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Preference-based decision making for personalised access to Learning Resources
Abstract
This article addresses the selection of Learning Resources (LRs; learning material, learning activities, etc.) through a preference-based decision-making framework. We consider the case where a set of LRs are maintained within a digital repository and are described in a common format (e.g. through learning technologies specifications and standards). We present a preference-based decision-making framework for selecting among these LRs according to the profile of each individual learner, thus facilitating personalised access to LRs. We argue that the proposed framework overcomes some of the problems caused by the rule-based approaches which are usually employed to facilitate adaptation and personalisation, in general.
In this chapter, we highlight some of the recent international debates informing personalised learning as it impacts on pedagogy, with a focus on e-mediated tools. We then reflect on the experience of being on-campus in the past, in the present, and then we look to the possible future. We consider significant challenges impeding technology adoption in higher education, drawing on a time-to-adoption frame. We provide insights into the challenges for effective pedagogy with specific focus on self-regulation, catering for diversity, and other important facets of inclusive education.
Automatic courseware authoring is recognized as among the most interesting research questions in intelligent web-based education. Automatic courseware authoring is the process of automatic learning object selection and sequencing. In most intelligent learning systems that incorporate course sequencing techniques, learning object selection and sequencing is based on a set of teaching rules according to the cognitive style or learning preferences of the learners. In spite of the fact that most of these rules are generic (i.e. domain independent), there are no well-defined and commonly accepted rules on how the learning objects should be selected and how they should be sequenced to make 'instructional sense'. Moreover, in order to design adaptive learning systems a huge set of rules is required, since dependencies between education characteristics of learning objects and learners are rather complex. In this paper, we address the learning object selection and sequencing problem in intelligent learning systems proposing a methodology that instead of forcing an instructional designer to manually define the set of selection and sequencing rules, it produces a decision model that mimics the way the designer decides, based on the observation of the designer's reaction over a small-scale learning object selection case.
The paper explains the basics of the current technology applied in Web-based education and how developments in computational intelligence can contribute to that technology. Web-based education is a popular research and development domain in educational technology. In recent years, a number of research groups have put a lot of efforts in developing suitable methodologies, approaches, tools, and practical systems to support Web-based education. There were also several introductory and survey papers covering the domain. However, there has been comparatively little research in applying the principles and achievements in computational intelligence - fuzzy
This paper addresses the issue of creating reusable adaptive courseware from the point of view of the author (teacher or instructor). We illustrate the idea of reusability under the motto "Authoring Once, Delivering Many" by showing the conversion process from a generic authoring system, MOT (My Online Teacher), to a different delivery system, WHURLE. This exercise is but one in a series of interfacing exercises, through which we try, step by step, to come closer to the goal of being able to reuse adaptive educational material, once created, on any adaptive delivery platform desired. The focus here is less on the technical aspects of the interfacing, as on the reduction of authoring burden, on one side, and on the maintenance of a certain level of pedagogical flexibility during the conversion, on the other.
The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners' experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective.
Adaptive learning object selection and sequencing is recognized as among the most interesting research questions in intelligent web-based education. In most intelligent learning systems that incorporate course sequencing techniques, learning object selection is based on a set of teaching rules according to the cognitive style or learning preferences of the learners. In spite of the fact that most of these rules are generic (i.e. domain independent), there are no well-defined and commonly accepted rules on how the learning objects should be selected and how they should be sequenced to make "instructional sense". Moreover, in order to design highly adaptive learning systems a huge set of rules is required, since dependencies between educational characteristics of learning objects and learners are rather complex. In this paper, we address the learning object selection problem in intelligent learning systems proposing a methodology that instead of forcing an instructional designer to manually define the set of selection rules, it produces a decision model that mimics the way the designer decides, based on the observation of the designer's reaction over a small-scale learning object selection case.
This paper suggests an alternative to the traditional 'as a whole' approach of evaluating adaptive learning systems (ALS), and adaptive systems, in general. We argue that the commonly recognised models of adaptive systems can be used as a basis for a layered evaluation that offers certain benefits to the developers of ALS. Therefore, we propose the layered evaluation framework, where the success of adaptation is addressed at two distinct layers: • user modelling • adaptation decision making. We outline how layered evaluation can improve the current evaluation practice of ALS. To build a stronger case for a layered evaluation we re-visit the evaluation of the InterBook where the layered approach can provide a difference and provide an example of its use in KOD learning system.
DT Tutor uses a decision-theoretic approach to select tutorial actions for coached problem solving that are optimal given the tutor's beliefs and objec- tives. It employs a model of learning to predict the possible outcomes of each action, weighs the utility of each outcome by the tutor's belief that it will occur, and selects the action with highest expected utility. For each tutor and student action, an updated student model is added to a dynamic decision network to re- flect the changing student state. The tutor considers multiple objectives, includ- ing the student's problem-related knowledge, focus of attention, independence, and morale, as well as action relevance and dialog coherence. Evaluation in a calculus domain shows that DT Tutor can select rational and interesting tutorial actions for real-world-sized problems in satisfactory response time. The tutor does not yet have a suitable user interface, so it has not been evaluated with human students.
This paper analyses one of the most well-known general purpose adaptive hypermedia systems, AHA!, and, based on this analysis, make some general observations about adaptive hypermedia systems and some improvement suggestions for the AHA! system. We suggest here a concept-based approach to the structuring of adaptive hypermedia systems, as well as an extension of the well-known rule-based overlay method for user-adaptation. This approach is another step towards flexible generic-purpose adaptive hypermedia.
