Use of Predictive Analytics within Learning Analytics Dashboards: A Review of Case Studies
| dc.contributor.author | Ramaswami G | |
| dc.contributor.author | Susnjak T | |
| dc.contributor.author | Mathrani A | |
| dc.contributor.author | Umer R | |
| dc.date.accessioned | 2023-11-20T01:38:37Z | |
| dc.date.available | 2022-08-26 | |
| dc.date.available | 2023-11-20T01:38:37Z | |
| dc.date.issued | 2023-09-01 | |
| dc.description | (c) The Author/s | |
| dc.description.abstract | Learning analytics dashboards (LADs) provide educators and students with a comprehensive snapshot of the learning domain. Visualizations showcasing student learning behavioral patterns can help students gain greater self-awareness of their learning progression, and at the same time assist educators in identifying those students who may be facing learning difficulties. While LADs have gained popularity, existing LADs are still far behind when it comes to employing predictive analytics into their designs. Our systematic literature review has revealed limitations in the utilization of predictive analytics tools among existing LADs. We find that studies leveraging predictive analytics only go as far as identifying the at-risk students and do not employ model interpretation or explainability capabilities. This limits the ability of LADs to offer data-driven prescriptive advice to students that can offer them guidance on appropriate learning adjustments. Further, published studies have mostly described LADs that are still at prototype stages; hence, robust evaluations of how LADs affect student outcomes have not yet been conducted. The evaluations until now are limited to LAD functionalities and usability rather than their effectiveness as a pedagogical treatment. We conclude by making recommendations for the design of advanced dashboards that more fully take advantage of machine learning technologies, while using suitable visualizations to project only relevant information. Finally, we stress the importance of developing dashboards that are ultimately evaluated for their effectiveness. | |
| dc.description.confidential | false | |
| dc.identifier.citation | Technology, Knowledge and Learning, 2022 | |
| dc.identifier.doi | 10.1007/s10758-022-09613-x | |
| dc.identifier.elements-id | 455656 | |
| dc.identifier.harvested | Massey_Dark | |
| dc.identifier.issn | 2211-1662 | |
| dc.identifier.uri | https://hdl.handle.net/10179/17552 | |
| dc.publisher | Springer Nature BV | |
| dc.relation.isPartOf | Technology, Knowledge and Learning | |
| dc.relation.uri | https://link.springer.com/article/10.1007/s10758-022-09613-x | |
| dc.rights | CC BY 4.0 | |
| dc.subject | Early Warning System | |
| dc.subject | Student Feedback System | |
| dc.subject | Systematic Review | |
| dc.subject | Learning Analytics Dashboard | |
| dc.title | Use of Predictive Analytics within Learning Analytics Dashboards: A Review of Case Studies | |
| dc.type | Journal article | |
| massey.relation.uri-description | Published version | |
| pubs.notes | Not known | |
| pubs.organisational-group | /Massey University | |
| pubs.organisational-group | /Massey University/College of Sciences | |
| pubs.organisational-group | /Massey University/College of Sciences/School of Mathematical and Computational Sciences |