By Topic

Learning Technologies, IEEE Transactions on

Issue 1 • Date Jan.-March 2009

Filter Results

Displaying Results 1 - 6 of 6
  • Introduction to the Special Issue on Personalization

    Page(s): 1 - 2
    Save to Project icon | Request Permissions | PDF file iconPDF (85 KB)  
    Freely Available from IEEE
  • Creating a Corpus of Targeted Learning Resources with a Web-Based Open Authoring Tool

    Page(s): 3 - 9
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1012 KB) |  | HTML iconHTML  

    Personalizing learning to studentspsila traits and interests requires diverse learning content. Previous studies have demonstrated the value of such materials in learning but a challenge remains in creating a corpus of content large enough to meet studentspsila varied interests and abilities. We present and evaluate a prototype Web-based tool for open authoring of learning materials. We conducted a study (an open web experiment) to evaluate whether specific student profiles presented in the toolpsilas interface increase the diversity of the contributions, and whether authors tailor their contributions to the features in the profiles. We report on the quality of materials produced, authorspsila facility in rating them, effects of author traits, and impact of the tailoring feature. Participants were professional teachers (math and non-math) and amateurs. Participants were randomly assigned to the tailoring tool or a simplified version without the tailoring feature. We find that while there are differences by teaching status, all three groups make contributions of worth. The tailoring feature leads contributors to tailor materials with greater potential to engage students. The experiment suggests that an open access Web-based tool is a feasible technology for developing a large corpus of materials for personalized learning. View full abstract»

    Open Access
  • Evaluating Learning Style Personalization in Adaptive Systems: Quantitative Methods and Approaches

    Page(s): 10 - 22
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1278 KB) |  | HTML iconHTML  

    It is a widely held assumption that learning style is a useful model for quantifying user characteristics for effective personalized learning. We set out to challenge this assumption by discussing the current state of the art, in relation to quantitative evaluations of such systems and also the methodologies that should be employed in such evaluations. We present two case studies that provide rigorous and quantitative evaluations of learning-style-adapted e-learning environments. We believe that the null results of both these studies indicate a limited usefulness in terms of learning styles for user modeling and suggest that alternative characteristics or techniques might provide a more beneficial experience to users. View full abstract»

    Open Access
  • Supporting the Development of Mobile Adaptive Learning Environments: A Case Study

    Page(s): 23 - 36
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1268 KB) |  | HTML iconHTML  

    In this paper, we describe a system to support the generation of adaptive mobile learning environments. In these environments, students and teachers can accomplish different types of individual and collaborative activities in different contexts. Activities are dynamically recommended to users depending on different criteria (user features, context, etc.), and workspaces to support the corresponding activity accomplishment are dynamically generated. In this article, we present the main characteristics of the mechanism that suggests the most suitable activities at each situation, the system in which this mechanism has been implemented, the authoring tool to facilitate the specification of context-based adaptive m-learning environments, and two environments generated following this approach will be presented. The outcomes of two case studies carried out with students of the first and second courses of ldquoComputer Engineeringrdquo at the ldquoUniversidad Autonoma de Madridrdquo are also presented. View full abstract»

    Open Access
  • Constraint-Based Validation of Adaptive e-Learning Courseware

    Page(s): 37 - 49
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (767 KB) |  | HTML iconHTML  

    Personalised e-learning allows the course creator to create courseware that dynamically adapts to the needs of individual learners or learner groupings. This dynamic nature of adaptive courseware makes it difficult to evaluate what the delivery time courseware will be for the learner. The course creator may attempt to validate adaptive courseware through dummy runs, but cannot eliminate the risk of pedagogical problems due to adaptive courseware's inherent variability. Courseware validation checks that adaptive courseware conforms to a set of pedagogical and non-pedagogical requirements. Validation of adaptive courseware limits the risk of pedagogical problems at delivery time. In this paper, we present our approach to adaptive courseware validation using the Courseware Authoring Validation Information Architecture (CAVIAr). We outline how CAVIAr captures adaptive courseware authoring concerns and validates courseware using a constraint-based approach. We also describe how CAVIAr can be integrated with the state of the art in adaptive e-learning and evaluate our validation approach. View full abstract»

    Open Access
  • Mood Recognition during Online Self-Assessment Tests

    Page(s): 50 - 61
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (462 KB) |  | HTML iconHTML  

    Individual emotions play a crucial role during any learning interaction. Identifying a student's emotional state and providing personalized feedback, based on integrated pedagogical models, has been considered to be one of the main limits of traditional tools of e-learning. This paper presents an empirical study that illustrates how learner mood may be predicted during online self-assessment tests. Here a previous method of determining student mood has been refined based on the assumption that the influence on learner mood of questions already answered declines in relation to their distance from the current question. Moreover, this paper sets out to indicate that ldquoexponential logicrdquo may help produce more efficient models, if integrated adequately with affective modelling. The results show that these assumptions may prove useful to future research. View full abstract»

    Open Access

Aims & Scope

IEEE Transactions on Learning Technologies (TLT) covers research on such topics as Innovative online learning systems, Intelligent tutors, Educational software applications and games, and Simulation systems for education and training.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Dr. Peter Brusilovsky