Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments

Learning and Instruction

Volumn 70, Issue , 2020, Pages

  Loderer, Kristina ^a   Pekrun, Reinhard ^a,b   Lester, James C ^c  

^a UNIVERSITY OF MUNICH   (Germany)

^b AUSTRALIAN CATHOLIC UNIVERSITY   (Australia)

^c North Carolina State University   (United States)

Author keywords

Affect; Control value theory; Emotions; Meta analysis; Technology based learning

Indexed keywords

EID: 85056297666     PISSN: 09594752     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.learninstruc.2018.08.002     Document Type: Article

References (107)

1. Acee, T.W., Kim, H., Kim, H.J., Kim, J., Hsiang-Ning, R.C., Kim, M., et al., The Boredom Research Group. Academic boredom in under- and overchallenging situations. Contemporary Educational Psychology 35 (2010), 17–27, 10.1016/j.cedpsych.2009.08.002.
2. Ahmed, W., van der Werf, G., Kuyper, H., Minnaert, A., Emotions, self-regulated learning, and achievement in mathematics: A growth-curve analysis. Journal of Educational Psychology 105 (2013), 150–161, 10.1037/a0030160.
3. Ainley, M., Hidi, S., Interest and enjoyment. Pekrun, R., Linnenbrink-Garcia, L., (eds.) International handbook of emotions in education, 2014, Taylor & Francis, New York, NY, 205–227.
4. Artino, A.R.J., Think, feel, act. Journal of Computing in Higher Education 21 (2009), 146–166, 10.1007/s12528-009-9020-9.
5. Astleitner, H., Leutner, D., Designing instructional technology from an emotional perspective. Journal of Research on Computing in Education 32 (2014), 497–510, 10.1080/08886504.2000.10782294.
6. Azevedo, R., Johnson, A., Chauncey, A., Burkett, C., Self-regulated learning with MetaTutor: Advancing the science of learning with MetaCognitive tools. Khine, M.S., Saleh, I.M., (eds.) New science of learning (Part II), 2010, Springer, New York, NY, 225–247.
7. Atkinson, J., Feather, N., A theory of achievement motivation. 1966, Wiley and Sons, New York, NY.
8. Baldwin, S.A., Christian, S., Berkeljon, A., Shadish, W.R., Bean, R., The effects of family therapies for adolescent delinquency and substance abuse: A meta-analysis. Journal of Marital and Family Therapy 38 (2012), 281–304 10.111/j.1752-0606.2011.00248.x.
9. Beasley, R.E., Waugh, M.L., Cognitive mapping architectures and hypermedia disorientation: An empirical study. Journal of Educational Multimedia and Hypermedia 4 (1995), 239–255.
10. Beilock, S.L., Gunderson, E.A., Ramirez, G., Levine, S.C., Female teachers' math anxiety affects girls' math achievement. Proceedings of the National Academy of Sciences 107 (2010), 1860–1863, 10.1073/pnas.
11. Bieg, M., Goetz, T., Hubbard, K., Can I master it and does it matter? An intraindividual analysis on control–value antecedents of trait and state academic emotions. Learning and Individual Differences 28 (2013), 102–108, 10.1016/j.lindif.2013.09.006.
12. Borenstein, M., Hedges, L.V., Higgins, J.P.T., Rothstein, H.R., Introduction to meta-analysis. 2009, John Wiley & Sons, Chichester, U.K.
13. Butz, N.T., Stupnisky, R.H., Pekrun, R., Students' emotions for achievement and technology use in synchronous hybrid graduate programmes. Research in Learning Technology, 23, 2015, 1629, 10.3402/rlt.v23.26097.
14. Calvo, R., D'Mello, S.K., New perspectives on affect and learning technologies. 2011, Springer, New York, NY.
15. Coburn, K.M., Vevea, J.L., Package “weightr”: Estimating weight-function models for publication bias in R. 2016 Retrieved from https://cran.r-project.org/web/packages/weightr/index.html.
