Name
Abstract | ||
|---|---|---|
This paper surveys the state of the art on prediction in MOOCs through a Systematic Literature Review (SLR). The main objectives are: (1) to identify the characteristics of the MOOCs used for prediction, (2) to describe the prediction outcomes, (3) to classify the prediction features, (4) to determine the techniques used to predict the variables, and (5) to identify the metrics used to evaluate the predictive models. Results show there is strong interest in predicting dropouts in MOOCs. A variety of predictive models are used, though regression and Support Vector Machines stand out. There is also wide variety in the choice of prediction features, but clickstream data about platform use stands out. Future research should focus on developing and applying predictive models that can be used in more heterogeneous contexts (in terms of platforms, thematic areas, and course durations), on predicting new outcomes and making connections among them (e.g., predicting learnersu0027 expectancies), on enhancing the predictive power of current models by improving algorithms or adding novel higher-order features (e.g., efficiency, constancy, etc.). |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tlt.2018.2856808 | IEEE Transactions on Learning Technologies |
Keywords | Field | DocType |
Predictive models,Education,Systematics,Measurement,Forecasting,Bibliographies,Object recognition | Systematic review,Predictive power,Clickstream,Regression,Computer science,Support vector machine,Knowledge management,Artificial intelligence,Thematic map,Machine learning,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
12 | 3 | 1939-1382 |
Citations | PageRank | References |
5 | 0.45 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro Manuel Moreno-Marcos | 1 | 6 | 0.80 |
| Carlos Alario-Hoyos | 2 | 181 | 30.68 |
| Pedro J. Muñoz-Merino | 3 | 189 | 22.13 |
| Carlos Delgado Kloos | 4 | 1121 | 172.07 |