Learning Analytics on Student Engagement to Enhance Students’ Learning Performance: A Systematic Review
Abstract
:1. Introduction
2. Literature Review
2.1. The Engagement and Learning Performance in Online Learning
2.2. Student Engagement and Learning Analytics on Students’ Learning Performance
3. Methodology
3.1. Inclusion and Exclusion Criteria
3.2. Literature Search
4. Analysis of the Articles
4.1. Year of Publication
4.2. Geographical Locations
4.3. Methodology of the Included Articles
5. Results
5.1. Research Question 1 (RQ1): The Types of Student Engagement in Online Learning
5.2. Research Question 2 (RQ2): The Purpose of Using Learning Analytics on Student Engagement
5.3. Research Question 3 (RQ3): The Effect of the Use of Learning Analytics on Student Engagement in Online-Learning Settings
6. Discussion
7. Conclusions
8. Limitations and Recommendations
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Label | Paper | Year of Publication | Origin | Methodology | Types of Engagement | Purpose of Research | Findings |
---|---|---|---|---|---|---|---|
A1 | [67] | 2011 | Canada | Quantitative | Emotional | To investigate and analyse student engagement in enhancing students’ learning performance. | Emotions related meaningfully to students’ learning and performance. |
A2 | [57] | 2012 | Hong Kong | Quantitative | Social | To promote students’ interaction and social networking in online learning. | Social interaction appeared to enhance social engagement but had a limited impact on cognitive engagement. |
A3 | [90] | 2012 | Malaysia | Quantitative | Collaborative | To promote students’ interaction and social networking in online learning. | Students participated actively in the online forum even with minimal intervention from the lecturer. |
A4 | [39] | 2012 | Canada | Quantitative | Emotional | To develop a sense of community in online learning. | Students’ emotions were present (1) when they were involved in discussion (2) in engaging experiences in online learning. |
A5 | [62] | 2013 | Malaysia | Quantitative | Behavioural | To develop learning persistence and engagement among students in online learning. | Students demonstrated positive behaviours in discussions due to the implementation of collaborative learning. |
A6 | [59] | 2014 | USA | Quantitative | Cognitive | To investigate and analyse student engagement in enhancing students’ learning performance. | There was a positive relationship between cognitive-engagement activities and learning outcomes due to participation in learning tasks. |
A7 | [60] | 2014 | Malaysia | Quantitative | Cognitive | To investigate students’ cognitive ability. | Students struggled to contribute messages at a high level of cognitive engagement. |
A8 | [56] | 2015 | UK | Quantitative | Social | To investigate and analyse student engagement in enhancing students’ learning performance. | There was a positive correlation between engagement and interaction in the online studio and students’ success. |
A9 | [27] | 2015 | Indonesia | Quantitative | Social | To promote students’ interaction and social networking in online learning. | Through social-media implementation in learning, students were more concerned with the quality of information, as comprehensive of communication enhanced engagement and promoted an active learning process for better performance. |
A10 | [73] | 2015 | USA | Quantitative | Social, cognitive, behavioural | To develop learning persistence and engagement among students in online learning. | The quality of student engagement influenced students’ performance in online learning. |
A11 | [58] | 2016 | Philippines | Quantitative | Cognitive | To investigate students’ cognitive ability. | Students’ cognitive engagement was quantified and could be promoted by providing quality responses and discussion questions. |
A12 | [65] | 2016 | Indonesia | Quantitative | Behavioural | To develop learning persistence and engagement among students in online learning. | Students showed positive perceptions and behaviours in online learning, and it resulted in good grades. |
A13 | [61] | 2017 | Australia | Quantitative | Social, emotional | To investigate and analyse student engagement in enhancing students’ learning performance. | Social and cognitive engagement could be fostered through feedback provided in the learning process and involvement in group activities. They helped to promote a sense of community, which in turn allowed students to share and gain more in their learning process. |
A14 | [48] | 2017 | China | Quantitative | Behavioural | To develop a sense of community in online learning. | Students’ communication behaviours in online courses reflected their sense of community. |
A15 | [71] | 2017 | USA | Quantitative | Cognitive, behavioural, emotional | To investigate and analyse student engagement in enhancing students’ learning performance. | Classroom size affected teachers’ instruction and students’ engagement. |
