Supporting students' self-regulated learning with an open learner model in a linear equation tutor

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7926 LNAI, Issue , 2013, Pages 219-228

  Long, Yanjin ^a   Aleven, Vincent ^a  

^a Carnegie Mellon University   (United States)

Author keywords

Classroom evaluation; Intelligent tutoring system; Open learner model; Self assessment; Self regulated learning; Study choice

Indexed keywords

COMPUTER AIDED INSTRUCTION; EDUCATION COMPUTING; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; LINEAR EQUATIONS; PROBLEM SOLVING; STUDENTS;

CLASSROOM EVALUATION; INTELLIGENT TUTORING SYSTEM; OPEN LEARNER MODELING; SELF ASSESSMENT; SELF-REGULATED LEARNING; STUDY CHOICE;

LEARNING SYSTEMS;

EID: 84880018673     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-39112-5_23     Document Type: Conference Paper

References (13)

1. Aleven, V., et al.: A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors. International Journal of Artificial Intelligence in Education 19, 105-154 (2009)
2. Brusilovsky, P., Sosnovsky, S., Shcherbinina, O.: QuizGuide: Increasing the Educational Value of Individualized Self-Assessment Quizzes with Adaptive Navigation Support. In: Nall, J., Robson, R. (eds.) Proceedings of World Conference on E-Learning, E-Learn 2004, pp. 1806-1813. AACE (2004)
3. Bull, S., Dimitrova, V., McCalla, G.: Open Learner Models: Research Questions (Special Issue of IJAIED Part 1). International Journal of Artificial Intelligence in Education 17(2), 83-87 (2007)
4. Bull, S., Jackson, T., Lancaster, M.: Students' Interest in Their Misconceptions in First Year Electrical Circuits and Mathematics Courses. International Journal of Electrical Engineering Education 47(3), 307-318 (2010)
5. Dunlosky, J., Lipko, A.: Metacomprehension: A Brief History and How to Improve Its Accuracy. Current Directions in Psychological Science 16, 228-232 (2007)
6. Hartley, D., Mitrovic, A.: Supporting Learning by Opening the Student Model. In: Cerri, S.A., Gouardères, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 453-462. Springer, Heidelberg (2002)
7. Koedinger, K.R., Baker, R.S.J.d., Cunningham, K., Skogsholm, A., Leber, B., Stamper, J.: 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. CRC Press, Boca Raton (2010)
8. Long, Y., Aleven, V.: Students' Understanding of Their Student Model. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds.) AIED 2011. LNCS, vol. 6738, pp. 179-186. Springer, Heidelberg (2011)
9. Mitrovic, A., Martin, B.: Scaffolding and Fading Problem Selection in SQL-Tutor. In: Hoppe, U., Verdejo, F., Kay, J. (eds.) Proceedings of the 11th International Conference on Artificial Intelligence in Education, pp. 479-481. Springer, Berlin (2003)
10. Schraw, G.: A Conceptual Analysis of Five Measures of Metacognitive Monitoring. Metacognition and Learning 4(1), 33-45 (2009)
11. Waalkens, M., Aleven, V., Taatgen, N.: Does Supporting Multiple Student Strategies Lead to Greater Learning and Motivation? Investigating a Source of Complexity in the Architecture of Intelligent Tutoring Systems. Computers & Education 60, 159-171 (2013)
12. White, B.C., Fredrickson, J.: Inquiry, Modeling, and Metacognition: Making Science Accessible to All Students. Cognition and Instruction 16, 39-66 (1998)
13. Zimmerman, B.J.: Investigating Self-Regulation and Motivation: Historical Background, Methodological Developments, and Future Prospects. American Educational Research Journal 45(1), 166-183 (2008)