16. Cohen, J., Statistical power analysis for the behavioral sciences. 2nd ed., 1988, Lawrence Erlbaum, Hillsdale, NJ.
17. Covington, M., The self-worth theory of achievement motivation: Findings and implications. The Elementary School Journal 85 (1984), 4–20, 10.1086/461388.
18. Cromley, J., Azevedo, R., Olson, E., Self-regulation of learning with multiple representations in hypermedia. Looi, C.-K., McCalla, G., Bredeweg, B., Breuker, J., (eds.) Artificial intelligence in education, 2005, IOS Press, Amsterdam, 184–191.
19. D'Mello, S.K., A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. Journal of Educational Psychology 105 (2013), 1082–1099, 10.1037/a0032674.
20. D'Mello, S.K., Graesser, A.C., Confusion. Pekrun, R., Linnenbrink-Garcia, L., (eds.) International handbook of emotions in education, 2014, Taylor & Francis, New York, NY, 289–310.
21. D'Mello, S.K., Lehman, B., Pekrun, R., Graesser, A.C., Confusion can be beneficial for learning. Learning and Instruction 29 (2014), 153–170, 10.1016/j.learninstruc.2012.05.003.
22. Daniels, L.M., Stupnisky, R.H., Not that different in theory: Discussing the control-value theory of emotions in online learning environments. Internet and Higher Education 15 (2012), 222–226, 10.1016/j.iheduc.2012.04.002.
23. Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13 (1989), 319–340, 10.2307/249008.
24. Fisher, Z., Tipton, E., robumeta: Robust variance meta-regression (R package version 1.6). 2015 Retrieved from http://CRAN.R-project.org/package=robumeta.
25. Fredrickson, B.L., The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist 56 (2001), 218–226, 10.1037/0003-066X.56.3.218.
26. Frenzel, A.C., Pekrun, R., Goetz, T., Girls and mathematics —a “hopeless” issue? A control-value approahc to gender differences in emotions towards mathematics. European Journal of Psychology of Education 22 (2007), 497–514, 10.1007/BF03173468.
27. Frenzel, A.C., Pekrun, R., Goetz, T., Perceived learning environment and students' emotional experiences: A multilevel analysis of mathematics classrooms. Learning and Instruction 17 (2007), 478–493, 10.1016/j.learninstruc.2007.09.001.
28. Friese, M., Frankenbach, J., Job, V., Loschelder, D.D., Does self-control training improve self-control? A meta-analysis. Perspectives on Psychological Science 12 (2017), 1077–1099, 10.1177/1745691617697076.
29. Gardella, J.H., Fisher, B.W., Teurbe-Tolon, A.R., A systematic review and meta-analysis of cyber-victimization and educational outcomes for adolescents. Review of Educational Research 87 (2017), 283–308, 10.3102/0034654316689136.
30. Gaspard, H., Dicke, A.-L., Flunger, B., Schreier, B., Häfner, I., Trautwein, U., et al. More value through greater differentiation: Gender differences in value beliefs about math. Journal of Educational Psychology 107 (2015), 663–677, 10.1037/edu0000003.
31. Goetz, T., Bieg, M., Lüdtke, O., Pekrun, R., Hall, N.C., Do girls really experience more anxiety in mathematics?. Psychological Science 24 (2013), 2079–2087, 10.1177/0956797613486989.
32. Goetz, T., Hall, N.C., Emotion and achievement in the classroom. Hattie, J., Anderman, E.M., (eds.) International guide to student achievement, 2013, Routledge, London, 192–195.
33. Goetz, T., Hall, N.C., Academic boredom. Pekrun, R., Linnenbrink-Garcia, L., (eds.) International handbook of emotions in education, 2014, Taylor & Francis, New York, NY, 311–330.
34. Goetz, T., Lüdtke, O., Nett, U.E., Keller, M.M., Lipnevich, A.A., Characteristics of teaching and students' emotions in the classroom: Investigating differences across domains. Contemporary Educational Psychology 38 (2013), 383–394, 10.1016/j.cedpsych.2013.08.001.
