@inProceedings{
 title = {Behavior Prediction in MOOCs using Higher Granularity Temporal Information},
 type = {inProceedings},
 year = {2015},
 identifiers = {[object Object]},
 keywords = {behavior prediction,machine learning,moocs},
 pages = {335-338},
 websites = {http://dl.acm.org/citation.cfm?id=2724660.2728687},
 month = {3},
 publisher = {ACM Press},
 day = {14},
 city = {New York, New York, USA},
 id = {b5319082-1a6e-3357-bd60-be427dd22e3a},
 created = {2015-08-16T20:36:58.000Z},
 accessed = {2015-07-16},
 file_attached = {false},
 profile_id = {15d5ede6-80ea-3b4c-a7e1-5b79ba86707d},
 group_id = {a7fbd406-18f3-3b0b-bf98-38c617d1e04e},
 last_modified = {2017-03-14T17:40:34.521Z},
 tags = {ayse,clustering,subgroup},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper, we present early research evaluating the predictive power of a variety of temporal features across student subpopulations with distinctive behaviors at the beginning of the course. Initial results illustrate that these features predict important differences across the subpopulations and over time in the courses. Ultimately, these results have implications for effectively targeting adaptive scaffolding tailored to the particular intentions and goals of subpopulations in MOOCs.},
 bibtype = {inProceedings},
 author = {Ye, Cheng and Kinnebrew, John S. and Biswas, Gautam and Evans, Brent J. and Fisher, Douglas H. and Narasimham, Gayathri and Brady, Katherine A.},
 booktitle = {Proceedings of the Second (2015) ACM Conference on Learning @ Scale - L@S '15}
}

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