The role of expressed emotions in online discussions

References

• Argaman, O., & Abu-Rabia, S. (2002). The influence of language anxiety on English reading and writing tasks among native Hebrew speakers. Language Culture and Curriculum, 15(2), 143–160. doi:10.1080/07908310208666640
   Google Scholar
• Artino, A. R. (2005). Review of the motivated strategies for learning questionnaire. Online Submission. Retrieved from http://www.eric.ed.gov/ERICWebPortal/detail?accno=ED499083
   Google Scholar
• Artino, A. R., Jr., & Jones, K. D. II, (2012). Exploring the complex relations between achievement emotions and self-regulated learning behaviors in online learning. The Internet and Higher Education, 15(3), 170–175. doi:10.1016/j.iheduc.2012.01.006
   Web of Science ®Google Scholar
• Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.
   Google Scholar
• Biondi, G., Franzoni, V., & Poggioni, V. (2017). A deep learning semantic approach to emotion recognition using the IBM Watson Bluemix Alchemy Language. In Proceedings of International Conference on Computational Science and Its Applications (pp. 718––729). Trieste, Italy. doi:10.1007/978-3-319-62398-6_51
   Google Scholar
• Blessing, R. H. (1997). Outlier treatment in data merging. Journal of Applied Crystallography, 30(4), 421–426. doi:10.1107/S0021889896014628
   Google Scholar
• Brett, A., Smith, M., Price, E., & Huitt, W. (2003). Overview of the affective domain. Educational Psychology Interactive, 1–21.
   Google Scholar
• Cagnol, B. (2017). Detecting and adapting conversational agent strategy to user’s emotions in video games. Edinburgh: Heriot-Watt University.
   Google Scholar
• Cela, K. L., Sicilia, M. Á., & Sánchez, S. (2015). Social network analysis in e-learning environments: A Preliminary systematic review. Educational Psychology Review, 27(1), 219–246. doi:10.1007/s10648-014-9276-0
   Web of Science ®Google Scholar
• Chauncey, A., & Azevedo, R. (2010). Emotions and motivation on performance during multimedia learning: How do I feel and why do I care?. In V. Aleven, J. Kay, & J. Mostow (Eds.), Intelligent tutoring systems (Vol. 6094, pp. 369–378). Berlin/Heidelberg, Germany: Springer.
   Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, New Jersey: Lawrence Erlbaum Associates.
   Google Scholar
• Cook, R. D., & Weisberg, S. (2009). Applied regression including computing and graphics (Vol. 488). New York, NY: John Wiley & Sons. INC.
   Google Scholar
• D’Mello, S., Kappas, A., & Gratch, J. (2018). The affective computing approach to affect measurement. Emotion Review, 10(2), 174–183. doi:10.1177/1754073917696583
   Web of Science ®Google Scholar
• Dado, M., Hecking, T., Bodemer, D., & Hoppe, H. U. (2017). On the adoption of social network analysis methods in CSCL research–a network analysis. Philadelphia. Presented at The International Conference of the Learning Sciences (ICLS) 2016, PA: International Society of the Learning Sciences. doi:10.22318/cscl2017.40
   Google Scholar
• Dillenbourg, P., Järvelä, S., & Fischer, F. (2009). The evolution of research on computer-supported collaborative learning. In N. Balacheff, S. Ludvigsen, T. Jong, A. Lazonder, & S. Barnes (Eds.), Technology-enhanced learning (pp. 3–19). Netherlands: Springer.
   Google Scholar
• Eccles, J. S., O’Neill, S. A., & Wigfield, A. (2005). Ability self-perceptions and subjective task values in adolescents and children. In K. A. Moore, & L. Lippman (Eds.), What do children need to flourish: Conceptualizing and measuring indicators of positive development (pp. 237–249). New York: Springer.
   Google Scholar
• Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. doi:10.3758/BRM.41.4.1149
   PubMed Web of Science ®Google Scholar
• Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2013). G* Power Version 3.1.7 [computer software]. Universität Kiel, Germany.
