LearnSphere: A Learning Data and Analytics CyberInfrastructure
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Published
Jun 27, 2024
John Stamper
Steven Moore
Carolyn Rose
Philip Pavlik
Kenneth Koedinger
https://orcid.org/0000-0002-2291-1468
Abstract
LearnSphere is a web-based data infrastructure designed to transform scientific discovery and innovation in
education. It supports learning researchers in addressing a broad range of issues including cognitive, social,
and motivational factors in learning, educational content analysis, and educational technology innovation.
LearnSphere integrates previously separate educational data and analytic resources developed by
participating institutions. The web-based workflow authoring tool, Tigris, allows technical users to
contribute sophisticated analytic methods, and learning researchers can adapt and apply those methods using
graphical user interfaces, importantly, without additional programming. As part of our use-driven design of
LearnSphere, we built a community through workshops and summer schools on educational data mining.
Researchers interested in particular student levels or content domains can find student data from elementary
through higher-education and across a wide variety of course content such as math, science, computing, and
language learning. LearnSphere has facilitated many discoveries about learning, including the importance of
active over passive learning activities and the positive association of quality discussion board posts with
learning outcomes. LearnSphere also supports research reproducibility, replicability, traceability, and
transparency as researchers can share their data and analytic methods along with links to research papers.
We demonstrate the capabilities of LearnSphere through a series of case studies that illustrate how analytic
components can be combined into research workflow combinations that can be developed and shared. We
also show how open web-accessible analytics drive the creation of common formats to streamline repeated
analytics and facilitate wider and more flexible dissemination of analytic tool kits.
How to Cite
Stamper, J., Moore, S., Rose, C., Pavlik, P., & Koedinger, K. (2024). LearnSphere: A Learning Data and Analytics CyberInfrastructure . Journal of Educational Data Mining, 16(1), 141–163. https://doi.org/10.5281/zenodo.11109638
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Keywords
algorithms, learning analytics, data repositories
References
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BIER, NORMAN, STAMPER, JOHN, MOORE, STEVEN, SIEGEL, DARREN, AND ANBAR, ARIEL. 2023. OLI Torus: a next-generation, open platform for adaptive courseware development, delivery, and research. In Companion Proceedings 13th International Conference on Learning Analytics & Knowledge (LAK 2023), ACM, 57-60.
BODILY, R., NYLAND, R., AND WILEY, D. 2017. The RISE Framework: Using Learning Analytics to Automatically Identify Open Educational Resources for Continuous Improvement. International Review of Research on Distance and Open Learning, Érudit, 18(2), 103-122.
DATAVYU TEAM. 2014. Datavyu: A Video Coding Tool. Databrary Project, New York University. URL http://datavyu.org.
FIACCO, J., COTOS, E. AND ROSÉ, C., 2019, March. Towards enabling feedback on rhetorical structure with neural sequence models. In Proceedings of the 9th international conference on learning analytics & knowledge (LAK 2019), ACM, 310-319.
FIACCO, J. AND ROSÉ, C., 2018, June. Towards domain general detection of transactive knowledge building behavior. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale, ACM, 1-11.
GARDNER, J., BROOKS, C., ANDRES, J.M. AND BAKER, R.S., 2018, December. MORF: A framework for predictive modeling and replication at scale with privacy-restricted MOOC data. In 2018 IEEE International Conference on Big Data (Big Data), IEEE, 3235-3244.
JIANG, W., PARDOS, Z. A., AND WEI, Q. 2019. Goal-based course recommendation. In Proceedings of the 9th international conference on learning analytics & knowledge (LAK 2019), ACM, 36-45.
JO, Y., TOMAR, G.S., FERSCHKE, O., ROSE, C.P., AND GAESEVIC, D. 2016. Pipeline for expediting learning analytics and student support from data in social learning. In Proceedings of Educational Data Mining (EDM 2016), T. Barnes, M., Chi, and M. Feng, Eds. International Educational Data Mining Society (IEDMS), 59-62.
JO, Y. AND ROSÉ, C. P. 2015. Time Series Analysis of Nursing Notes for Mortality Prediction via State Transition Topic Models, In Proceedings of The 24th ACM International Conference on Information and Knowledge Management (CIKM 2015), ACM, 1171-1180.
