The Impact of Learning Style Adaptivity in Teaching Computer Security

Alshammari, Mohammad T.; Anane, Rachid; Hendley, Robert J.

doi:10.1145/2729094.2742614

Cited by 16 publications

(8 citation statements)

References 19 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, the system randomly assigned participants to the adaptive or nonadaptive group and directed them to complete a pretest. The next step involved the study by participants of learning material on computer security, as the application domain of the system [4]. When each learning lesson is completed, a post-test was provided by the system to learners, so that the scores of these tests could be used to measure the learning effectiveness at the end of interaction with the system.…”

Section: Methodsmentioning

confidence: 99%

Usability and Effectiveness Evaluation of Adaptivity in E-Learning Systems

Alshammari

Anane

Hendley

2016

Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Usability and Effectiveness Evaluation of Adaptivity in E-Learning Systems

Alshammari

Anane

Hendley

2016

Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

“…The main feature of the adaptive order technique is the customized sequencing and arrangements of LOs. Figure 1 presents a simplified example of how the adaptive order, according to the information perception dimension of learning style, is applied to a subset of the computer security course, the application domain of the AES (Alshammari, Anane, & Hendley, 2015b). For example, the learning unit of symmetric key encryption (i.e., one element of the course) contains four LOs (concept, mathematical notation, an example, and an interactive tool), which are classified as either concrete or abstract objects.…”

Section: Learning Style Adaptivitymentioning

confidence: 99%

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

Alshammari

Qtaish

2019

JITE:Research

View full text Add to dashboard Cite

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.

show abstract

“…Basically, since each lecture is an ensemble of LOs, the lecture could be reordered and assembled according to the LS of each student. There are studies claiming positive impacts on learning outcomes [Alshammari et al 2015, El-Bishouty et al 2014]. On the other hand, there have been studies that disagree with its efficacy [Kirschner 2017, An andCarr 2017].…”

Section: Content Personalizationmentioning

confidence: 99%

A Computational Architecture for Learning Objects Authoring and Personalization in Ubiquitous Learning Environments

Araújo¹,

Dorça²,

Cattelan³

2018

Workshops Do Congresso Brasileiro De Informática Na Educação

View full text Add to dashboard Cite

Computers have become everyday tools in the contemporary classroom to support both instructors and students in the learning process. Ubiquitous Learning Environments (ULEs) are created to combine real and virtual learning environments in order to produce richer study artifacts composed multimedia documents structured as Learning Objects (LOs). This work presents a computational architecture grounded on ULEs and Intelligent Tutoring Systems concepts, capable of structuring LOs captured in classrooms and their metadata through a collaborative approach, and offering content personalization and recommendation features to students. The resulting LO repository, with metadata in the IEEE-LOM format, is made publicly available.Resumo. Os computadores se tornaram ferramentas cotidianas nas salas de aulas contemporâneas e apoiam tanto professores quanto estudantes no processo de ensino e aprendizagem. Ambientes Educacionais Ubíquos (AEUs) combinam ambientes reais e virtuais de aprendizagem visando produzir artefatos de estudo mais ricos, na forma de documentos multimídia estruturados como Objetos de Aprendizagem (OAs). Este trabalho apresenta uma arquitetura computacional, fundamentada nos conceitos de AEUs e de Sistemas Tutores Inteligentes, capaz de estruturar OAs capturados em salas de aula e seus metadados utilizando uma abordagem colaborativa e também personalizar e recomendar conteúdo aos estudantes. O repositório de OAs resultante, com metadados no formato IEEE-LOM,é disponibilizado publicamente.

show abstract

The Impact of Learning Style Adaptivity in Teaching Computer Security

Cited by 16 publications

References 19 publications

Usability and Effectiveness Evaluation of Adaptivity in E-Learning Systems

Usability and Effectiveness Evaluation of Adaptivity in E-Learning Systems

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

A Computational Architecture for Learning Objects Authoring and Personalization in Ubiquitous Learning Environments

Contact Info

Product

Resources

About