{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:30:47Z","timestamp":1787027447213,"version":"3.56.0"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Robot. AI"],"abstract":"<jats:p>Creating an accurate model of a user\u2019s skills is an essential task for Intelligent Tutoring Systems (ITS) and robotic tutoring systems. This allows the system to provide personalized help based on the user\u2019s knowledge state. Most user skill modeling systems have focused on simpler tasks such as arithmetic or multiple-choice questions, where the user\u2019s model is only updated upon task completion. These tasks have a single correct answer and they generate an unambiguous observation of the user\u2019s answer. This is not the case for more complex tasks such as programming or engineering tasks, where the user completing the task creates a succession of noisy user observations as they work on different parts of the task. We create an algorithm called Time-Dependant Bayesian Knowledge Tracing (TD-BKT) that tracks users\u2019 skills throughout these more complex tasks. We show in simulation that it has a more accurate model of the user\u2019s skills and, therefore, can select better teaching actions than previous algorithms. Lastly, we show that a robot can use TD-BKT to model a user and teach electronic circuit tasks to participants during a user study. Our results show that participants significantly improved their skills when modeled using TD-BKT.<\/jats:p>","DOI":"10.3389\/frobt.2023.1249241","type":"journal-article","created":{"date-parts":[[2024,2,25]],"date-time":"2024-02-25T23:14:55Z","timestamp":1708902895000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Time-dependant Bayesian knowledge tracing\u2014Robots that model user skills over time"],"prefix":"10.3389","volume":"10","author":[{"given":"Nicole","family":"Salomons","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Scassellati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.3923\/itj.2008.1055.1060","article-title":"Developing an intelligent tutoring system for students learning to program in c++","volume-title":"Information Technology Journal","author":"Abu-Naser","year":"2008"},{"key":"B2","volume-title":"Design and development of an intelligent tutoring system for c# language","author":"Al-Bastami","year":"2017"},{"key":"B3","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1126\/science.228.4698.456","article-title":"Intelligent tutoring systems","volume":"228","author":"Anderson","year":"1985","journal-title":"Science"},{"key":"B4","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1207\/s15327809jls0402_2","article-title":"Cognitive tutors: lessons learned","volume":"4","author":"Anderson","year":"1995","journal-title":"J. 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