KOEDINGER, K., BAKER, R., CUNNINGHAM, K., SKOGSHOLM, A., LEBER, B., AND STAMPER, J. 2010. A Data Repository for the EDM community: The PSLC DataShop. In Handbook of Educational Data Mining, C. Romero, S., Ventura, M., Pechenizkiy, and R.S.J.d. Baker, Eds. Boca Raton, FL: CRC Press, 43-56.
KOEDINGER, K. R., CORBETT, A. T., AND PERFETTI, C. (2012). The Knowledge‐Learning‐Instruction framework: Bridging the science‐practice chasm to enhance robust student learning. Cognitive science. Wiley Online Library, 36(5), 757-798.
KOEDINGER, K. R., BRUNSKILL, E., BAKER, R. S., MCLAUGHLIN, E. A., AND STAMPER, J. 2013. New potentials for data-driven intelligent tutoring system development and optimization. AI Magazine, AAAI, 34(3), 27-41.
KOEDINGER, K.R., KIM, J., JIA, J., MCLAUGHLIN, E.A., AND BIER, N.L. 2015. Learning is not a spectator sport: Doing is better than watching for learning from a MOOC. In Proceedings of the Second ACM Conference on Learning at Scale, ACM, 111-12.
KOEDINGER, K.R., MCLAUGHLIN, E. A., JIA, J. Z., AND BIER, N. L. 2016. Is the Doer Effect a causal relationship? How can we tell and why it’s important. In Proceedings of the Sixth International Conference on Learning, Analytics and Knowledge (LAK 2016), ACM, 388- 397.
KOEDINGER, K. R., SCHEINES, R., AND SCHALDENBRAND, P. 2018. Is the doer effect robust across multiple data sets? In Proceedings of the 11th International Conference on Educational Data Mining, K. Boyer and M. Yudelson, Eds. International Educational Data Mining Society (IEDMS), 369–375.
LIU, R., AND KOEDINGER, K. R. 2017. Towards Reliable and Valid Measurement of Individualized Student Parameters. In Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017), X. Hu, T. Barnes, A. Hershkovitz, and L. Paquette, Eds. International Educational Data Mining Society (IEDMS), 135-142.
LOHSE, J. J., MCMANUS, C. A., AND JOYNER, D. A. 2019. Surveying the MOOC data set universe. In 2019 IEEE Learning With MOOCS (LWMOOCS), Meier, R. et al. Eds. IEEE, Madrid, Spain, 159-164.
MACLELLAN, C.J., LIU, R., AND KOEDINGER, K.R. 2015. Accounting for Slipping and Other False Negatives in Logistic Models of Student Learning. In Proceedings of the 8th International Conference on Educational Data Mining. O.C. Santos, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J.M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, M. Desmarais, Eds. International Educational Data Mining Society (IEDMS), 53-60.
MATZ, R. L., KOESTER, B. P., FIORINI, S., GROM, G., SHEPARD, L., STANGOR, C. G., ... AND MCKAY, T. A. 2017. Patterns of gendered performance differences in large introductory courses at five research universities. Aera Open, 3(4), 2332858417743754.
MAYFIELD, E. AND ROSÉ, C. P. (2013). LightSIDE: Open Source Machine Learning for Text Accessible to Non-Experts, Handbook of Automated Essay Grading. Shermis, M. and Burstein. J. Eds. Routledge, New York, 124-135.
O’REILLY, U.M., AND VEERAMACHANENI, K. 2014. Technology for Mining the Big Data of MOOCs. Research & Practice in Assessment, 9(2), 29-37.
PAVLIK, P., CEN, H., AND KOEDINGER, K. 2009. Performance Factors Analysis - A New Alternative to Knowledge Tracing. In Proceedings of the 14th International Conference on Artificial Intelligence in Education (AIED 2009), V. Dimitrova, R.Mizoguchi, B. du Boulay, and A. C. Graesser, Eds. IOS Press, 531–538.