   Google Scholar
• Festinger, L. (1962). Cognitive dissonance. Scientific American, 207(4), 93–102. doi:10.1038/scientificamerican1062-93
   PubMed Web of Science ®Google Scholar
• Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. California: Sage Publications.
   Google Scholar
• Fogel, A., Nwokah, E., Dedo, J. Y., Messinger, D., Dickson, K. L., Matusov, E., & Holt, S. A. (1992). Social process theory of emotion: A dynamic systems approach. Social Development, 1(2), 122–142. doi:10.1111/j.1467-9507.1992.tb00116.x
   Google Scholar
• Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. doi:10.3102/00346543074001059
   Web of Science ®Google Scholar
• Garcia-Garcia, J. M., Penichet, V. M., & Lozano, M. D. (2017). Emotion detection: A technology review. In Proceedings of the XVIII International Conference on Human Computer Interaction (p. 8:1-8:8). New York, NY.
   Google Scholar
• George, J. (2017, December 6). Natural language processing with IBM Watson and Stanford NLP. Retrieved May, 23 2019, from https://zerone-consulting.blog/2017/12/06/natural-language-processing-with-ibm-watson-and-stanford-nlp/
   Google Scholar
• Götz, T., Cronjäger, H., Frenzel, A. C., Lüdtke, O., & Hall, N. C. (2010). Academic self-concept and emotion relations: Domain specificity and age effects. Contemporary Educational Psychology, 35(1), 44–58. doi:10.1016/j.cedpsych.2009.10.001
   Web of Science ®Google Scholar
• Gujarati, D. N., & Porter, D. C. (2003). Basic econometrics (ed.). New York: McGraw-HiII.
   Google Scholar
• Hannula, M. S. (2006). Motivation in mathematics: Goals reflected in emotions. Educational Studies in Mathematics, 63(2), 165–178. doi:10.1007/s10649-005-9019-8
   Google Scholar
• IBM Cloud. (2018). The science behind the service. Retrieved from https://console.bluemix.net/docs/services/tone-analyzer/science.html#the-science-behind-the-service
   Google Scholar
• Järvenoja, H., Järvelä, S., & Malmberg, J. (2015). Understanding regulated learning in situative and contextual frameworks. Educational Psychologist, 50(3), 204–219. doi:10.1080/00461520.2015.1075400
   Web of Science ®Google Scholar
• Järvenoja, H., Järvelä, S., & Malmberg, J. (2017). Supporting groups’ emotion and motivation regulation during collaborative learning. Learning and Instruction, 101090. doi:10.1016/j.learninstruc.2017.11.004
   Google Scholar
• Järvenoja, H., Volet, S., & Järvelä, S. (2013). Regulation of emotions in socially challenging learning situations: An instrument to measure the adaptive and social nature of the regulation process. Educational Psychology, 33(1), 31–58. doi:10.1080/01443410.2012.742334
   Web of Science ®Google Scholar
• Joksimović, S., Poquet, O., Kovanović, V., Dowell, N., Mills, C., Gašević, D., … Brooks, C. (2018). How do we model learning at scale? A systematic review of research on MOOCs. Review of Educational Research, 88(1), 43–86. doi:10.3102/0034654317740335
   Web of Science ®Google Scholar
• Kim, C., Park, S. W., & Cozart, J. (2014). Affective and motivational factors of learning in online mathematics courses. British Journal of Educational Technology, 45(1), 171–185. doi:10.1111/j.1467-8535.2012.01382.x
   Web of Science ®Google Scholar
• King, R. B., McInerney, D. M., & Watkins, D. A. (2012). How you think about your intelligence determines how you feel in school: The role of theories of intelligence on academic emotions. Learning and Individual Differences, 22(6), 814–819. doi:10.1016/j.lindif.2012.04.005
   Web of Science ®Google Scholar
• Kratzwald, B., Ilić, S., Kraus, M., Feuerriegel, S., & Prendinger, H. (2018). Deep learning for affective computing: Text-based emotion recognition in decision support. Decision Support Systems, 115, 24–35. doi:10.1016/j.dss.2018.09.002
   Web of Science ®Google Scholar
• Krosnick, J. A. (1999). Survey research. Annual Review of Psychology, 50(1), 537–567. doi:10.1146/annurev.psych.50.1.537
   PubMed Web of Science ®Google Scholar
• Lazowski, R. A., & Hulleman, C. S. (2016). Motivation interventions in education: A meta-analytic review. Review of Educational Research, 86(2), 602–640. doi:10.3102/0034654315617832
   Web of Science ®Google Scholar
• Lehman, B., D’Mello, S., Cade, W., & Person, N. (2012, June). How do they do it? Investigating dialogue moves within dialogue modes in expert human tutoring. In International Conference on Intelligent Tutoring Systems (pp. 557–562). Berlin, Heidelberg: Springer.
