Adaptive Learning Path Recommendation based on Graph Theory and an Improved Immune Algorithm

Abstract

Adaptive learning in e-learning has garnered researchers' interest. In it, learning resources could be recommended automatically to achieve a personalized learning experience. There are various ways to realize it. One of the realistic ways is adaptive learning path recommendation, in which learning resources are provided according to learners' requirements. This paper summarizes existing works and proposes an innovative approach. Firstly, a learner-centred concept map is created using graph theory based on the features of the learners and concepts. Then, the approach generates a linear concept sequence from the concept map using the proposed traversal algorithm. Finally, Learning Objects (LOs), which are the smallest concrete units that make up a learning path, are organized based on the concept sequences. In order to realize this step, we model it as a multi-objective combinatorial optimization problem, and an improved immune algorithm (IIA) is proposed to solve it. In the experimental stage, a series of simulated experiments are conducted on nine datasets with different levels of complexity. The results show that the proposed algorithm increases the computational efficiency and effectiveness. Moreover, an empirical study is carried out to validate the proposed approach from a pedagogical view. Compared with a self-selection based approach and the other evolutionary algorithm based approaches, the proposed approach produces better outcomes in terms of learners' homework, final exam grades and satisfaction.

A Novel Personalized Learning Framework With Interactive e-Mentoring

“…The Algorithm is designed based on the Felder and Silverman proposed model. [44]. The learning style questionnaire is categorized into four categories with each category representing one of the four learning dimensions.…”

“…The time they are required to learn the material, the suitable learning object to learn the material based upon the learner's style, and the learning goal or objective. [44].…”

“…[44] . The tablebelowshows the index of leaning style by Felder & Silverman Therefore, LSi di € [1,0] ={(LS1, d1), (LS2,d2), (LS3,d3), (LS4,d4)} (1) Where, LSi = Learning Style Dimension i.e.…”

Aportes de la Tecnología al Aprendizaje Personalizado

“…En la actualidad, los estudiantes están acostumbrados a adquirir conocimientos de diferentes formas, pudiendo escoger los temas que necesitan y el tipo de material que más se ajuste a sus necesidades. Esto es posible con apoyo de la tecnología, pero se requiere habilidad del docente para analizar e interpretar correctamente la información, y luego construir el material suficiente para que el estudiante elija los que más favorecen su comprensión (Bian et al, 2019;Liu et al, 2020;Zhao, 2018).…”

Student Learning Satisfaction During The Covid-19 Pandemic: Experience Of Russian Universities

“…However, until recently, these studies have been conducted primarily in mixed learning conditions (Al Awamleh, 2020;Herrador-Alcaide et al, 2019;Zhou et al, 2020b) or continuing education in the workplace (Gopinathan et al, 2019;Rodríguez-Santero et al, 2020). A large number of studies were devoted to analyzing the effectiveness of individual E-learning tools (Bian et al, 2019;Choe et al, 2019;Codilan, 2019;Dovbenko et al, 2020;Padmo & Sri Ardiasih, 2019). Previous researches have highlighted that students have a positive attitude towards E-learning (Alamri et al, 2019;Smolyaninova & Bezyzvestnykh, 2019).…”