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“I am Really Good at It” or “I am Just Feeling Lucky”: the effects of emotions on information problem-solving

Abstract

The ability to search, process, extract, evaluate and integrate information for learning purposes has clearly become the basic skills of the twenty first century. Although this process is often taken as a cognitive process, research has shown a strong connection between emotion and cognition. Recent research has suggested that positive emotions can influence the way cognitive material is organized and processed. This study examined the relationship between students’ emotional states prior to task engagement to their problem-solving patterns. Results revealed that students with positive emotions, compared to the negative and mixed emotion groups, were characterized as regulatory problem-solvers who were more engaged in self-regulatory activities. Students with negative emotions were characterized by less variety of search activities as well as little or no regulatory activities.

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References

  • Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. In P. S. Yu, & A. L. P. Chen (Eds.), In Proceedings of the 11th international conference on data engineering (pp. 3–14). Washington, DC: IEEE Computer Society.

  • Alias, S., Razali, M. N., Fun, T. S., & Sainin, M. S. (2011). Sequential pattern mining using personalized minimum support threshold with minimum items. In Proceedings of 2011 international conference on research and innovation in information systems (ICRIIS) (pp. 1–6). Kuala Lumpur, Malaysia.

  • Ambady, N., & Gray, H. M. (2002). On being sad and mistaken: Mood effects on the accuracy of thin-slice judgments. Journal of Personality and Social Psychology, 83, 947–961.

    Article  Google Scholar 

  • Arapakis, I., Jose, J. M., & Gray, P. D. (2008). Affective feedback: An investigation into the role of emotions in the information seeking process. In Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval (pp. 395–402). New York, NY: ACM.

  • Austin, M. P., Mitchell, P., & Goodwin, G. M. (2001). Cognitive deficits in depression: Possible implications for functional neuropathology. British Journal of Psychiatry, 178, 200–206.

    Article  Google Scholar 

  • Bilal, D., & Kirby, J. (2002). Differences and similarities in information seeking: Children and adults as web users. Information Processing and Management, 38, 649–670.

    Article  Google Scholar 

  • Branch, J. L. (2001). Junior high students and thinks alouds: Generating information-seeking process data using concurrent verbal protocols. Library and Information Science Research, 23, 107–122.

    Article  Google Scholar 

  • Brand-Gruwel, S., Wopereis, I., & Vermetten, Y. (2005). Information problem solving by experts and novices: Analysis of a complex cognitive skill. Computers in Human Behavior, 21, 487–508.

    Article  Google Scholar 

  • Brand-Gruwel, S., Wopereis, I., & Walraven, A. (2009). A descriptive model of information problem solving while using Internet. Computers and Education, 53, 1207–1217.

    Article  Google Scholar 

  • Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65, 245–281.

    Article  Google Scholar 

  • Choo, C. W., Detlor, B., & Turnbull, D. (1999). Information seeking on the web: An integrated model of browsing and searching. Paper presented at the annual meeting of the american society for information science (ASIS), Washington, D C.

  • De Houwer, J., & Hermans, D. (Eds.). (2001). Automatic affective processing. New York: Psychology Press.

    Google Scholar 

  • Ellis, D. (1989). A behavioural model for information retrieval system design. Journal of Information Science, 15, 237–247.

    Article  Google Scholar 

  • Ertmer, P. A., & Newby, T. J. (1996). The expert learner: Strategic, self-regulated, and reflective. Instructional Science, 24, 1–24.

    Article  Google Scholar 

  • Forgas, J. P. (1991). Affect and social judgments: An introductory review. In J. P. Forgas (Ed.), Emotion and social judgments (pp. 3–30). Tarrytown, NY: Pergamon.

    Google Scholar 

  • Forgas, J. P. (1995). Mood and judgment: The affect infusion model (AIM). Psychological Bulletin, 116, 39–66.

    Article  Google Scholar 

  • Fredrickson, B. L. (2004). The broaden-and-build theory of positive emotions. Philosophical Transactions of the Royal Society of London, 359, 1367–1378.

