing features that Brusilovsky calls
traits (Stanford Aptitude Seminar, 2002). Nevertheless, relatively stable qualities
such as these present special challenges to adaptive systems because they often
require special psychological tests to extract.
The environment in which the user is accessing the adaptive system offers
possibilities for adaptation along several dimensions. User environments can
differ, for example, in terms of the software platform, hardware capacities, and
network bandwidth. Adapting successfully to variables such as these can
influence comprehension of material presented. The growth of mobile devices
adds two other aspects of the environment connected to the smaller screen and the
capacity to identify location based on the global positioning system. Location
information has adaptive potential for delivering guide information in terms of
both the general area and the finer-grained positioning within buildings.
Having addressed the broad range of types of information to drive adaptation, we
can turn to the complementary issue of what can be adapted in adaptive systems.
Brusilovsky (1996; 2001) is instructive in this regard as well. Brusilovsky (1996)
notes the limitations of adaptive hypermedia systems, a point that will be
highlighted by our later consideration of adaptive teaching where there is a much
more expansive repertoire of adaptivity. He identifies two broad classes of
adaptive strategies: adaptive presentation and adaptive navigation.
Adaptive presentation includes adaptive multimedia presentation, adaptive text
presentation, and adaptation of modality. Adaptive multimedia presentation
involves tailoring the presentation of content within online media based on user
characteristics. A familiar example might be a system that recognizes the
bandwidth of a user and alters the density and size of multimedia files to
accommodate bandwidth limitations to provide a viewable media asset to the user.
Adaptive text presentation entails presenting users represented by different user
models with different text. Brusilovsky (2001) divides adaptive text presentation
into canned text adaptation and natural language adaptation. Canned text
adaptation can involve inserting/removing text fragments, altering fragments,
stretchtext (i.e., providing more detailed text treatment of particular content to
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facilitate user understanding), sorting text fragments, or dimming fragments.
Natural language adaptation may make similar use of fragments through the use of
natural language technologies.
Adaptive navigation support refers to those techniques that assist users in finding
their paths through hypermedia content adapting the way that links are presented
in response to differences in user characteristics. Brusilovsky (2001) identifies six
techniques: direct guidance, adaptive link sorting, adaptive link hiding (including
hiding, disabling, and removal), adaptive link annotation, adaptive link generation,
and map adaptation.
Direct guidance involves showing the user the best link or node based on user
characteristics as represented in the user model. Adaptive ordering or sorting
positions the best link according to the user model at the top of the list of links.
Adaptive hiding restricts the navigation possibilities available to the user by hiding
links to pages that are not relevant or appropriate according to the user model.
Adaptive link annotation provides additional information to the user about the
node or page behind the link either through additional text or visual cues. Map
adaptation entails ways of adapting the form of hypermedia maps based on the
characteristics of the user as represented in the user model.
Brusilovsky and Maybury (2002) and Brusilovsky (2004) discuss the challenge of
adaptive navigation support in situations other than assisting users in moving
through a closed body of hypertext. Two situations which press the limitations of
adaptive systems are the open body of resources represented by the web and the
complex graphical dimensions in virtual reality environments. Providing adaptive
support in these situations pose new challenges and opportunities. Brusilovsky
(2004) discusses the use of both content-based and social technologies to address
the open body of resources on the web. Brusilovsky (2004) notes that adaptive
navigation support technologies developed for hypertext have analogs in similar
approaches in virtual environments.
Adaptive hypermedia systems based on user characteristics and types of adaptivity
discussed in this section have taken diverse forms. Brusilovsky (1996) identifies
six kinds of adaptive hypermedia systems: online information systems, online
help systems, information retrieval systems, institutional hypermedia, systems for
managing personalized views of information spaces, and educational hypermedia.
Online information systems operate to provide reference access to information for
diverse users. Online help systems operate much the same way, but the
information they provide is directly related to the online system the user is
accessing. Information retrieval hypermedia systems combine information
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retrieval techniques with access from an index of terms to a set of documents.
Institutional hypermedia systems seek to provide online access to all of the
information necessary to support the work of an institution. Systems for managing
personalized views of information spaces provide access to a subset of hyperspace
selected as a result of user characteristics represented in a user model. Finally,
educational hypermedia seek to provide adaptive access to the hyperspaces
representing a particular course or part of a curriculum. We consider educational
hypermedia under the learning domain or learning task discussion in a later
section.
Writing in 2009, Knutov, De Bra, and Pechenizkly consider progress in adaptive
hypermedia systems over the preceding twelve years. They organize their
discussion around six major questions of adaptation:
Why do we need adaptation (Why?)
What can we adapt? (What?)
What can we adapt to? (To What?)
Where can we apply adaptation? (Where?)
When can we apply adaptation? (When?)
How do we adapt? (How?) (pp. 7-8)
Knutov, De Bra, and Pechenizkly (2009) note that reference models used to
describe adaptive hypermedia architectures employ a tower model that represents
different elements as different levels or layers. Along these lines, they discuss
different layers as they relate to the key questions.
The Goal Model (GM) layer (addressing the “Why?” question) potentially
involves not just goals, but also a hierarchical structure that includes “goals,
objectives, tasks, requirements, workflows” (Knutov, De Bra, and Pechenizkly,
2009, p. 23) of the sort shown in our model of adaptive learning opportunities as
(3) goals, (4) tasks, (5) learner, and (6) performance. A generalized view of the
goal centered approach might include a “hierarchy of goals and corresponding
tasks comprising this goal, workflows that need to be followed to complete a
requirement (Knutov, De Bra, and Pechenizkly, 2009, pp. 23-24). In addition to
systems in which the goal is assigned by some expert or selected by users from the
outset, Knutov, De Bra, and Pechenikly (2009) note that it is possible for a system
to infer a goal from the interactions of other users of the system.
The Domain Model (DM) layer (addressing the “What?” question) consists of
concepts and the relationships among concepts, most often organized in a
hierarchy. When the Domain Model is structured so that concepts are fine grained
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and structured hierarchically it is possible to apply adaptive techniques. As
Knutov, De Bra, and Pechenizkly (2009, p. 14) observe, “Each concept
relationship has a type (e.g., direct link, inhibitor, ‘part-of’, or prerequisite) which
may play a role in the adaptation.” Two issues receiving current attention are the
challenge of dealing with an open body of documents and the idea of higher order
adaptation. Domain Models applied to a closed body of material can be defined in
advance by a domain expert, but when we are dealing with an open body of
material, as in the case of the entire Internet, then the problem of mapping
concepts to content must be addressed as elements of the open body of material
are brought into play. Thinking about higher order adaptation presents its own
challenges. Systems with higher order adaptation are able to monitor the user as a
basis for determining the parameters around which adaptation will occur.
