demonstration

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Studying Learning at Scale with the ASSISTments TestBed

Authors:

Korinn S. Ostrow,

Neil T. HeffernanAuthors Info & Claims

L@S '16: Proceedings of the Third (2016) ACM Conference on Learning @ Scale

Pages 333 - 334

https://doi.org/10.1145/2876034.2893404

Published: 25 April 2016 Publication History

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Abstract

An interactive demonstration on how to design and implement randomized controlled experiments at scale within the ASSISTments TestBed, a new collaborative for educational research funded by the National Science Foundation (NSF). The Assessment of Learning infrastructure (ALI), a unique data retrieval and analysis tool, is also demonstrated.

References

[1]

Heffernan, N. (2014). SI2-SSE: Adding Research Accounts to the ASSISTments Platform: Helping Researchers Do Randomized Controlled Studies with Thousands of Students. (NSF #1440753). http://goo.gl/lv2Ndi

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[2]

Heffernan, N. & Heffernan, C. (2014). The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching. Int J of AIED. 24(4), pp. 470--497.

Google Scholar

[3]

Ostrow, K. & Heffernan, C. (2015). ASSISTments TestBed Resource Guide. https://goo.gl/9GHuiS

Google Scholar

[4]

Ostrow, K., Selent, D., Wang, Y., Van Inwegen, E., Heffernan, N., & Williams, J.J. (In Press). The Assessment of Learning Infrastructure (ALI): The Theory, Practice, and Scalability of Automated Assessment. To be included in the Proceedings of the 6th Int Conf on LAK.

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Hutt SBaker RAshenafi MAndres‐Bray JBrooks C(2022) Controlled outputs, full data: A privacy‐protecting infrastructure for MOOC data British Journal of Educational Technology10.1111/bjet.1323153:4(756-775)Online publication date: 11-May-2022
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Baker RHutt SMogessie MValayaputtar H(2022)Research Using the MOOC Replication Framework and E-TRIALS2022 IEEE Learning with MOOCS (LWMOOCS)10.1109/LWMOOCS53067.2022.9927934(131-136)Online publication date: 29-Sep-2022
https://doi.org/10.1109/LWMOOCS53067.2022.9927934
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Studying Learning at Scale with the ASSISTments TestBed

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Information & Contributors

Information

Published In

L@S '16: Proceedings of the Third (2016) ACM Conference on Learning @ Scale

April 2016

446 pages

ISBN:9781450337267

DOI:10.1145/2876034

General Chair:
Jeff Haywood
The University of Edinburgh, UK
,
Program Chairs:
Vincent Aleven
Carnegie Mellon University, USA
,
Judy Kay
University of Sydney, Australia
,
Ido Roll
University of British Columbia, Canada

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 April 2016

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Demonstration

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L@S 2016

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L@S 2016: Third (2016) ACM Conference on Learning @ Scale

April 25 - 26, 2016

Scotland, Edinburgh, UK

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L@S '16 Paper Acceptance Rate 18 of 79 submissions, 23%;

Overall Acceptance Rate 117 of 440 submissions, 27%

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Cited By

View all

Guan QYu YHuang XFang LHe CWu LLuo WChen GChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Generating Privacy-preserving Educational Data Records with Diffusion ModelCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651511(806-809)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651511
Hutt SBaker RAshenafi MAndres‐Bray JBrooks C(2022) Controlled outputs, full data: A privacy‐protecting infrastructure for MOOC data British Journal of Educational Technology10.1111/bjet.1323153:4(756-775)Online publication date: 11-May-2022
https://doi.org/10.1111/bjet.13231
Baker RHutt SMogessie MValayaputtar H(2022)Research Using the MOOC Replication Framework and E-TRIALS2022 IEEE Learning with MOOCS (LWMOOCS)10.1109/LWMOOCS53067.2022.9927934(131-136)Online publication date: 29-Sep-2022
https://doi.org/10.1109/LWMOOCS53067.2022.9927934
Bernacki MGreene MLobczowski N(2021)A Systematic Review of Research on Personalized Learning: Personalized by Whom, to What, How, and for What Purpose(s)?Educational Psychology Review10.1007/s10648-021-09615-8Online publication date: 27-Apr-2021
https://doi.org/10.1007/s10648-021-09615-8
Preet Kamal Sachin Ahuja (2017)A Review on Prediction of Academic Performance of Students at-Risk Using Data Mining TechniquesJournal on Today's Ideas - Tomorrow's Technologies10.15415/jotitt.2017.510025:1(30-39)Online publication date: 28-Jun-2017
https://doi.org/10.15415/jotitt.2017.51002

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The assessment of learning infrastructure (ALI): the theory, practice, and scalability of automated assessment

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