Difference between revisions of "MORF:Data Studies"

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== Published Studies ==
== Published Studies ==
====== Hutt et al. (2022)<ref>Hutt, S., Baker, R. S., Ashenafi, M. M., Andres‐Bray, J. M., & Brooks, C. (2022). Controlled outputs, full data: A privacy‐protecting infrastructure for MOOC data. ''British Journal of Educational Technology''.</ref> ======
Title - Controlled outputs, full data: A privacy-protecting infrastructure for MOOC data.


====== Andres-Bray (2021)<ref>Andres-Bray, J. M. L. (2021). ''Replication in Massive Open Online Course Research Using the MOOC Replication Framework'' (Doctoral dissertation, University of Pennsylvania).</ref> ======
====== Andres-Bray (2021)<ref>Andres-Bray, J. M. L. (2021). ''Replication in Massive Open Online Course Research Using the MOOC Replication Framework'' (Doctoral dissertation, University of Pennsylvania).</ref> ======
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== Ongoing Studies ==
== Ongoing Studies ==


* Investigating algorithmic bias in predicting dropout from MOOCs for intersectional identities (led by researcher at CMU)
* Investigating algorithmic bias in predicting dropout from MOOCs for intersectional identities (led by Shamya Karumbaiah, CMU and Haripriya Valayaputtur, UPenn)
* Detecting which MOOC forum posts should be responded to by course staff (led by former graduate student at Penn)
* Detecting which MOOC forum posts should be responded to by course staff  
* Other ongoing projects involving researchers at University of Florida, Northern Kentucky University, SUNY Albany, University of Pennsylvania
* Applying foundation models to MOOC Data (led by Anthony Botelho, U. Florida and Seth Adjei, Northern Kentucky University)
* Other projects by researchers at SUNY Albany, University of Pennsylvania
 
== References ==

Latest revision as of 16:39, 19 July 2022

This page lists all known MORF based data studies since 2020.

Published Studies

Andres-Bray (2021)[1]

Title - Replication in Massive Open Online Course Research Using the MOOC Replication Framework (Doctoral dissertation, University of Pennsylvania).

Zhao, Wang, & Sahebi (2020)[2]

Title - Modeling knowledge acquisition from multiple learning resource types.

Wang et al. (2021)[3]

Title - Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization.

Ongoing Studies

  • Investigating algorithmic bias in predicting dropout from MOOCs for intersectional identities (led by Shamya Karumbaiah, CMU and Haripriya Valayaputtur, UPenn)
  • Detecting which MOOC forum posts should be responded to by course staff
  • Applying foundation models to MOOC Data (led by Anthony Botelho, U. Florida and Seth Adjei, Northern Kentucky University)
  • Other projects by researchers at SUNY Albany, University of Pennsylvania

References

  1. Andres-Bray, J. M. L. (2021). Replication in Massive Open Online Course Research Using the MOOC Replication Framework (Doctoral dissertation, University of Pennsylvania).
  2. Zhao, S., Wang, C., & Sahebi, S. (2020). Modeling knowledge acquisition from multiple learning resource types. arXiv preprint arXiv:2006.13390.
  3. Wang, C., Sahebi, S., Zhao, S., Brusilovsky, P., & Moraes, L. O. (2021, June). Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (pp. 179-188).