Exploring the Effectiveness of Learning Path Recommendation based on Felder-Silverman Learning Style Model: A Learning Analytics Intervention Approach

Abstract

A fixed learning path for all learners is a major drawback of virtual learning systems. An online learning path recommendation system has the advantage of offering flexibility to select appropriate learning content. Learning Analytics Intervention (LAI) provides several educational benefits, particularly for low-performing students. Researchers employed an LAI approach in this work to recommend personalised learning paths to students pursuing online courses depending on their learning styles. It was accomplished by developing a Learning Path Recommendation Model (LPRM) based on the Felder–Silverman Learning Style Model (FSLSM) and evaluating its efficacy. The data were analysed with the help of a dataset from the Moodle Research Repository, and different learning paths were recommended using a sequence matching algorithm. The effectiveness of this approach was tested in two groups of learners using the independent two-sample t-test, a statistical testing tool. The experimental evaluation revealed that learners who followed the suggested learning path performed better than those who followed the learning path without any recommendations. This enhanced learning performance exemplifies the effects of learning analytics intervention .

Personalized Recommendation in the Adaptive Learning System: The Role of Adaptive Testing Technology

“…The experimental results did demonstrate the effectiveness of the proposed method, which is consistent with the results of other studies on personalized recommendation (Hsu et al, 2013; Joseph et al, 2022). However, more empirical studies are needed to determine whether the results are generalizable.…”

Set-to-Sequence Ranking-Based Concept-Aware Learning Path Recommendation

“…1970;Inhelder et al 1976;Pinar et al 1995) reveal that cognitive structure greatly influences adaptive learning, which includes both the relationship between items (e.g., premise relationship and synergy relationship) and the characteristics of students' dynamic development with learning. Most existing methods to solve learning path planning are either based on a knowledge graph (or some relationship between concepts) to constrain path generation (Liu et al 2019;Shi et al 2020;Wang et al 2022), or based on collaborative filtering of features to search for paths (Joseph, Abraham, and Mani 2022;Chen et al 2022;Nabizadeh, Jorge, and Leal 2019). However, these models can not penetrate into the important features of the cognitive structure perfectly, and the model is relatively simple, resulting in the path generated either with a low degree of individuation or with a poor learning effect.…”

“…As shown in Figure1, we expect students to achieve the best improvement in the target concept D (Machine Learning). However, the existing path recommendation algorithms either do not use this feedback but use indirect factors such as similarity degree and occurrence probability (Joseph, Abraham, and Mani 2022;Shao, Guo, and Pardos 2021), or lack of excellent generation algorithms (Zhou et al 2018;Nabizadeh, Jorge, and Leal 2019). As a result, it is difficult for them to provide an efficient learning path.…”

“…(ii) Plan a certain length of the learning path for students at one time. The latter is because users sometimes want to know the entire learning path at the beginning (for example, universities need to organize courses for students) (Joseph, Abraham, and Mani 2022;Chen et al 2022;Shao, Guo, and Pardos 2021;Bian et al 2019;Dwivedi, Kant, and Bharadwaj 2018). As the latter direction is more restricted and complex (e.g., larger search space, less available feedback), it is more challenging and is also the main focus of this paper.…”

Learning Styles Impact Students’ Perceptions on Active Learning Methodologies: A Case Study on the Use of Live Coding and Short Programming Exercises

“…In their study, the FSLSM is used to represent both the students' learning styles and the profiles of the learning objects. Other recent research studies [27,40,41] recommend different personalization approaches based on the FSLSM when developing an e-Learning system. Isal et al [42] create and assess an adaptive mobile learning application designed to accommodate diverse student needs, with the FSLSM serving as the benchmark for identifying learning styles.…”