Research Article
Technology-enhanced Feedback Profiles and their Associations with Learning and Academic Well-being Indicators in Basic Education
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1 Centre for Educational Assessment, Faculty of Educational Sciences, University of Helsinki, Finland2 Faculty of Education and Culture, University of Tampere, Finland3 University of Tampere, Finland* Corresponding Author
Contemporary Educational Technology, 12(2), October 2020, ep271, https://doi.org/10.30935/cedtech/8202
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ABSTRACT
As a variety of commercial educational applications are currently being taken into daily use to provide technology-enhanced feedback, research is needed to observe whether pedagogical evidence of the impact of feedback on learning and well-being is being utilized. To this end, this study explores the connections between technology-enhanced feedback, motivation, competence and the relationship with teachers. A nationally representative sample of pupils undertaking Finnish basic education (N=2031) was analyzed using latent profile analysis. Seven patterns for receiving technology-enhanced feedback were identified. Most girls (80%) and boys (55%) belonged to groups receiving mainly positive feedback in the form of teacher praise, which was connected to the highest scores in all measured indicators. Although the results indicate teachers’ efforts to encourage pupils through technology-enhanced feedback, we also identified profiles in which pupils (up to 30%) repeatedly received negative feedback related to behavior problems or forgotten matters, as well as profiles in which pupils (5%) reported that they never received any technology-enhanced feedback at all. Pupils who did not receive any feedback reported the lowest values in all scales. The relationship with teachers was particularly weak for pupils receiving negative feedback or no feedback. The results indicate that current technology-enhanced feedback practices do not fully meet pedagogical knowledge concerning efficient feedback.
CITATION (APA)
Oinas, S. E., Thuneberg, H., Vainikainen, M.-P., & Hotulainen, R. (2020). Technology-enhanced Feedback Profiles and their Associations with Learning and Academic Well-being Indicators in Basic Education. Contemporary Educational Technology, 12(2), ep271. https://doi.org/10.30935/cedtech/8202
REFERENCES
- Bathke, A.C., Friedrich, S., Pauly, M., Konietschke, F., Staffen, W., Strobl, N., & Höller, Y. (2018). Testing mean differences among groups: multivariate and repeated measures analysis with minimal assumptions. Multivariate Behavioral Research, 53(3), 348-359. https://doi.org/10.1080/00273171.2018.1446320
- Bergman, P., & Chan, E.W. (2017). Leveraging parents: The impact of high-frequency information on student achievement. AERA conference-paper, 1-59. Retrieved from http://www.columbia.edu/~psb2101/ParentRCT.pdf
- Cochran, K. F., Reinsvold, L. A., & Hess, C. A. (2017). Giving students the power to engage with learning. Research in Science Education, 47, 1379-1401.
- Corno, L. (2009). Work habits and self-regulated learning: Helping students to find “will” from a “why”. In D. H. Shunck & B. J. Zimmerman (Eds.) Motivation and self-regulated learning, theory, research and applications (pp. 197-222). Routledge.
- Cutumisu, M., Chin, D. B., & Schwartz, D. L. (2019). A digital game-based assessment of middle-school and college students’ choices to seek critical feedback and to revise. British Journal of Educational Technology, 50(6), 2977-3003. https://doi.org/10.1111/bjet.12796
- Cutumisu, M. (2019). The association between feedback-seeking and performance is moderated by growth mindset in digital assessment game. Computers in Human Behavior, 93, 267-278.
- Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum.
- Doss, C., Fahle, E., Loeb, S., & York, B. (2017). Supporting parenting through differentiated and personalized text-messaging: testing effects on learning during kindergarten. Working Paper No.16-18. Retrieved from http://cepa.stanford.edu/wp16-18
- Elliot, A. J., & Dweck, C. S. (2005). Competence and motivation: Competence as the core of achievement motivation. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 3-12). New York, NY: Guilford.
- ElShaer, A., Casanova, D., Freestone, N. S., & Calabrese, G. (2019). Students’ perceptions of the value of electronic feedback - does disciplinary background really matter? British Journal of Educational Technology, 1-17. https://doi.org/10.1111/bjet.12881
- Finch, H. (2015). A comparison of statistics for assessing model invariance in latent class analysis. Open Journal of Statistic, 5, 191-210. https://doi.org/10.4236/ojs.2015.53022
- Finch, H. (2015). A comparison of statistics for assessing model invariance in latent class analysis. Open Journal of Statistics, 5(3), 1-19. https://doi.org/10.4236/ojs.2015.53022
- Gambari, I. A., Gbodi, B. E., Olakanmi, E. U., & Abalaka, E. N. (2016). Promoting intrinsic and extrinsic motivation among chemistry students using computer-assisted instruction. Contemporary Educational Technology, 7(1), 25-46.
- Gasser, L., Grütter, J., Buholzer, A., & Wettstein, A. (2018). Emotionally supportive classroom interactions and students’ perceptions of their teachers as caring and just. Learning & Instruction, 54, 82-92.
- Griffin, N. L. (2018). Using assessment feedback for motivation among early adolescents: a grounded theory study. Liberty University.
- Hagger, M. S., Koch, S., & Chatzisarantis, N. J. D. (2015). The effect of causality orientations and positive competence-enhancing feedback on intrinsic motivation: a test of additive and interactive effects. Personality and individual differences, 72, 107-111.
