Skip to main content
SpringerLink
Log in

Latent profile analysis of Korean undergraduates’ academic emotions in e-learning environment

  • Cultural and Regional Perspectives
  • Published:
Educational Technology Research and Development Aims and scope Submit manuscript

Abstract

This study aimed to classify latent profiles of Korean undergraduates’ academic emotions in an e-learning environment, and to examine the effects of instructional variables on these profiles as well as the differences in their learning outcomes. A survey was conducted among 777 students who participated in online courses offered by a Korean university. Latent profile analysis revealed four types of emotional profiles: a moderate type (MT); a positive type (PT); a negative type (NT); and an ambivalent type (AT). MT comprised 72.5% of the total number of participants and showed medium levels of both positive emotions (PE) and negative emotions (NE). PT comprised 13.1% of the participants and showed high levels of PE and low levels of NE. NT comprised 10.2% of the participants and showed low levels of PE and high levels of NE. AT comprised 4.2% of the participants and both showed high levels of both PE and NE. Further analysis showed that the quality of instructional content, interaction, the system, and evaluation all proved to be predictors of emotional profiles. Moreover, they indicated differences in perceived achievement and in learner satisfaction. Based on these results, this study provides a discussion and suggestions for further studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Ahn, J. H. (2015). A qualitative study on characteristics of “good” college classes. The Journal of Yeolin Education,2(1), 163–191.

    Google Scholar 

  • Allan, B. I. B., Jerome, O., & Maria Guadalupe, C. S. (2009). What is an academic emotion? Insights from Filipino bilingual students’ emotion words associated with learning. Psychological Studies,54, 28–37.

    Article  Google Scholar 

  • Asparouhov, T., & Muthén, B. (2014a). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal,21(3), 329–341.

    Article  Google Scholar 

  • Asparouhov, T., & Muthén, B. (2014b). Auxiliary variables in Mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary secondary model. Mplus Web Notes,21(2), 1–22.

    Google Scholar 

  • Assor, A., Kaplan, H., Kanat-Maymon, Y., & Roth, G. (2005). Directly controlling teacher behaviors as predictors of poor motivation and engagement in girls and boys: The role of anger and anxiety. Learning and Instruction,15, 397–413.

    Article  Google Scholar 

  • Astleitner, H. (2000). Designing emotionally sound instruction: The FEASP-approach. Journal of Instructional Psychology,28(4), 209–219.

    Google Scholar 

  • Bakk, Z., Tekle, F. B., & Vermunt, J. K. (2013). Estimating the association between latent class membership and external variables using bias adjusted three-step approaches. Sociological Methodology,43(1), 272–311.

    Article  Google Scholar 

  • Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis,12(1), 3–27.

    Article  Google Scholar 

  • Bradburn, N. M. (1969). The structure of psychological well-being. Chicago: Aldine.

    Google Scholar 

  • Chei, M. J. (2016). Analysis of structural relationship among instructional quality, academic emotions, perceived achievement and learning satisfaction in offline & online university lectures. Unpublished Doctoral Dissertation, Konkuk University, South Korea.

  • Cho, E. A., & Lee, H. K. (2011). The Effect of optimism and pessimism on college students` adaptation and positive/negative affect: Achievement goal orientation as a mediating variable. Korean Journal of Youth Studies,18(2), 351–374.

    Google Scholar 

  • Choi, M. N., & Rho, H. L. (2011). Effect of the registration motivation and learning satisfaction on persistent intention in e-learning courses of university. Journal of Educational Technology,27(4), 653–673.

    Article  Google Scholar 

  • Cleveland-Innes, M., & Campbell, P. (2012). Emotional presence, learning, and the online learning environment. The International Review of Research in Open and Distributed Learning, 13(4), 269–292.

    Article  Google Scholar 

  • Cookson, P. (2000). Implications of internet technologies for higher education: North American perspectives. Open Learning,15(1), 71–81.

    Article  Google Scholar 

  • Dirkx, J. M. (2008). The meaning and role of emotions in adult learning. New Directions for Adult and Continuing Education,120, 7–17.

    Article  Google Scholar 

  • Do, S. L., Son, S. K., Byun, J. H., & Lim, J. (2011). Development and construct validation of the Korean Achievement Emotions Questionnaire(K-AEQ). The Korean Journal of Educational Psychology,25(4), 945–970.

    Google Scholar 

  • Efklides, A., & Petkaki, C. (2005). Effects of mood on students’ metacognitive experiences. Learning and Instruction,15, 415–431.

