Model-driven assessment of learners in open-ended learning environments

ACM International Conference Proceeding Series

Volumn , Issue , 2013, Pages 200-204

  Segedy, James R ^a   Loretz, Kirk M ^a   Biswas, Gautam ^a  

^a VANDERBILT UNIVERSITY   (United States)

Author keywords

adaptive scaffolding; model driven assessment; open ended learning environment; performance metrics

Indexed keywords

ADAPTIVE SCAFFOLDING; ANALYSIS TECHNIQUES; INITIAL DEVELOPMENT; LEARNING ENVIRONMENTS; METACOGNITIVE PROCESS; MIDDLE SCHOOL STUDENTS; MODEL-DRIVEN; PERFORMANCE METRICS;

SCAFFOLDS; STUDENTS;

COMPUTER AIDED INSTRUCTION;

EID: 84876464500     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2460296.2460336     Document Type: Conference Paper

References (15)

1. R. Azevedo and A. Hadwin. Scaffolding self-regulated learning and metacognition - implications for the design of computer-based scaffolds. Instructional Science, 33(5-6):367-379, 2005. (Pubitemid 41455843)
2. R. Azevedo and M. Jacobson. Advances in scaffolding learning with hypertext and hypermedia: A summary and critical analysis. Educational Technology Research and Development, 56(1):93-100, 200
3. R. Azevedo, A. Johnson, A. Chauncey, and C. Burkett. Self-regulated learning with metatutor: Advancing the science of learning with metacognitive tools. In M. Khine and I. Saleh, editors, New Science of Learning, pages 225-247, 2010.
4. C. Bravo, W. van Joolingen, and T. de Jong. Modeling and simulation in inquiry learning: Checking solutions and giving intelligent advice. Simulation, 82(11):769-784, 2006. (Pubitemid 46321698)
5. G. Clarebout and J. Elen. Advice on tool use in open learning environments. Journal of Educational Multimedia and Hypermedia, 17(1):81-97, 2008.
6. J. S. Kinnebrew, K. M. Loretz, and G. Biswas. A contextualized, differential sequence mining method to derive students' learning behavior patterns. Journal of Educational Data Mining, in press.
7. K. Koedinger and A. Corbett. Cognitive tutors: Technology bringing learning science to the classroom. In K. Sawyer, editor, The Cambridge Handbook of the Learning Sciences, pages 61-78. Cambridge University Press, 2006.
8. S. Lajoie and R. Azevedo. Teaching and learning in technology-rich environments. In P. Alexander and P. Winne, editors, Handbook of Educational Psychology, pages 803-821. Erlbaum, Mahwah, NJ, second edition, 2006.
9. S. Land. Cognitive requirements for learning with open-ended learning environments. Educational Technology Research and Development, 48(3):61-78, 2000. (Pubitemid 33238096)
10. K. Leelawong and G. Biswas. Designing learning by teaching agents: The betty's brain system. International Journal of Artificial Intelligence in Education, 18(3):181-208, 2008.
11. R. E. Mayer. Should there be a three-strikes rule against pure discovery learning? American Psychologist, 59(1):14-19, 2004.
12. S. McQuiggan, J. Rowe, and J. Lester. Story-based learning: The impact of narrative on learning experiences and outcomes. In B. Woolf, E. Aimeur, R. Nkambou, and S. Lajoie, editors, Lecture Notes in Computer Science, volume 5091, pages 530-539, 2008.
13. R. Moreno and R. Mayer. Interactive multimodal learning environments. Educational Psychology Review, 19(3):309-326, 2007.
14. J. R. Segedy, J. S. Kinnebrew, and G. Biswas. Modeling learner's cognitive and metacognitive strategies in an open-ended learning environment. In Advances in cognitive systems: Papers from the AAAI Fall Symposium, pages 297-304. AAAI Press, 2011.
15. J. R. Segedy, J. S. Kinnebrew, and G. Biswas. The effect of contextualized conversational feedback in a complex open-ended learning environment. Educational Technology Research and Development, 61(1):71-89, 2013.