    Article  Google Scholar 

  • Goldin, G. A. (1998). Representations, learning, and problem solving in mathematics. Journal of Mathematical Behavior, 17, 137–165.

    Article  Google Scholar 

  • Goleman, D. (1995). Emotional intelligence. London: Bloomsbury.

    Google Scholar 

  • Hartlage, S., Alloy, L. B., Vazquez, C., & Dykman, B. (1993). Automatic and effortful processing in depression. Psychological Bulletin, 113, 247–278.

    Article  Google Scholar 

  • Isen, A. M., Daubman, K. A., & Nowicki, G. P. (1987). Positive affect facilitates creative problem solving. Journal of Personality and Social Psychology, 52, 1122–1131.

    Article  Google Scholar 

  • Jenkins, C., Corritore, C. L., & Wiedenbeck, S. (2003). Patterns of information seeking on the Web: A qualitative study of domain expertise and Web expertise. IT and Society, 1, 64–89.

    Google Scholar 

  • Kalbach, J. (2006). “I’m feeling lucky”: The role of emotions in seeking information on the web. Journal of the American Society for Information Science, 57(6), 813–818.

    Google Scholar 

  • Kort B., Reilly, R., & Picard R. (2001). An affective model of interplay between emotions and learning: Reengineering educational pedagogy—Building a learning companion. In T. Okamoto, R. Hartley, Kinshuk, & J. P. Klus (Eds.), Proceedings of the IEEE international conference on advanced learning technology: Issues, achievements and challenges (pp. 43–48). Madison, WI: IEEE Computer Society.

  • Kuhlthau, C. C. (1993). Seeking meaning: A process approach to library and information services. Norwood, NJ: Ablex.

    Google Scholar 

  • Land, S. M., & Greene, B. A. (2000). Project-based learning with the World Wide Web: A qualitative study of resource integration. Educational Technology Research and Development, 48, 45–68.

    Article  Google Scholar 

  • Lavie, T., & Tractinsky, N. (2004). Assessing dimensions of perceived visual aesthetics of web sites. International Journal of Human Computer Studies, 60, 269–298.

    Article  Google Scholar 

  • Lazarus, R. S. (1991). Emotion and adaptation. New York: Oxford University Press.

    Google Scholar 

  • Lazonder, A. W., Biemans, H. J. A., & Wopereis, I. G.-J. H. (2000). Differences between novice and experienced users in searching information on the World Wide Web. Journal of the American Society for Information Science, 51, 576–581.

    Article  Google Scholar 

  • Linnenbrink, E. A., & Pintrich, P. R. (2004). Role of affect in cognitive processing in academic contexts. In D. Y. Dai & R. J. Sternberg (Eds.), Motivation, emotion, and cognition: Integrative perspectives on intellectual functioning and development (pp. 57–87). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Mandler, G. (1984). Mind and body: Psychology of emotion and stress. New York: Norton.

    Google Scholar 

  • Marchionini, G. N. (1995). Information seeking in electronic environments. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Martinez, R., Yacef, K., Kay, J., Al-Qaraghuli, A., & Kharrufa, A. (2011). Analysing frequent sequential patterns of collaborative learning activity around an interactive tabletop. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, & J. Stamper (Eds.), Proceedings of the fourth international conference on educational data mining (pp. 111–120). Netherlands: Eindhoven.

    Google Scholar 

  • Matsumoto, D., & Sanders, M. (1988). Emotional experiences during engagement in intrinsically and extrinsically motivated tasks. Motivation and Emotion, 12, 353–369.

    Article  Google Scholar 

  • McLeod, D. B. (1988). Affective issues in mathematical problem solving: Some theoretical considerations. Journal for Research in Mathematics Education, 19, 134–141.

    Article  Google Scholar 

  • Morzy, T., Wojciechowski, M., & Zakrzewicz, M. (2000). Data mining support in database management systems. In Y. Kambayashi, M. K. Mohania, & A. M. Tjoa (Eds.), Proceedings of the 2nd DaWaK conference (pp. 382–392). London: Springer.