PAVLIK JR, P. I., OLNEY, A. M., BANKER, A., EGLINGTON, L., AND YARBRO, J. 2020. The Mobile Fact and Concept Textbook System (MoFaCTS). In Proceedings of the Second International Workshop on Intelligent Textbooks 2020 co-located with 21st International Conference on Artificial Intelligence in Education (AIED 2020), S. Sosnovsky, P. Brusilovsky, R. Baraniuk, and A. Lan, Eds. CEUR Vol. 2674, 35-49.
PAVLIK, P. I., EGLINGTON, L. G., AND HARRELL-WILLIAMS, L. M. 2021. Logistic knowledge tracing: A constrained framework for learner modeling. IEEE Transactions on Learning Technologies, IEEE, 14(5), 624-639.
PAVLIK JR., P. I., KELLY, C., AND MAASS, J. K. 2016. Using the mobile fact and concept training system (MoFaCTS). In Proceedings of the 13th International Conference on Intelligent Tutoring Systems. A. Micarelli and J. Stamper, Eds. Springer, 247-253.
RAD, B. B., BHATTI, H. J., AND AHMADI, M. 2017. An introduction to docker and analysis of its performance. International Journal of Computer Science and Network Security (IJCSNS), 17(3), 228.
ROSÉ, C. P. 2017. Expediting the cycle of data to intervention, Learning: Research and Practice 3(1), special issue on Learning Analytics, Taylor and Francis, 59-62.
ROSÉ, C. P. AND FERSCHKE, O. 2016. Technology Support for Discussion Based Learning: From Computer Supported Collaborative Learning to the Future of Massive Open Online Courses. International Journal of Artificial Intelligence in Education, 25th Anniversary Edition, Springer, volume 26(2). 660-678.
ROSÉ, C. P., WANG, Y.C., CUI, Y., ARGUELLO, J., STEGMANN, K., WEINBERGER, A., AND FISCHER, F. 2008. Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning, Springer, 3(3), 237-271.
SANKARANARAYANAN, S., DASHTI, C., BOGART, C., WANG, X., MARSHALL AN, CLARENCE NGOH, MICHAEL HILTON, SAKR, M., AND ROSÉ, C. 2019. Online Mob Programming: Bridging the 21st Century Workplace and the Classroom, In Proceedings of Computer- Supported Collaborative Learning. Vol.2, K. Lund, G. P. Niccolai, E. Lavoué, C. Hmelo- Silver, G. Gweo, and M. Baker, Eds. Lyon, FR, 855-856.
SANKARANARAYANAN, S., DASHTI, C., BOGART, C., WANG, X., SAKR, M., AND ROSÉ, C. 2018. When Optimal Team Formation is a Choice - Self-Selection versus Intelligent Team Formation Strategies in a Large Online Project-Based Course, In the 19th International Conference on Artificial Intelligence in Education (AIED 2018), C. Penstein Rosé, R. Martínez Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. M. McLaren, and B. du Boulay, Eds. Springer International Publishing. London, UK. Part I. 518-531.
SELENT, D., PATIKORN, T., AND HEFFERNAN, N. 2016. Assistments dataset from multiple randomized controlled experiments. In Proceedings of the Third ACM Conference on Learning@ Scale, ACM, 181-184.
SINATRA, A.M. 2022. Proceedings of the 10th Annual GIFT Users Symposium. Orlando, FL: US Army Combat Capabilities Development Command - Soldier Center. ISBN 978-0- 9977258-2-7. Available at: https://gifttutoring.org/documents/ SOTTILARE, R. A., LONG, R. A., AND GOLDBERG, B. S. 2017. Enhancing the Experience Application Program Interface (xAPI) to improve domain competency modeling for adaptive instruction. In Proceedings of the Fourth ACM Conference on Learning@ Scale, ACM, 265-268.
SPIRTES, P., GLYMOUR, C., SCHEINES, R. AND RAMSEY, J., 1990. The TETRAD project. Acting and Reflecting. 183-207.
STAMPER, J., AND PARDOS, Z. A. 2016. The 2010 KDD Cup Competition Dataset: Engaging the machine learning community in predictive learning analytics. Journal of Learning Analytics, 3(2), 312-316.
