{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T16:39:11Z","timestamp":1779122351546,"version":"3.51.4"},"publisher-location":"New York, New York, USA","reference-count":21,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100006642","name":"U.S. Department of Education","doi-asserted-by":"publisher","award":["R305B090023"],"award-info":[{"award-number":["R305B090023"]}],"id":[{"id":"10.13039\/100006642","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005246","name":"Institute of Education Sciences","doi-asserted-by":"publisher","award":["R305B110003"],"award-info":[{"award-number":["R305B110003"]}],"id":[{"id":"10.13039\/100005246","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1145\/2883851.2883967","type":"proceedings-article","created":{"date-parts":[[2016,4,22]],"date-time":"2016-04-22T13:54:08Z","timestamp":1461333248000},"page":"369-377","source":"Crossref","is-referenced-by-count":10,"title":["Modeling common misconceptions in learning process data"],"prefix":"10.1145","author":[{"given":"Ran","family":"Liu","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rony","family":"Patel","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenneth R.","family":"Koedinger","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","reference":[{"key":"key-10.1145\/2883851.2883967-1","doi-asserted-by":"crossref","unstructured":"Adams, D., McLaren, B. M., Durkin, K., Mayer, R. E., Rittle-Johnson, B., Isotani, S., & Van Velsen, M. (2014). Using erroneous examples to improve mathematics learning with a web-based tutoring system.Computers in Human Behavior, 36, 401--411.","DOI":"10.1016\/j.chb.2014.03.053"},{"key":"key-10.1145\/2883851.2883967-2","doi-asserted-by":"crossref","unstructured":"Akaike, H. (1985). Prediction and entropy. In Atkinson, A. & Fienberg, S., (Eds.), A Celebration of Statistics. Springer: New York, 1--24.","DOI":"10.1007\/978-1-4613-8560-8_1"},{"key":"key-10.1145\/2883851.2883967-3","unstructured":"Barnes, T. (2005). The Q-matrix Method: Mining Student Response Data for Knowledge.Proceedings of AAAI 2005: Educational Data Mining Workshop."},{"key":"key-10.1145\/2883851.2883967-4","doi-asserted-by":"crossref","unstructured":"Cen, H., Koedinger, K. R., & Junker, B. (2006). Learning Factors Analysis: A general method for cognitive model evaluation and improvement.Proceedings of the 8th International Conference of Intelligent Tutoring Systems, 164--175.","DOI":"10.1007\/11774303_17"},{"key":"key-10.1145\/2883851.2883967-5","doi-asserted-by":"crossref","unstructured":"Cen, H., Koedinger, K. R., Junker, B. (2008). Comparing two IRT models for conjunctive skills.Proceedings of the 9th International Conference of Intelligent Tutoring Systems.","DOI":"10.1007\/978-3-540-69132-7_111"},{"key":"key-10.1145\/2883851.2883967-6","doi-asserted-by":"crossref","unstructured":"Corbett, A. T., & Anderson, J. R. (1995). Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge.User Modeling and User-Adapted Interaction, 4, 253--278.","DOI":"10.1007\/BF01099821"},{"key":"key-10.1145\/2883851.2883967-7","unstructured":"Embretson, S. E., & Reise, S. P. (2000). Item Response Theory for Psychologists. Mahwah, NJ: Erlbaum."},{"key":"key-10.1145\/2883851.2883967-8","unstructured":"Gelman, R., & Williams, E. (1998). Enabling constraints for cognitive development and learning: Domain specificity and epigenesis. In D. Kuhn and R. Siegler, (Eds.), Cognition, perception and language. Vol. 2. Handbook of Child Psychology. New York: John Wiley and Sons, 575--630."