Name
Title | ||
|---|---|---|
Data-driven Learner Modeling to Understand and Improve Online Learning: MOOCs and technology to advance learning and learning research (Ubiquity symposium) |
Abstract | ||
|---|---|---|
Advanced educational technologies are developing rapidly and online MOOC courses are becoming more prevalent, creating an enthusiasm for the seemingly limitless data-driven possibilities to affect advances in learning and enhance the learning experience. For these possibilities to unfold, the expertise and collaboration of many specialists will be necessary to improve data collection, to foster the development of better predictive models, and to assure models are interpretable and actionable. The big data collected from MOOCs needs to be bigger, not in its height (number of students) but in its widthmore meta-data and information on learners' cognitive and self-regulatory states needs to be collected in addition to correctness and completion rates. This more detailed articulation will help open up the black box approach to machine learning models where prediction is the primary goal. Instead, a data-driven learner model approach uses fine grain data that is conceived and developed from cognitive principles to build explanatory models with practical implications to improve student learning. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2591682 | Ubiquity |
Keywords | DocType | Volume |
computer uses in education,design,education,experimentation,human factors,measurement,performance,world wide web | Journal | 2014 |
Issue | Citations | PageRank |
May | 3 | 0.81 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kenneth R. Koedinger | 1 | 3551 | 403.07 |
| Elizabeth A. McLaughlin | 2 | 147 | 11.51 |
| John C. Stamper | 3 | 452 | 59.07 |