A16 | [72] | 2017 | Canada | Quantitative | Cognitive, behavioural, emotional | To identify how the length of time spent by students on MOOC can affect their behavioural, cognitive, and emotional engagement. | The level of anxiety influenced students’ engagement in online learning. |
A17 | [64] | 2018 | Africa | Quantitative | Collaborative | To promote students’ interaction and social networking in online learning. | The use of social media to promote students’ collaboration and engagement was vital, as it lay the foundation for constructive solutions in which social media could help enhance students’ learning, competence, and ultimately performance. |
A18 | [70] | 2018 | Malaysia | Quantitative | Cognitive, behavioural, emotional | To investigate and analyse student engagement in enhancing students’ learning performance. | Social media had the potential to engage students in learning. It only facilitated cognitive engagement, but not behavioural or emotional engagement. |
A19 | [63] | 2020 | Malaysia | Quantitative | Collaborative | To develop learning persistence and engagement among students in online learning. | The use of discussion forums as an intervention in online learning aided students’ engagement. |
A20 | [22] | 2020 | Finland | Qualitative | Collaborative | To promote students’ interaction and social networking in online learning. | Positive interaction could be meaningful in the collaborative learning progress. |
A21 | [66] | 2020 | USA | Quantitative | Emotional | To investigate and analyse student engagement in enhancing students’ learning performance. | Students were emotionally engaged in their learning, and competence was the only predictor of emotional engagement. |
A22 | [68] | 2020 | China | Quantitative | Emotional | To develop learning persistence and engagement among students in online learning. | Students’ online interaction and emotional engagement were the central determinants of learning persistence. |
A23 | [69] | 2020 | Malaysia | Quantitative | Cognitive, behavioural, emotional | To develop learning persistence and engagement among students in online learning. | Demographic factors such as age, gender, etc. were closely related to the level of engagement (cognitive, behavioural, and emotional) in online-learning activities. |
A24 | [74] | 2020 | USA | Quantitative | Behavioural, emotional | To promote students’ interaction and social networking in online learning. | Student–faculty interactions were positively linked to the effort expended by students and behavioural and emotional engagement. |
A25 | [3] | 2020 | Malaysia | Quantitative | Cognitive | To investigate students’ cognitive ability. | Students displayed low cognitive engagement due to the lack of cognitive demand and mental effort. |
A26 | [25] | 2021 | China | Quantitative | Cognitive, behavioural, emotional | To predict students’ performance from LMS data by analysing 17 different courses on Moodle. | Students’ performance varied across courses. |
A27 | [36] | 2017 | China | Quantitative | Collaborative | To use learning analytics to analyse students’ writing behaviours in collaborative writing activities. | The visualisation from learning analytics offered students a chance to reflect on their writing process. |
A28 | [84] | 2017 | Netherlands | Quantitative | Unknown | To study the potentials and pitfalls of learning analytics as a tool for supporting students’ well-being. | LA was used to assist staff in supporting students’ learning. |
A29 | [49] | 2018 | UK | Quantitative | Unknown | To analyse factors influencing learner performance with learning analytics. | The online tasks or activities were effective in predicting students’ learning performance. |
A30 | [76] | 2020 | Spain | Quantitative | Unknown | To predict students’ performance in higher-education institutions using video-learning analytics and data-mining techniques. | Students’ academic performance could be predicted using learning analytics. |
A31 | [29] | 2020 | Malaysia | Quantitative | Unknown | To study the engagement in LMSs and students’ performance through learning analytics. | LA could predict high learning performance in LMSs, and clear objectives from instructors increased students’ LMS usage and performance. |
A32 | [47] | 2019 | USA | Quantitative | Behavioural | To examine the necessary conditions for engagement in online-learning environments based on a learning-analytics approach. | Course design and instructor guidance were the keys to students’ positive behaviours in task completion. |
A33 | [50] | 2015 | China | Quantitative | Behavioural | To integrate learning analytics to predict students’ performance. | LA could significantly predict students’ performance in learning. |
