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Adaptive learning in computer science education: A scoping review.

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Název: Adaptive learning in computer science education: A scoping review.
Autoři: Barbosa, Pedro Luis Saraiva, Carmo, Rafael Augusto Ferreira do, Gomes, João P. P., Viana, Windson
Zdroj: Education & Information Technologies; 2024, Vol. 29 Issue 8, p9139-9188, 50p
Témata: INSTRUCTIONAL systems, COMPUTER science, HIGHER education, FUZZY logic
Abstrakt: Adaptive learning is a teaching approach aiming to personalize the learning experience for each student. In Computer Science Education (CSE), Adaptive Learning Systems (ALS) can provide students with customized lessons, exercises, and assessments based on their previous knowledge, strengths, and weaknesses. Some literature reviews focus on adaptive learning techniques, but none specifically analyze the methods used in adaptive systems in CSE. Our overall objective is to identify the computing techniques that are implemented within ALS to provide adaptive computer education in higher education. We conducted a scoping review (SR) based on the Guidelines for performing Systematic Literature Reviews in Software Engineering, defining six research questions and a search string. We performed searches in four databases, retrieving 512 documents. After applying the selection criteria, we selected 29 works. The results present quantitative and qualitative data from those 29 studies. Most research has used adaptive learning in programming education (12 articles) and databases (9 papers). Nine articles used learning styles as the core of adaptation, with the Felder-Silverman model being used by seven of them. The most commonly used approach was the rule-based system approach built by experts within the proposal, which was used in 17.24% of the articles. Clustering, collaborative filtering, fuzzy logic, and K-NN were used in 10.34% of the proposals. The positive aspect of "delivering adaptive content" was pointed out in seven articles. As for negative aspects, the problem of lack of student engagement was mentioned in four articles. This study discusses the application of dynamic and static models based on learning styles (LS) and the challenges involved in their use. Additionally, another aspect pointed out is that it is crucial to carefully consider pedagogical aspects in learning systems and have mixed development teams. The study also discusses student motivation and the use of gamification elements as a strategy to engage students in electronic learning systems. Finally, the results also indicate that the level of student knowledge is typically represented by a student model (SM), and the growing trend is the use of hybrid SMs. The research identified the most studied areas in ALS in CSE, such as computer programming and databases. The most common techniques used in ALS were rule-based systems built by experts, fuzzy logic, and clustering. The most common feature of ALS identified was learning resources, and the majority of studies focused on investigating the impact of system accuracy. This research can help researchers design better adaptive learning systems and understand challenges that need to be addressed. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:Adaptive learning is a teaching approach aiming to personalize the learning experience for each student. In Computer Science Education (CSE), Adaptive Learning Systems (ALS) can provide students with customized lessons, exercises, and assessments based on their previous knowledge, strengths, and weaknesses. Some literature reviews focus on adaptive learning techniques, but none specifically analyze the methods used in adaptive systems in CSE. Our overall objective is to identify the computing techniques that are implemented within ALS to provide adaptive computer education in higher education. We conducted a scoping review (SR) based on the Guidelines for performing Systematic Literature Reviews in Software Engineering, defining six research questions and a search string. We performed searches in four databases, retrieving 512 documents. After applying the selection criteria, we selected 29 works. The results present quantitative and qualitative data from those 29 studies. Most research has used adaptive learning in programming education (12 articles) and databases (9 papers). Nine articles used learning styles as the core of adaptation, with the Felder-Silverman model being used by seven of them. The most commonly used approach was the rule-based system approach built by experts within the proposal, which was used in 17.24% of the articles. Clustering, collaborative filtering, fuzzy logic, and K-NN were used in 10.34% of the proposals. The positive aspect of "delivering adaptive content" was pointed out in seven articles. As for negative aspects, the problem of lack of student engagement was mentioned in four articles. This study discusses the application of dynamic and static models based on learning styles (LS) and the challenges involved in their use. Additionally, another aspect pointed out is that it is crucial to carefully consider pedagogical aspects in learning systems and have mixed development teams. The study also discusses student motivation and the use of gamification elements as a strategy to engage students in electronic learning systems. Finally, the results also indicate that the level of student knowledge is typically represented by a student model (SM), and the growing trend is the use of hybrid SMs. The research identified the most studied areas in ALS in CSE, such as computer programming and databases. The most common techniques used in ALS were rule-based systems built by experts, fuzzy logic, and clustering. The most common feature of ALS identified was learning resources, and the majority of studies focused on investigating the impact of system accuracy. This research can help researchers design better adaptive learning systems and understand challenges that need to be addressed. [ABSTRACT FROM AUTHOR]
ISSN:13602357
DOI:10.1007/s10639-023-12066-z