Predicting Learning Styles Using Machine Learning Classifiers

Assiry, Khadeja Ali Hamamy; Muniasamy, Anandhavalli

doi:10.1109/icecet55527.2022.9872971

Cited by 2 publications

(5 citation statements)

References 30 publications

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“…In particular, four databases were systematically searched and the reference lists of relevant studies were screened, ultimately resulting in the 48 studies involved in this review. The following references were the studies involved: [5], [6], [8], [10], [12], [14], [15], [16], [17], [18], [29], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [46], [47], [48], [49], [50], [51], [52], [53], [55], [61], [62], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79].…”

Section: Discussionmentioning

confidence: 99%

“…PAL platforms based on LS have also been applied to various platforms such as the ITS [33], [34]; the Conversational Intelligent Tutoring System (CITS) [17] and online mobile applications [35], [36]. In [35], the proposed approach enabled elementary students to learn materials in an online mobile application and adjust the delivery method according to their preferred LS.…”

Section: A What Platforms Are Frequently Investigated In Personalised...mentioning

confidence: 99%

“…On the other hand, [37] detected LSs using attributes related to assessments such as quiz grades, chats, mouse click counts and keyboard input counts within the virtual machine window. Reference [34] used online reactions on an e-learning platform to predict LSs. All three studies ( [12], [34], [37]) used Felder-Silverman's LSM.…”

Section: A: Data Source and Corresponding Attributesmentioning

confidence: 99%

“…Reference [34] used online reactions on an e-learning platform to predict LSs. All three studies ( [12], [34], [37]) used Felder-Silverman's LSM. -Users' history and background data: This source includes static information (SI) [25].…”

Section: A: Data Source and Corresponding Attributesmentioning

confidence: 99%

“…These include Linear Discriminant Analysis [55], semantic clustering [29], simple logistics [61], Kstar, OneR, JRIP and Decision Table [62]. In Reference [34], three different classification methods namely Classifier Chains, Binary Relevance, and Label Powerset were applied to make a model for LS prediction. Reference [53] modified a collaborative filtering model, which is typically used for recommendation tasks, to also predict the learners' LSs.…”

Section: ) Frequency Of Machine Learning Used To Detect Learning Stylesmentioning

confidence: 99%

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Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles: A Systematic Literature Review

2023

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Artificial intelligence (AI) approaches have been used in personalised adaptive education systems to overcome the limitations of statically determined learning styles (LSs). These approaches utilise algorithms from machine learning (ML) to tackle the challenge of personalising e-learning by mapping students' behavioural attributes to a particular LS automatically and dynamically to optimise the individual learning process. Motivated by the many influential studies in this field and the current developments in ML and AI, a comprehensive systematic literature review was conducted from 2015 to 2022. Influential scientific literature was analysed to identify the emerging trends and gaps in the literature in terms of LS models and possible ML techniques employed for personalised adaptive learning platforms. The outcomes of this paper include a review and analysis of the current trends of this emerging field in terms of the applications and developments in using ML approaches to implement more intelligent and adaptive e-learning environments to detect learners' LSs automatically for enhancing learning. In addition, the following issues were also investigated: the platforms that stimulated research; identifying LS models utilised in e-learning; the evaluation methods used; and the learning supports provided. The results indicated an increasing interest in using artificial neural network approaches to identify LSs. However, limited work has been conducted on the comparison of deep learning methods in this context. The findings suggest the need to consider and stimulate further empirical investigation in documenting the adoption and comparison of deep learning algorithms in classifying LSs to provide higher adaptability.

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