A34 | [24] | 2017 | Oman | Quantitative | Behavioural | To study the combination of self-regulated learning indicators and engagement with online-learning events to predict academic performance. | Students’ behaviours in online learning indicated noteworthy results on their academic performance in self-regulated learning. |
A35 | [78] | 2017 | Taiwan | Quantitative | Behavioural | To predict behaviour-based grades for MOOC via time-series neural networks. | Students’ behaviours in online learning influenced their learning performance. |
A36 | [75] | 2017 | UK | Quantitative | Behavioural | To validate a theorised model of engagement in learning analytics. | Students’ participation and academic-oriented behaviours were positively associated with their grades. |
A37 | [87] | 2019 | China | Quantitative | Behavioural | To predict academic performance based on multi-sourced and multi-featured behavioural data. | The augmented model could predict students’ performance. |
A38 | [77] | 2020 | China | Quantitative | Unknown | To study learning analytics in collaborative learning. | Social influence and teamwork engagement had positive effects on students’ success. |
A39 | [86] | 2019 | China | Quantitative | Unknown | To predict students’ performance from LMS data by analysing 17 different courses on Moodle. | Students’ performance varied across courses. |
A40 | [13] | 2018 | UK | Quantitative | Behavioural | To use learning analytics to explore students’ insights and behaviours in online learning. | Learning analytics could be used to determine students’ engagement in online learning, and course design affected students’ learning experience and performance. |
A41 | [82] | 2020 | UK | Quantitative | Behavioural | To analyse black minority ethnicities’ (BMEs’) and white students’ behavioural engagement on their attainment differences in online distance learning. | There were gaps between BMEs’ and white students’ academic attainment based on their behavioural engagement in online learning using learning analytics. |
A42 | [81] | 2018 | UK | Quantitative | Behavioural | To investigate the modules delivered and monitor students’ behaviours in a virtual learning environment (VLE). | Learning analytics helped to monitor student engagement in the VLE. |
Appendix B
Label | Author | Description | Types of Engagement |
---|---|---|---|
A1 | [86] | To predict students’ performance from LMS data by analysing 17 different courses in Moodle. | Unknown |
A2 | [29] | To predict student performance in higher-education institutions using video-learning analytics and data-mining techniques. | |
A3 | [76] | To analyse factors influencing learners’ performance with learning analytics. | |
A4 | [49] | To study the potentials and pitfalls of learning analytics as a tool for supporting student wellbeing. | |
A5 | [77] | To study learning analytics in collaborative learning. | |
A6 | [41] | To integrate learning analytics to predict students’ performance behaviours. | Behavioural |
A7 | [24] | To study the combination of self-regulated-learning indicators and engagement with online-learning events to predict academic performance. | |
A8 | [78] | To predict behaviour-based grades for MOOC via time-series neural networks. | |
A9 | [36] | To use learning analytics to analyse students’ writing behaviours in collaborative writing activities. | |
A10 | [47] | To study the engagement in LMSs and students’ performance through learning analytics. | |
A11 | [50] | To examine the necessary conditions for engagement in online-learning environments based on a learning-analytics approach. | |
A12 | [75] | To validate a theorised model of engagement in learning analytics. | |
A13 | [13] | To explore students’ insights and behaviours in online learning. | |
A14 | [82] | To analyse students’ behavioural engagement in terms of their attainment differences in online distance learning. | |
A15 | [81] | To investigate the modules delivered and monitor students’ behaviours in a virtual learning environment (VLE). | |
A16 | [87] | To predict academic performance based on multi-sourced and multi-featured behavioural data. | Social |
A17 | [58] | To investigate students’ cognitive engagement in the discussion forum. | Cognitive |
A18 | [63] | Student engagement in online discussions. | Collaborative |
Objectives | Description |
---|---|
Monitoring/analysis | To track students’ activities and generate reports to support decision-making. It is also related to teachers’ evaluations of the learning process to improve the learning environment. Moreover, it examines and analyses the ways students use a learning system that can assist teachers in detecting patterns and making decisions on the future design of learning activities. |
Prediction/intervention | The aim is to develop a model that can predict the knowledge absorption and future performance of students, which can be used for intervention purposes. An example of intervention includes suggesting actions that should be taken to help students who need additional assistance. |
