Recommending suitable learning paths according to learners’ preferences: Experimental research results

Kurilovas, Eugenijus; Žilinskienė, Inga; Dagienė, Valentina

doi:10.1016/j.chb.2014.10.027

Cited by 56 publications

(23 citation statements)

References 25 publications

(20 reference statements)

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“…Other sophisticated evolutionary algorithms are also applied to solve this problem. Kurilovas et al applied the ant colony optimization (ACO) method to generate a learning path based on students' learning styles, which observably improved students' learning outcomes and saved their learning time [24]. Wan & Niu proposed a learner oriented learning recommendation approach based on mixed concept mapping and an immune algorithm (IA) [3].…”

Section: Related Workmentioning

confidence: 99%

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

2019

KSII TIIS

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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.

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Section: Related Workmentioning

confidence: 99%

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

2019

KSII TIIS

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“…Авторы статьи [13] разрабатывали подход, основанный на модели нечеткой логики на основе алгоритма Е. Мамдани, а в работе [14] представлены результаты исследования, проведенного в процессе индивидуализации обучения с внедрением индивидуальных траекторий и основанного на методе искусственного интеллекта.…”

Section: формирование индивидуальных маршрутов обучения как фактор вunclassified

Method for Constructing an Individual Educational Route By Completing Tasks for Training Professional Skills in Compiling SQL-queries

Govorov¹,

Govorova²,

Slizen³

et al. 2018

CTE

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“…In the educational environment, these systems filter and provide educational contents based on the preferences of a student and are known as adaptive educational hypermedia systems [1,2]. e user model in these systems is confined and focuses on the features that are associated with the learning aspect of a student [2,3], and learning styles are exploited in the adaptive hypermedia domain as a source of content adaptation [4][5][6]. By integrating learning styles in the user model, it covers the cognition aspect of human learning, such as how a user learns [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Computationally Efficient User Model for Effective Content Adaptation Based on Domain-Wise Learning Style Preferences: A Web-Based Approach

et al. 2021

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In the educational hypermedia domain, adaptive systems try to adapt educational materials according to the required properties of a user. The adaptability of these systems becomes more effective once the system has the knowledge about how a student can learn better. Studies suggest that, for effective personalization, one of the important features is to know precisely the learning style of a student. However, learning styles are dynamic and may vary domain-wise. To address such aspects of learning styles, we have proposed a computationally efficient solution that considers the dynamic and nondeterministic nature of learning styles, effect of the subject domain, and nonstationary aspect during the learning process. The proposed model is novel, robust, and flexible to optimize students’ domain-wise learning style preferences for better content adaptation. We have developed a web-based experimental prototype for assessment and validation. The proposed model is compared with the existing available learning style-based model, and the experimental results show that personalization based on incorporating discipline-wise learning style variations becomes more effective.

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Recommending suitable learning paths according to learners’ preferences: Experimental research results

Cited by 56 publications

References 25 publications

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

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

Method for Constructing an Individual Educational Route By Completing Tasks for Training Professional Skills in Compiling SQL-queries

A Computationally Efficient User Model for Effective Content Adaptation Based on Domain-Wise Learning Style Preferences: A Web-Based Approach

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