VAN CAMPENHOUT, R., JOHNSON, B.G., AND OLSEN, J. 2021. The Doer Effect: Replicating Findings that Doing Causes Learning. The Thirteenth International Conference on Mobile, Hybrid, and On-line Learning, Think Mind Digital Library, 1-6.
VAN CAMPENHOUT, R., JEROME, B., DITTEL, J. S., AND JOHNSON, B. G. 2023. The Doer Effect at Scale: Investigating Correlation and Causation Across Seven Courses. In LAK23: 13th International Learning Analytics and Knowledge Conference, ACM. 357-365.
VEERAMACHANENI, K., DERNONCOURT, F., TAYLOR, C., PARDOS, Z.A., AND O’REILLY, U.M. 2015. Moocdb: Developing data standards for MOOC data science. MOOCShop at Artificial Intelligence in Education, 2015a. URL http://ceur-ws.org/Vol-1009/0104.pdf, Madrid, Spain. 17-24.
WANG, X.., YANG, D., WEN, M., KOEDINGER, K. R., AND ROSÉ, C. P. 2015. Investigating how student’s cognitive behavior in MOOC discussion forums affect learning gains, The 8th International Conference on Educational Data Mining, O.C. Santos, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J.M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, M. Desmarais, Eds. International Educational Data Mining Society (IEDMS), 226-233.
WARLAUMONT, A. S., VANDAM, M., BERGELSON, E., AND CRISTIA, A. 2017. HomeBank: A Repository for Long-Form Real-World Audio Recordings of Children. In Proceedings of INTERSPEECH 2017, Stockholm, Sweden, F. Lacerda, Ed. International Speech Communication Association (ISCA), 815-816.
WILEY, D. 2018. RISE: An R package for RISE analysis. Journal of Open Source Software. The Open Journal, 3(28), 846, https://doi.org/10.21105/joss.00846.
YANG, D., WEN, M., KUMAR, A., XING, E., AND ROSÉ, C. P. 2014. Towards an Integration of Text and Graph Clustering Methods as a Lens for Studying Social Interaction in MOOCs. In The International Review of Research in Open and Distance Learning 15(5), Special Issue on the MOOC Research Initiative. 215-234.
YUDELSON, M., KOEDINGER, K.R., AND GORDON, G.J. 2013. Individualized Bayesian Knowledge Tracing Models. In Artificial Intelligence in Education: 16th International Conference, AIED 2013, H. C. Lane, K. Yacef, J. Mostow, and P. I. Pavlik, Eds. Springer Berlin Heidelberg. Memphis, TN, USA, 171-180.
AMBROSE, S. A., BRIDGES, M. W., DIPIETRO, M., LOVETT, M. C., AND NORMAN, M. K. 2010. How learning works: Seven research-based principles for smart teaching. John Wiley & Sons.
BAKER, R. S. D., DUVAL, E., STAMPER, J., WILEY, D., AND SHUM, S. B. 2012. Educational data mining meets learning analytics. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK 2012). ACM, 20-20.
BECK, J.E. AND GONG, Y., 2013. Wheel-spinning: Students who fail to master a skill. In In Artificial Intelligence in Education: 16th International Conference, AIED 2013, Memphis, TN, USA, H. C. Lane, K. Yacef, J. Mostow, and P. I. Pavlik, Eds. Springer Berlin Heidelberg, 431-440.
BIER, NORMAN, STAMPER, JOHN, MOORE, STEVEN, SIEGEL, DARREN, AND ANBAR, ARIEL. 2023. OLI Torus: a next-generation, open platform for adaptive courseware development, delivery, and research. In Companion Proceedings 13th International Conference on Learning Analytics & Knowledge (LAK 2023), ACM, 57-60.
BODILY, R., NYLAND, R., AND WILEY, D. 2017. The RISE Framework: Using Learning Analytics to Automatically Identify Open Educational Resources for Continuous Improvement. International Review of Research on Distance and Open Learning, Érudit, 18(2), 103-122.
DATAVYU TEAM. 2014. Datavyu: A Video Coding Tool. Databrary Project, New York University. URL http://datavyu.org.