The User Model (UM) layer (addressing the “To What?” question) is made up of
entities that have values stored in a structure (typically a table) that overlays the
Domain Model, in effect creating a map of some attribute (such as user
knowledge) over the domain. Systems can use both domain dependent properties
such as user knowledge and domain independent properties such as user
characteristics. Systems can respond to both dynamic properties such as user
knowledge gathered from user interaction with the system and static properties
such as user personal characteristics. Systems can simply draw on the static
properties but they must process ongoing changes in dynamic properties (Knutov,
De Bra, and Pechenizkly, 2009, pp.19-20).
The Context Model (CM) layer (addressing the “Where?” and “When?” questions)
refer to system features that take account of the context in which the adaption is
applied and the environment in which the application is used. Adaptation
processes and decisions that are dependent on an application address the “Where?”
question. Adaptation processes and decisions that are independent of an
application (e.g., time, day of the week, network bandwidth) address the “When?”
question. Although Knutov, De Bra, and Pechenizkly (2009, pp. 24-25) discuss
these issues, they do not treat them as a layer in the general model.
The Adaptation Model (AM) layer (addressing the “How?” question) deals with
techniques and methods of providing adaptation within a system. According to
Knutov, De Bra, and Pechenizkly (2009, pp. 25), “Methods represent
generalizations of a technique.” They identify techniques of content adaptation
(inserting/removing fragments, altering fragments), techniques of adaptive
presentation (dimming fragments, sorting fragments, stretchtext, zoom/scale,
layout, link sorting/ordering, link annotation, combinatorial techniques), and
techniques of adaptive navigation (link generation, guidance, link hiding).
17
Knutov, De Bra, and Pechenizkly (2009, pp. 30-34) conclude their review of
progress in adaptive systems by highlighting a number of emerging trends likely to
drive work on such systems in the immediate future. These include:
 Using ontologies to support bringing together user models from different
applications so that user experience in one application can be shared with
another application
 Applying adaptive methods to an unknown or open document space such as
the web
 Extending adaptive processes by applying them not just to individuals, but
to groups of users
 Using data mining techniques to support adaptation, notably the updating of
event-condition-action rules that drive the adaptive system
 Applying higher order adaptation in which monitoring of the user results in
modifications of the adaptation itself.
 Developing the Context Model to allow the system to operate under
different rules in different contexts
 Extending adaptation techniques to multimedia content
To these anticipated areas of development we would add the possibility of
extending adaptive systems to include the altering of not only content, but
applications or processes. As applications come to be a more prominent element
of the world wide web, the ability to alter applications based on knowledge users
before they initiate interaction with an application will become an important way
to enhance the user experience.
B. The Constructed (Learning) Task Perspective
The Constructed Task Perspective calls our attention to development and research
that includes construction of tasks specific to the application in question. Such
tasks can be more or less learning oriented. For example, some elaborately
constructed task environments are created for purposes of entertainment rather
18
than learning. But learning oriented tasks are well represented among constructed
tasks, and a good deal of theoretical and practical work focuses on developing task
environments that promote learning.
As we move from the domain of natural tasks to the domain of constructed tasks,
it is immediately evident that developers of such systems have conceived and
planned the task environment presented to user/learners/students in a very
deliberate manner. Indeed, work in this area places great emphasis on the
conscious creation or design of tasks and task environments. A key aspect of the
deliberate nature of the design of these environments is the connection to different
theoretical traditions for guiding the design of adaptive systems.
For our present purposes we can divide the theoretical traditions guiding the
design of tasks for adaptive learning opportunities into two major categories:
objectivist and constructivist. These traditions differ in their major assumptions
about how individuals learn and consequently they carry different implications for
the design of adaptive systems. One of the major traditions is rooted in what
Jonassen (1999) terms “objectivist conceptions” of learning. This tradition has
been dominant in instructional design; authors such as Papanikolaou, Grigoriadou,
and Samarakou (2005) refer to it as the “instructional design approach.” In the
objectivist tradition knowledge is assumed to be transferred from teachers to
students or transmitted from instructional technologies and acquired by learners.
Papanikolaou, Grigoriadou, and Samarakou (2005) describe such approaches as
entailing definition of specific learning objectives, the design of materials and
procedures to address these objectives, and assessments to determine if the
objectives have been met. Instructional systems with these elements go back to
early military training programs and the curriculum tradition of Ralph Tyler
(1969). Papanikolaou, Grigoriadou, and Samarakou (2005) note that such
approaches entail the definition of specific learning objectives, the design of
materials and procedures to address these objectives, and assessments to determine
if the objectives have been met. Papanikolaou, Grigoriadou, and Samarakou
(2005, p. 2) note that in the instructional design or objectivist approach learners
are provided with structured content consisting of various knowledge modules
differing in format and interactivity level along with individually tailored support
for acquiring the intended knowledge, skills, and/or attitude objectives.
Numerous adaptive learning systems, or what Brusilovsky and Peylo (2003) term
adaptive and intelligent web-based educational systems (AIWBES), and
Papanikolaou, Grigoriadou, and Samarakou (2005) refer to as adaptive educational
hypermedia systems (AEHS)), have been developed based on the objectivist
perspective. Papanikolaou, Grigoriadou, and Samarakou (2005) argue that the
19
majority of early adaptive learning systems, including DCG (Vassileva, 1997),
ELM-ART (Weber & Bruskolvsky, 2001), INSPIRE (Papanikolaou, Grigoriadou,
Kornilakis, & Majoulas, 2003), AST (Specht, M., Weber, G., Heitmeyer, S., &
Schöch, V.,1997), and KnowledgeSea (Brusilovsky & Rizzo, 2002), were designed
from the objectivist perspective. A similar claim may be made about WebMA, a
system designed to provide drill and practice in mathematics (Nguyen & Kulm,
2005).
Constructivist approaches have been employed in a range of projects to design
technology mediated or enhanced learning environments. Central to constructivism is the
assumption that knowledge and understanding are constructed individually and
collectively by learners as a result of their experiences. The implication of this view is
that learning is facilitated by experiences that allow individuals to construct knowledge.
Jonassen (1999) summarizes some key aspects of the design of constructivist
environments. He highlights the problem-focused or case-oriented approach in which
learners are presented with an interesting problem or a challenge and provided relevant
information along with tools to scaffold required skills, tools to support communication
and collaboration, as well as social or contextual support for learning.
A variety of related emphases are captured in work on constructivist learning
environments. For example, McClintock (1971) and Black and McClintock (1995) have
envisioned and developed digital information infrastructures that constitute environments
for supported study opportunities for students. Schank and colleagues (Shank, Fano,
Bell, & Jona, 1993/94 emphasize the use of goal-based scenarios to orient and guide
activities in learning environments. Bransford and colleagues (Bransford, Sherwood,
Hasselbring, Kinzer, & Willams, 1990) used anchored instruction in which learning is
focused through a problem entailing realistic tasks. Work on problem-based learning
(Baron, et al., 1998) and project-based learning (Blumenfeld, Soloway, Marx, Krajcik,
Guzdial, & Palincsar (1991) leads to the design of learning environments oriented around
realistic and more open-ended challenges to engage learners along with resources to
support their learning. Shaffer (2004; 2006) focused on development environments and
experiences for students that resemble real world work environments associated with the
professions as well as games that hone skills that will be useful in real world
applications. Papert and colleagues (Harel & Papert, 1991; Kafai & Resnick, 1996)
championed a constructionist perspective stressing that individuals learn effectively when
they are engaged in building, in making things in the real world. All of these approaches
in the constructivist tradition incorporate the idea of situated learning, i.e., learning in the
context of realistic situations, and scaffolding, i.e., personalized support from an
individual or a resource that scaffolds or assists in learning. In a web-based environment,
the scaffolding is provided by the website and its multimedia options (Own, 2006).
