Teaching the Teacher: Tutoring SimStudent Leads to More Effective Cognitive Tutor Authoring

SimStudent is a machine-learning agent initially developed to help novice authors to create cognitive tutors without heavy programming. Integrated into an existing suite of software tools called Cognitive Tutor Authoring Tools (CTAT), SimStudent helps authors to create an expert model for a cognitiv...

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Vydané v:	International journal of artificial intelligence in education Ročník 25; číslo 1; s. 1 - 34
Hlavní autori:	Matsuda, Noboru, Cohen, William W., Koedinger, Kenneth R.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer New York 01.03.2015 Springer Springer Nature B.V
Predmet:	Accuracy Artificial Intelligence Authoring Cognition & reasoning Cognitive ability Computer Science Computer Simulation Computers and Education Context Educational Strategies Educational Technology Feedback Feedback (Response) Intelligent Tutoring Systems Knowledge Machine learning Negative feedback Pedagogy Problem solving Programming Research Article Skills Software Student Needs Subject specialists Task analysis Teaching Methods Thinking Skills Tutoring Tutors User interface User Interfaces and Human Computer Interaction Intelligent authoring system Programming by demonstration Inductive logic programming SimStudent Machine learning Cognitive tutor authoring tools
ISSN:	1560-4292, 1560-4306
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Abstract	SimStudent is a machine-learning agent initially developed to help novice authors to create cognitive tutors without heavy programming. Integrated into an existing suite of software tools called Cognitive Tutor Authoring Tools (CTAT), SimStudent helps authors to create an expert model for a cognitive tutor by tutoring SimStudent on how to solve problems. There are two different ways to author an expert model with SimStudent. In the context of Authoring by Tutoring, the author interactively tutors SimStudent by posing problems to SimStudent, providing feedback on the steps performed by SimStudent, and also demonstrating steps as a response to SimStudent’s hint requests when SimStudent cannot perform steps correctly. In the context of Authoring by Demonstration, the author demonstrates solution steps, and SimStudent attempts to induce underlying domain principles by generalizing those worked-out examples. We conducted evaluation studies to investigate which authoring strategy better facilitates authoring and found two key results. First, the expert model generated with Authoring by Tutoring is better and has higher accuracy while maintaining the same level of completeness than the one generated with Authoring by Demonstration. The reason for this better accuracy is that the expert model generated by tutoring benefits from negative feedback provided for SimStudent’s incorrect production applications. Second, authoring by Tutoring requires less time than Authoring by Demonstration. This enhanced authoring efficiency is partially because (a) when Authoring by Demonstration, the author needs to test the quality of the expert model, whereas the formative assessment of the expert model is done naturally by observing SimStudent’s performance when Authoring by Tutoring, and (b) the number of steps that need to be demonstrated during tutoring decreases as learning progresses.
AbstractList	SimStudent is a machine-learning agent initially developed to help novice authors to create cognitive tutors without heavy programming. Integrated into an existing suite of software tools called Cognitive Tutor Authoring Tools (CTAT), SimStudent helps authors to create an expert model for a cognitive tutor by tutoring SimStudent on how to solve problems. There are two different ways to author an expert model with SimStudent. In the context of Authoring by Tutoring, the author interactively tutors SimStudent by posing problems to SimStudent, providing feedback on the steps performed by SimStudent, and also demonstrating steps as a response to SimStudent’s hint requests when SimStudent cannot perform steps correctly. In the context of Authoring by Demonstration, the author demonstrates solution steps, and SimStudent attempts to induce underlying domain principles by generalizing those worked-out examples. We conducted evaluation studies to investigate which authoring strategy better facilitates authoring and found two key results. First, the expert model generated with Authoring by Tutoring is better and has higher accuracy while maintaining the same level of completeness than the one generated with Authoring by Demonstration. The reason for this better accuracy is that the expert model generated by tutoring benefits from negative feedback provided for SimStudent’s incorrect production applications. Second, authoring by Tutoring requires less time than Authoring by Demonstration. This enhanced authoring efficiency is partially because (a) when Authoring by Demonstration, the author needs to test the quality of the expert model, whereas the formative assessment of the expert model is done naturally by observing SimStudent’s performance when Authoring by Tutoring, and (b) the number of steps that need to be demonstrated during tutoring decreases as learning progresses.
Author	Matsuda, Noboru Koedinger, Kenneth R. Cohen, William W.
Author_xml	– sequence: 1 givenname: Noboru surname: Matsuda fullname: Matsuda, Noboru email: nmazda@gmail.com organization: Human-Computer Interaction Institute, Carnegie Mellon University – sequence: 2 givenname: William W. surname: Cohen fullname: Cohen, William W. organization: Machine Learning Department, Carnegie Mellon University – sequence: 3 givenname: Kenneth R. surname: Koedinger fullname: Koedinger, Kenneth R. organization: Human-Computer Interaction Institute, Carnegie Mellon University
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Cites_doi	10.1207/s15327809jls0701_2 10.1109/MIS.2007.74 10.1207/s15327809jls1302_1 10.1016/0743-1066(94)90035-3 10.1016/S0004-3702(99)00067-3 10.1016/S0883-0355(97)81235-9 10.1007/978-3-540-30139-4_51 10.1207/s15327809jls0402_2 10.1007/978-94-017-0819-7_17 10.3758/BF03194060 10.1037/0022-0663.93.3.579 10.1037/0022-0663.95.4.774 10.1207/s15327809jls0603_1 10.1016/B978-0-444-70536-5.50012-9 10.1007/11774303_7 10.1007/s10648-007-9049-0 10.1007/978-3-540-69132-7_16 10.1023/A:1026505626690 10.1007/978-3-540-30139-4_16 10.21236/ADA280011
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SubjectTerms	Accuracy Artificial Intelligence Authoring Cognition & reasoning Cognitive ability Computer Science Computer Simulation Computers and Education Context Educational Strategies Educational Technology Feedback Feedback (Response) Intelligent Tutoring Systems Knowledge Machine learning Negative feedback Pedagogy Problem solving Programming Research Article Skills Software Student Needs Subject specialists Task analysis Teaching Methods Thinking Skills Tutoring Tutors User interface User Interfaces and Human Computer Interaction
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Title	Teaching the Teacher: Tutoring SimStudent Leads to More Effective Cognitive Tutor Authoring
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