Using Learning Analytics to Predict Academic Outcomes of First-year Students in Higher Education
| dc.contributor.author | Sander, Pete | |
| dc.date.accessioned | 2016-12-06T18:56:12Z | |
| dc.date.available | 2016-12-06T18:56:12Z | |
| dc.date.issued | 2016-05 | |
| dc.description | 49 Pages | en_US |
| dc.description.abstract | This annotated bibliography explores scholarly literature published between 2010 and 2016 that addresses the analysis of student-generated data, called learning analytics (Fiaidhi, 2014), with the intention of providing early intervention to promote better academic outcomes. It provides information to higher-education instructors and administrators who are interested in learning about (a) reducing attrition of first year students, (b) when the application of learning analytics produces the best results, and (c) predicting academic outcomes using learning analytics. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1794/21969 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | UO | en_US |
| dc.relation.ispartofseries | AIM Capstone;2016 | |
| dc.rights | Creative Commons BY-NC-ND 4.0-US | en_US |
| dc.subject | Big data | en_US |
| dc.subject | Learning analytics | en_US |
| dc.subject | Prediction | en_US |
| dc.subject | Higher education | en_US |
| dc.subject | LMS data | en_US |
| dc.title | Using Learning Analytics to Predict Academic Outcomes of First-year Students in Higher Education | en_US |
| dc.type | Terminal Project | en_US |