FIACCO, J., COTOS, E. AND ROSÉ, C., 2019, March. Towards enabling feedback on rhetorical structure with neural sequence models. In Proceedings of the 9th international conference on learning analytics & knowledge (LAK 2019), ACM, 310-319.
FIACCO, J. AND ROSÉ, C., 2018, June. Towards domain general detection of transactive knowledge building behavior. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale, ACM, 1-11.
GARDNER, J., BROOKS, C., ANDRES, J.M. AND BAKER, R.S., 2018, December. MORF: A framework for predictive modeling and replication at scale with privacy-restricted MOOC data. In 2018 IEEE International Conference on Big Data (Big Data), IEEE, 3235-3244.
JIANG, W., PARDOS, Z. A., AND WEI, Q. 2019. Goal-based course recommendation. In Proceedings of the 9th international conference on learning analytics & knowledge (LAK 2019), ACM, 36-45.
JO, Y., TOMAR, G.S., FERSCHKE, O., ROSE, C.P., AND GAESEVIC, D. 2016. Pipeline for expediting learning analytics and student support from data in social learning. In Proceedings of Educational Data Mining (EDM 2016), T. Barnes, M., Chi, and M. Feng, Eds. International Educational Data Mining Society (IEDMS), 59-62.
JO, Y. AND ROSÉ, C. P. 2015. Time Series Analysis of Nursing Notes for Mortality Prediction via State Transition Topic Models, In Proceedings of The 24th ACM International Conference on Information and Knowledge Management (CIKM 2015), ACM, 1171-1180.
KOEDINGER, K., BAKER, R., CUNNINGHAM, K., SKOGSHOLM, A., LEBER, B., AND STAMPER, J. 2010. A Data Repository for the EDM community: The PSLC DataShop. In Handbook of Educational Data Mining, C. Romero, S., Ventura, M., Pechenizkiy, and R.S.J.d. Baker, Eds. Boca Raton, FL: CRC Press, 43-56.
KOEDINGER, K. R., CORBETT, A. T., AND PERFETTI, C. (2012). The Knowledge‐Learning‐Instruction framework: Bridging the science‐practice chasm to enhance robust student learning. Cognitive science. Wiley Online Library, 36(5), 757-798.
KOEDINGER, K. R., BRUNSKILL, E., BAKER, R. S., MCLAUGHLIN, E. A., AND STAMPER, J. 2013. New potentials for data-driven intelligent tutoring system development and optimization. AI Magazine, AAAI, 34(3), 27-41.
KOEDINGER, K.R., KIM, J., JIA, J., MCLAUGHLIN, E.A., AND BIER, N.L. 2015. Learning is not a spectator sport: Doing is better than watching for learning from a MOOC. In Proceedings of the Second ACM Conference on Learning at Scale, ACM, 111-12.
KOEDINGER, K.R., MCLAUGHLIN, E. A., JIA, J. Z., AND BIER, N. L. 2016. Is the Doer Effect a causal relationship? How can we tell and why it’s important. In Proceedings of the Sixth International Conference on Learning, Analytics and Knowledge (LAK 2016), ACM, 388- 397.
KOEDINGER, K. R., SCHEINES, R., AND SCHALDENBRAND, P. 2018. Is the doer effect robust across multiple data sets? In Proceedings of the 11th International Conference on Educational Data Mining, K. Boyer and M. Yudelson, Eds. International Educational Data Mining Society (IEDMS), 369–375.
LIU, R., AND KOEDINGER, K. R. 2017. Towards Reliable and Valid Measurement of Individualized Student Parameters. In Proceedings of the 10th International Conference on Educational Data Mining (EDM 2017), X. Hu, T. Barnes, A. Hershkovitz, and L. Paquette, Eds. International Educational Data Mining Society (IEDMS), 135-142.
LOHSE, J. J., MCMANUS, C. A., AND JOYNER, D. A. 2019. Surveying the MOOC data set universe. In 2019 IEEE Learning With MOOCS (LWMOOCS), Meier, R. et al. Eds. IEEE, Madrid, Spain, 159-164.
