Connecting the dots: Predicting student grade sequences from bursty MOOC interactions over time

L@S 2015 - 2nd ACM Conference on Learning at Scale

Volumn , Issue , 2015, Pages 249-252

  Sinha, Tanmay ^a   Cassell, Justine ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

E-LEARNING; FORECASTING; RANDOM PROCESSES;

COMPARATIVE ANALYSIS; CONDITIONAL RANDOM FIELD; DISCRIMINATIVE CLASSIFIERS; DISCUSSION FORUM; INTERACTION FEATURES; MASSIVE OPEN ONLINE COURSE; PROBABILISTIC FRAMEWORK; STUDENT PERFORMANCE;

STUDENTS;

EID: 84928036623     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2724660.2728669     Document Type: Conference Paper

References (6)

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2. Kim, J., Guo, P. J., Seaton, D. T., Mitros, P., Gajos, K. Z., and Miller, R. C. Understanding in video dropouts and interaction peaks inonline lecture videos. In Proceedings of ACM L@S (2014), 31-40.
3. Laérty, J., McCallum, A., and Pereira, F. C. Conditional random fields: Probabilistic models for segmenting and labeling sequence data.
4. MITx, and HarvardX. Harvardx-mitx person-course academic year 2013 de-identified dataset, version 2.0. In http://dx.doi.org/10.7910/DVN/26147, Harvard Dataverse Network [Distributor] V10 [Version] (2014).
5. Sinha, T., Li, N., Jermann, P., and Dillenbourg, P. Capturing attrition intensifying structural traits from didactic interaction sequences of MOOC learners. Proceedings of EMNLP (2014).
6. Yang, D., Sinha, T., Adamson, D., and Rosé, C. P. Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses. In Proceedings of the NIPS (2013).