35. Gogol, K., Brunner, M., Goetz, T., Martin, R., Ugen, S., Keller, U., Preckel, F., “My questionnaire is too long!” the assessments of motivational-affective constructs with three-item and single-item measures. Contemporary Educational Psychology 39 (2014), 188–205, 10.1016/j.cedpsych.2014.04.002.
36. Graesser, A.C., Chipman, P., King, B., McDaniel, B., D'Mello, S., Emotions and learning with AutoTutor. Frontiers in Artificial Intelligence and Applications 158 (2007), 569–571.
37. Graesser, A.C., D'Mello, S.K., Strain, A.C., Emotions in advanced learning technologies. Pekrun, R., Linnenbrink-Garcia, L., (eds.) International handbook of emotions in education, 2014, Taylor & Francis, New York, NY, 473–493.
38. Graham, S., Taylor, A.Z., An attributional approach to emotional life in the classroom. Pekrun, R., Linnenbrink-Garcia, L., (eds.) International handbook of emotions in education, 2014, Taylor & Francis, New York, NY, 96–119.
39. Gunn, C., French, S., McLeod, H., McSporran, M., Conole, G., Gender issues in computer-supported learning. Research in Learning Technology 10 (2002), 32–44, 10.1080/0968776020100106.
40. Hagtvet, K.A., Benson, J., The motive to avoid failure and test anxiety responses: Empirical support for integration of two research traditions. Anxiety, Stress & Coping 10 (1997), 35–57, 10.1080/10615809708249294.
41. Hedges, L.V., Tipton, E., Johnson, M.C., Robust variance estimation in meta-regression with dependent effect size estimates. Research Synthesis Methods 1 (2010), 39–65, 10.1002/jrsm.5.
42. Hedges, L.V., Vevea, J.L., Fixed and random-effects models in meta-analysis. Psychological Methods 3:4 (1998), 486–504.
43. Hembree, R., Correlates, causes, effects, and treatment of test anxiety: A meta-analysis. Review of Educational Research 58 (1988), 47–77, 10.2307/1170348.
44. Hidi, S., Renninger, K.A., The four-phase model of interest development. Educational Psychologist 41 (2006), 111–127, 10.1207/s15326985ep4102_4.
45. Homer, B.D., Hayward, E.O., Frye, J., Plass, J.L., Gender and player characteristics in video game play of preadolescents. Computers in Human Behavior 28 (2012), 1782–1789, 10.1016/j.chb.2012.04.018.
46. Kepes, S., Banks, G.C., McDaniel, M., Whetzel, D., Publication bias in the organizational sciences. Organizational Research Methods 15 (2012), 624–662, 10.1177/1094428112452760.
47. Kepes, S., Banks, G.C., Oh, I.-S., Avoiding bias in publication bias research: The value of “null” findings. Journal of Business and Psychology 29 (2014), 183–203, 10.1007/s10869-012-9279-0.
48. King, R.B., Gaerlan, J.M., High self-control predicts more positive emotions, better engagement, and higher achievement in school. European Journal of Psychology of Education 29 (2014), 81–100, 10.1007/s10212-013-0188-z.
49. Lajoie, S.P., Azevedo, R., Teaching and learning in technology-rich environments. Alexander, P.A., Winne, P.H., (eds.) Handbook of educational psychology, 2nd ed., 2006, Erlbaum, Mahwah, N.J, 803–821.
50. Lazarus, R.S., Folkman, S., Stress, appraisal, and coping. 1984, Guilford Press, New York, N.Y.
51. Lester, J.C., Spires, H.A., Nietfeld, J.L., Minogue, J., Mott, B.W., Lobene, E.V., Designing game-based learning environments for elementary science education: A narrative-centered learning perspective. Information Sciences 264 (2014), 4–18, 10.1016/j.ins.2013.09.005.
52. Lipsey, M.W., Wilson, D.B., Practical meta-analysis. 2001, Sage, Thousand Oaks, CA.
