Penn Center for Learning Analytics Wiki - User contributions [en]

MORF:Data Studies

2022-07-19T21:39:08Z

Haripriya: /* Published Studies */

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

== Published Studies ==

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

====== Zhao, Wang, & Sahebi (2020)<ref>Zhao, S., Wang, C., & Sahebi, S. (2020). Modeling knowledge acquisition from multiple learning resource types. ''arXiv preprint arXiv:2006.13390''.</ref> ======
Title - Modeling knowledge acquisition from multiple learning resource types.

====== Wang et al. (2021)<ref>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).</ref> ======
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 ==

MORF

2022-07-19T21:38:28Z

Haripriya:

The MOOC Replication Framework (MORF) is a framework that facilitates the replication of previously published findings across multiple data sets. It facilitates the construction and evaluation of end-to-end pipelines from raw data to evaluation. MORF is designed to ensure the seamless integration of new findings as new research is conducted or new hypotheses are generated, and to support the generation of novel research in the learning sciences.

MORF is a joint project between multiple research laboratories, with primary implementation in recent years occurring at the University of Pennsylvania Center for Learning Analytics, the etc lab at the University of Michigan School of Information, and the Human-Computer Interaction Institute at Carnegie Mellon University. Other universities also have instances of MORF.

MORF has now partnered with the ASSISTments E-TRIALS infrastructure to create RAILKaM, an integration where researchers will be able to link MOOC and intelligent tutor data.<ref>https://educational-technology-collective.github.io/morf/about/</ref>

[[MORF:Studies|Studies]]

[[MORF:Data Studies|Data 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.
== References ==

=== Citations ===
<references />

MORF:Data Studies

2022-07-17T17:05:25Z

Haripriya:

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

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

====== Zhao, Wang, & Sahebi (2020)<ref>Zhao, S., Wang, C., & Sahebi, S. (2020). Modeling knowledge acquisition from multiple learning resource types. ''arXiv preprint arXiv:2006.13390''.</ref> ======
Title - Modeling knowledge acquisition from multiple learning resource types.

====== Wang et al. (2021)<ref>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).</ref> ======
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 researcher at CMU)
* Detecting which MOOC forum posts should be responded to by course staff (led by former graduate student at Penn)
* Other ongoing projects involving researchers at University of Florida, Northern Kentucky University, SUNY Albany, University of Pennsylvania

== References ==

MORF:Data Studies

2022-07-17T17:04:56Z

Haripriya: added studies

MORF:Studies

2022-07-16T14:57:39Z

Haripriya: typo

This page lists all known MORF based studies since 2020.

MORF

2022-07-16T14:55:06Z

Haripriya:

MORF:Data Studies

2022-07-16T14:53:38Z

Haripriya: created a sub-page for MORF based data studies

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

MORF:Studies

2022-07-16T14:52:58Z

Haripriya: created a new page for morf based studies

This page all known studies since 2020

MORF

2022-07-16T14:48:29Z

Haripriya:

MORF

2022-07-16T14:47:26Z

Haripriya:

MORF

2022-07-16T14:43:03Z

Haripriya: added some intro text from the MORF about page