Adapting handwriting recognition for applications in algebra learning

Proceedings of the ACM International Multimedia Conference and Exhibition

Volumn , Issue , 2007, Pages 47-56

  Anthony, Lisa ^a   Yang, Jie ^a   Koedinger, Kenneth R ^a  

^a Carnegie Mellon University ^*  (United States)

Author keywords

Handwriting input; Handwriting recognition; Mathematics learning; Recognition accuracy evaluation

Indexed keywords

HANDWRITING INPUT; HANDWRITING RECOGNITION; MATHEMATICS LEARNING; RECOGNITION ACCURACY EVALUATION;

INTELLIGENT SYSTEMS; PATTERN RECOGNITION; STUDENTS; TEXT PROCESSING;

ALGEBRA;

EID: 38049024452     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1290144.1290153     Document Type: Conference Paper

References (39)

1. Anderson, J.R., Corbett, AT., Koedinger, K.R., and Pelletier, R. (1995) Cognitive Tutors: Lessons Learned. Journal of the Learning Sciences, 4, 167-207.
2. Anderson, R.H. (1968) Syntax-directed recognition of hand-printed two-dimensional mathematics. In Interactive Systems for Experimental Applied Mathematics (eds. Klerer, M. & Reinfelds, J.), Academic Press, NY.
3. Anthony, L., Yang, J., and Koedinger, K. R. (2005) Evaluation of Multimodal Input for Entering Mathematical Equations on the Computer. In Proceedings of the SIGCHI Conf. on Human Factors in Computing Systems (CHI'05), 1184-1187.
4. Anthony, L., Yang, J., and Koedinger, K. R. (2006) Towards the Application of a Handwriting Interface for Mathematics Learning. In Proceedings of the IEEE Conf. on Multimedia and Expo (ICME'06), 2077-2080.
5. Anthony, L., Yang, J., and Koedinger, K. R. (2007) Benefits of Handwritten Input for Students Learning Algebra Equation Solving. In Proceedings of the Int'l Conf. on Artificial Intelligence in Education (AIEd'07), to appear.
6. Anthony, L., Yang, J., and Koedinger, K. R. Data-Driven Methodology for Handwriting Recognition in Mathematics. Under review.
7. Bloom, B.S. (1984) The Two Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring. Educational Researcher, Vol. 13, Nos. 4-6.
8. Blum, A. and Mitchell, T. (1998). Combining Labeled and Unlabeled Data with Co-Training. Proceedings of the Workshop on Computational Learning Theory (COLT'98), 92-100.
9. Chan, K.-F. and Yeung, D.-Y. (2000) Mathematical Expression Recognition: A Survey. International Journal of Document Analysis & Recognition 3, 1, 375-384.
10. Corbett, A.T. and Trask, H. (2000) Instructional Interventions in Computer-based Tutoring: Differential Impact on Learning Time and Accuracy. In Proceedings of the SIGCHI Conf. on Human Factors in Computing Systems (CHI'00), 97-104.
11. Corbett, A.T. (2001) Cognitive Computer Tutors: Solving the Two-Sigma Problem. Proceedings of User Modeling, 137-147.
12. Corbett, AT., Koedinger, K.R., Hadley, W.H. (2001) Cognitive Tutors: From the Research Classroom to All Classrooms. In: P. Goodman (ed.): Technology Enhanced Learning: Opportunities for Change. L. Erlbaum, Mahwah New Jersey, 235-263.
13. Frankish, C., Hull, R., and Morgan, P. (1995) Recognition Accuracy and User Acceptance of Pen Interfaces. In Proceedings of the SIGCHI Conf. on Human Factors in Computing Systems (CHI'95), 503-510.
14. Goldberg, D. and Goodisman, A. (1991) Stylus User Interfaces for Manipulating Text. Proceedings of UIST 1991, 127-135.
15. Graesser, A., Moreno, K.N., Marineau, J.C, Adcock, A.B., Olney, A.M. and Person, N.K. (2003) AutoTutor Improves Deep Learning of Computer Literacy: Is it the Dialog or the Talking Head? Proceedings of the Int'l Conference on Artificial Intelligence in Education (AIEd 2003), 47-54
16. Hausmann, R.G.M. and Chi, M.T.H. (2002) Can a Computer Interface Support Self-explaining? Cognitive Technology 7, 1, 4-14.
