Formation of individual educational trajectory of university student based on modified ant colony optimization algorithm

Individual educational trajectory allows to provide each learner with a sequence of learning materials in accordance with their pace, learning style and existing gaps in knowledge. Under the conditions of education digitalization, there is an opportunity to use modern ways to improve learning quality to create an individual educational trajectory. The article presents a model of learning content adaptation which contains an algorithm for building an individual educational trajectory based on a modified ant colony optimization algorithm that provides a personalized selection of a sequence of educational materials. The model of learning content adaptation is illustrated on the example of learning SQL within the framework of the course “Databases”. The proposed modification of the ant colony optimization algorithm allows to increase in the efficiency of competence formation in the course of academic disciplines which have a significant duration and consist of loosely related modules by assigning each student to the most appropriate subgroup in accordance with the chosen professional role and organizing full­fledged repetition throughout the discipline. It is proposed to use the desired qualification level of mastering the SQL (basic or professional) or the direction of training (specialty) as specifiers of student groups. The repetition of the basics of problem solving in the process of studying the discipline is proposed to be performed based on pheromone values determined by the ant colony optimization algorithm. The calculating formula for these values provides a high probability of redirecting the student to the solution of problems from the list for repetition when exceeding the permissible number of taken hints or attempts to solve.

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Publication Details

Journal
Informatics and Education
Published
2024-08-25
DOI
https://doi.org/10.32517/0234-0453-2024-39-3-53-65
Citations
1
Primary Topic
Scheduling and Timetabling Solutions
Type
article
Field-Weighted Citation Impact
0.49
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article

Formation of individual educational trajectory of university student based on modified ant colony optimization algorithm

V.V. Kutuzov, K. V. Zakharchenkov, T. V. Mrochek
1 citations
Informatics and Education
Scheduling and Timetabling Solutions
0.49
article

Formation of individual educational trajectory of university student based on modified ant colony optimization algorithm

V.V. Kutuzov, K. V. Zakharchenkov, T. V. Mrochek
article en
1 citations

Abstract

Individual educational trajectory allows to provide each learner with a sequence of learning materials in accordance with their pace, learning style and existing gaps in knowledge. Under the conditions of education digitalization, there is an opportunity to use modern ways to improve learning quality to create an individual educational trajectory. The article presents a model of learning content adaptation which contains an algorithm for building an individual educational trajectory based on a modified ant colony optimization algorithm that provides a personalized selection of a sequence of educational materials. The model of learning content adaptation is illustrated on the example of learning SQL within the framework of the course “Databases”. The proposed modification of the ant colony optimization algorithm allows to increase in the efficiency of competence formation in the course of academic disciplines which have a significant duration and consist of loosely related modules by assigning each student to the most appropriate subgroup in accordance with the chosen professional role and organizing full­fledged repetition throughout the discipline. It is proposed to use the desired qualification level of mastering the SQL (basic or professional) or the direction of training (specialty) as specifiers of student groups. The repetition of the basics of problem solving in the process of studying the discipline is proposed to be performed based on pheromone values determined by the ant colony optimization algorithm. The calculating formula for these values provides a high probability of redirecting the student to the solution of problems from the list for repetition when exceeding the permissible number of taken hints or attempts to solve.

Informatics and EducationVol. 39(3)
Belarusian Russian University (BY)
Quality Education
Openalex Percentile: Top 32%
Scheduling and Timetabling Solutions
0.49
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Formation of individual educational trajectory of university student based on modified ant colony optimization algorithm — V.V. Kutuzov, K. V. Zakharchenkov, et al. · Informatics and Education (2024) | TGRS Research Map | TGRS