   Google Scholar
• Lin, C. H., Zhang, Y., & Zheng, B. (2017). The roles of learning strategies and motivation in online language learning: A structural equation modeling analysis. Computers & Education, 113, 75–85. doi:10.1016/j.compedu.2017.05.014
   Web of Science ®Google Scholar
• Linnenbrink-Garcia, L., & Pekrun, R. (2011). Students’ emotions and academic engagement: Introduction to the special issue. Contemporary Educational Psychology, 36(1), 1–3. doi:10.1016/j.cedpsych.2010.11.004
   Web of Science ®Google Scholar
• Loderer, K., Pekrun, R., & Lester, J. C. (2018). Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments. Learning and Instruction, 101162.
   Web of Science ®Google Scholar
• Luhrs, C., & McAnally-Salas, L. (2016). Collaboration levels in asynchronous discussion Forums: A social network analysis approach. Journal of Interactive Online Learning, 14(1), 29–44.
   Web of Science ®Google Scholar
• Majeski, R. A., Stover, M., & Valais, T. (2018). The community of inquiry and emotional presence. Adult Learning, 29(2), 53–61. doi:10.1177/1045159518758696
   Web of Science ®Google Scholar
• Malekzadeh, M., Mustafa, M. B., & Lahsasna, A. (2015). A review of emotion regulation in intelligent tutoring systems. Journal of Educational Technology & Society, 18(4), 435–445.
   Web of Science ®Google Scholar
• Marchand, G. C., & Gutierrez, A. P. (2012). The role of emotion in the learning process: Comparisons between online and face-to-face learning settings. The Internet and Higher Education, 15(3), 150–160. doi:10.1016/j.iheduc.2011.10.001
   Web of Science ®Google Scholar
• Marcos-García, J.-A., Martínez-Monés, A., & Dimitriadis, Y. (2015). DESPRO: A method based on roles to provide collaboration analysis support adapted to the participants in CSCL situations. Computers & Education, 82, 335–353. doi:10.1016/j.compedu.2014.10.027
   Web of Science ®Google Scholar
• Marsh, H. W., & Shavelson, R. (1985). Self-concept: Its multifaceted, hierarchical structure. Educational Psychologist, 20(3), 107–123. doi:10.1207/s15326985ep2003_1
   Web of Science ®Google Scholar
• Mostafa, M., Crick, T., Calderon, A. C., & Oatley, G. (2016). December). Incorporating emotion and personality-based analysis in user-centered modelling. In International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 383–389). Cham: Springer.
   Google Scholar
• Münchow, H., & Bannert, M. (2019). Feeling good, learning better? Effectivity of an emotional design procedure in multimedia learning. Educational Psychology, 39(4), 530–549. doi:10.1080/01443410.2018.1524852
   Web of Science ®Google Scholar
• Murphy, P. K., & Alexander, P. A. (2000). A motivated exploration of motivation terminology. Contemporary Educational Psychology, 25(1), 3–53. doi:10.1006/ceps.1999.1019
   PubMed Web of Science ®Google Scholar
• Osborne, J., & Waters, E. (2002). Four assumptions of multiple regression that researchers should always test. Practical Assessment, Research & Evaluation, 8(2), 1–9.