    Article  Google Scholar 

  • Efklides, A., & Volet, S. (2005). Emotional experiences during learning: Multiple, situated and dynamic. Learning and Instruction,15, 377–380.

    Article  Google Scholar 

  • Fisher, C. D. (1993). Boredom at work: A neglected concept. Human Relations,46(3), 395–417.

    Article  Google Scholar 

  • Frenzel, A. C., Thrash, T. M., Pekrun, R., & Goetz, T. (2007). Achievement emotions in Germany and China: A cross-cultural validation of the academic emotions questionnaire-mathematics. Journal of Cross-Cultural Psychology,38(3), 302–309.

    Article  Google Scholar 

  • Glaser-Zikuda, M., Fub, S., Laukenmann, M., Metz, K., & Randler, C. (2005). Promoting students’ emotions and achievement-instructional design and evaluation of the ECOLE-approach. Learning and Instruction,15, 481–495.

    Article  Google Scholar 

  • Govaerts, S., & Grégoire, J. (2008). Development and construct validation of an academic emotions scale. International Journal of Testing,8, 34–54.

    Article  Google Scholar 

  • Gumora, C., & Arsenio, W. F. (2002). Emotionality, emotion regulation, and school performance in middle school children. Journal of School Psychology,40(5), 395–413.

    Article  Google Scholar 

  • Hawkins, A., Barbour, M. K., & Graham, C. R. (2012). Everybody is their own island: Teacher disconnection in a virtual school. International Review of Research in Open and Distance Learning,13(2), 123–144.

    Article  Google Scholar 

  • Herbert, W. M., Oliver, L., Ulrich, T., & Alexander, J. S. M. (2009). Classical latent profile analysis of academic self-concept dimensions: Synergy of person- and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling,16, 191–225.

    Article  Google Scholar 

  • Hughes, M., Ventura, S., & Dando, M. (2007). Assessing social presence in online discussion groups: A replication study. Innovations in Education and Teaching International,44(1), 17–29.

    Article  Google Scholar 

  • Jarrell, A., Harley, J. M., Lajoie, S., & Naismith, L. (2017). Success, failure and emotions: Examining the relationship between performance feedback and emotions in diagnostic reasoning. Educational Technology Research and Development,65(5), 1263–1284.

    Article  Google Scholar 

  • Jo, E. Y. (2014). Effects of positive and negative emotion on psychological well-being of college students: Mediation by cognitive flexibility. Unpublished Master’s Thesis, Korea University, South Korea.

  • Kaplan, S., Bradley, J. C., Luchman, J. N., & Haynes, D. (2009). On the role of positive and negative affectivity in job performance: A meta-analytic investigation. Journal of Applied Psychology,94(1), 162–176.

    Article  Google Scholar 

  • Kim, J.-H. (2007). EFL learners’ anxiety as an affective focus in the study of e-learning. English Language Teaching,19(2), 183–213.

    Article  Google Scholar 

  • Kim, M. S. (2008). Processes of emotional experiences in online discussions: Emotional changes through interacting with other students. The Korean Journal of Educational Psychology,22(4), 697–722.

    Google Scholar 

  • Kim, M. S. (2009). Emotion in learning context: Its origins and functions. Asian Journal of Education,10(1), 73–98.

    Article  Google Scholar 

  • Kim, E. J. (2013). The influence of emotional regulation on academic achievement through academic emotions and learning strategies. Unpublished Doctoral Dissertation, Kyung-Hee University, South Korea.

  • Kim, Y. S., & Cho, H. (2015). A meta-analysis of the relationship between achievement emotions and academic achievement. The Korean Journal of Educational Psychology,29(1), 85–111.

    Article  Google Scholar 

  • Kim, H. J., & Kim, H. J. (2011). Relations among teaching factors, student`s interest, enjoyment, intrinsic motivation, student learning. The Korean Journal of Educational Psychology,25(3), 569–589.

    Google Scholar 

  • Kim, S. H., & Lee, I. S. (2014). The effects of academic emotions on motivation in e-learning. Korean Journal of Educational Technology,30(4), 749–775.

    Article  Google Scholar 

  • Kim, C. M., Park, S. W., & Cozart, J. (2014). Affective and motivational factors of learning in online mathematics courses. British Journal of Educational Technology,46(1), 171–185.