Efforts at developing adaptive learning opportunities in the constructivist tradition locate
the adaptive dimensions in the environments provided for learners rather than in a direct
20
instructional interaction. As Lee and Park (2008) observe, when designers of computerbased learning environments begin with a constructivist perspective instead of an
objectivist approach, they emphasize the facilitative role of the system in place of the
tutoring role. For example Akhras and Self (2002a) note that while environments rooted
in objectivist conceptions of knowledge have architectures that emphasize representations
of the knowledge to be learned (i.e., a domain model), inferences regarding the current
knowledge state of the learner (i.e., a learner model), and instructional activities for the
learner (i.e., a teaching model), environments based on constructivist perspectives might
have quite different architectures.
Akhras and Self (2002a) propose such an architecture that includes different but parallel
components: situations or interactional contexts that facilitate the activities of learners to
construct their own knowledge, properties of interactions and sequences of such
interactions that reveal the processes by which learners are constructing knowledge, and
opportunities provided by affordances of learning situations to learners whose learning
process is in a particular state. Akhras and Self (2002a) view the domain, learner, and
teaching models characteristic of systems grounded in the objectivist perspective as being
contained within broader categories of the situation, interaction, and affordance models
they offer as compatible with the constructivist approach. Akhras and Self (2002a)
illustrate their approach with two systems: SAMPLE, a system to help students learn
concepts of salad making, and INCENSE, a system to help students learn software
engineering.
The proposal for a constructivist architecture by Akhras and Self (2002a) has been
challenged from several perspectives. Azevedo (2002) called on Akhras and Self to
enhance the conceptual clarity of their approach and apply their constructivist perspective
to systems intended to foster self-regulated learning in students. Young Depalma and
Garrett (2002) drew on an ecological psychology perspective to call attention to some
additional complexities inherent in understanding a learning context. Akhras and Self
(2002b) responded to these challenges by clarifying their original arguments, and in a
later paper Akhras (2005) offered additional formalization to strengthen the approach to
modeling learning interactions.
Henze and Nejdl (1999) and Henze, Nejdl, and Wolpers (1999) used a constructivist
approach that incorporates elements of project-based learning, group work and
discussions in the design of the KBS Hyperbook System. The system allows students to
select their own learning goals and the review suggestions for appropriate projects along
with the information necessary to achieve their learning goals.
Papanikolaou, Grigoriadou and colleagues (Papanikolaou, Grigoriadou, 2006;
Papanikolaou, Grigoriadou, Alexi, & Samarakou, 2005; Papanikolaou, Grigoriadou, &
Samarakou, 2005) discuss the development of ProSys, a web-based adaptive educational
system to support project-based curricula and case-based learning. In ProSys learners are
engage in project work. As they engage, the system presents them with learning aids to
21
support their completion of the project. Among the aids is a library of case structures to
be consistent with the conceptual structure of the knowledge domain. Learners are
provided with navigation aids adapted to their knowledge level and degree of progress.
In addition, learners are offered a sequence of activities tailored for them.
It is clear that constructed tasks offer important points of entry into research and
development of adaptive learning opportunities. Whatever the theoretical perspective
underling the tasks, the structure of the task environment plays a defining role in many
adaptive systems. Moreover, tasks and task environments are targets for the creative
energies of designers and developers in all areas.
C. The Learning Research Perspective
Many research and development efforts to create adaptive learning opportunities
have been driven by a learning process research perspective; that is, they have
been influenced by the base of knowledge on patterns of human learning and
strategies to apply technologies to leverage those patterns. Inherent in these
approaches is a model of the learner maintained by the adaptive system and the
capacity of the system to adjust itself in response to the range of processes
employed by individual learners (Shute & Zapata-Rivera, 2007).
An understanding of learner differences, resources, and responses to those
differences is essential to creating an effective adaptive system. Several broad
classes of factors on which learners may be anticipated to differ have been
considered over the years. These include, for example (as defined by Shute &
Zapata-Rivera, 2007): 1) cognitive factors such as knowledge, skills, and abilities
(see also Carroll, 1993; Stanford Aptitude Seminar, 2002); 2) motivational and
affective factors such as interest, attachment, and success expectations (Deci,
Koestner, & Ryan, 2001; Pintrich & Shunk, 1996); 3) demographic factors such as
race, gender and social class (Natriello, 1994), and 4) cultural factors such as
ethnicity and language (Boykin, 1994; Hurley, Allen, & Boykin, 2009; Rogoff,
2003).
Developments in several areas promise increasingly detailed information about a
growing range of learner characteristics. First, more and finer grained information
on learners is available as a result of students spending more time in online
learning environments. Online environments capture massive amounts of
information on learners by tracking student use of the learning resources through
their direct interactions with the software programs and augmented by such
biologically based means as eye-tracking, speech-patterns, and head- and facial22
gesture captures. These large data pools can be explored through data mining
techniques that can be used to uncover patterns and trends in student behavior
(Shute & Zapata-Rivera, 2007).
Second, progress in the development of models of learning and learners will lead
to the identification of both new learner characteristics and new modeling
techniques. For example, in response to questions posed by Shute and ZapataRivera, (2007, p. 287), a panel of experts in the field identified the following
learner variables as promising for consideration:
Cognitive abilities (e.g., math skills, reading skills, cognitive
developmental level, problem solving, analogical reasoning)
Metacognitive skills (e.g., self-explanation, self-assessment,
reflection, planning)
Motivational and Affective states (e.g., engagement, flow, anxiety
level)
Additonal variables (e.g., personality types, learner styles, social
skills such as collaboration, and perceptual skills)
In their consideration of constructivist learning environments, Akhras and Self
(2002) identify four dimensions of learner behavior, or what they term “properties
of interactions and of sequences of interactions,” (p. 8) that may be more likely to
lead to learning: cumulative, constructive, self-regulated, and reflective. Lee and
Park (2007) call attention to systems that include motivational variables, citing De
Vicente and Pain (2002), whose motivation model includes control, challenge,
independence, fantasy, confidence, sensory interest, cognitive interest, effort, and
satisfaction as variables to indicate a learner’s motivational state. In considering
the issue of control, Lee and Park (2007) noted the distinction made between
adaptable systems that allow users to choose certain parameters, and adaptive
systems where options are determined by the system based on measures of user
needs. Lee and Park (2007) also highlight approaches and systems that consider
metacognitive skills such as SCIWISE (White, Shimoda, & Frederiksen, 1999),
which encourages students to express their metacognitive ideas and Help-Seeking
Tutor Agent (Aleven, Stahl, Schworm, Fischer, & Wallace, 2003; Aleven,
McLaren, Roll & Koedinger (2004), which supports learners to become better help
seekers.