MACLELLAN, C.J., LIU, R., AND KOEDINGER, K.R. 2015. Accounting for Slipping and Other False Negatives in Logistic Models of Student Learning. In Proceedings of the 8th International Conference on Educational Data Mining. O.C. Santos, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J.M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, M. Desmarais, Eds. International Educational Data Mining Society (IEDMS), 53-60.
MATZ, R. L., KOESTER, B. P., FIORINI, S., GROM, G., SHEPARD, L., STANGOR, C. G., ... AND MCKAY, T. A. 2017. Patterns of gendered performance differences in large introductory courses at five research universities. Aera Open, 3(4), 2332858417743754.
MAYFIELD, E. AND ROSÉ, C. P. (2013). LightSIDE: Open Source Machine Learning for Text Accessible to Non-Experts, Handbook of Automated Essay Grading. Shermis, M. and Burstein. J. Eds. Routledge, New York, 124-135.
O’REILLY, U.M., AND VEERAMACHANENI, K. 2014. Technology for Mining the Big Data of MOOCs. Research & Practice in Assessment, 9(2), 29-37.
PAVLIK, P., CEN, H., AND KOEDINGER, K. 2009. Performance Factors Analysis - A New Alternative to Knowledge Tracing. In Proceedings of the 14th International Conference on Artificial Intelligence in Education (AIED 2009), V. Dimitrova, R.Mizoguchi, B. du Boulay, and A. C. Graesser, Eds. IOS Press, 531–538.
PAVLIK JR, P. I., OLNEY, A. M., BANKER, A., EGLINGTON, L., AND YARBRO, J. 2020. The Mobile Fact and Concept Textbook System (MoFaCTS). In Proceedings of the Second International Workshop on Intelligent Textbooks 2020 co-located with 21st International Conference on Artificial Intelligence in Education (AIED 2020), S. Sosnovsky, P. Brusilovsky, R. Baraniuk, and A. Lan, Eds. CEUR Vol. 2674, 35-49.
PAVLIK, P. I., EGLINGTON, L. G., AND HARRELL-WILLIAMS, L. M. 2021. Logistic knowledge tracing: A constrained framework for learner modeling. IEEE Transactions on Learning Technologies, IEEE, 14(5), 624-639.
PAVLIK JR., P. I., KELLY, C., AND MAASS, J. K. 2016. Using the mobile fact and concept training system (MoFaCTS). In Proceedings of the 13th International Conference on Intelligent Tutoring Systems. A. Micarelli and J. Stamper, Eds. Springer, 247-253.
RAD, B. B., BHATTI, H. J., AND AHMADI, M. 2017. An introduction to docker and analysis of its performance. International Journal of Computer Science and Network Security (IJCSNS), 17(3), 228.
ROSÉ, C. P. 2017. Expediting the cycle of data to intervention, Learning: Research and Practice 3(1), special issue on Learning Analytics, Taylor and Francis, 59-62.
ROSÉ, C. P. AND FERSCHKE, O. 2016. Technology Support for Discussion Based Learning: From Computer Supported Collaborative Learning to the Future of Massive Open Online Courses. International Journal of Artificial Intelligence in Education, 25th Anniversary Edition, Springer, volume 26(2). 660-678.
ROSÉ, C. P., WANG, Y.C., CUI, Y., ARGUELLO, J., STEGMANN, K., WEINBERGER, A., AND FISCHER, F. 2008. Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning, Springer, 3(3), 237-271.
SANKARANARAYANAN, S., DASHTI, C., BOGART, C., WANG, X., MARSHALL AN, CLARENCE NGOH, MICHAEL HILTON, SAKR, M., AND ROSÉ, C. 2019. Online Mob Programming: Bridging the 21st Century Workplace and the Classroom, In Proceedings of Computer- Supported Collaborative Learning. Vol.2, K. Lund, G. P. Niccolai, E. Lavoué, C. Hmelo- Silver, G. Gweo, and M. Baker, Eds. Lyon, FR, 855-856.
SANKARANARAYANAN, S., DASHTI, C., BOGART, C., WANG, X., SAKR, M., AND ROSÉ, C. 2018. When Optimal Team Formation is a Choice - Self-Selection versus Intelligent Team Formation Strategies in a Large Online Project-Based Course, In the 19th International Conference on Artificial Intelligence in Education (AIED 2018), C. Penstein Rosé, R. Martínez Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. M. McLaren, and B. du Boulay, Eds. Springer International Publishing. London, UK. Part I. 518-531.