Tutoring/mentoring | Tutoring focuses on the teaching process (control of the tutor), whereas mentoring focuses on the learning process of students. |
Assessment/feedback | To provide feedback to both students and teachers based on the assessment of the efficacy and efficiency of the learning process. |
Adaptation | Adaptation is to be carried out by the teacher/tutoring system or educational institution. It is concerned with guiding students with the next move by organising and establishing instructional activities and learning resources based on individual needs. |
Personalisation/recommendation | Personalisation refers to assisting students in making decisions about their own learning and continuously shaping their PLEs to achieve learning goals. Meanwhile, the recommender system plays a role in fostering self-directed learning by recommending explicit and tacit knowledge nodes based on individual preferences and activities of other learners with similar preferences. |
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No. | Research Question | Purpose |
---|---|---|
1 | What types of student engagement in online learning have been studied using learning analytics? | This question aims to discover the types of student engagement in online learning that were studied in past research using the application of learning analytics. |
2 | What is the purpose of using learning analytics on student engagement in online learning? | This question aims to explore the objective of using learning analytics to determine student engagement in online learning through past research. |
3 | What is the effect of the use of learning analytics for student engagement in online learning? | This question aims to discover whether the utilisation of learning analytics for student engagement could enhance students’ learning performance in online learning and the role of learning analytics. |
Inclusion | Exclusion |
---|---|
Empirical studies | Non-empirical studies such as reviews |
Studies that were written in English | Studies that were not written in English |
Studies or publications that intended to use learning analytics on student engagement in online learning | Studies that were not conducted in universities, such as schools and other institutions |
Studies or publications that were published from 2011 to 2021 |
Student Engagement | Article | Count | Percentage (%) |
---|---|---|---|
Social engagement | [27,56,57] | 3 | 11.54 |
Cognitive engagement | [3,58,59,60,61] | 5 | 19.23 |
Behavioural engagement | [48,62] | 2 | 7.69 |
Collaborative engagement | [14,22,63,64] | 4 | 15.38 |
Emotional engagement | [39,65,66,67,68] | 5 | 19.23 |
Cognitive, emotional, and behavioural engagement | [25,69,70,71,72] | 5 | 19.23 |
Social, cognitive, and behavioural engagement | [73] | 1 | 3.84 |
Behavioural and emotional engagement | [74] | 1 | 3.84 |
Total | 26 | 100% |
Article | Purpose | Description |
---|---|---|
[19,20,24,41,66,75,76,77,78] | Predict | The aim is to develop a model to predict the knowledge absorption and future performance of students, which can be used for intervention purposes. An example of intervention includes suggesting actions that should be taken to help students who need additional assistance. |
[66] | Analyse | It tracks students’ activities and generates reports to support decision-making. It is also related to teacher’s evaluation of the learning process, which can help improve the learning environment. Examining and analysing the ways students use a learning system can assist teachers in detecting patterns and making decisions in terms of future learning activities. |
[13,32,50,79,80] | To examine the necessary conditions geared toward engagement in online-learning environments based on the learning-analytics approach. | |
[58] | Feedback | To provide feedback to both students and teachers based on the analysis outcome of the efficiency of the learning process. |
[63] | ||
[49] | Others | To study the potentials and pitfalls of learning analytics as a tool for promoting students’ well-being in online learning. |
[47] | To study the engagement in LMSs and students’ performance through learning analytics. | |
[65] | To validate a theorised model of engagement in learning analytics. | |
[48] | To study learning analytics in collaborative learning. |
Article | Type of Engagement | Online-Learning Platform/Tool | LA Role | LA Intervention | Findings |
---|---|---|---|---|---|
[58] | Cognitive | Discussion forum | LA discovered that students’ cognitive engagement can be promoted by providing feedback through quality responses and discussion questions. | Yes (feedback) | Students’ cognitive engagement was promoted due to feedback given. |