Third, more and better data on students along with new conceptual and theoretical
models together with new modeling techniques (e.g., Bayseian networks), can
only be employed to create more powerful adaptive learning opportunities
centered on learning if the bandwidth available to exchange the information
23
increases. Fortunately, the bandwidth available for such systems is increasing
although the distribution of capacity is uneven throughout the world.
With rapid advances in computing and data transfer technologies, a broader set of
research efforts to develop systems that intentionally involve adapting to
individual learning patterns, and attendant increases in learner agility in the use of
these technologies, it is becoming evident that, not only can the broader set of
tools be better leveraged to help provide more effective adaptable learning
environments, but also that additional learner attributes might well be taken into
consideration in the quest for evolving better and more effective adaptive teaching
and learning practices.
As Shute and Zapata-Rivera (2007) note, adaptive learning technologies can be
considered in terms of a four-process or four-stage adaptive cycle. The four
processes are capture, analyze, select, and present. The cycle begins with the
capturing of personal information on the learner as the learner interacts with the
environment. Such information is used to update internal learner models in the
system. A model of the learner organizes the analysis process used to understand
the current state of the learner in relation to the domain. In work representative of
the learning research area, these learner models are well developed and substantial
in driving the adaptive cycle. In the selection stage content is identified in terms
of the model of the learner held by the system and the overall goals. In work
representative of the learning research area, the emphasis in the selection process
is on the learner model. This drives the presentation of the next sequence of
information to the learner.
In their discussion of technology-rich environments, Lajoie and Azevedo (2006) stress
the utility of research and development driven from well-developed learning theories.
They highlight approaches that involve both cognitive and metacognitive tools to support
learners. In particular, they argue that in the wake of the growing development of open
electronic learning environments accompanying the spread of the Internet, students are
increasingly being called upon to regulate their own learning more effectively. This leads
them to call attention to the substantial body of work on self-regulated learning (e.g.,
Boekaerts, Pintrich, & Zeidner, 2000; Butler & Winne, 1995; Paris & Paris, 2001;
Schunk & Zimmerman, 2001; Zimmerman & Schunk, 2001).
Lajoie and Azevedo (2006) draw on the synthesis and conceptual work of Pintrich (2000)
which they offer as a sound foundation for organizing extant work on self-regulation that
can be relevant to technology-rich environments. Four assumptions common to models
of self-regulated learning identified by Pintrich are that: 1) learners are active
participants in the learning process, 2) effective learners are able to monitor, control, and
regulate aspects of their own cognition, motivation, behavior, and context, 3) various
24
constraints, whether biological, developmental, contextual, or otherwise individual, can
interfere with learner control and regulation; and 4) there is a goal or standard used by
learners to determine whether to continue or alter the current approach to learning. In
addition to these assumptions, Lajoie and Azevedo (2006) report on a four stage
conceptual framework that guides much research on learner self-regulation: Phase 1 –
planning and goal setting, Phase 2 – monitoring, Phase 3 – efforts to control and regulate,
and Phase 4 – reactions and reflections on the self, the learning task, and the context.
Research on the impact of hypermedia environments on student self-regulated learning
provides a good example of work that expands the view of student learning while
simultaneously attempting to create more powerful learning opportunities. The work
attends not only to learning gains, but also to the learning process. Self-regulated student
learning refers to a host of processes (e.g., planning, monitoring, strategy use, handling of
task difficulty and demands) (see, e.g., Greene & Azevedo, 2009) that learners can
employ to become more effective. However, as Lajoie and Azevedo (2006) note, despite
the range of work being done on technology-rich environments from a self-regulation
perspective (e.g., Hill & and Hannafin, 1997; Chou & Lin, 1997; Jackson, Krajcik, &
Soloway 2000, Hadwin & Winne, 2001; Azevedo, Guthrie, & Seibert, 2004), there has
been little research that would allow us to understand the entire cycle of activities
involved in self-regulated learning in technology-rich environments. Pursuing such work
as part of a broader class of efforts to enhance adaptive learning opportunities could be
very fruitful.
D. The Assessment Perspective
In contrast to other perspectives where the adaptive elements are designed to alter
learning resources (e.g., relationships, content) in response to learner/user problem
solving and behavior, sometimes gathered in an assessment episode, the assessment
perspective focuses our attention on those instances where the adaptation is directed to
the assessment itself, i.e., where the later elements of an assessment are altered in light of
learner/user performance on a previous assessment element. Such arrangements can be
multistage tests in which a set of tests or “testlets” of differing difficulty are organized
along various paths with performance at one stage determining the assignment of the
testlet in the next stage (Mead, 2006; Luecht, Brumfield, & Breithaupt, 2006). They can
also be computer adaptive tests where the test items are of varying known difficulty, and
students are assigned them in the course of the test; the assignment is determined by
performance on earlier items of known difficulty (Kreitzberg, Stocking, & Swanson,
1978; Wainer, 2000; Challis, 2009). As Van Horn (2003, p. 567) clearly puts it: “If a
student misses an item, a slightly easier one is given. If a student gets an item correct, a
slightly more difficult one is given.” The estimate of the level at which the student is
functioning is adjusted with each answer to guide the selection of the next test item from
the item pool.
25
In these assessment situations, there are typically no learning resources per se since the
goal is to assess learner/user capabilities in the absence of such resources. In the realm of
assessment a considerable amount of work has been devoted to creating adaptive
dimensions within single assessments or programs of assessments, and our consideration
of adaptive learning opportunities can draw on the unique developments in the
assessment field to enrich adaptive learning.
Computer adaptive testing has been employed successfully in large-scale testing
programs such as the Armed Services Vocational Aptitude Battery (Sands & Waters,
1997) and the Graduate Record Examination (Schaeffer, Steffen, Golub-Smith, Mills, &
Durso, 1995; Mills & Steffen, 2000). Such large-scale programs have both the resources
and size to support the necessary test development activities. To address some of the
constraints on the application of computer adaptive testing, Welch and Frick (1993)
consider several methods for using computer-adaptive testing in instruction settings such
as corporate education and training and public school classrooms. Although they
emphasize the possibilities for employing the various approaches they review, they also
identify numerous barriers such as the need to generate data on test items from large
numbers of students prior to using the approach in the context of ongoing programs.
Indeed, these kinds of barriers have tended to limit the use of computer-adaptive
approaches to more formal testing programs.
The situation is changing with the introduction of learning systems that involve
large numbers of teachers and students with the capacity to capture and retain
large amounts of performance information. Lilley, Barker, and Britton (2004)
highlight some of the advantages of using computer-adaptive testing as part of the
managed learning environments made possible by online systems. These
advantages include greater motivation for students who are not confronted with
test questions that are too easy or too hard, greater efficiency in testing requiring
less time from learners, and better information on learner performance to drive the
educational experience.