17. Jackson, Lorrie. (2004) Laptops, Handhelds, or Tablet PCs? Education World Technology Article: http://www.education-world.com/a_tech/tech/ tech198.shtml
18. Jumping Minds. (2003) Practice Series. http://www.jumpingminds.com.
19. Kanahori, T., Tabata, K., Cong, W., Tamari, F., and. Suzuki, M. (2000) On-Line Recognition of Mathematical Expressions Using Automatic Rewriting Method. Proc. ICMI 2000, 394-401.
20. LaLomia, M.J. (1994) User acceptance of handwritten recognition accuracy. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI'94), 107.
21. LaViola, J.J. (2006) An Initial Evaluation of a Pen-Based Tool for Creating Dynamic Mathematical Illustrations. Proc. Eurographics Workshop on Sketch-Based Interfaces and Modeling, EG Workshop Series, 157-164.
22. Liwicki, M. and Bunke, H. (2005) Enhancing Training Data for Handwriting Recognition of Whiteboard Notes with Samples from a Different Database. Proceedings of the Int'l Conference on Document Analysis and Recognition (ICDAR '05), 550-554.
23. MacKenzie, I.S., and Chang, L. (1999) A Performance Comparison of Two Handwriting Recognizers. Interacting with Computers 11, 283-297.
24. th International Workshop on Frontiers in Handwriting Recognition, CENPARMI.
25. Matsakis, N.E. (1999) Recognition of Handwritten Mathematical Expressions. Master's thesis, Massachusetts Institute of Technology, Cambridge, MA.
26. Mayer, R. E. (2001) Multimedia Learning. New York: Cambridge University Press, Boston, MA.
27. Oviatt, S. (2000) Taming Recognition Errors with a Multimodal Interface. Communications of the ACM, 45-51.
28. Oviatt, S., Arthur, A. and Cohen, J. (2006) Quiet Interfaces that Help Students Think. In Proeedings of UIST 2006, 191-200.
29. Paas, F. and Van Merrienboer, J. (1994) Variability of worked examples and transfer of geometry problem-solving skills: A cognitive-load approach. Journal of Educational Psychology, 86, 122-133.
30. PISA (2005). International Outcomes of Learning in Mathematics Literacy and Problem Solving. http://nces.ed.gov/pubs2005/2005003.pdf
31. Ringenberg, M.A. and VanLehn. K. (2006) Scaffolding Problem Solving with Annotated, Worked-Out Examples to Promote Deep Learning. Proceedings of the International Conference on Intelligent Tutoring Systems (ITS 2006), 625-634.
32. Santos, P.J., Baltzer, A.J., Badre, A.N., Henneman, R.L., and Miller, M.S. (1992) On handwriting recognition performance: Some experimental results. Proceedings of the Human Factors Society 36th Annual Meeting, 283-287.
33. Smithies, S., Novins, K. and Arvo, J. (2001) Equation Entry and Editing via Handwriting and Gesture Recognition. Behaviour and Information Technology, 20, 53-67.
34. Sweller, J. (1988) Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science 12, 2, 257-285.
35. Tapia, E. and Rojas. R. (2004) Recognition of On-Line Handwritten Mathematical Expressions using a Minimum Spanning Tree Construction and Symbol Dominance. In Graphics Recognition: Recent Advances & Perspectives (eds. Liados, J. & Kwon, Y.-B.), Lecture Notes in Computer Science 3088, 329-340.
36. VanLehn, K., Lynch, C., Taylor, L., Weinstein, A., Shelby, R., Schulze, K., Treacy, D. and Wintersgill, M. (2002) Minimally Invasive Tutoring of Complex Physics Problem Solving. Proceedings of the Intelligent Tutoring Systems Conference, 367-376.
37. Wood, C. (2002) Technology and Education. PC Magazine: http://www.pcmag.com/article2/0,4149,15154,00.asp.
38. xThink MathJournal. http://www.xthink.com/MathJournal.html
39. Zanibbi, R., Blostein, D., and Cordy, J.R. (2002) Recognizing Mathematical Expressions Using Tree Transformation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 11, 1455-1467.