Adaptive e-Learning System for Slow Learners Based on Felder-Silverman Learning Style Model

Joseph, Lumy; Abraham, Sajimon

doi:10.1007/978-981-15-0108-1_13

Cited by 6 publications

(8 citation statements)

References 13 publications

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“…For instance, the Felder-Silverman Learning Style Model (FSLSM) in [57,67,69], the pre-test of multiple-choice questions in [57], and the Myers-Briggs Type Indicator (MBTI) questionnaire in [67] were adopted in several personalized learning software systems. According to these assumptions, formal learning software systems were established, attempting to model learning processes and activities similar to the ones carried out in a classroom [10,12,[14][15][16][17][18][19][30][31][32][33][34]37,39,[45][46][47][49][50][51][52][54][55][56][58][59][60][63][64][65][66]68,[70][71][72][73][74]92]. In such cases, the learning software system implements well-defined learning content, learning outcomes, and assessment measures [42,57,…”

Section: Formal Learning Software Systemsmentioning

confidence: 99%

Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models

et al. 2023

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This paper presents a comprehensive systematic review of personalized learning software systems. All the systems under review are designed to aid educational stakeholders by personalizing one or more facets of the learning process. This is achieved by exploring and analyzing the common architectural attributes among personalized learning software systems. A literature-driven taxonomy is recognized and built to categorize and analyze the reviewed literature. Relevant papers are filtered to produce a final set of full systems to be reviewed and analyzed. In this meta-review, a set of 72 selected personalized learning software systems have been reviewed and categorized based on the proposed personalized learning taxonomy. The proposed taxonomy outlines the three main architectural components of any personalized learning software system: learning environment, learner model, and content. It further defines the different realizations and attributions of each component. Surveyed systems have been analyzed under the proposed taxonomy according to their architectural components, usage, strengths, and weaknesses. Then, the role of these systems in the development of the field of personalized learning systems is discussed. This review sheds light on the field’s current challenges that need to be resolved in the upcoming years.

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Section: Formal Learning Software Systemsmentioning

confidence: 99%

Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models

et al. 2023

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“…Numerous systems have been developed that incorporate penalisation options in e-learning systems using many learning styles models, namely, Felder & Silverman model [92,99,137,144,161,202], Kolb method [64,239] or VAK model [78]. Those systems classify learners in several dimensions or categories and tailor the course accordingly.…”

Section: Adaptation To Learning Modalitiesmentioning

confidence: 99%

“…Resource sequencing Genetic Algorithms PLS-ML [135], QUESTOURnament [5,10,230] Learning path, Course structure 4 Memetic Algorithms TPG [206] Learning path, Search process, Learner preferences 1 Ant Colony Optimisation ACSEL [41], AACS [286], LPR [207] Learning CS388 [34], TANGOW/WOTAN [214], PLORS [137], POLCA [92], Protus [99,161], ULEARN [202], AeLS [144] Learning style, Content, Learner preferences, Assignments 8 VARK [78] Learning style, Content, Learner Classification…”

Section: Recommender Systemsmentioning

confidence: 99%

Personalisation methods in e‐learning‐A literature review

Mikić¹,

Ilić²,

Kopanja³

et al. 2022

Comp Applic In Engineering

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Over the years, personalisation in e‐learning has evolved as a promising paradigm for matching learners' specific requirements. Although many literature reviews on personalised e‐learning have been conducted, there are still voids and limitations in the comprehensive literature survey regarding how personalisation might occur in e‐learning environments and benefit the teaching and learning process. In addition, no design guidelines, frameworks, or tools to guide its implementation exist, so practice can vary widely. The principal purpose of this literature review is to examine the subject, provide insights into the use of personalised techniques and systems in e‐learning, and investigate its impact on the engagement and success of the students. This study applies methods of precise selection criteria to determine which of the selected publications have the strongest interconnection and relevance with the topic of e‐learning personalisation. It also categorises the personalisation techniques employed by each identified system and highlights new perspectives and advances in the area.

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“…However, if the switch from traditional to online classes is considered a change in the educational process, then this change may as well fall under supported hypothesis, according to which school shifts are associated with an increase in behavioral problems Sullivan et al, 2010. Additionally, assuming that traditional learning may sometimes constrain teachers to comply to a fixed syllabus without adapting to students' preferences or learning styles, online learning might as well amplify this issue (Lumy & Sajimon, 2019). Consequently, the quality of online courses should be a prevalent pedagogical objective (Reevy & Bursten, 2015;Xu & Jaggars, 2014)…”

Section: Student Academic Progressmentioning

confidence: 99%

The Effects of Online Teaching on Students’ Academic Progress in Stem

Pânișoară

Chirca

Lazar

2020

JBSE

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Online teaching environment is a challenge for science, technology, engineering, and mathematics (STEM) in-service teachers who feel worried about their ability to succeed in what might be an unfamiliar learning environment. This research focused on testing the effects of perceived risks and concerns by in-service teachers during online-only classes due to the coronavirus outbreak on student academic progress requirements across the different STEM disciplines. The research hypotheses were tested on a sample of 1444 in-service teachers teaching exclusively in the online environment, in Romania. A structural equation model was used to explore the possible links among two external variables (pedagogical and technological perceived risks), one mediator variable (student engagement), one control variable (school settings), and one output variable (student academic progress requirements). The results revealed significant negative paths from challenges mediated by student engagement to student academic progress, as well as positive paths from them to mediator factor. The school setting categories were negatively correlated with both perceived risk dimensions. The moderator role of student engagement on the challenges-outcome link was supported. The online teaching effects on student’s academic progress varied across the different STEM disciplines. The relevant common features for all STEM disciplines were further then demonstrated. Keywords: online class, pandemic emergency, STEM, structural equation modelling, student academic progress, in-service teachers

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Adaptive e-Learning System for Slow Learners Based on Felder-Silverman Learning Style Model

Cited by 6 publications

References 13 publications

Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models

Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models

Personalisation methods in e‐learning‐A literature review

The Effects of Online Teaching on Students’ Academic Progress in Stem

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