Nirmalakhandan (2007) describes the use of computer-adaptive testing in the
context of a computerized adaptive tutorial and assessment system used for a
university level hydraulic engineering course. Triantafillou, Georgiadou,
Anastasios, & Economides (2008) offer a design in which computer-adaptive
testing is implemented on mobile devices that can be accessed by students in
diverse settings. Speaking to the increasingly diverse applications that may be
possible for computer-adaptive testing, McGlohen &Chang (2008) and Cheng
(2009) present data on the use of computer-adaptive testing in the context of
cognitively diagnostic assessments that can provide not just an assessment to drive
the next test item, but also diagnostic feedback to learners. Although problems
with computer adaptive testing remain to be addressed (e.g., Rulison & Loken,
26
2009), continuing work in this area of assessment may offer important benefits to
the overall goal of providing adaptive learning opportunities for students.
E. The Content Model Perspective
The content model perspective calls attention to a variety of strategies and approaches to
controlling and shaping content and the presentation of content to achieve a variety of
goals. Such work places emphasis on the selection, organization, and presentation of
content to learners. In the present case the most relevant goals for driving actions to
control content are learning goals. Attempts to control and shape content for educational
purposes have a long and robust role in the history of education and formal schooling,
and they have been the subject of a good deal of analysis and theorizing (e.g., Tyler,
1969; Pinar, 2004; Tanner & Tanner, 2006; Schiro, 2007). We briefly consider some of
this work as a foundation for the insights it might provide to contemporary efforts to
create adaptive learning opportunities.
While our immediate interests are more focused on those dimensions of curriculum or
content models that form the basis for the kinds of differentiation that can be associated
with adaptive learning opportunities for students, it is important to note that much of the
effort around theorizing and policy making concerning curriculum has been devoted to
specifying commonalities of content experience (Tyack, 1974; Reese, 2005) rather than
diversity. Exposing individuals to common content with the intention to accomplish
common learning goals has been a strategy to build group cohesion and solidarity. This
strategy has been applied at the level of the nation state (Meyer, Tyack, Nagel, &
Gordon, 1979), the community (Servgiovanni, 1996), the professional group (Boyer &
Mitgang, 1996), the corporation (Chrusciel, 2006), and the classroom (Edward & Mercer,
1987). Even these attempts to convey a common curriculum or content to diverse groups
have led to differentiated efforts to secure the attention and engagement of all concerned
on the common content agenda.
The history of curriculum differentiation is of interest because it includes important
dimensions for adapting content. The major dimensions along which content has been
differentiated include student background, including cultural background (LadsonBillings and Brown, 2008), student destinations, including career aspirations (Grubb,
1995; Gordon 2007), and differentiation by personal qualities such as interest
(Tomlinson, 2001), and student intellectual ability (Reiser, 1987). Efforts to differentiate
educational opportunities have differed along each of these dimensions and often among
several at the same time.
Differentiation efforts have occurred at the system level, the school level, and the
classroom level. At the system level, attempts to differentiate based on student
characteristics have led to the development of different types of schools. For example, in
some systems students are sorted into vocational and college preparatory schools with
27
possible gradations within each type. In the U.S. we have witnessed the emergence of
special theme schools as part of magnet school policies or more recently as charter
schools (Buckley & Schneider, 2009). At the school level instructional alternatives have
been selected on the basis of learning goals and students' general cognitive abilities and
achievement levels in the curriculum structure. Emerging in the early 1900s, this
approach was meant to be an alternative to the traditional lock-step system, and so
different instructional and assessment methods were adopted to better accommodate
students' learning abilities (Reiser, 1987). The ultimate result, however, was that students
were simply grouped by grades or scores on standardized tests (i.e., “tracked” by ability
or achievement level), while instructional strategies that generally involved the instructor
presenting information, asking questions to monitor learning and then providing feedback
based on students' individual performances, remained essentially the same. It is therefore
not surprising that school level adaptive instructional systems are not well-received in
mainstream education today; nevertheless, some form of tracking continues to exist
nationwide (Oakes, Gamoran, & Page, 1992; Natriello, 1994; Lucas, 1999; Oakes, 2005).
At the level of the classroom attempts at differentiation include both strategies of
grouping and individualization. For example, an early paper by Glaser (1977)
recommended a system combing several modes of instruction suitable for individuals and
small groups, ranging from tutoring to cooperative learning. These included managerial
steps to be taken by teachers – curriculum planning, pacing, task assignment, and
monitoring, into a program designed to encompass an entire school day.
Efforts to facilitate learning growing out of a content model perspective have been rooted
in attempts to generate some understanding of how content can be arranged to facilitate
learning through mastery of material. Such efforts connected with the development of
adaptive learning opportunities are only a small and recent subset of the full range of
approaches connected with preparing content as part of educational processes and
systems. These approaches have been influenced by variety of forces and factors over
the years. Some influences over the school curriculum may be described as the result of
social processes related to the general organization of societies, such as the prominence
of religions (Young, 1971; Bernstein, 1975; Meyer, Kamens, & Benovot, 1994). Other
influences have a more decidedly political dimension, such as the roles of public and
private entities (Whitty & Young 1976; Eggleston, 1977; Cody 1990). The content of
education has also been shaped by professional perspectives, both those proffered by the
academic disciplines (Gumport & Snydman, 2002) and those emanating from
professional educators (Tyler 1969).
Within education traditional curriculum development has relied on logical examination of
content or professional judgment to organize sequences of materials for students to move
through as a way of developing competence in a subject. More recently, efforts have
been made to base the development of such content sequences on empirical studies of
student experiences moving through particular content sequences or along particular
paths. These empirically based sequences are referred to as “learning progressions”
(Corcoran, Mosher, & Rogat, 2009), and they constitute an evidence-based method for
28
identifying effective patterns for the organization and presentation of content.
The various factors influencing the nature of content or curriculum highlight the
malleability of content, ranging from decisions governing the selection of content to be
included in any learning system or environment, to the shaping, sequencing, and leveling
of content within a curriculum, to the matching of content to particular students or groups
of students. It is this malleability of content that can be an advantage within an adaptive
learning system. Content can be arranged in any number of ways, and the content model
perspective sensitizes us to the prospects for arranging content to achieve particular
learning ends.
Web-based education seems to offer some particularly useful options for adapting content
to students, and we have discussed much of this work in earlier sections. Here we
mention only a few approaches that seem particularly connected to content and its use to
foster learning.
