Sequences of frustration and confusion, and learning

Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013

Volumn , Issue , 2013, Pages

  Liu, Zhongxiu ^a   Baker, Ryan S J D ^b   Pataranutaporn, Visit ^a   Ocumpaugh, Jaclyn ^a  

^a WORCESTER POLYTECHNIC INSTITUTE   (United States)

^b Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

Affect; Affect detection; Affect sequences; Affective persistence; Confusion; Discovery with models; Frustration; Learning outcomes

Indexed keywords

LEARNING SYSTEMS; STUDENTS;

AFFECT; AFFECT DETECTION; AFFECT SEQUENCES; AFFECTIVE PERSISTENCE; CONFUSION; FRUSTRATION; LEARNING OUTCOME;

DATA MINING;

EID: 85084012674     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Arroyo, I., Cooper, D., Burleson, W., Woolf, B. 2010. Bayesian Networks and Linear Regression Models of Students’ Goals, Moods, and Emotions. Handbook of educational data mining (Oct. 2010). Taylor and Francis Group, London, UK, 323.
2. th International Conference on Intelligent Tutoring System (Jhongli, Taiwan, June 26-30, 2006), 29-36.
3. Baker, R.S.J.d., D'Mello, S., Rodrigo, M., Graesser, A. 2010. Better to be frustrated than bored: The incidence and persistence of affect during interactions with three different computer-based learning environments. International Journal of Human-computer Studies, 68, 4 (Dec. 2010). Elsevier B.V., Oxford, UK, 223-241.
4. Baker, R.S.J.d., Gowda, S.M., Wixon, M., Kalka, J., Wagner, A.Z., Salvi, A., Aleven, V., Kusbit, G., Ocumpaugh, J., Rossi, L. 2012. Sensor-free automated detection of affect in a Cognitive Tutor for Algebra. Proceedings of the 5th International Conference on Educational Data Mining (Chania, Greece, June 19-21, 2012), 126-133.
5. Baker, R.S.J.d., Moore, G., Wagner, A., Kalka, J., Karabinos, M., Ashe, C., Yaron, D. 2011. The Dynamics Between Student Affect and Behavior Occuring Outside of Educational Software. Proceedings of the 4th bi-annual International Conference on Affective Computing and Intelligent Interaction. (Memphis, TN, Oct 9-16, 2011).
6. Benjamini, Y.,Hochberg, Y. 1995. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing Author(s). Journal of the Royal Statistical Society, Series B, 58, 1 (1995), London, UK, 289-300.
7. Craig, S., Graesser, A., Sullins, J., Gholson, B. 2004. Affect and Learning: an Exploratory Look into the Role of Affect in Learning with AutoTutor. Journal of Educational Media, 29, 3 (Oct. 2004). Taylor & Francis, London, UK, 241-250.
8. D’Mello, S.K., Graesser, A.C. 2011. The Half-Life of Cognitive-Affective States during Complex Learning. Cognition and Emotion, 25, 7 (2011). Taylor and Francis Group, London, UK, 1299-1308.
9. D’Mello, S.K.., Lehman, B., Pekrun, R., Graesser, A.T. In Press. Confusion Can Be Beneficial For Learning. Learning and Instruction. Elsevier B.V., Oxford, UK.
10. D’Mello, S.K., Person, N., Lehman, B.A. 2009. Antecedent-Consequent Relationships and Cyclical Patterns between Affective States and Problem Solving Outcomes. Proceedings of 14th International Conference on Artificial Intelligence in Education (Brighton, UK, July 6-10, 2009), 57-64.
11. Forbes-Riley, K., Litman D. 2009. Adapting to Student Uncertainty Improves Tutoring Dialogues. In Proceeding of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling (Brighton, UK, July 6-10, 2009), 33-40.
12. Gee, J.P. 2007. Good video games+ good learning: Collected essays on video games, learning, and literacy (Mar. 2007). Peter Lang Pub Incorporated, Bern, Switzerland.
13. Hone, K. 2006. Empathic Agents to Reduce User Frustration: The Effects of Varying Agent Characteristics. Interacting with Computers, 18, 2 (Mar. 2006). Elsevier B.V., Oxford, UK, 227-245.
14. Koedinger, K.R., Corbett, A.T. 2006. Cognitive Tutors: Technology bringing learning science to the classroom. The Cambridge Handbook of the Learning Sciences (Apr. 2006). Cambridge University Press, Cambridge, UK, 61-78.
15. Klein, J., Moon, Y., Picard, R. 2002. This computer responds to user frustration – Theory, design, and results. Interacting with Computers, 14, 2 (Feb. 2002). Elsevier B.V., Oxford, UK, 119-140.
16. Kort, B., Reilly, R., Picard R. 2001. An Affective Model of Interplay between Emotions and Learning: Reengineering Educational Pedagogy—Building a Learning Companion. Proceedings of the 1st IEEE International Conference on Advanced Learning Technology: Issues, Achievements and Challenges (Madison, WI, Aug 06-08, 2001), 43-48.
17. Lee, D.M., Rodrigo, M.M., Baker, R.S.J.d., Sugay, J., Coronel, A. 2011. Exploring the Relationship between Novice Programmer Confusion and Achievement. Proceedings of the 4th bi-annual International Conference on Affective Computing and Intelligent Interaction (Memphis, TN, Oct 9-12, 2011).
18. McQuiggan, S.W., Robison, J.L., Lester, J.C. 2010. Affective Transitions in Narrative-centered Learning Environments. Educational Technology & Society, 13, 1(Jan. 2010). International Forum of Educational Technology & Society, 40-53.
19. Ocumpaugh, J., Baker, R.S.J.d., Rodrigo, M.M.T. 2012. Baker-Rodrigo Observation Method Protocol (BROMP) 1.0. Training Manual version 1.0. Technical Report. New York, NY: EdLab. Manila, Philippines: Ateneo Laboratory for the Learning Sciences.
20. Perneger, T.V. 1998. What's Wrong with Bonferroni Adjustments. British Medical Journal, 316 (Apr. 1998). BMJ Publishing Group, London, UK, 1236-1238.
21. Rodrigo, M.M.T., Baker, R.S.J.d., Jadud, M.C., Amarra, A.C.M., Dy, T., Espejo-Lahoz, M.B.V., Lim, S.A.L., Pascua, S.A.M.S., Sugay, J.O., Tabanao, E.S. 2009. Affective and Behavioral Predictors of Novice Programmer Achievement. Proceedings of the 14th ACM-SIGCSE Annual Conference on Innovation and Technology in Computer Science Education (Paris, France, July 06-09, 2009), 156-160.
22. Rodrigo, M.M.T., Baker, R.S.J.d., Nabos, J.Q. 2010. The Relationships between Sequences of Affective States and Learner Achievements. Proceedings of the 18th International Conference on Computers in Education (Putrajaya, Malaysia, Nov 29 - Dec 3, 2010).
23. Sabourin, J., Rowe, J., Mott, B., Lester, J. 2011. When Off-Task is On-Task: the Affective Role of Off-Task Behavior in Narrative-Centered Learning Environments. Artificial Intelligence in Education, 21 (2011). Springer, Berlin/Heidelbery, Germany, 534-536.
24. Sao Pedro, M. A., Baker, R.S.J.d., Montalvo, O., Nakama, A., Gobert, J.D. 2010. Using Text Replay Tagging to Produce Detectors of Systematic Experimentation Behavior Patterns. Proceedings of the 3rd International Conference on Educational Data Mining, 181-190.