53. Loderer, K., Pekrun, R., Plass, J.L., Emotional foundations of game-based learning. Plass, J.L., Homer, B.D., Mayer, R.E., (eds.) Handbook of game-based learning, 2018, MIT Press, Cambridge, MA (in press).
54. Mandler, G., Sarason, S.B., A study of anxiety and learning. Journal of Abnormal and Social Psychology 47 (1952), 166–173, 10.1037/h0062855.
55. Marsh, H.W., Academic self-concept: Theory, measurement, and research. Suls, J.M., (eds.) Psychological perspectives on the self The self in social perspective, Vol. 4, 1993, Lawrence Erlbaum Associates, Inc, Hillsdale, NJ, US, 59–98.
56. McKeachie, W.J., Anxiety in the college classroom. Journal of Educational Research 45 (1951), 153–160.
57. McNamara, D.S., Jackson, G.T., Graesser, A.C., Intelligent tutoring and games (ITaG). Baek, Y.K., (eds.) Gaming for classroom-based learning: Digital role-playing as a motivator of study, 2010, IGI Global, Hershey, PA, 44–65.
58. Moos, D.C., Azevedo, R., Learning with computer-based learning environments: A literature review of computer self-efficacy. Review of Educational Research 79 (2009), 576–600, 10.3102/0034654308326083.
59. Moos, D.C., Marroquin, E., Multimedia, hypermedia, and hypertext: Motivation considered and reconsidered. Computers in Human Behavior 26 (2010), 265–276, 10.1016/j.chb.2009.11.004.
60. Moreno, R., Does the modality principle hold for different media? A test of the method-affects-learning hypothesis. Journal of Computer Assisted Learning 22 (2006), 149–158, 10.1111/j.1365-2729.2006.00170.x.
61. Moreno, R., Application of technology to learning. Harris, K.R., Graham, S., Urdan, T., Bus, A.G., Major, S., Swanson, H.L., (eds.) APA educational psychology handbook Applications to learning and teaching, Vol. 3, 2012, APA, Washington, D. C, 427–449.
62. Muis, K.R., Chevrier, M., Singh, C.A., The role of epistemic emotions in personal epistemology and self-regulated learning. Educational Psychologist. 2018, Advance online publication, 10.1080/00461520.2017.1421465.
63. Muis, K.R., Psaradellis, C., Lajoie, S.P., Di Leo, I., Chevrier, M., The role of epistemic emotions in mathematics problem solving. Contemporary Educational Psychology 42 (2015), 172–185, 10.1016/j.cedpsych.2015.06.003.
64. Murayama, K., Miyatsu, T., Buchli, D., Storm, B.C., Forgetting as a consequence of retrieval: A meta-analytic review of retrieval-induced forgetting. Psychological Bulletin 140 (2014), 1383–1409, 10.1037/a0037505.
65. Niculescu, A.C., Tempelaar, D., Dailey-Hebert, A., Segers, M., Gijselaers, W., Exploring the antecedents of learning-related emotions and their relations with achievement outcomes. Frontline Learning Research 3 (2015), 1–17, 10.14786/flr.v3i1.136.
66. Noteborn, G., Bohle Carbonell, K., Dailey-Hebert, A., Gijselaers, W., The role of emotions and task significance in Virtual Education. The Internet and Higher Education 15 (2012), 176–183, 10.1016/j.iheduc.2012.03.002.
67. Pekrun, R., Expectancy-value theory of anxiety: Overview and implications. Forgays, D.G., Sosnowski, T., Wrzesniewski, K., (eds.) Anxiety: Recent developments in self-appraisal, psychophysiological and health research, 1992, Hemisphere, Washington, DC, 23–41.
68. Pekrun, R., Progress and open problems in educational emotion research. Learning and Instruction 15 (2005), 497–506, 10.1016/j.learninstruc.2005.07.014.
69. Pekrun, R., The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review 18 (2006), 315–341, 10.1007/s10648-006-9029-9.
