Automatic Detection of Learning Styles: A Decade Review on Data-driven Approaches

Ngatirin, Nor Rahayu; Zainol, Zurinahni

doi:10.1088/1742-6596/1997/1/012001

Cited by 6 publications

(4 citation statements)

References 33 publications

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“…Cultural barriers present intricate challenges in cultivating autonomous learning within educational settings [17]. The varying learning styles and preferences rooted in diverse cultural backgrounds [18] can lead to disparities in the effectiveness of autonomous learning strategies, as different cultures prioritise collaborative or individualised approaches to education. Additionally, linguistic diversity poses a significant hurdle, with language differences influencing comprehension and engagement with autonomous learning materials [19].…”

Section: Cultural Barriersmentioning

confidence: 99%

“…Some challenges relate to cultural barriers in fostering independent learning. First, Diverse Learning Styles and Preferences: Cultural diversity often translates into a myriad of student learning styles and preferences [18]. Some cultures may prioritise collaborative and grouporiented learning, while others may value individualised approaches.…”

Section: Cultural Barriersmentioning

confidence: 99%

See 1 more Smart Citation

Cultivating Autonomous Learning Skills in Socially Diverse Classrooms: Challenges and Solutions

Omodan

2024

Proceedings of the 3rd International Conference on Education, Humanities, Health and Agriculture, ICEHHA 2023, 15-16 December 2

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This article delves into the intricacies of autonomous learning within university classrooms, particularly addressing obstacles associated with cultural and socioeconomic diversity. Through an extensive literature review, the paper identifies key challenges such as varied learning styles, linguistic barriers, and unequal access to resources. Proposed solutions encompass fostering inclusivity, effective technology integration, and targeted support to establish an environment conducive to autonomous learning. Concluding insights emphasize the collaborative efforts needed from educators and policymakers to implement these strategies and cultivate inclusive, technologically enriched, and supportive learning environments in diverse university settings.

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Section: Cultural Barriersmentioning

confidence: 99%

Section: Cultural Barriersmentioning

confidence: 99%

Cultivating Autonomous Learning Skills in Socially Diverse Classrooms: Challenges and Solutions

Omodan

2024

Proceedings of the 3rd International Conference on Education, Humanities, Health and Agriculture, ICEHHA 2023, 15-16 December 2

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“…A static approach for learning style detection is carried out by filling out the questionnaires [15][16][17]. It is possible that some of the questionnaires will not be filled in properly, which may lead to inaccurately detecting the learning styles [18][19][20]. The other approaches are based on data-driven methods to build classification models that use the sample data and ILS.…”

Section: Introductionmentioning

confidence: 99%

ILS Validity Analysis for Secondary Grade through Factor Analysis and Internal Consistency Reliability

et al. 2022

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In differentiated learning, the teacher needs to be aware of the learning styles of students in the classroom to accommodate specific learning preferences, e.g., the Felder–Silverman learning style model. The corresponding instrument, i.e., the Felder–Silverman Index of Learning Style (ILS), was designed to assess learning styles, specifically for engineering students. The ILS has been tested at the middle school level to identify the learning styles; however, validity/reliability had not been established in earlier studies with large samples. The focus of this study was to identify the validity and reliability of an ILS instrument for middle school students (N=450) by investigating the factor structure through factor analysis. This includes internal consistency reliability and constructing validity report of the ILS. An exploratory and confirmatory factor analysis was undertaken to investigate the factor structure to establish validity. As a result of the study, the reliability of the instrument was established. Five-factors emerged through exploratory factor analysis (EFA), which were subjected to confirmatory factor analysis (CFA). The outcome provided five-factors (i.e., Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Residual (SRMR), and Goodness of Fit (GFI)), out of which four factors were related to the four dimensions of the Felder–Silverman model, and the fifth factor was related to the association of sensing/intuitive and sequential/global dimensions of the model, which is in agreement with the theoretical construct of ILS. As a result of CFA, ILS entailing 24 items indicates a good fit with five-factor structure. CFI=0.922;TLI=0.927; RMSEA=0.026; SRMR=0.585;GFI=0.911;X2=277;df=42;p=0.60. This study suggests that the ILS for the secondary-grade students needs to be revised with fewer items to improve the reliability, as supported by empirical evidence through the EFA and CFA.

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“…As a result, detecting learners' learning styles can lead to less accuracy and make it difficult to use in online environments. To address these limitations, research has focused on an automatic approach to identifying the learner's learning style based on their behavior in online educational systems [6], [7], [8]. They employed various machine/deep learning techniques to predict the learner's learning style.…”

Section: Introductionmentioning

confidence: 99%

GNN-LS: A Learning Style Prediction in Online Environments using Graph Neural Networks

Muhammad¹,

Liu²,

Ahmad³

et al. 2022

J-NaNA

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Prediction of learners’ learning styles in online environments has several advantages, including steering learners on the proper path, motivating and engaging them while learning, and improving their learning results. It also helps instructors in the formation of personalized resource recommendations. As a result, predicting learners learning styles is necessary to aid in the personalization process. Existing approaches use either conventional or automatic approaches for learning style identifications. However, the large volume of data stored in online platforms has become a challenge in analyzing the behavior of learners and predicting their learning styles in the real world. Also, most of the existing approaches rely on a particular learning platform and can not be used in other platforms without technical assistance. In this paper, we propose GNN-LS, a new approach to identify and predict learners learning styles using a graph neural network. First, the graph embedding technique is used to capture the representation of learners and resources as a bipartite graph and encode them into low-dimensional representation. The encoded L-R sequences were given as input to the K-means clustering algorithm to identify and obtain labels as per FSLSM dimensions. Then, Graph neural network is trained to predict the learner’s learning style in the real world. The GNN-LS technique can be applied in a variety of educational systems and adapted to fit a variety of learning style models. Extensive experiments are run using the 2015 KDD Cup public available dataset to demonstrate the capabilities of GNN-LS. 5.31-15.68% improvements are achieved across all four FSLSM dimensions in accuracy.

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Automatic Detection of Learning Styles: A Decade Review on Data-driven Approaches

Cited by 6 publications

References 33 publications

Cultivating Autonomous Learning Skills in Socially Diverse Classrooms: Challenges and Solutions

Cultivating Autonomous Learning Skills in Socially Diverse Classrooms: Challenges and Solutions

ILS Validity Analysis for Secondary Grade through Factor Analysis and Internal Consistency Reliability

GNN-LS: A Learning Style Prediction in Online Environments using Graph Neural Networks

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