Brusilovsky, Eklund, and Schwarz (1998) suggest that course material becomes more
flexible through the use of adaptive hypermedia systems (AHS), which allow students
access to personalized content and personalized order of presentation. This flexibility
manifests in adaptive hypermedia systems through a network of static hypertext pages,
some of which include the use of media. Adaptive hypermedia systems present material
in two ways: as adaptive presentation and as adaptive navigation support (Brusilovsky,
Eklund, & Schwarz, 1998). Adaptive presentation adapts the page content to such
individual characteristics as student knowledge and goals. Adaptive navigation support
assists students in finding the best path, based on their growing subject matter
knowledge, through the learning material. An example of adaptive hypermedia system
technology is when a user is given an online presentation in a course that caters to his or
her knowledge of the topic and then receives relevant and learning-level appropriate links
that provide further information on the subject with which students can follow-up
(Brusilovsky, 2003). Any one or more of many curricular organizational frameworks can
be employed in creating adaptive systems. A particularly intriguing approach might be to
combine the empirical methods of learning progressions with the meta-adaptive strategy
advocated by Brusilovsky (2003). This would allow empirically derived paths for
learning to be associated with learners with different profiles so that the method of
adaptation itself is adapted.
Beyond distributing content and navigating content in ways that adapt to students, a more
micro strategy focuses on the adaptation of small parts of text itself in response to
learners. Researchers have created a development framework (Biedert, Buscher,
Schwarz, Moller, Dengel, & Lottermann, 2010) and a reading device (Biedert, Buscher,
& Dengel, in press) that uses eye tracking data to adapt text to the reader to create a more
powerful reading experience. Adaptations include elements such as annotations keyed to
the sections of text being read, the addition of sounds and images, alterations in color,
and translations. Because the connections between eye movements and cognitive
29
processes are well established, future developments in eye tracking equipment and
algorithms that support immediate text adaptations and the tailoring of text on-the-fly
may increasingly come to characterize the reading experience. Such micro adaptations of
content can be linked to broader content models and curriculum strategies.
Earlier we highlighted some of the dilemmas for controlling content and its presentation
in learning environments posed by the open environment of the web. Indeed, as
communication networks and utilities advance, it is increasingly clear that not even the
content of live talks can be constrained from network effects (Parry, 2009). With learners
increasingly in control of the content that supports their learning, the pressure to develop
content perspectives and models that support high performance learning will continue to
grow. It seems that it is no longer possible or worthwhile to attempt to wall out content;
the only strategy is to create more engaging and persuasive curricular experiences.
F. The Teaching Perspective
Work oriented by the teaching perspective has focused on developing insight into
differences in students that can be attuned to a broadened repertoire of instructional
strategies to achieve optimal performance. At least three broad approaches can be
considered. First, there have been numerous efforts over decades of research on teaching
and instruction that examine the interaction of instructional methods and student abilities.
Second, more recent research addresses the micro strategies of teaching that can be used
to adapt to student differences in real time in the context of real classrooms, assessing
their efficacy and feasibility. Third, technology based solutions to creating adaptive
learning opportunities derived from instructional models have taken the form of
intelligent tutoring systems (Winne, 1989).
A substantial body of work follows the logic of research in the tradition of aptitudetreatment interactions (ATIs) (see Cronbach, 1957; Cronbach & Snow, 1977). ATI
studies conducted throughout the sixties and seventies illustrated the importance of
varying instructional methods for students of different ability and motivation levels; this
research also showed that a lack of main effects in studies of instructional treatments
could frequently be explained by aptitude (student characteristics such as ability and
motivation) treatment interaction effects. Cronbach & Snow (1977) define aptitude as any
individual characteristic that increases or impairs the student's probability of success in
response to instruction. Aptitude generally comprises three major categories - cognitive
abilities, which includes intellectual abilities and declarative knowledge;
affective/conative aptitude, which includes motivation, such as interests and efficacy, and
action-control constructs, and affective factors such as temperament and mood; and
physical and psychomotor abilities, including sensory-perceptual abilities. The classic
“ATI hypothesis” is that some portion of students at a high end of a given aptitude
measure may be found to do well with a given form of instruction, while other students,
30
scoring at lower levels, do poorly. The logical implication from an ATI perspective is
that specific instructional procedures and strategies should be adapted by teachers to
specific student characteristics. However, implementing an ATI-based approach to
adaptation requires identifying the most relevant learner characteristics (or aptitudes) for
a given form of instruction and then selecting instructional strategies that best facilitate
the learning processes of particular students exhibiting those aptitudes. Unfortunately, at
the conclusion of their exhaustive review of hundreds of ATI studies, Cronbach and
Snow (1977) concluded that there were so few student aptitudes able to be linked
systematically to particular methods, that matching was an impractical goal.
Other scholars have developed theoretical notions about linkages between different
learner aptitudes and corresponding appropriate instructional strategies that will facilitate
learning (see, e.g., Gagne, 1967; Gallagher, 1994). These ideas have resulted in the
proposal of several broad strategies, guidelines and activities that instructors can adopt
based on the unique aptitudes of different students involved in a given learning context.
For example, Jonassen (1988) proposed a taxonomy of instructional strategies theorized
to correspond to different processes of cognitive learning.
Another issue that came to light in the review of research in the ATI tradition is that ATI
research tended to lay emphasis on exploring simple input/output relations between
measured learner characteristics and learning outcomes based on some instructional
interventions (Tobias, 1989). However, individual difference variables are difficult to
measure; therefore, results may not only be questionable in terms of validity, they may
also not be generalizable to other areas not covered by the particular research studies.
Indeed, (Carrier & Jonassen, 1988) recommend that teachers become proficient teaching
subject matter using several types of instructional strategies, pointing out that since
several individual differences between students do influence learning, selecting a specific
strategy for a given situation will inevitably be impractical; the outcomes may be
different for different instructional contexts. Research is ongoing, however, as Park &
Lee (2004) indicate, "the outlook is not optimistic for the development of a
comprehensive ATI model or set of principles for developing adaptive instruction that is
empirically traceable and theoretically coherent in the near future" (p. 661).
Some researchers have attempted to develop more micro-adaptive instructional models
using on-task measures of individual student behavior and performances. The belief is
that diagnosing students' learning preferences, needs, and styles through on-task
monitoring of how they learn including cognition, behavior, and emotional states, can
lead to instructional strategies based on these diagnoses. This then is the basis for
making adaptive instructional decisions, which does not rely on holistic models and
guidelines prescribed prior to tasks. This micro-level approach is hypothesized to ensure
learning outcomes by continuously monitoring, manipulating and tailoring instructional
treatments based on the diagnosis of student learning concerns, information processing,
and behavior during each ongoing learning cycle. One-on-one tutoring best exemplifies
the model underlying this micro-level adaptive instruction approach, and indeed the
31
effectiveness of one-on-one tutoring in promoting learning has been empirically
supported (Bloom, 1984; Winne & Nesbit, 2009).
As Corno (2008) notes in her discussion of adaptive teaching, one-on-one tutoring or any
form of true individualization is practically impossible to implement in current formal
educational settings (e.g., public school classrooms) where there are both inadequate
resources and few available qualified tutors. Corno described how practicing teachers
make such micro-adaptations on an ongoing basis, based on their own formations of
student strengths and weaknesses. Other research programs have come at the micro-level
adaptation question by capitalizing on advances in computing and communication
technologies. These bodies of research are aimed at developing instructional models that
include the use of new technologies in ways that are expected to support effective
individualized learning and instructional activities.