70. Pekrun, R., Global and local perspectives on human affect: Implications of the control-value theory of achievement emotions. Wosnitza, M., Karabenick, S.A., Efklides, A., Nenninger, P., (eds.) Contemporary motivation research: From global to local perspectives, 2009, Hogrefe, Cambridge, MA, 97–115.
71. Pekrun, R., Goetz, T., Frenzel, A.C., Barchfeld, P., Perry, R.P., Measuring emotions in students' learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology 36 (2011), 36–48, 10.1037/t21196-000.
72. Pekrun, R., Goetz, T., Titz, W., Perry, R.P., Academic emotions in students' self-regulated learning and achievement. Educational Psychologist 37 (2002), 91–105, 10.1207/S15326985EP3702_4.
73. Pekrun, R., Hall, N.C., Goetz, T., Perry, R.P., Boredom and academic achievement: Testing a model of reciprocal causation. Journal of Educational Psychology 106 (2014), 696–710, 10.1037/a0036006.
74. Pekrun, R., Perry, R.P., Control-value theory of achievement emotions. Pekrun, R., Linnenbrink-Garcia, L., (eds.) International handbook of emotions in education, 2014, Taylor & Francis, New York, NY, 120–141.
75. Pekrun, R., Stephens, E.J., Academic emotions. Harris, K.R., Graham, S., Urdan, T., Graham, S., Royer, J.M., Zeidner, M., (eds.) APA educational psychology handbook, Vol 2: Individual differences and cultural and contextual factors, 2012, American Psychological Association, Washington, 3–31.
76. Pekrun, R., Vogl, E., Muis, K.R., Sinatra, G.M., Measuring emotions during epistemic activities: The epistemically-related emotion scales. Cognition & Emotion, 2017, 10.1080/02699931.2016.1204989.
77. Peters, J.L., Sutton, A.J., Jones, D.R., Abrams, K.R., Rushton, L., Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry. Journal of Clinical Epidemiology 61 (2008), 991–996, 10.1016/j.jclinepi.2007.11.010.
78. Plass, J.L., Kaplan, U., Emotional design in digital media for learning. Tettegah, S.Y., McCreery, M.P., (eds.) Emotions, technology, and learning, 2016, Academic Press, London, 131–161.
79. Polanin, J.R., Pigott, T.D., The use of meta-analytic statistical significance testing. Research Synthesis Methods 6 (2014), 63–73, 10.1002/jrsm.1124.
80. Putwain, D.W., Deconstructing test anxiety. Emotional & Behavioural Difficulties 13 (2008), 141–155, 10.1080/13632750802027713.
81. Putwain, D.W., Sander, P., Larkin, D., Academic self-efficacy in study-related skills and behaviours: Relations with learning-related emotions and academic success. British Journal of Educational Psychology 83 (2013), 633–650, 10.1111/j.2044-8279.2012.02084.x.
82. R Core Team, R: A language and environment for statistical computing. 2015, R Foundation for Statistical Computing, Vienna, Austria Retrieved from https://www.R-project.org/.
83. Ranellucci, J., Hall, N.C., Goetz, T., Achievement goals, emotions, learning, and performance: A process model. Motivation Science 1 (2015), 98–120, 10.1037/mot0000014.
84. Sabourin, J.L., Lester, J.C., Affect and engagement in game-based learning environments. IEEE Transactions on Affective Computing 5 (2014), 45–56, 10.1109/T-AFFC.2013.27.
85. Scammacca, N., Roberts, G., Stuebing, K.K., Meta-analysis with complex research designs: Dealing with dependence from multiple measures and multiple group comparisons. Review of Educational Research 84 (2014), 328–364, 10.3102/0034654313500826.
86. Schmidt, F.L., Hunter, J.E., Methods of meta-analysis: Correcting error and bias in research findings. 3rd ed., 2015, Sage, Los Angeles, CA.
87. Schukajlow, S., Rakoczy, K., The power of emotions: Can enjoyment and boredom explain the impact of individual preconditions and teaching methods on interest and performance in mathematics?. Learning and Instruction 44 (2016), 117–127, 10.1016/j.learninstruc.2016.05.001.