SELENT, D., PATIKORN, T., AND HEFFERNAN, N. 2016. Assistments dataset from multiple randomized controlled experiments. In Proceedings of the Third ACM Conference on Learning@ Scale, ACM, 181-184.
SINATRA, A.M. 2022. Proceedings of the 10th Annual GIFT Users Symposium. Orlando, FL: US Army Combat Capabilities Development Command - Soldier Center. ISBN 978-0- 9977258-2-7. Available at: https://gifttutoring.org/documents/ SOTTILARE, R. A., LONG, R. A., AND GOLDBERG, B. S. 2017. Enhancing the Experience Application Program Interface (xAPI) to improve domain competency modeling for adaptive instruction. In Proceedings of the Fourth ACM Conference on Learning@ Scale, ACM, 265-268.
SPIRTES, P., GLYMOUR, C., SCHEINES, R. AND RAMSEY, J., 1990. The TETRAD project. Acting and Reflecting. 183-207.
STAMPER, J., AND PARDOS, Z. A. 2016. The 2010 KDD Cup Competition Dataset: Engaging the machine learning community in predictive learning analytics. Journal of Learning Analytics, 3(2), 312-316.
VAN CAMPENHOUT, R., JOHNSON, B.G., AND OLSEN, J. 2021. The Doer Effect: Replicating Findings that Doing Causes Learning. The Thirteenth International Conference on Mobile, Hybrid, and On-line Learning, Think Mind Digital Library, 1-6.
VAN CAMPENHOUT, R., JEROME, B., DITTEL, J. S., AND JOHNSON, B. G. 2023. The Doer Effect at Scale: Investigating Correlation and Causation Across Seven Courses. In LAK23: 13th International Learning Analytics and Knowledge Conference, ACM. 357-365.
VEERAMACHANENI, K., DERNONCOURT, F., TAYLOR, C., PARDOS, Z.A., AND O’REILLY, U.M. 2015. Moocdb: Developing data standards for MOOC data science. MOOCShop at Artificial Intelligence in Education, 2015a. URL http://ceur-ws.org/Vol-1009/0104.pdf, Madrid, Spain. 17-24.
WANG, X.., YANG, D., WEN, M., KOEDINGER, K. R., AND ROSÉ, C. P. 2015. Investigating how student’s cognitive behavior in MOOC discussion forums affect learning gains, The 8th International Conference on Educational Data Mining, O.C. Santos, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, J.M. Luna, C. Mihaescu, P. Moreno, A. Hershkovitz, S. Ventura, M. Desmarais, Eds. International Educational Data Mining Society (IEDMS), 226-233.
WARLAUMONT, A. S., VANDAM, M., BERGELSON, E., AND CRISTIA, A. 2017. HomeBank: A Repository for Long-Form Real-World Audio Recordings of Children. In Proceedings of INTERSPEECH 2017, Stockholm, Sweden, F. Lacerda, Ed. International Speech Communication Association (ISCA), 815-816.
WILEY, D. 2018. RISE: An R package for RISE analysis. Journal of Open Source Software. The Open Journal, 3(28), 846, https://doi.org/10.21105/joss.00846.
YANG, D., WEN, M., KUMAR, A., XING, E., AND ROSÉ, C. P. 2014. Towards an Integration of Text and Graph Clustering Methods as a Lens for Studying Social Interaction in MOOCs. In The International Review of Research in Open and Distance Learning 15(5), Special Issue on the MOOC Research Initiative. 215-234.
YUDELSON, M., KOEDINGER, K.R., AND GORDON, G.J. 2013. Individualized Bayesian Knowledge Tracing Models. In Artificial Intelligence in Education: 16th International Conference, AIED 2013, H. C. Lane, K. Yacef, J. Mostow, and P. I. Pavlik, Eds. Springer Berlin Heidelberg. Memphis, TN, USA, 171-180.
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Section
EDM 2024 Journal Track
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