A Systematic Review of the Role of Learning Analytics in Supporting Personalized Learning
Personalized learning is becoming more important in today’s diverse classrooms. It is a strategy that tailors instruction to each student’s abilities and interests. The benefits of personalized learning include students’ enhanced motivation and academic success. The average teacher-to-student ratio...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Education Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7102/14/1/51 |
| _version_ | 1827372482958458880 |
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| author | Ean Teng Khor Mutthulakshmi K |
| author_facet | Ean Teng Khor Mutthulakshmi K |
| author_sort | Ean Teng Khor |
| collection | DOAJ |
| description | Personalized learning is becoming more important in today’s diverse classrooms. It is a strategy that tailors instruction to each student’s abilities and interests. The benefits of personalized learning include students’ enhanced motivation and academic success. The average teacher-to-student ratio in classes is 1:15.3, making it challenging for teachers to identify each student’s areas of strength (or weakness). Learning analytics (LA), which has recently revolutionized education by making it possible to gather and analyze vast volumes of student data to enhance the learning process, has the potential to fill the need for personalized learning environments. The convergence of these two fields has, therefore, become an important area for research. The purpose of this study is to conduct a systematic review to understand the ways in which LA can support personalized learning as well as the challenges involved. A total of 40 articles were included in the final review of this study, and the findings demonstrated that LA could support personalized instruction at the individual, group, and structural levels with or without teacher intervention. It can do so by (1) gathering feedback on students’ development, skill level, learning preferences, and emotions; (2) classifying students; (3) building feedback loops with continuously personalized resources; (4) predicting performance; and (5) offering real-time insights and visualizations of classroom dynamics. As revealed in the findings, the prominent challenges of LA in supporting personalized learning were the accuracy of insights, opportunity costs, and concerns of fairness and privacy. The study could serve as the basis for future research on personalizing learning with LA. |
| first_indexed | 2024-03-08T10:59:19Z |
| format | Article |
| id | doaj.art-8d16d564b47545fdab11c4929df06541 |
| institution | Directory Open Access Journal |
| issn | 2227-7102 |
| language | English |
| last_indexed | 2024-03-08T10:59:19Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj.art-8d16d564b47545fdab11c4929df065412024-01-26T16:09:55ZengMDPI AGEducation Sciences2227-71022023-12-011415110.3390/educsci14010051A Systematic Review of the Role of Learning Analytics in Supporting Personalized LearningEan Teng Khor0Mutthulakshmi K1National Institute of Education, Nanyang Technological University, Singapore 637616, SingaporeNational Institute of Education, Nanyang Technological University, Singapore 637616, SingaporePersonalized learning is becoming more important in today’s diverse classrooms. It is a strategy that tailors instruction to each student’s abilities and interests. The benefits of personalized learning include students’ enhanced motivation and academic success. The average teacher-to-student ratio in classes is 1:15.3, making it challenging for teachers to identify each student’s areas of strength (or weakness). Learning analytics (LA), which has recently revolutionized education by making it possible to gather and analyze vast volumes of student data to enhance the learning process, has the potential to fill the need for personalized learning environments. The convergence of these two fields has, therefore, become an important area for research. The purpose of this study is to conduct a systematic review to understand the ways in which LA can support personalized learning as well as the challenges involved. A total of 40 articles were included in the final review of this study, and the findings demonstrated that LA could support personalized instruction at the individual, group, and structural levels with or without teacher intervention. It can do so by (1) gathering feedback on students’ development, skill level, learning preferences, and emotions; (2) classifying students; (3) building feedback loops with continuously personalized resources; (4) predicting performance; and (5) offering real-time insights and visualizations of classroom dynamics. As revealed in the findings, the prominent challenges of LA in supporting personalized learning were the accuracy of insights, opportunity costs, and concerns of fairness and privacy. The study could serve as the basis for future research on personalizing learning with LA.https://www.mdpi.com/2227-7102/14/1/51learning analyticspersonalized learningsystematic reviewmethodschallengeslearning technologies |
| spellingShingle | Ean Teng Khor Mutthulakshmi K A Systematic Review of the Role of Learning Analytics in Supporting Personalized Learning Education Sciences learning analytics personalized learning systematic review methods challenges learning technologies |
| title | A Systematic Review of the Role of Learning Analytics in Supporting Personalized Learning |
| title_full | A Systematic Review of the Role of Learning Analytics in Supporting Personalized Learning |
| title_fullStr | A Systematic Review of the Role of Learning Analytics in Supporting Personalized Learning |
| title_full_unstemmed | A Systematic Review of the Role of Learning Analytics in Supporting Personalized Learning |
| title_short | A Systematic Review of the Role of Learning Analytics in Supporting Personalized Learning |
| title_sort | systematic review of the role of learning analytics in supporting personalized learning |
| topic | learning analytics personalized learning systematic review methods challenges learning technologies |
| url | https://www.mdpi.com/2227-7102/14/1/51 |
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