    Article  Google Scholar 

  • Kim, E. J., & Yang, M. H. (2011). Development and validation of an emotional regulation scale in academic settings. Korean Journal of Educational Research,25(3), 501–521.

    Google Scholar 

  • Kleine, M., Goetz, T., Pekrun, R., & Hall, N. (2005). The structure of students’ emotions experienced during a mathematical achievement test. ZDM Mathematics Education,37(3), 221–225.

    Article  Google Scholar 

  • Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Lanza, S. T., Tan, X., & Bray, B. C. (2013). Latent class analysis with distal outcomes: A flexible model-based approach. Structural Equation Modeling,20, 1–26.

    Article  Google Scholar 

  • Lazarus, R. S. (1994). Emotion and adaptation. New York: Oxford University Press.

    Google Scholar 

  • Lee, H., Jo, I., Kim, J., & Park, Y. (2018). Development of prediction models based on clustered online learners’ behavioral patterns in a university e-Learning environment. Journal of Educational Studies,49(1), 127–150.

    Article  Google Scholar 

  • Lee, J. Y., & Kim, J. R. (2013). Analysis on structural relationships among learners’ perceived usefulness, learner satisfaction and related factors in mobile learning in universities. Journal of Korean Education,40(1), 49–79.

    Google Scholar 

  • Lee, M. Y., & Lee, H. C. (1990). The structural analysis of adjective meanings: Related to affective vocabulary. Korean Journal of Experimental and Cognitive Psychology,2, 118–138.

    Google Scholar 

  • Lee, J. Y., & Park, S. (2016). Analysis of the structural relationships among self-efficacy, experience, mobile learning quality, and learner satisfaction in universities. Educational Technology International,17(2), 203–228.

    Google Scholar 

  • Levi, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers & Education,48(2), 185–204.

    Article  Google Scholar 

  • Linnenbrink, E. A., & Pintrich, P. R. (2002). Achievement goal theory and affect: An asymmetrical bidirectional model. Educational Psychologist,37(2), 69–78.

    Article  Google Scholar 

  • Lovegrove, P. J., & Cornell, D. G. (2014). Patterns of bulling and victimization associated with other problem behaviors among high school student: A conditional latent class approach. Journal of Crime and Justice,37(1), 5–22.

    Article  Google Scholar 

  • MacFadden, R. J. (2005). Souls on Ice: Incorporating emotion in web-based education. In R. J. MacFadden, B. Moore, M. Herie, & D. Schoech (Eds.), Web-based education in the human services: Models, methods, and best practices (pp. 79–98). New York, London, Victoria: The Haworth Press.

    Google Scholar 

  • MacFadden, R. J., Herie, M., Maiter, S., & Dumbrill, G. (2005). Achieving high touch in high tech: A constructivist, emotionally-oriented model of web-based instruction. Journal of Teaching in Social Work,25(1–2), 21–44.

    Article  Google Scholar 

  • Magidson, J., & Vermunt, J. (2002). Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing Research,20, 37–44.

    Google Scholar 

  • Markus, H. R., & Kitayama, S. (1991). Culture and the self: Implications for cognition, emotion, and motivation. Psychological Review,98, 224–253.

    Article  Google Scholar 

  • McCrae, J. S., Chapman, M. V., & Christ, S. L. (2006). Profile of children investigated for sexual abuse: Association with psychopathology symptoms and services. American Journal of Orthopsychiatry,76(4), 468–481.

    Article  Google Scholar 

  • Medina, J. (2008). Brain rules. Seattle, WA: Pear Press.

    Google Scholar 

  • Mesquita, B., & Frijda, N. H. (1992). Culture variations in emotions: A review. Psychological Bulletin,112, 179–204.

    Article  Google Scholar 

  • Meyer, D. K., & Turner, J. C. (2002). Discovering emotion in classroom motivation research. Educational Psychologist,37(2), 107–114.

    Article  Google Scholar 

  • Meyer, D. K., & Turner, J. C. (2006). Re-conceptualizing emotion and motivation to learn in classroom contexts. Educational Psychology Review,18(4), 377–390.

    Article  Google Scholar 

  • Moneta, G. B., & Kekkonen-Moneta, S. S. (2007). Affective learning in online multimedia and lecture versions of an introductory computing course. Educational Psychology,27(1), 51–74.

    Article  Google Scholar 

  • Muthén, B. O. (2004). Latent variable analysis. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social science (pp. 345–368). Thousand Oaks, CA: Sage.

