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The latest issue of IEEE Transactions on Learning Technologies (Vol. 2, No. 1) is online
EDITORIAL
Introduction to the Special Issue on Personalization
Paul De Bra, Judy Kay, Stephan Weibelzahl
http://doi.ieeecomputersociety.org/10.1109/TLT.2009.15
PAPERS
Creating a Corpus of Targeted Learning Resources with a Web-Based Open Authoring Tool
Turadg Aleahmad, Vincent Aleven, Robert Kraut
http://doi.ieeecomputersociety.org/10.1109/TLT.2009.8
Abstract:
Personalizing learning to students’ 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 students’ 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 tool’s 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, authors’ 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.
Evaluating Learning Style Personalization in Adaptive Systems: Quantitative Methods and Approaches
Elizabeth J. Brown, Timothy J. Brailsford, Tony Fisher, Adam Moore
http://doi.ieeecomputersociety.org/10.1109/TLT.2009.11
Abstract:
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.
Supporting the Development of Mobile Adaptive Learning Environments: A Case Study
Estefanía Martín, Rosa M. Carro
http://doi.ieeecomputersociety.org/10.1109/TLT.2008.24
Abstract:
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 “Computer Engineering” at the “Universidad Autónoma de Madrid” are also presented.
Constraint-Based Validation of Adaptive e-Learning Courseware
Mark Melia, Claus Pahl
http://doi.ieeecomputersociety.org/10.1109/TLT.2009.7
Abstract:
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.
Mood Recognition during Online Self-Assessment Tests
Christos N. Moridis, Anastasios A. Economides
http://doi.ieeecomputersociety.org/10.1109/TLT.2009.12
Abstract:
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 “exponential logic” may help produce more efficient models, if integrated adequately with affective modelling. The results show that these assumptions may prove useful to future research.
Subject:
The latest issue of IEEE Transactions on Learning Technologies (Vol. 2, No. 1) is online
From:
<MBartosik@computer.org>
Date:
Mon, 6 Apr 2009 20:07:15 +0200
To:
<tlt-subscribers@computer.org>
Greetings,
This is to notify you that the January-March 2009 (Vol. 2, No. 1) issue of IEEE Transactions on Learning Technologies is now online. You can access any of the following articles and columns individually from the table of contents, or you can download the entire issue as a zip file (3.738 MB). To login, please click the text link "Register/Login," located in the blue bar at the top of the page.
To access the articles in this issue, use your IEEE user name and password at
http://opac.ieeecomputersociety.org/opac?year=2009&volume=2&issue=1&acronym=tlt
For information on using your account, visit the IEEE at http://www.ieee.org/web/accounts/. If you need information on your subscription or have membership questions, please email help@computer.org.
Regards,
Mark Bartosik
Digital Production Specialist
IEEE Computer Society
www.computer.org
-----------------------------------------------------------------------------------------------------------------
EDITORIAL
Introduction to the Special Issue on Personalization
Paul De Bra, Judy Kay, Stephan Weibelzahl
http://doi.ieeecomputersociety.org/10.1109/TLT.2009.15
PAPERS
Creating a Corpus of Targeted Learning Resources with a Web-Based Open Authoring Tool
Turadg Aleahmad, Vincent Aleven, Robert Kraut
http://doi.ieeecomputersociety.org/10.1109/TLT.2009.8
Abstract:
Personalizing learning to students’ 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 students’ 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 tool’s 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, authors’ 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.
Evaluating Learning Style Personalization in Adaptive Systems: Quantitative Methods and Approaches
Elizabeth J. Brown, Timothy J. Brailsford, Tony Fisher, Adam Moore
http://doi.ieeecomputersociety.org/10.1109/TLT.2009.11
Abstract:
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.
Supporting the Development of Mobile Adaptive Learning Environments: A Case Study
Estefanía Martín, Rosa M. Carro
http://doi.ieeecomputersociety.org/10.1109/TLT.2008.24
Abstract:
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 “Computer Engineering” at the “Universidad Autónoma de Madrid” are also presented.
Constraint-Based Validation of Adaptive e-Learning Courseware
Mark Melia, Claus Pahl
http://doi.ieeecomputersociety.org/10.1109/TLT.2009.7
Abstract:
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.
Mood Recognition during Online Self-Assessment Tests
Christos N. Moridis, Anastasios A. Economides
http://doi.ieeecomputersociety.org/10.1109/TLT.2009.12
Abstract:
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 “exponential logic” may help produce more efficient models, if integrated adequately with affective modelling. The results show that these assumptions may prove useful to future research.