Technology based solutions grow out of a history of work on computer assisted
instruction. Computer assisted instruction/learning (CAI/CAL) marked an effort to use
computers to enhance learning based upon the psychological theories extant at different
periods of time. For example, in the 1920’s Sidney Pressey created a machine
that claimed it “tested and also taught” (Skinner, 1986). This machine directed the
student through a multiple choice test, and monitored responses. If the student chose the
correct answers the machine progressed to a higher level. However, if some responses
were determined to be incorrect, the student had the opportunity to repeat the test, but the
machine only presented the items that the student got incorrect on the first trial. This
simple tracking of a student's responses was considered a form of adapting future content
to students' personal areas of weakness. This idea remained foundational to teaching
machines as they developed.
In the 1950's, as Skinner’s idea of behaviorism was reflected not only in instructional
methods in schools but also in design frameworks for educational technology systems,
learning processes were generally conceptualized as forming connections between stimuli
and responses, hence the drive towards the reward and punishment frameworks
(Bransford, Brown, & Cocking, 1999). Skinner himself dabbled with teaching machines
and showcased in 1954 his arithmetic machine at the University of Pittsburgh (Skinner,
1986). The machine was an awkward box that had a knob that students turned to have
questions scroll into a designated area with sliders that the students used to create their
numerical answers. Once they were done with answering one question, they would try to
turn the knob and get another question, and if they had placed the sliders in the correct
places (a correct numerical answer) then they were given the next question. If what they
produced was wrong, they had to keep repeating the process of placing the sliders until
the right response was obtained before they could continue. This machine built
upon Pressey's (1926) earlier work in two important ways. First, it was not only a test
taking machine, in that students who had not previously studied material could also use it,
and so the machine both tested and taught. Second, the students composed their responses
instead of choosing from a preexisting set, and the material was arranged hierarchically,
32
such that once learners completed the questions in the first frame, they were prepared to
deal with questions in the next (Skinner, 1986).
Early prototypes of these programmable learning machines created during this time were
"linear programs," in part because they neglected to provide rich feedback or
individualization. They were not designed to develop a model of whom they were
teaching, what they were teaching, and how to teach it (Nwana, 1990). The programs
were organized into frames that were presented to the learner and were set up to take the
learner toward a desired behavior. As the frames were presented the learner had to
complete very simple operations such as filling in a blank space, and was immediately
given knowledge of results. Regardless of the student response, frames would continue in
a predetermined order, thus the computer was really doing little more than what a
programmed textbook could do (Nwana, 1990), and early studies of student motivation
with programmed instruction showed that boredom led to skipped items and lost interest.
The next step in the evolution of these machines involved taking the individual learner
into consideration by regarding their input in terms of content and actions. This idea led
to what is known as “branching programs” that modify the interaction between the
system and the user by being able to comment on a response and also use it to choose a
next frame. Branching programs still used the frame design but made decisions on what
material should be shown to the student based on their knowledge and actions. Pattern
matching techniques thus allowed learners' answers to be considered on an acceptability
scale rather than the correct/incorrect model of the Skinnerian systems (Nwana, 1990).
The next development along the spectrum of intelligent tutoring grew out of the
recognition that teaching material could itself be generated by the computer. This school
of thought emerged by the late 1960’s and the corresponding systems that were
developed were known as “generative” or “adaptive systems” (Nwana, 1990). Such
systems were able to generate meaningful problems and monitor their solution as well.
For instance, Urh (1969) implemented a series of such systems that generated problems
in arithmetic that were tailored to a student's performance. Suppes (1967), and Woods
and Hartley (1971) also produced systems with similar capabilities.
Generally however, these "generative" systems also relied on a frame-oriented design in
which the course author specified the formats. The system generated parameters for these
formats and upon receiving a response to a particular question, searched a database and
presented students with information on whether the answer was right or wrong. Due to
the obvious shortcomings of these systems, their usage was limited to domains with wellstructured content like mathematics that allowed for drill-type exercises (Nwana, 1990).
Such computer assisted instruction provided the foundation for the development of
intelligent tutoring systems (Winne, 1989). Through further development the systems
gradually improved on their individualization and feedback features, though critics
maintained that the systems could not fully mimic human knowledge (Winne, 1989;
33
Nwana, 1990), and also failed to incorporate many valuable learning principles and
instructional strategies (Park, Perez, & Siedel, 1987). To such critics, computer tutoring
systems should have a representation of what is being taught, who is being taught and
how such a person should be taught. Thus, computer programs called Intelligent Tutoring
Systems (ITSs) were devised, and these aimed to incorporate techniques from artificial
intelligence (AI) in order to provide "tutors" that know what they teach and how to teach
it in an ‘intelligent’ way (Nwana, 1990). Apart from the practical ideals of one-on-one
customized tutoring and immediate feedback to learners, intelligent tutoring systems
were thought to be more economical in the long run, and this contributed to heightened
interest in these systems.
Research and development on intelligent tutoring systems spans three different
disciplines - computer science, cognitive psychology and educational research, and this
results in major differences in research prototypes and goals, terminologies, theoretical
frameworks, and emphases among researchers (Nwana, 1990). Currently, prototype and
operational intelligent tutoring systems provide practice-based instruction to support
individualized learning in K-12 and college education, although on a limited scale.
Work from the teaching or instructional perspective shows promise along several lines.
First, there seems to be the potential for intelligent tutoring systems to be developed and
refined to include more complete adaptive elements and meta-adaptive dimensions.
Second, strategies to prepare and equip classroom teachers to teach adaptively may be
supported by the development of technology-based assistive devices and adaptive
teaching tools and systems. We highlight some recent developments in each area to
illustrate the potential for further progress from the teaching perspective.
Although current uses of intelligent tutoring systems remain modest, there is a good deal
of ongoing research designed to make such systems more effective components of the
learning process (Self, 1999). Recently, intelligent tutoring systems have moved towards
reading the user's emotional state and conveying the emotional state of the intelligent
tutor (Nkambou, 2006; Sarrafzadeh, Alexander, Dadgostar, Fan, & Bigdeli, 2008). The
idea is to for the system to “sense” when learners are frustrated, confused, or angry and in
need of remedial assistance and to respond appropriately, including an affective
dimension. This added adaptivity is expected to improve students’ affective response or
satisfaction as well as their performance. Other work to increase the sensitivity and
responsiveness of intelligent tutoring systems is drawing on evolving techniques of data
mining to (Nkambou, Fournier-Viger, & Nguifo, 2009) to take into account the wider
array of data generated by students in the act of problem solving.