   Google Scholar
• Pacella, D., & López-Pérez, B. (2018). Assessing children’s interpersonal emotion regulation with virtual agents: The serious game Emodiscovery. Computers & Education, 123, 1–12. doi:10.1016/j.compedu.2018.04.005
   Web of Science ®Google Scholar
• Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. doi:10.1007/s10648-006-9029-9
   Web of Science ®Google Scholar
• Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The achievement emotions questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48. doi:10.1016/j.cedpsych.2010.10.002
   Web of Science ®Google Scholar
• Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of quantitative and qualitative research. Educational Psychologist, 37(2), 91–106. doi:10.1207/S15326985EP3702_4
   Web of Science ®Google Scholar
• Pennebaker, J. W., Francis, M. E., & Booth, R. J. (2003). Linguistic inquiry and word count: LIWC2001 manual. Mahwah, NJ: Erlbaum.
   Google Scholar
• Piaget, J. (1976). Science of education and the psychology of the child. New York: Orion Press.
   Google Scholar
• Pintrich, P. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. doi:10.1007/s10648-004-0006-x
   Web of Science ®Google Scholar
• Pugmire, D. (1998). Rediscovering emotion. Edinburg: Edinburg University Press.
   Google Scholar
• Salmela, M. (2005). What is emotional authenticity?. Journal for the Theory of Social Behaviour, 35(3), 209–230. doi:10.1111/j.1468-5914.2005.00273.x
   Web of Science ®Google Scholar
• Schutz, P. A., & Lanehart, S. L. (2002). Introduction: Emotions in Education. Educational Psychologist, 37(2), 67–68. doi:10.1207/S15326985EP3702_1
   Web of Science ®Google Scholar
• Schutz, P. A., Hong, J. Y., Cross, D. I., & Osbon, J. N. (2006). Reflections on investigating emotion in educational activity settings. Educational Psychology Review, 18(4), 343–360. doi:10.1007/s10648-006-9030-3
   Web of Science ®Google Scholar
• Tabachnick, B. G., Fidell, L. S., & Osterlind, S. J. (2001). Using multivariate statistics. Boston: Allyn & Bacon.
   Google Scholar
• Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54.
   Web of Science ®Google Scholar
• Trueck, S., Weron, R., & Wolff, R. (2007). Outlier treatment and robust approaches for modeling electricity spot prices, Working paper 4711, MPRA.
   Google Scholar
• Wang, Y., & Pal, A. (2015, July). Detecting emotions in social media: A constrained optimization approach. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015) (pp. 996–1002).
   Google Scholar
• Woo, M. M., Chu, S. K. W., & Li, X. (2013). Peer-feedback and revision process in a wiki mediated collaborative writing. Educational Technology Research and Development, 61(2), 279–309.
   Web of Science ®Google Scholar
• Xie, K., Di Tosto, G., Lu, L., & Cho, Y. S. (2018). Detecting leadership in peer-moderated online collaborative learning through text mining and social network analysis. The Internet and Higher Education, 38, 9–17. doi:10.1016/j.iheduc.2018.04.002
   Web of Science ®Google Scholar
• Xie, K., Lu, L., Cheng, S. L., & Izmirli, S. (2017). The interactions between facilitator identity, conflictual presence, and social presence in peer-moderated online collaborative learning. Distance Education, 38(2), 230–244. doi:10.1080/01587919.2017.1322458
   Web of Science ®Google Scholar
• Xu, J., Du, J., & Fan, X. (2014). Emotion management in online groupwork reported by Chinese students. Educational Technology Research & Development, 62(6), 795–819.
   Web of Science ®Google Scholar
• Yamauchi, T., & Xiao, K. (2018). Reading emotion from mouse cursor motions: Affective computing approach. Cognitive Science, 42(3), 771–819.
   PubMed Web of Science ®Google Scholar
• Yip, M. W. (2009). Differences between high and low academic achieving university students in learning and study strategies: A further investigation. Educational Research and Evaluation, 15(6), 561–570.
   Google Scholar
• Zschocke, K., Wosnitza, M., & Bürger, K. (2016). Emotions in group work: Insights from an appraisal-oriented perspective. European Journal of Psychology of Education, 31(3), 359–384. doi:10.1007/s10212-015-0278-1.
   Web of Science ®Google Scholar