    Google Scholar 

  • No, U. K., & Hong, S. H. (2013). Identifying latent profiles in the motivations of adolescents’ use of computers and testing the effects of eco-system variables. Studies on Korean Youth,23(3), 51–76.

    Google Scholar 

  • Nummenmaa, M. (2007). Emotions in a web-based learning environment. Unpublished Doctoral Dissertation, University of Turku, Finland.

  • Nummenmaa, M., & Nummenmaa, L. (2008). University students’ emotions, interest and activities in a web-based learning environment. British Journal of Educational Psychology,78(1), 163–178.

    Article  Google Scholar 

  • O’Regan, K. (2003). Emotion and e-Learning. Journal of Asynchronous Learning Networks,7(3), 78–92.

    Google Scholar 

  • Park, S. H., & Sohn, W. S. (2014). Types of academic emotion and its relationships with self-regulated learning: Math attitude and achievement for 5 to 6th Graders. The Journal of Elementary Education,27(3), 49–73.

    Google Scholar 

  • Pekrun, R. (2000). A social-cognitive, control-value theory of achievement emotions. In J. Heckhausen (Ed.), Motivational psychology of human development: Developing motivation and motivating development (pp. 143–163). New York: Elsevier.

    Chapter  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, 315–341.

    Article  Google Scholar 

  • Pekrun, R., Frenzel, A., Goetz, T., & Perry, R. P. (2007). The control-value theory of achievement emotions: An integrative approach to emotions in education. In P. A. Schutz & R. Pekrun (Eds.), Emotion in education (pp. 13–36). San Diego, CA: Academic Press.

    Chapter  Google Scholar 

  • Pekrun, R., Goetz, T., Frenzel, A., Barchfeld, P., & Perry, R. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questonnaire (AEQ). Contemporary Educational Psychology,36(1), 36–48.

    Article  Google Scholar 

  • Pekrun, R., Goetz, T., & Perry, R. P. (2005). Academic emotions questionnarie (AEQ): User’s manual. Munich: University of Munich.

    Google Scholar 

  • Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist,37(2), 91–105.

    Article  Google Scholar 

  • Pekrun, R., Hall, N. C., Goetz, T., & Perry, R. P. (2014). Boredom and academic achievement: Testing a model of reciprocal causation. Journal of Educational Psychology,106(3), 696–710.

    Article  Google Scholar 

  • Perry, R. P., Schonwetter, D. J., Magnusson, J. L., & Struthers, C. W. (1994). Students’ explanatory schemas and the quality of college instruction: Some evidence for buffer and compensation effects. Research in Higher Education,35(3), 349–371.

    Article  Google Scholar 

  • Plutchik, R. (2001). The nature of emotions. American Scientist,89, 344–350.

    Article  Google Scholar 

  • Power, M. (2006). The structure of emotion: An empirical comparison of six models. Cognition and Emotion,20, 694–713.

    Article  Google Scholar 

  • Reichert, F. (2017). young adults’ conceptions of ‘good’ citizenship behaviors: a latent class analysis. Journal of Civil Society,13(1), 90–110.

    Article  Google Scholar 

  • Rowe, J. (2005). Since feeling is first: Narrative Inquiry toward understanding emotion in online teaching and learning. Unpublished Doctoral Dissertation, University of Wisconsin-Madison.

  • Rowe, A. D., Fitness, J., & Wood, L. N. (2015). University student and lecturer perceptions of positive emotions in learning. International Journal of Qualitative Studies in Education,28, 1–20.

    Article  Google Scholar 

  • Scherer, K. R. (2009). The dynamic architecture of emotion: Evidence for the component process model. Cognition and Emotion,23(7), 1307–1351.

    Article  Google Scholar 

  • Schutz, P. A., & DeCuir, J. T. (2002). Inquiry on emotions in education. Educational Psychologist,37(2), 125–134.

    Article  Google Scholar 

  • Schutz, P. A., & Lanehart, S. L. (2002). Introduction: Emotions in education. Educational Psychologist,37(2), 67–68.

    Article  Google Scholar 

  • Shelton, C., & Stern, R. (2003). Understanding emotions in the classroom. New York: National Professional Resources.

    Google Scholar 

  • Smith, P. J., Coldwell, J., Smith, S. N., & Murphy, K. L. (2005). Learning through computer-mediated communication: A comparison of Australian and Chinese heritage students. Innovations in Education and Teaching International,42(2), 123–134.

