Coarse-grained detection of student frustration in an introductory programming course

ICER'09 - Proceedings of the 2009 ACM Workshop on International Computing Education Research

Volumn , Issue , 2009, Pages 75-79

  Rodrigo, Ma Mercedes T ^a   Baker, Ryan Shaun J D ^b  

^a ATENEO DE MANILA UNIVERSITY   (Philippines)

^b Carnegie Mellon University ^*  (United States)

Author keywords

Affect; Frustration detection; Novice programmers

Indexed keywords

COARSE-GRAINED; GRAINSIZE; INTRODUCTORY PROGRAMMING; INTRODUCTORY PROGRAMMING COURSE; LEARNING ENVIRONMENTS; NOVICE PROGRAMMER; STUDENT BEHAVIOR;

COMPUTER SCIENCE; TEACHING;

STUDENTS;

EID: 70449730111     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1584322.1584332     Document Type: Conference Paper

References (31)

1. Baker, R. S., Corbett, A. T., Koedinger, K. R., K.R., and Wagner, A. Z. 2004. Off-task behavior in the Cognitive Tutor classroom: When students "Game The System". ACM CHI 2004: Computer-Human Interaction, 383-390, 2004.
2. Barker, L. J., Garvin-Doxas, K. and Roberts E. 2005. What can computer science learn from a fine arts approach to teaching?. In Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education (St. Louis, Missouri, USA, February 23-27, 2005). SIGCSE '05. ACM, New York, NY, 421-425.
3. Barnes, T., Richter, H., Powell, E., Chaffin, A. and Godwin, A.. 2007. Game2Learn: building CS1 learning games for retention. In Proceedings of the 12th Annual SIGCSE Conference on innovation and Technology in Computer Science Education (Dundee, Scotland, June 25-27, 2007). ITiCSE '07. ACM, New York, NY, 121-125.
4. Bergin, S. and Reilly, R. 2005. Programming: factors that influence success. In Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education (St. Louis, Missouri, USA, February 23-27, 2005). SIGCSE '05. ACM, New York, NY, 411-415.
5. Blum, A. and Mitchell, T.. Combining labeled and unlabeled data with co-training.1998. In Proceedings of the 11th Annual Conference on Computational Learning Theory (COLT '98), 92-100.
6. Cohen, J. 1960. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20, 37-46.
7. Corbett, A. T. and Anderson, J. R. 1992. The LISP intelligent tutoring system: Research in skill acquisition. In J. Larkin, R. Chabay, and C. Scheftic (Eds.), Computer Assisted Instruction and Intelligent Tutoring Systems: Establishing Communication and Collaboration. Hillsdale, NJ: Erlbaum.
8. Csikszentmihalyi, M.. 1990. Flow: The Psychology of Optimal Experience. New York: Harper and Row.
9. D'Mello, S. K., Craig, S. D. , Witherspoon, A., McDaniel, B. and Graesser, A. 2008. Automatic detection of learner's affect from conversational cues. User Modeling and User-Adapted Interaction, 18, 45-80.
10. Dahlberg, T., Barnes, T., Rorrer, A., Powell, E. and Cairco, L. 2008. Improving retention and graduate recruitment through immersive research experiences for undergraduates. In Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education (Portland, OR, USA, March 12-15, 2008). SIGCSE '08. ACM, New York, NY, 466-470.
11. Dragon, T., Arroyo, I., Woolf, B. P., Burleson, W., Kaliouby, R. e., and Eydgahi, H. 2008. Viewing student affect and learning through classroom observation and physical sensors. In B. P. Woolf, E. Aimeur, R. Nkambou and S. P. Lajoie (Eds), Intelligent Tutoring Systems 2008.
12. Dweck, C.. 2000. Self-theories: Their Role in Motivation, Personality and Development. Philadelphia, PA: Psychology Press.
13. Guo. J. 2008. Using group-based projects to improve retention of students in computer science major. Journal of Computing in Small Colleges, 23(6), 187-193, 2008
14. Kapoor, A., Burleson, W., and Picard, R. W. 2007. Automatic prediction of frustration. International Journal of Human-Computer Studies, 65, 724-736.
