The Learning Object Triangle

Anane, Rachid

doi:10.1109/icalt.2014.209

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The learning material was implemented as Learning Objects (LOs), where a LO is defined as a selfcontained component of instruction (Anane, 2014). The ordering of links to LOs is produced for each student depending on their information perception dimension of learning style.…”

Section: Learning Style Adaptivitymentioning

confidence: 99%

Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

Alshammari

Qtaish

2019

JITE:Research

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Aim/Purpose: Effective e-learning systems need to incorporate student characteristics such as learning style and knowledge level in order to provide a more personalized and adaptive learning experience. However, there is a need to investigate how and when to provide adaptivity based on student characteristics, and more importantly, to evaluate its value in learning enhancement. This study aims to bridge that gap by examining the effect of different modes of learning material adaptation and their sequences to the learning style and knowledge level of students in e-learning systems. Background: E-learning systems aim to provide acceptability and interactivity between students, instructors, and learning content anytime and anywhere. However, traditional systems are typically designed for generic students irrespective of individual requirements. Successful e-learning systems usually consider student characteristics such as learning style and knowledge level to provide more personalized and adaptive student-system interaction. Methodology: A controlled experiment was conducted in a learning context with 174 subjects to evaluate the learning effectiveness of adaptivity in e-learning systems. Contribution: The main contributions of the paper are threefold. First, a novel adaptive approach is proposed based on a specific learning style model and knowledge level. Second, the approach is implemented in an e-learning system to teach computer security, the application domain. Third, a rigorous experimental evaluation of the learning effect of the adaptive approach is offered. Findings: The results indicate that adaptation according to the combination of learning style and knowledge level produces significantly better learning gains, both in the short-term and medium-term, than adaptation according to either trait individually. Recommendations for Practitioners: Practitioners should consider the combination of learning style and knowledge level when delivering and presenting learning material to their students. Recommendation for Researchers: Researchers should consider sound educational models when designing adaptive e-learning systems. Also, rigorous and carful experimental design evaluations should be taken into account. Impact on Society: Universities and e-learning industries can benefit from the proposed adaptive approach and the findings in designing and developing more personalized and adaptive e-learning systems. The incorporation of student characteristics, especially learning style and knowledge level, may be used to enhance learning. Future Research: The experiment might be duplicated with a focus on longer-term learning gains by including more subjects and more learning resources. Also, the study might be expanded to application domains other than computer security. Moreover, other variables such as student satisfaction, motivation, and affective state might be explored to further the understanding of the effect of adaptivity on learning gains.

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Section: Learning Style Adaptivitymentioning

confidence: 99%

Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

Alshammari

Qtaish

2019

JITE:Research

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“…A learning unit focuses entirely on one topic of the course. Each learning unit contains a set of interrelated learning objects in level two [5]. The learning objects are annotated to support adaptation to match the information perception learning stylefollowing the Felder-Silverman model [13].…”

Section: Learning Style Adaptivitymentioning

confidence: 99%

The Impact of Learning Style Adaptivity in Teaching Computer Security

Alshammari

Anane

Hendley

2015

Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education

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Teaching computer security is one of the most challenging tasks in computer science, because of the need to successfully integrate abstract concepts and practical applications. Several e-learning systems have been developed to address this issue. However, they usually provide the same material in the same sequence irrespective of the characteristics of the students, such as their knowledge level and learning style. In this paper, an approach to learning style adaptivity is proposed for the teaching of computer security. An e-learning system was developed to provide more personalised and adaptive learning, based on the information perception style of the Felder-Silverman model. This is the dimension of learning style, which has received the least attention in published research. In the approach, a personalised sequence of learning material is generated based on an individual learning style. The approach is evaluated in order to determine its effectiveness in learning provision. An experiment conducted with sixty subjects produced significant results. They indicate that matching computer security learning material, according to the learning style of the students, yields significantly better learning gain and student satisfaction than without matching.

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Educational Concept Maps for Personalized Learning Path Generation

Adorni

Koceva

2016

AI*IA 2016 Advances in Artificial Intelligence

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The Learning Object Triangle

Cited by 3 publications

References 7 publications

Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

The Impact of Learning Style Adaptivity in Teaching Computer Security

Educational Concept Maps for Personalized Learning Path Generation

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