    Article  Google Scholar 

  • Song, I. S., & Choi, J. H. (2011). Exploring the domain issue of academic emotions: A comparison of domain generality, domain specificity and domain complexity. Journal of Learner-Centered Curriculum and Instruction,11(4), 227–243.

    Google Scholar 

  • Tiedens, L. Z., Ellsworth, P. C., & Mesquita, B. (2000). Stereotypes about sentiments and status: Emotional expectations for high- and low-status group members. Personality and Social Psychology Bulletin,26, 560–574.

    Article  Google Scholar 

  • Tze, V. M. C., Daniels, L. M., Buhr, E., & Le, L. (2017). Affective profiles in a massive open online course and their relationship with engagement. Frontiers in Education,2(65), 1–13.

    Google Scholar 

  • Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis,18, 450–469.

    Article  Google Scholar 

  • Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis (pp. 89–106). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Videbeck, S. L. (2006). Psychiatric mental health nursing (3rd ed.). Philadelphia: Lippincott Williams & Wilkins.

    Google Scholar 

  • Villavicencio, F. T., & Bernardo, A. B. I. (2013). Positive academic emotions moderate the relationship between self-regulation and academic achievement. British Journal of Educational Psychology,83(2), 329–340.

    Article  Google Scholar 

  • Vuorela, M., & Nummenmaa, L. (2004). Experienced emotions, emotion regulation and student activity in a web-based learning environment. European Journal of Psychology of Education,14(4), 423–436.

    Article  Google Scholar 

  • Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology,54(6), 1063–1070.

    Article  Google Scholar 

  • Watson, D., & Tellegen, A. (1985). Toward a consensual structure of mood. Psychological Bulletin,98(2), 219–235.

    Article  Google Scholar 

  • Wosnitza, M., & Volet, S. (2005). Origin, direction and impact of emotions in social online learning. Learning and Instruction,15(5), 449–464.

    Article  Google Scholar 

  • Xu, J., Du, J., & Fan, X. (2014). Emotion management in online groupwork reported by Chinese students. Educational Technology Research and Development,62, 395–819.

    Google Scholar 

  • Yang, M. H., & Kwon, J. K. (2013). A study on the profile analysis of academic emotion and it’s relation to self-regulated learning. Studies on Korean Youth,24(4), 203–229.

    Google Scholar 

  • You, J. W., & Kang, M. (2014). The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning. Computers & Education,77, 125–133.

    Article  Google Scholar 

  • You, J. W., Kim, H. J., & Park, S. H. (2012). Development and construct validation of e-learning academic emotions scale (e-AES). The Journal of Yeolin Education,20(3), 19–44.

    Google Scholar 

  • Yu, J. H., & Lee, S. J. (2012). Development and validation of academic emotion regulation scale. The Korean Journal of Educational Psychology,26(4), 1137–1159.

    Google Scholar 

  • Zeidner, M. (1998). Test anxiety: The state of the art. New York: Plenum Press.

    Google Scholar 

  • Zembylas, M. (2008). Adult learners’ emotions in online learning. Distance Education,29(1), 71–87.

    Article  Google Scholar 

  • Zhou, M. (2013). “I am Really Good at It” or “I am Just Feeling Lucky”: The effects of emotions on information problem-solving. Educational Technology Research and Development,61, 505–520.

    Article  Google Scholar 

  • Zull, J. E. (2011). From brain to mind using neuroscience to guide change in education. Sterling, VA: Stylus Publishing.

    Google Scholar 

Download references

Acknowledgements

This study was supported by Konkuk University in 2016.

Author information

Authors and Affiliations

  1. Department of Educational Technology, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul, 05029, Korea

    Jong-Yeon Lee

  2. Education Advancement Center, Hankuk University of Foreign Studies, 107 Imun-ro, Dongdaemun-gu, Seoul, 02450, Korea

    Min Jung Chei

Authors
  1. Jong-Yeon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Jung Chei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Min Jung Chei.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This study was carried out under the approval of the instructors in charge of the target courses. A survey was administered to their participants after explicitly explaining its purpose. The participants were clearly informed that their participation was on a totally voluntary basis and they would not be disadvantaged based on their responses. The participants were asked to respond to the survey items anonymously if they decided to participate in the study.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, JY., Chei, M.J. Latent profile analysis of Korean undergraduates’ academic emotions in e-learning environment. Education Tech Research Dev 68, 1521–1546 (2020). https://doi.org/10.1007/s11423-019-09715-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11423-019-09715-x

Keywords

Search

Navigation