15. Khan, I. A., Hierons, R. M., and Brinkman, W. P. 2007. Mood independent programming. In Proceedings of the 14th European Conference on Cognitive Ergonomics: invent! Explore! (London, United Kingdom, August 28-31, 2007). ECCE '07, vol. 250. ACM, New York, NY, 269-272.
16. Klein, J., Moon, Y. and Picard, R.W. 2002. This computer responds to user frustration: Theory, design, results, and implications, Interacting with Computers, 14, 119-140.
17. Kolling, M. and Rosenberg, J. 2004. BlueJ v. 2.1.3. Computer Software.
18. Jadud, M. C. 2006. An Exploration of Novice Compilation Behavior in BlueJ. Doctoral thesis. University of Kent, 2006.
19. Lane, H. C. and VanLehn, K. 2005. Intention-based scoring: an approach to measuring success at solving the composition problem. In Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education (St. Louis, Missouri, USA, February 23-27, 2005). SIGCSE '05. ACM, New York, NY, 373-377.
20. McKinney, D. and Denton, L. F. 2004. Houston, we have a problem: there's a leak in the CS1 affective oxygen tank. In Proceedings of the 35th SIGCSE Technical Symposium on Computer Science Education (Norfolk, Virginia, USA, March 03-07, 2004). SIGCSE '04. ACM, New York, NY, 236-239.
21. McQuiggan, S. W., Lee, S., and Lester, J. C. 2007. Early prediction of student frustration. In A. Paiva, R. Prada, and R. W. Picard (Eds.), Affective Computing and Intelligent Interaction, 698-709.
22. Ng Cheong Vee, M.-H., Meyer, B., and Mannock, K. L. 2006. Understanding novice errors and error paths in Object-oriented programming through log analysis. Proceedings of the Workshop on Educational Data Mining at Intelligent Tutoring Systems 2006, 13-20.
23. Perkins, D. N., Hancock, C., Hobbs, R., Martin, F. and Simmons, R. 1985. Conditions of Learning in Novice Programmers. Concept Paper. Educational Technology Center, Harvard Graduate School of Education..
24. Raftery, A. E.. Bayesian model selection in social research. Sociological Methodology, 25, 111-163, 2003.
25. Rodrigo, M. M. T., Baker, R. S. J. d., Lagud, M. C. V., Lim, S. A. L., Macapanpan, A. F., Pascua, S. A. M. S., Santillano, J. Q., Sevilla, L. R. S., Sugay, J. O., Tep, S. and Viehland, N. J. B. 2007. Affect and usage choices in simulation problem-solving environments. In R. Luckin, K. R. Koedinger, J. Greer (Eds.), 13th International Conference on Artificial Intelligence in Education, 145-152.
26. Stephenson, P., Peckham, J. , Hervé, J., Hutt, R. and Encarnação, L. M. 2006. Increasing student retention in computer science through research programs for undergraduates. In ACM SIGGRAPH 2006 Educators Program (Boston, Massachusetts, July 30 - August 03, 2006). SIGGRAPH '06. ACM, New York, NY, 10.
27. Tabanao, E., Rodrigo, M. M. T. and Jadud, M. 2008. Identifying at-risk novice programmers through the analysis of online protocols. Philippine Computing Society Congress 2008, (UP Diliman, Quezon City, February 23-24, 2008).
28. Vegso, J. 2008. Enrollments and degree production at US CS departments drop further in 2006/2007. CRA Bulletin. Accessed 3 December 2008 from the CRA Bulletin web site: http://www.cra.org/wp/index.php?p=139,.
29. Whittington, K. J. and Bills, D. P. 2004. Alternative pacing in an introductory java sequence. In Proceedings of the 5th Conference on information Technology Education (Salt Lake City, UT, USA, October 28-30, 2004). CITC5 '04. ACM, New York, NY, 118-121.
30. Williams, L. and Upchurch, R. L. 2001. In support of student pair-programming. ACM SIGCSE Bulletin, 33(1), 327-331, 2001.
31. Witten, I. H. and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition, Morgan Kaufmann: San Francisco, 2005.