Adaptive learning resources selection and sequencing is recognized as among the most interesting research questions in adaptive educational hypermedia systems (AEHS). In order to adaptively select and sequence learning resources in AEHS, the definition of adaptation rules contained in the Adaptation Model, is required. Although, some efforts have been reported in literature aiming to support the Adaptation Model design by providing AEHS designers direct guidance or semi-automatic mechanisms for making the design process less demanding, still it requires significant effort to overcome the problems of inconsistency, confluence and insufficiency, introduced by the use of rules. Due to the problems of inconsistency and insufficiency of the defined rule sets in the Adaptation Model, conceptual "holes" can be generated in the produced learning resource sequences (or learning paths). In this paper, we address the design problem of the Adaptation Model in AEHS proposing an alternative sequencing method that, instead of generating the learning path by populating a concept sequence with available learning resources based on pre-defined adaptation rules, it first generates all possible learning paths that match the learning goal in hand, and then, adaptively selects the desired one, based on the use of a decision model that estimates the suitability of learning resources for a targeted learner. In our simulations we compare the learning paths generated by the proposed methodology with ideal ones produced by a simulated perfect rule-based AEHS. The simulation results provide evidence that the proposed methodology can generate almost accurate learning paths avoiding the need for defining complex rule sets in the Adaptation Model of AEHS.
We focus on well-behaved Adaptive Hypermedia Systems, which means the adaptation engine that executes adaptation rules always terminates and produces predictable (confluent) adaptation results. Unfortunately termination and confluence are undecidable in general. In this paper we discuss sufficient conditions to help authors to write adaptation rules that satisfy termination and confluence.
Intelligent user interfaces are characterised by their capability to adapt at run-time and make several communication decisions concerning `what', `when', `why' and `how' to communicate, through a certain adaptation strategy. In this paper, we present a methodological approach to assist this decision making process, which is based on a clear separation of the important attributes that characterise the adaptation strategy, namely the adaptation determinants, constituents, goals and rules. Based on this separation, we also present a methodological approach for the formulation of adaptation rules, which utilises techniques from the domain of multiple criteria decision making. It is argued that, following the proposed approach, the adaptation strategy can be easily customised to the requirements of different application domains and user groups, and can be re-used with minor modifications in different applications. As a result, developers of intelligent user interfaces can be significantl...
Currently, adaptive educational hypermedia systems (AEHSs) are described using nonuniform methods, depending on the specific view of the system, the application, or other parameters. There is no common language for expressing the functionality of AEHSs, hence these systems are difficult to compare and analyze. In this paper we investigate how a logical description can be employed to characterize adaptive educational hypermedia. We propose a definition of AEHSs based on first-order logic, characterize some AEHSs resulting from this formalism, and discuss the applicability of this approach.
817 p., fig., ref. bib. : 17 p.1/2 This volume is the direct result of a seminar on Multiple Criteria Decision Making held at the University of South Carolina on October 26-27, 1972. Support for this seminar and for production of this book was provided by the University of South Carolina Business Partnership Foundation. All the papers and abridgements of papers included in this volume were read at the seminar. They represent a major addition to decision making literature. Although the multiplicity of objectives in many areas of human decision making and judgment is readily observable in reality, multiple goal behavior has traditionally been approximated by single, unchanging, and technically manageable criteria. A typical example of this is the profit maximization criterion. "Profit" is really a surrogate for a number of complex variables such as earnings per share, stock price, debt-equity ratio, market share, goodwill, labor relations, product quality, and ecological impact of operations. Such a compression of several noncommensurable entities reduces their information content. Similarly, attempts . to translate nonmonetary terms has been considerably overdone. Dealing with such "incommensurables" as quality of life, pollution impact, educational level, personal health, esthetic value, job variety, and national security cannot be avoided any longer. Obviously, mathematicians' concept of "optimality" is not quite sufficient. The optimum optimorum is graduallybeing replaced by fuzzy solution concepts such as compromise, arbitration, interaction, prominence, dominance, satisficing, negotiation, and bargaining. The compromise resolution of multiple goal conflict is usually missing from theories of decision making. The search for theories, concepts, and techniques applicable to decision making processes under multiple criteria has steadily been intensified during the past few years. The time has come to review the results, to compare them, and to outline possible future directions. The seminar at which the papers in this volume were read was the first meeting of this nature in the United States. Since the problem is highly general, the participants represented many areas of applied decision making. Most were from business and economics, but a number of engineers, psychologists, sociologists, behavioral scientists, mathematicians, statisticians, and political scientists also contributed. The important part of the discussions explored the relation between formalized decision making techniques (utilizing computer and mathematical analyisi) and of human judgment, based on intuition, experience, and "professional insight." Rejuvenation of the role of human judgment seems to be one of the main aspects of the literature on multiple criteria decision making but many participants seem to be skeptical about man's ability to choose among multiattributed alternatives-suggesting an interaction.
Libraries of learning objects may serve as basis for deriving course offerings that are customized to the needs of different learning communities or even individuals. Several ways of organizing this course composition process are discussed. Course composition needs a clear understanding of the dependencies between the learning objects. Therefore we discuss the metadata for object relationships proposed in different standardization projects and especially those suggested in the Dublin Core Metadata Initiative. Based on these metadata we construct adjacency matrices and graphs. We show how Gozinto-type computations can be used to determine direct and indirect prerequisites for certain learning objects. The metadata may also be used to define integer programming models which can be applied to support the instructor in formulating his specifications for selecting objects or which allow a computer agent to automatically select learning objects. Such decision models could also be helpful for a learner navigating through a library of learning objects. We also sketch a graph-based procedure for manual or automatic sequencing of the learning objects.
IMS Meta-data Best Practice Guide for IEEE 1484.12. 1-2002 Standard for Learning Object Metadata, Version 1.3 Final Specification
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Learning Objects: Difficulties and Opportunities. Available at
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