Efforts to support classroom teachers to adapt their instructional approach to individual
learners have relied on professional development (e.g., Wang & Gennari, 1983; Wang,
Rubinstein, & Reynolds, 1985), but as Corno (2008) notes, the conditions found in most
schools make individualization at the level envisioned by fully adaptive teaching
impossible. Fortunately, the same advances in computing and communications
34
technologies that support digital learning environments can also be employed to create a
new generation of tools to support classroom teachers in successful adaptations. The inclassroom use of handheld computing and communications devices is one early example
of a means for creating opportunities for teachers to engage in dynamic assessment of
student performance that supports their efforts to adapt instruction to individual students
(Penuel & Yarnall, 2005; Hupert Heinze, Gunn, & Stewart, 2008).
G. The Social Network Perspective
Social relationships among individuals have always provided major opportunities for
learning, and, indeed, the social groups in which we are all embedded have long been
used to explain differences in educational attainment (Lin, 1999). Studies of the
structures and processes of human social networks have been joined more recently by
studies of the network processes that are growing along with new developments in
computing and communications technologies. Moreover, the network possibilities
connected with the Internet and the world wide web have brought forth a new generation
of research on network processes and properties (Barabasi, 2003; Watts, 2004).
Technology mediated or supported social networks offer the capacity to connect learners
with other individuals who can support and enhance their learning. These networks
present new possibilities and opportunities for adaptive learning.
There are at least three streams of activity that bear on adaptive learning opportunities
with varying degrees of intention and structure. First, the increasing size and density of
the Internet and the world wide web has the potential to offer expanded learning
opportunities. Second, within the broader Internet there are naturally forming online
communities that offer rich collections of individuals who can be resources for the
learning of others within the same community. Third, there are deliberately assembled
and purposively structured online communities of individuals committed to facilitating
the learning of one another.
First, with the growth of the number of individuals actively online, there is an enormous
set of individuals who may serve as resources to support the learning of others on the
network. These individuals are not necessarily clustered within the network in proximity
to any particular learner in need of assistance. Moreover, there is reason to believe that
opportunities for learning from less directly connected individuals may be powerful
(Granovetter, 1973). Opportunities for enhancing the learning value of connections on the
open Internet seem to lie with the development of new understandings of the operations
of structure and processes operating within networks, including those factors that affect
the distribution of resources and access to them (Barabasi, 2003; Watts, 2004). If we are
to enhance the learning value of elements of a network, we need to develop strategies for
exposing the learning opportunities available in various resources (Alesso & Smith, 2009;
Antiniou & van Harmelen, 2008), as well as more powerful search utilities to connect
learner needs to far flung individuals (Morville, 2005).
35
Second, there are naturally forming communities within the larger Internet that offer
more specialized collections of individuals. These communities form around common
interests and pursuits such as common problems or professional interests (Wenger, 1999).
With the rise of social networking services such as MySpace, Facebook, and Flickr, the
opportunities for social processes leading to exchange of relevant information appear to
be increasing. Such networks convey various benefits to participants (Ellison, Steinfield,
& Lampe, 2007), including learning opportunities (Constant, Sproull, & Kiesler, 1996).
In these networks the process of assembling the appropriate resources predates particular
efforts to access the resources for an immediate learning purpose. Nonetheless, with the
continuing development of technologies for enhancing both the quality of information on
resources and the quality of information on individual users and their learning needs
(Breslin, Passant, & Decker, 2009), the opportunities to facilitate learning are increasing
in substantial ways.
Third, there are more deliberately assembled online communities that create rich
collections of individuals who can be accessed to assist each other’s learning. These
more deliberate efforts encompass a wide range of activities from targeted research and
development programs to the rapidly growing number of online courses and learning
environments. These efforts all share the explicit goal of facilitating learning. Stahl,
Koshmann, & Suthers (2002) and Lee and Park (2007) discuss the range of research and
development efforts within the field of education that seek to enhance learning
opportunities through the support of interaction and collaboration. Bruckman (2002)
describes the learning possibilities of online communities deliberately organized for
educational purposes.
Focusing more directly on the adaptive learning dimensions of deliberately
assembled educational collectivities, Brusilovsky and Peylo (2003) discuss a
group of technologies that they term “intelligent collaborative learning.” They
view these approaches as lying at the crossroads of the fields of computer
supported collaborative learning and intelligent tutoring systems. Brusilovsky and
Peylo (2003, pp. 164-165) identify three distinct technologies underlying this
work. Adaptive group formation and peer help approaches identify appropriate
matches to support the specific learning needs of individuals and groups by
drawing on knowledge about potential collaborating peers contained in their
student models. Masthoff (2008) notes the task of dividing a group into smaller
teams for optimal collaboration as a target for such support as well as the task of
identifying a suitable partner for working together.
Adaptive collaboration support approaches draw on information about more or
less successful collaboration patterns taken from communication logs to provide
interactive support for collaboration processes. Masthoff (2008) distinguishes
further between interaction support and decision support. Interaction support may
36
involve promoting equal and appropriate contributions through modeling and
incentives as well as assisting learners in asynchronous collaboration
environments, and sorting through a large number of contributions made in their
absence by providing relevance information. Decision support may involve
modeling common knowledge among group members, using individual user
model information to guide adaptation to groups, and facilitating negotiation
among group members.
Virtual student approaches rely on the introduction of virtual peers into the
learning environment to support student learning. For example, Uresti and du
Boulay (2004) introduced a learning companion requiring instruction and review
from real students to promote their engagement. Gulz (2004) examined the
evidence available on the learning impact of virtual students as of 2004, describing
the overall results for the early research as uncertain. She identified a number of
avenues for additional research as this approach is developed further.
CONCLUSION
As we have said, any parceling of the broad set of activities that may contribute to the
evolution of adaptive learning opportunities is necessarily somewhat arbitrary. The seven
perspectives we have defined are at times over lapping, and many adaptive learning
activities will draw on multiple perspectives. Nevertheless, the seven perspectives
identified provide a starting point for our efforts to scour the terrain of activities that
might be brought into a conversation to advance thinking about adaptive learning
possibilities. They may also serve to suggest new avenues for development, both for
those starting on the path of developing adaptive applications and practices and for those
who are already deeply engaged in such development.
The sheer breadth and extent of the development of adaptive learning applications
suggests that we are ready to take the next step to bring such applications and their
creators into a sustainable conversation with both the increasingly multi-disciplinary
community of developers and with practicing educators in the evolving education and
learning sector. The question before us now is how best to facilitate such a conversation.
Our next step is to engage key constituencies (e.g., developers, researchers, publishers,
educators, learners) in activities to develop prototype designs for these and other
elements of the infrastructure for facilitating the growth of adaptive learning
opportunities.
We believe that the current period is ripe for fundamental change in the education sector.
Indeed, such change seems likely to involve what Lawrence Cremin termed the
“configurations of education,” that is, the ways in which the various institutions involved
in education interact with each other and with other major social sectors. If we are to
37
foster the development, refinement, delivery, and use of adaptive learning applications on
a large scale, we might need to imagine one or more new institutional entities added to
the configurations of the contemporary education sector. And we will certainly need to
imagine changes in the relationships among the parts of the sector. It is time to begin,
and we invite you to join us.
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