88. Seipp, B., Anxiety and academic performance: A meta-analysis of findings. Anxiety Research 4 (1991), 27–41, 10.1080/08917779108248762.
89. Seipp, B., Schwarzer, C., Cross-cultural anxiety research: A review. Schwarzer, C., Zeidner, M., (eds.) Stress, anxiety, and coping academic settings, 1996, Francke, Tuebingen, 13–68.
90. Shuman, V., Scherer, K.R., Concepts and Structures of Emotions. Pekrun, R., Linnenbrink-Garcia, L., (eds.) International Handbook of Emotions in Education, 2014, Taylor and Francis, New York, N.Y.; London, 13–35.
91. Sieber, J.E., O'Neil, H.F., Tobias, S., Anxiety, learning, and instruction. 1977, Routledge, New York, NY.
92. Silvia, P.J., Interest - the curious emotion. Current Directions in Psychological Science 17:1 (2008), 57–60, 10.1111/j.1467-8721.2008.00548.x.
93. Spielberger, C.D., Vagg, P.R., Test anxiety: A transactional process. Spielberger, C.D., Vagg, P.R., (eds.) Test anxiety: Theory, assessment, and treatment, 1995, Taylor & Francis, Washington, DC, 3–14.
94. Stengel, E., Prüfungsangst und Prüfungsneurose. Zeitschrift für Psychoanalytische Pädagogik 10 (1936), 300–320.
95. Sterne, J.A.C., Sutton, A.J., Ioannidis, J.P.A., Terrin, N., Jones, D.R., Lau, J., et al., Higgins, J.P.T., Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. British Medical Journal, 343, 2011, d4002, 10.1136/bmj.d4002.
96. Tanner-Smith, E.E., Tipton, E., Polanin, J.R., Handling complex meta-analytic data structures using robust variance estimates: a tutorial in R. Journal of Developmental and Life-Course Criminology 2 (2016), 85–112, 10.1007/s40865-016-0026-5.
97. Tipton, E., Small sample adjustments for robust variance estimation with meta-regression. Psychological Methods 20 (2015), 375–393, 10.1037/met0000011.
98. Turner, J.E., Schallert, D.L., Expectancy-value relationships of shame reactions and shame resiliency. Journal of Educational Psychology 93 (2001), 320–329, 10.1037/0022-0663.93.2.320.
99. Tze, V.M.C., Daniels, L.M., Klassen, R.M., Evaluating the relationship between boredom and academic outcomes: A meta-analysis. Educational Psychology Review 28 (2016), 119–144, 10.1007/s10648-015-9301-y.
100. Vevea, J.L., Woods, C.M., Publication bias in research synthesis: Sensitivity analysis using a prior weight functions. Psychological Methods 10 (2005), 428–443, 10.1037/1082-989X.10.4.428.
101. Viechtbauer, W., Conducting meta-analyses in R with the metafor package. Journal of Statistical Software 36:3 (2010), 1–48.
102. Weiner, B., Examining emotional diversity on the classroom: An attribution theorist considers the moral emotions. Schutz, P.A., Pekrun, R., (eds.) Emotions in education, 2007, Academic Press, San Diego, CA, 73–88.
103. Wigfield, A., Rosenzweig, E.Q., Eccles, J.S., Achievement values: Interactions, interventions, and future directions. Elliot, A.J., Dweck, C.S., Yeager, D.S., (eds.) Handbook of competence and motivation, 2nd ed., 2017, Guilford, New York, NY, 116–134.
104. Wine, J.D., Test anxiety and the direction of attention. Psychological Bulletin 76 (1971), 92–104, 10.1037/h0031332.
105. Zeidner, M., Test anxiety. The state of the art. 1998, Plenum, New York, NY.
106. Zeidner, M., Anxiety in education. Pekrun, R., Linnenbrink-Garcia, L., (eds.) International handbook of emotions in education, 2014, Taylor & Francis, New York, NY, 265–288.
107. Zeidner, M., Matthews, G., Evaluative anxiety. Elliot, A.J., Dweck, C.S., (eds.) Handbook of competence and motivation, 2005, Guilford Press, New York, NY, 141–166.