},{"key":"key-10.1145\/2883851.2883967-9","doi-asserted-by":"crossref","unstructured":"Koedinger, K. R., Baker, R. S. J. d., Cunningham, K., Skogsholm, A., Leber, B., & Stamper, J. (2010). A Data Repository for the EDM community: The PSLC DataShop. In Romero, C., Ventura, S., Pechenizkiy, M., Baker, R. S. J. d. (Eds.) Handbook of Educational Data Mining. Boca Raton, FL: CRC Press.","DOI":"10.1201\/b10274-6"},{"key":"key-10.1145\/2883851.2883967-10","doi-asserted-by":"crossref","unstructured":"Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning.Cognitive Science, 36(5), 757--798.","DOI":"10.1111\/j.1551-6709.2012.01245.x"},{"key":"key-10.1145\/2883851.2883967-11","unstructured":"Koedinger, K. R., McLaughlin, E. A., & Stamper, J. C. (2012). Automated Student Model Improvement.Proceedings of the 5th International Conference on Educational Data Mining."},{"key":"key-10.1145\/2883851.2883967-12","doi-asserted-by":"crossref","unstructured":"Koedinger, K. R., McLaughlin, E. A., Stamper, J. C., & Nixon, T. (2013). Using data-driven discovery of better student models to improve student learning.Proceedings of the 16th International Conference on Artificial Intelligence in Education.","DOI":"10.1007\/978-3-642-39112-5_43"},{"key":"key-10.1145\/2883851.2883967-13","doi-asserted-by":"crossref","unstructured":"Kulikowich, J. M., & Alexander, P. A. (1994). Evaluating students' errors on cognitive tasks: Applications of polytomous Item Response Theory and log-linear modeling. In Reynolds, C. R. (Ed.), Cognitive Assessment: A Multidisciplinary Perspective. New York, NY: Springer Science, 137--154.","DOI":"10.1007\/978-1-4757-9730-5_7"},{"key":"key-10.1145\/2883851.2883967-14","unstructured":"Liu, R., & Koedinger, K. R. (2015). Variations in learning rate: Student classification based on systematic residual error patterns across practice opportunities.Proceedings of the 8thInternational Conference on Educational Data Mining."},{"key":"key-10.1145\/2883851.2883967-15","unstructured":"Ni Y., & Zhou, Y. D. (2005). Teaching and learning fraction and rational numbers: the origins and implications of whole number bias.Educational Psychologist, 40, 27--52."},{"key":"key-10.1145\/2883851.2883967-16","unstructured":"Patel, R. Fraction Addition and Multiplication.pslcdatashop.web.cmu.edu\/DatasetInfo?datasetId=1190."},{"key":"key-10.1145\/2883851.2883967-17","unstructured":"Pavlik, P. I., Jr., Cen, H., & Koedinger, K. R. (2009). Performance factors analysis - A new alternative to knowledge tracing.Proceedings of the 14th International Conference on Artificial Intelligence in Education, 531--538."},{"key":"key-10.1145\/2883851.2883967-18","unstructured":"R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http:\/\/www.R-project.org\/."},{"key":"key-10.1145\/2883851.2883967-19","doi-asserted-by":"crossref","unstructured":"Siegler, R. S., & Crowley, K. (1991). The microgenetic method: a direct means for studying cognitive development.American Psychologist, 46(6), 606--620.","DOI":"10.1037\/0003-066X.46.6.606"},{"key":"key-10.1145\/2883851.2883967-20","doi-asserted-by":"crossref","unstructured":"Tatsuoka, K. K. (1983) Rule space: An approach for dealing with misconceptions based on item response theory.Journal of Educational Measurement, 20, 345--354.","DOI":"10.1111\/j.1745-3984.1983.tb00212.x"},{"key":"key-10.1145\/2883851.2883967-21","unstructured":"Van Lehn, K. (2006). The behavior of tutoring systems.International Journal of Artificial Intelligence in Education, 16, 227--265."}],"event":{"name":"the Sixth International Conference","location":"Edinburgh, United Kingdom","acronym":"LAK '16","number":"6","start":{"date-parts":[[2016,4,25]]},"end":{"date-parts":[[2016,4,29]]}},"container-title":["Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK '16"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2883851.2883967","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=2883967&ftid=1708853&dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:54:08Z","timestamp":1750222448000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=2883851.2883967"}},"subtitle":[],"proceedings-subject":"Learning Analytics & Knowledge","short-title":[],"issued":{"date-parts":[[2016]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1145\/2883851.2883967","relation":{},"subject":[],"published":{"date-parts":[[2016]]}}}