Informing and Performing: A Study Comparing Adaptive Learning to Traditional Learning

10.28945/2140 ◽

2015 ◽

Author(s):

Meg Coffin Murray ◽

Jorge Pérez

Keyword(s):

Learning Outcomes ◽

Learning Style ◽

Adaptive Learning ◽

Digital Literacy ◽

Learning Systems ◽

Potential Game ◽

Learning Tools ◽

Literacy Course ◽

Delivery Options ◽

Adaptive Learning Systems

Technology has transformed education, perhaps most evidently in course delivery options. However, compelling questions remain about how technology impacts learning. Adaptive learning tools are technology-based artifacts that interact with learners and vary presentation based upon that interaction. This paper compares adaptive learning with a conventional teaching approach implemented in a digital literacy course. Current research explores the hypothesis that adapting instruction to an individual’s learning style results in better learning outcomes. Computer technology has long been seen as an answer to the scalability and cost of individualized instruction. Adaptive learning is touted as a potential game-changer in higher education, a panacea with which institutions may solve the riddle of the iron triangle: quality, cost and access. Though the research is scant, this study and a few others like it indicate that today’s adaptive learning systems have negligible impact on learning outcomes, one aspect of quality. Clearly, more research like this study, some of it from the perspective of adaptive learning systems as informing systems, is needed before the far-reaching promise of advanced learning systems can be realized. A revised version of the paper was published in Informing Science: the International Journal of an Emerging Transdiscipline, Volume 18, 2015

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Evaluation of Clustering Methods for Adaptive Learning Systems

Artificial Intelligence Applications in Distance Education - Advances in Mobile and Distance Learning ◽

10.4018/978-1-4666-6276-6.ch014 ◽

2015 ◽

pp. 237-260 ◽

Cited By ~ 1

Author(s):

Wilhelmiina Hämäläinen ◽

Ville Kumpulainen ◽

Maxim Mozgovoy

Keyword(s):

Data Mining ◽

Adaptive Learning ◽

Clustering Algorithms ◽

Educational Data Mining ◽

Optimal Choice ◽

Learning Systems ◽

Learning Tools ◽

Clustering Methods ◽

Central Task ◽

Adaptive Learning Systems

Clustering student data is a central task in the educational data mining and design of intelligent learning tools. The problem is that there are thousands of clustering algorithms but no general guidelines about which method to choose. The optimal choice is of course problem- and data-dependent and can seldom be found without trying several methods. Still, the purposes of clustering students and the typical features of educational data make certain clustering methods more suitable or attractive. In this chapter, the authors evaluate the main clustering methods from this perspective. Based on the analysis, the authors suggest the most promising clustering methods for different situations.

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A Study of a Learning Style Index to Support an Intelligent and Adaptive Learning Systems

Intelligent and Adaptive Educational-Learning Systems - Smart Innovation, Systems and Technologies ◽

10.1007/978-3-642-30171-1_5 ◽

2013 ◽

pp. 109-132 ◽

Cited By ~ 2

Author(s):

Mohamed Hamada ◽

Kuseke Nishikawa ◽

John Brine

Keyword(s):

Learning Style ◽

Adaptive Learning ◽

Learning Systems ◽

Adaptive Learning Systems

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Evaluation of Clustering Methods for Adaptive Learning Systems

Business Intelligence ◽

10.4018/978-1-4666-9562-7.ch027 ◽

2016 ◽

pp. 519-542

Author(s):

Wilhelmiina Hämäläinen ◽

Ville Kumpulainen ◽

Maxim Mozgovoy

Keyword(s):

Data Mining ◽

Adaptive Learning ◽

Clustering Algorithms ◽

Educational Data Mining ◽

Optimal Choice ◽

Learning Systems ◽

Learning Tools ◽

Clustering Methods ◽

Central Task ◽

Adaptive Learning Systems

Clustering student data is a central task in the educational data mining and design of intelligent learning tools. The problem is that there are thousands of clustering algorithms but no general guidelines about which method to choose. The optimal choice is of course problem- and data-dependent and can seldom be found without trying several methods. Still, the purposes of clustering students and the typical features of educational data make certain clustering methods more suitable or attractive. In this chapter, the authors evaluate the main clustering methods from this perspective. Based on the analysis, the authors suggest the most promising clustering methods for different situations.

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Identifying learning style through eye tracking technology in adaptive learning systems

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i5.pp4408-4416 ◽

2019 ◽

Vol 9 (5) ◽

pp. 4408

Author(s):

Inssaf El Guabassi ◽

Zakaria Bousalem ◽

Mohammed Al Achhab ◽

Ismail Jellouli ◽

Badr Eddine EL Mohajir

Keyword(s):

Eye Movements ◽

Learning Styles ◽

Eye Tracking ◽

Learning Style ◽

Adaptive Learning ◽

Good Condition ◽

Learning Systems ◽

Gaze Behavior ◽

Adaptive Learning Systems ◽

Tracking Technology

Learner learning style represents a key principle and core value of the adaptive learning systems (ALS). Moreover, understanding individual learner learning styles is a very good condition for having the best services of resource adaptation. However, the majority of the ALS, which consider learning styles, use questionnaires in order to detect it, whereas this method has a various disadvantages, For example, it is unsuitable for some kinds of respondents, time-consuming to complete, it may be misunderstood by respondent, etc. In the present paper, we propose an approach for automatically detecting learning styles in ALS based on eye tracking technology, because it represents one of the most informative characteristics of gaze behavior. The experimental results showed a high relationship among the Felder-Silverman Learning Style and the eye movements recorded whilst learning.

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Detecting Learning Style through Biometric Technology for Mobile GBL

International Journal of Game-Based Learning ◽

10.4018/ijgbl.2012040104 ◽

2012 ◽

Vol 2 (2) ◽

pp. 55-74 ◽

Cited By ~ 5

Author(s):

Tracey J. Mehigan ◽

Ian Pitt

Keyword(s):

Learning Styles ◽

Learning Style ◽

Adaptive Learning ◽

Learning Systems ◽

Interaction Patterns ◽

Mobile Game ◽

Big Five Model ◽

Improved Learning ◽

The Individual ◽

Adaptive Learning Systems

Adaptive learning systems tailor content delivery to meet specific needs of the individual for improved learning-outcomes. Learning-styles and personalities are usually determined through the completion of questionnaires. There are a number of models available for this purpose including the Myer-Briggs Model (MBTI), the Big Five Model, and the Felder Silverman Learning-Style Model (FSLSM). Most models classify the student on a number of scales. Recently, a number of studies have investigated the possibility of determining an individual’s learning-style directly through their interaction patterns when using a system. Automatic learning-style detection could play a significant role in the advancement of educational gaming through personalized learning environments. Biometric devices, such as accelerometers and eye-trackers, are now available for use with mobile devices. These provide an opportunity to move toward adaptive mobile gaming environments, giving potential to track learning-styles directly through avatar movement. This paper examines mobile learning (mLearning) with an emphasis on mobile game-based environments. Adaptive learning systems are introduced. The results of studies conducted to assess the potential of biometric devices as a means of automatically detecting students’ learning-styles are discussed. The potential of this research for mobile game-based learning is outlined.

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