Automatic learning styles prediction: a survey of the State-of-the-Art (2006–2021)

Raleiras, Monica; Nabizadeh, Amir Hossein; Costa, Fernando Albuquerque

doi:10.1007/s40692-021-00215-7

Cited by 23 publications

(25 citation statements)

References 100 publications

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“…Such systems require progressive improvements to make learners learn effectively during their classes following a preferred style and method [39]. As a consequence, more research has been conducted to address this issue and improve learning efficiency using learning style aspects [18]. There are numerous approaches to the learning styles of learners, that is why a variety of learning styles have been proposed.…”

Section: Learning Styles Concepts and Classificationmentioning

confidence: 99%

“…The rule-based approach uses learners' traces to define their learning styles [17]. The model that determines learning styles is defined by using a combination of pre-defined rules to the learners' reactions within the learning system [18]. These pre-defined rules expressed as "if... then... else" statements, are applied to compute the preference level of the learning style.…”

Section: Approaches To Identifying Learning Stylesmentioning

confidence: 99%

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New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS

Benabbes¹,

Hmedna²,

Housni³

et al. 2022

Int. J. Interact. Mob. Technol.

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Learning style is a significant learner-difference factor. Each learner has a preferred learning style and a different way of processing and understanding the novelty. In this paper, a new approach that automatically identify learners learning styles based on their interaction with the Learning Management System (LMS) is introduced. To implement this approach, the traces of 920 enrolled learners in three agronomy courses were exploited using an unsupervised clustering method to group learners according to their degree of engagement. The decision tree classification algorithm relies on the decision rules construction, which is widely adopted to identify the accurate learning style. As missing good decision rules would lead to learning style misclassification, the Felder-Silverman Learning Style Model (FSLSM) is used as it is among the most adopted models in the technology of quality improvement process. The results of this research highlight that most learners prefer the global learning style.

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Section: Learning Styles Concepts and Classificationmentioning

confidence: 99%

Section: Approaches To Identifying Learning Stylesmentioning

confidence: 99%

New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS

Benabbes¹,

Hmedna²,

Housni³

et al. 2022

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…Numerous research has been conducted on the topic of student activities in an e-learning environment and its relationship to learning styles [5,6]. In [7], authors made a comprehensive presentation of different approaches and algorithms used to predict learning styles along with their classification and the analysis of their advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Students’ Perceptions of ILS as a Learning-Style-Identification Tool in E-Learning Environments

et al. 2022

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This paper presents the evaluation of the Index of Learning Styles, an assessment tool of the Felder–Silverman learning model. A few studies have previously evaluated this tool, but as far as we know, none of them considered the learners’ opinion to achieve their goals. Considering that many studies suggest continuing with the Index of Learning Styles’ evaluation, an experimental study was conducted using Protus, developed as an adaptive learning system. Analysing the concurrent validity of the Index of Learning Styles, students’ learning preferences were acquired via two different tools: the Index of Learning Styles and the subjective questionnaire. Results suggest that the Index of Learning Styles is valid for defining learning style at the beginning of the learning process, resolving the cold-start problem. We found some differences between the results of the Index of Learning Styles and subjective assessment. By enhancing the Protus user interface with new functionality, which allows a free choice of the learning style during the learning process, we overcome the observed limitations of the Index of Learning Styles. This solution could be implemented in different personalised e-learning environments, regardless of the applied assessment tool, leading to a more reliable student model.

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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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Automatic learning styles prediction: a survey of the State-of-the-Art (2006–2021)

Cited by 23 publications

References 100 publications

New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS

New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS

Students’ Perceptions of ILS as a Learning-Style-Identification Tool in E-Learning Environments

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

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