[63] | Collaborative | Online discussion using Blackboard | LA suggested that feedback aids students’ engagement and assists their learning. | Yes (feedback) | Students’ engagement and learning was strengthened by feedback given. |
[36] | Collaborative | VLE tools: OpenDesignStudio (ODS) to post forums, live chat, etc. | The visualisation from LA gave students a chance to reflect on their writing process to promote engagement and learning performance. | No | Students learning was improved and supported due to visualisation. |
[84] | Behavioural | Moodle LMS | LA predicted students’ performance by analysing students’ grade performance in the LMS based on their learning traces. | No | Students’ final grades were increased with prediction but only slightly. |
[76] | Behavioural | MOOC courses | LA was used to predict students’ performance through participation in online tasks. | No | Students’ final exam grades were harder to predict than final grades due to different assignments during the course. |
[29] | Behavioural | Video learning and datamining through a student-information system (SIS), student online activities from the LMS Moodle and student video-interaction data from eDify (mobile application) | LA predicted students’ performance through online behaviour. | No | Students’ performances and interaction were improved. |
[77] | Behavioural and Social | Case-analysis report through Slack | LA predicted students’ performance based on task participation and interaction among students. | No | Students’ teamwork engagement was promoted and positively influenced students’ success. |
[47] | Behavioural | Student data on the LMS | LA predicted that high learning performance can be achieved with clear learning objectives from instructors. | No | Students’ performance was predicted to be influenced by good learning engagement. |
[50] | Behavioural | Instructors teaching through an LMS, Tsinghua Educational Online (THEOL) | LA analysed that course design and instructor’s guidance are the keys to students’ positive behaviours in task completion, which leads to improved learning performance. | No | Students’ actions in online learning was reported. |
[41] | Behavioural | Online platform | LA predicted students’ performance through the CPT+ model based on their behavioural engagement in learning. | No | Students’ performance behaviour was significantly predicted. |
[24] | Behavioural | Interaction in an LMS | LA predicted students’ academic performance through their online behaviour of self-regulated learning and interactions. | No | Students’ engagement and performance was positively influenced with better learning approaches. |
[78] | Behavioural | Clickstream and answer quiz on the MOOC platform | LA predicted students’ grades based on students’ online behaviour and involvement. | No | Students’ behaviour was predicted to not correlated with performance. |
[75] | Behavioural | Activity on MOOC | LA analysed students’ grades based on participation and academic-oriented behaviour. | No | Students’ grades coincided with positive participation and learning behaviour. |
[13] | Behavioural | Posts and discussion on Canvas | LA was used to assess students’ learning experience and performance based on their behavioural engagement through self-directed learning and collaborative learning. | No | Students’ performance was predicted to not be associated with their engagement. |
[79] | Behavioural | Activity in VLE | LA was used to identify the study materials and the causes that affect students’ academic achievement. | No | Students’ interaction was identified to influence engagement in learning. |
[80] | Behavioural | Daily trace data in VLE | LA was used to monitor student engagement considering factors affecting engagement, learning experience, and performance. | No | A comparison of students’ grades was carried out. |
[85] | Social | Academic data in VLE | LA predicted students’ performance based on students’ academic data using the AugmentED model. | Yes (visualised feedback) | Students’ academic performance was highly predicted, especially for at-risk students. |
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Johar, N.A.; Kew, S.N.; Tasir, Z.; Koh, E. Learning Analytics on Student Engagement to Enhance Students’ Learning Performance: A Systematic Review. Sustainability 2023, 15, 7849. https://doi.org/10.3390/su15107849
Johar NA, Kew SN, Tasir Z, Koh E. Learning Analytics on Student Engagement to Enhance Students’ Learning Performance: A Systematic Review. Sustainability. 2023; 15(10):7849. https://doi.org/10.3390/su15107849
Chicago/Turabian StyleJohar, Nurul Atiqah, Si Na Kew, Zaidatun Tasir, and Elizabeth Koh. 2023. "Learning Analytics on Student Engagement to Enhance Students’ Learning Performance: A Systematic Review" Sustainability 15, no. 10: 7849. https://doi.org/10.3390/su15107849