    Google Scholar 

  • Nahl, D. (1998). Ethnography of novices’ first use of Web search engines: Affective control in cognitive processing. Internet Reference Services Quarterly, 3, 51–72.

    Article  Google Scholar 

  • Nahl, D. (2004). Measuring the affective information environment of web searchers. Proceedings of the American Society for Information Science and Technology, 41, 191–197.

  • Nahl, D., & Meer, M. P. (1997). User-centered assessment of two web browsers: Errors, perceived self-efficacy, and success. In Proceedings of the ASIS annual meeting (Vol. 34, pp. 89–97). Silver Spring, MD: American Society for Information Science and Technology.

  • Nahl, D., & Tenopir, C. (1996). Affective and cognitive searching behavior of novice end-users of a full-text database. Journal of the American Society for Information Science, 47, 276–286.

    Article  Google Scholar 

  • Nesbit, J. C., Zhou, M., Xu, Y., & Winne, P. H. (2007, August). Advancing log analysis of student interactions with cognitive tools. Paper presented at the European Association for Research on Learning and Instruction (EARLI) 2007 Conference, Budapest, Hungary.

  • Nist, S. L., & Holschuh, J. (2000). Comprehension strategies at the college level. In R. Flippo & D. Caverly (Eds.), Handbook of college reading and study strategy research (pp. 75–104). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Pahl, C., & Donnellan, C. (2003). Data mining technology for the evaluation of web-based teaching and learning systems. In Proceedings of the congress e-learning (pp. 1–7). Montreal, Canada.

  • Pekrun, R. (1992). The impact of emotions on learning and achievement: Towards a theory of cognitive/motivational mediators. Applied Psychology, 41, 359–376.

    Article  Google Scholar 

  • Pekrun, R., Goetz, T., Titz, W., & Perry, R. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational psychologist, 37, 91–105.

    Article  Google Scholar 

  • Pekrun, R., & Linnenbrink-Garcia, L. (2012). Academic emotions and student engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 259–282). New York: Springer.

    Chapter  Google Scholar 

  • Pekrun, R., Molfenter, S., Titz, W., & Perry, R. P. (2000, April). Emotion, learning, and achievement in university students: Longitudinal studies. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

  • Peterson, S. E. (1992). The cognitive functions of underlining as a study technique. Reading Research and Instruction, 31, 49–56.

    Article  Google Scholar 

  • Pitkow, J., & Pirolli, P. (1999). Mining longest repeating subsequences to predict World Wide Web surfing. In Proceedings of USENIX Symposium (pp. 139–150), Boulder, CO.

  • Power, M. J., & Dalgleish, T. (1997). Cognition and emotion: From order to disorder. Hove: Psychology Press.

    Google Scholar 

  • Reschly, A. L., Huebner, E. S., Appleton, J. J., & Antaramian, S. (2008). Engagement as flourishing: The contribution of positive emotions and coping to adolescents’ engagement at school and with learning. Psychology in the Schools, 45, 419–431.

    Article  Google Scholar 

  • Romero, C., Ventura, S., Delgado, J. A., & Bra, P. D. (2007). Personalized links recommendation based on data mining in adaptive educational hypermedia systems. Lecture Notes in Computer Science, 4753, 292–306.

  • Schutz, P. A., & Davis, H. A. (2000). Emotions and self-regulation during test taking. Educational Psychologist, 35, 243–256.

    Article  Google Scholar 

  • Sideridis, G. D. (2005). Goal orientations, academic achievement, and depression: Evidence in favor of revised goal theory. Journal of Educational Psychology, 97, 366–375.

    Article  Google Scholar 

  • Sideridis, G. D., Mouzaki, A., Simos, P., & Protopapas, A. (2006). Classification of students with reading comprehension difficulties: The roles of motivation, affect, and psychopathology. Learning Disabilities Quarterly, 29, 159–180.

