An Approach for the Identification and Tracking of Learning Styles in MOOCs

Hmedna, Brahim; Mezouary, Ali El; Baz, Omar

doi:10.1007/978-3-319-46568-5_13

Cited by 11 publications

(11 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[4] defines personalised adaptive learning as a ''technology-empowered effective pedagogy which can adaptively adjust teaching strategies timely based on real-time monitored (enabled by smart technology) learners' differences and changes in individual characteristics, individual performance, and personal development''. Adaptive education systems aim to enhance learning efficiency and performance and reduce cognitive overload issues by providing an optimal learning path and individualised content based on the knowledge, behaviour and profile of each learner [5], [6], [7]. Thus, personalised adaptive educational systems are built based on the fact that the learning process is different for each learner [8], [9].…”

Section: Introductionmentioning

confidence: 99%

“…Differences inLSs are determined by the different approaches that students use to engage with learning materials and internalise information [10]. Therefore, to 'personalise' e-learning, it is important to understand the types of learners and evaluate and classify their LSs to adapt the content and learning techniques according to their preferred way of learning to support learners more effectively and efficiently [6], [11]. To determine LSs, well-known and used learning style theory models (LSMs) have been suggested to identify the initial LSs of learners [2].…”

Section: Introductionmentioning

confidence: 99%

“…To overcome these limitations, artificial intelligence (AI) approaches have been used in personalised adaptive education systems to detect LSs automatically [5], [6], [10]. The automatic detection of LSs to classify students according to the way they prefer to learn is beneficial as it is not only more efficient than filling in questionnaires, but it is also dynamic and can be changed according to the students' behaviours [10].…”

Section: Introductionmentioning

confidence: 99%

“…After the identification of accurate LSs, adaptive learning systems utilise the information to provide accurate personalisation leading to benefits for students, including increased student performance, satisfaction, learner engagement and time efficiency [2], [6], [8], [12], [13]. By students being aware of their LSs, they can self-regulate their learning, capitalise on their strengths and understand why they are struggling [14].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

2023

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

2023

View full text Add to dashboard Cite

show abstract

“…We can mention in particular works related to MOOCs' taxonomies [1], massive traces' analysis [2], learner profiles [3], adaptation through various criteria, namely: learning styles, learners' preferences, etc. [4], modalities of updating MOOC scenario and scenario aspects [5] etc.…”

Section: Introduction Since 2008 Mooc (Massive Open Online Courses)mentioning

confidence: 99%