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Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization
- Source :
- Computers in Human Behavior. 58:119-129
- Publication Year :
- 2016
- Publisher :
- Elsevier BV, 2016.
-
Abstract
- Massive open online courses (MOOCs) have recently taken center stage in discussions surrounding online education, both in terms of their potential as well as their high dropout rates. The high attrition rates associated with MOOCs have often been described in terms of a scale-efficacy tradeoff. Building from the large numbers associated with MOOCs and the ability to track individual student performance, this study takes an initial step towards a mechanism for the early and accurate identification of students at risk for dropping out. Focusing on struggling students who remain active in course discussion forums and who are already more likely to finish a course, we design a temporal modeling approach, one which prioritizes the at-risk students in order of their likelihood to drop out of a course. In identifying only a small subset of at-risk students, we seek to provide systematic insight for instructors so they may better provide targeted support for those students most in need of intervention. Moreover, we proffer appending historical features to the current week of features for model building and to introduce principle component analysis in order to identify the breakpoint for turning off the features of previous weeks. This appended modeling method is shown to outperform simpler temporal models which simply sum features. To deal with the kind of data variability presented by MOOCs, this study illustrates the effectiveness of an ensemble stacking generalization approach to build more robust and accurate prediction models than the direct application of base learners. Propose a temporal modeling approach for students' dropout behavior in MOOCs.Demonstrate the advantage of appended feature modeling space based on PCA over a summed features modeling space.Explore the power of the ensemble learning method (stacking generalization) in enhancing the prediction ability.
- Subjects :
- Computer science
Generalization
business.industry
05 social sciences
Learning analytics
050301 education
02 engineering and technology
Space (commercial competition)
Machine learning
computer.software_genre
Ensemble learning
Human-Computer Interaction
Identification (information)
Arts and Humanities (miscellaneous)
ComputingMilieux_COMPUTERSANDEDUCATION
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
0503 education
computer
Model building
General Psychology
Dropout (neural networks)
Predictive modelling
Subjects
Details
- ISSN :
- 07475632
- Volume :
- 58
- Database :
- OpenAIRE
- Journal :
- Computers in Human Behavior
- Accession number :
- edsair.doi...........449260e02fffa50635608f4405b26d24
- Full Text :
- https://doi.org/10.1016/j.chb.2015.12.007