    Article  Google Scholar 

  • Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of Educational Psychology, 85, 571–581.

    Article  Google Scholar 

  • Smith, B., & Schutz, P. A. (1991). The future of teacher education: An ecocultural perspective. In J. J. Van Pattern (Ed.), Human energy shaping the future (pp. 49–58). Fayetteville, AR: University of Arkansas.

    Google Scholar 

  • Su, A. Y. S., Yang, S. J. H., Hwang, W.-Y., & Zhang, J. (2010). A Web 2.0-based collaborative annotation system for enhancing knowledge sharing in collaborative learning environments. Computers and Education, 55, 752–766.

    Article  Google Scholar 

  • Sutcliffe, A. G., Ennis, M., & Watkinson, S. J. (2000). Empirical studies of end-user information searching. Journal of the American Society for Information Science, 51, 1211–1231.

    Article  Google Scholar 

  • Tenopir, C., Wang, P., Zhang, Y., Simmons, B., & Pollard, R. (2008). Academic users’ interactions with ScienceDirect in search tasks: Affective and cognitive behaviors. Information Processing and Management, 44, 105–121.

    Article  Google Scholar 

  • Tharp, R. G., Estrada, P., Dalton, S. S., & Yamanchi, L. A. (2000). Teaching transformed: Achieving excellence, fairness, inclusion and harmony. Bolder, CO: Westview.

    Google Scholar 

  • Tiedens, L. Z., & Linton, S. (2001). Judgment under emotional certainty and uncertainty: The effects of specific emotions on information processing. Journal of Personality and Social Psychology, 81, 973–988.

    Article  Google Scholar 

  • Tu, Y., Shih, M., & Tsai, C. (2008). Eighth graders’ web searching strategies and outcomes: The role of task types, web experiences and epistemological beliefs. Computers and Education, 51, 1142–1153.

    Article  Google Scholar 

  • Veiel, H. O. F. (1997). A preliminary profile of neuropsychological deficits associated with major depression. Journal of Clinical and Experimental Neuropsychology, 19, 587–603.

    Article  Google Scholar 

  • Walraven, A., Brand-Gruwel, S., & Boshuizen, H. P. A. (2009). How students evaluate information and sources when searching the World Wide Web for information. Computers and Education, 52, 234–246.

    Article  Google Scholar 

  • Wang, P., Hawk, W. P., & Tenopir, C. (2000). Users’ interaction with World Wide Web resources: An exploratory study using a holistic approach. Information Processing and Management, 36, 229–251.

    Article  Google Scholar 

  • Weinreich, H., Obendorf, H., Herder, E., & Mayer, M. (2006). Off the beaten tracks: Exploring three aspects of web navigation. In Proceedings of the 15th international conference on World Wide Web (pp. 133–142). New York: ACM.

  • Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Zhou, M., & Winne, P. H. (2009). Designing multimedia to trace goal-setting in studying. In R. Zheng (Ed.), Cognitive effects of multimedia learning (pp. 288–311). Hershey, PA: IGI Global.

    Google Scholar 

  • Zhou, M., Xu, Y., Nesbit, J. C., & Winne, P. H. (2010). Sequential pattern analysis of learning logs: Methodology and applications. In C. Romero et al. (Eds.), Handbook of educational data mining (pp. 107–121). London: Chapman & Hall/CRC Press.

  • Zhou, M., Xu, Y., Su, Y., & Liu, L. (2011). A research tool for exploring student online search strategies and performance. In Proceedings of international conference on education and educational psychology (pp. 159). Istanbul, Turkey.

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Correspondence to Mingming Zhou.

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Zhou, M. “I am Really Good at It” or “I am Just Feeling Lucky”: the effects of emotions on information problem-solving. Education Tech Research Dev 61, 505–520 (2013). https://doi.org/10.1007/s11423-013-9300-y

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Keywords

  • Emotion
  • Information problem-solving
  • Trace data