- Harter, S. (2012). The construction of the self, developmental and sociocultural foundations. Second edition. Guildford Press.
- Harter, S., Waters, P., & Whitesell, N.R. (1998). Relational self-worth: differences in perceived worth as a person across interpersonal context among adolescents. Child Development, 69(3), 756-766. https://doi.org/009-3920/98/6903-0019$01.00
- Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77, 81. https://doi.org/10.3102/003465430298487
- Hautamäki, J., & Kupiainen, S. (2014). Learning to learn in Finland, theory and policy, research and practice. In R.D. Crick, C. Stringher & K. Ren (Eds.) Learning to learn, international perspectives from theory and practice (pp. 179-205). Routledge.
- Heimo, O. I., Rantanen, M. M., & Kimppa, K. K. (2015). Wilma ruined my life: how an educational system became the criminal record for the adolescents. SIGCAS Computers & Society, 45(3), 138-146.
- Hoffman, J. (2008). I know what you did last math class. The New York Times. May 4, 2008. Retrieved from https://www.nytimes.com/2008/05/04/fashion/04edline.html
- Hughes, G. B. (2010). Formative assessment practices that maximize learning for students at risk. In H. L. Andrack & G. J. Cizek (Eds.) Handbook of formative assessment (pp. 212-232). Routledge.
- Jeno, L. M., Grytnes, J.-A., & Vandvik, V. (2017). The effect of a mobile-application tool on biology students’ motivation and achievement in species identification: a self-determination theory perspective. Computers & Education, 107, 1-12. https://doi.org/10.1016/j.compedu.2016.12.011
- Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: a historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254-284.
- Korhonen, J., Linnanmäki, K., & Aunio, P. (2014). Learning difficulties, academic well-being and educational dropout: a person-centred approach. Learning and Individual Differences, 31, 1-10. https://doi.org/10.1016/j.lindif.2013.12.011
- Kreuter, F., & Muthén, B.O.L (2008). Longitudinal modeling of population heterogeneity: Methodological challenges to the analysis of empirically derived criminal trajectory profiles. In G. R. Hancock, & K. M. Samuelsen (Eds.) Advances in latent variable mixture models (pp. 53-75). Information Age Publishing, Inc.
- Kuusimäki, A.-M., Uusitalo-Malmivaara, L., & Tirri, K. (2019). Parents’ and teachers’ views on digital communication in Finland. Education Research International, 1-7. https://doi.org/10.1155/2019/8236786
- Leary, M. R., & Terry, M. L. (2012). Interpersonal aspects of receiving evaluative feedback. In R. Sutton, M. J. Hornsey, & K. M. Douglas (Eds.) Feedback (pp. 15-28). Peter Lang Publishing.
- Lenhard, W., & Lenhard, A. (2016). Computation of Effect Sizes. Psychometrica. https://doi.org/10.13140/RG.2.2.17823.92329
- Lindfors, P., Minkkinen, J., Rimpelä, A., & Hotulainen, R. (2017). Family and school social capital, school burnout and academic achievement: a multilevel longitudinal analysis among Finnish pupils. International Journal of Adolescence and Youth, 23(3), 368-381. https://doi.org/10.1080/02673843.2017.1389758
- Linnakylä, P., & Malin, A. (2008). Finnish students’ school engagement profiles in the light of PISA 2003. Scandinavian Journal of Educational Research, 52(6), 583-602.
- Marsh, H. W., Lütdke, O., Trautwein, U., & Morin, A. J. S. (2009). Classical latent profile analysis of academic self-concept dimensions: synergy of person- and variable-centered approaches in theoretical models of self-concept. Structural Equation Modelling: A Multidisciplinary Journal, 16, 191-225. https://doi.org/10.1808/10705510902751010
- McKenna, K., Pouska, B., Moraes, M. C., & Folkestad, J. E. (2019). Visual-form learning analytics: a tool for critical reflection and feedback. Contemporary Educational Technology, 10(3), 214-228. https://doi.org/10.30935/cet.589989
- Niemivirta, M. (2004). Habits of mind and academic endeavours: The correlates and consequences of achievement goal orientations. University of Helsinki, Department of Education, Research Report 196. Helsinki University Press.
- Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: a monte carlo simulation study. A structural Equation Modeling 14(4), 535-569. https://doi.org/10.1080/10705510701575396
- Oberski, D. (2016). Mixture models: latent profile and latent class analysis. In J. Robertson & M. Kaptein (Eds.) Modern Statistical Methods for HCI (275-287). Springer.
- Oinas, S., Vainikainen, M.-P. & Hotulainen, R. (2017). Technology-enhanced feedback for pupils and parents in Finnish basic education. Computers & Education, 108, 59-70. https://doi.org/10.1016/J.compedu.2017.01.012
- Oinas, S., Vainikainen, M.-P., & Hotulainen, R. (2018). Is technology-enhanced feedback encouraging for all? A person-centred approach. Learning & Instruction, 58, 12-21. https://doi.org/10.1016/j.learninstruc.2018.05.002
- Pekrun, R. (2009). Emotions at school. In K. R. Wentzel & A. Wigfield (Eds.) Handbook of motivation at school (pp. 575-604). Routledge.
- Pekrun, R., Cusack, A., Murayma, K., Elliot, A. J., & Thomas, K. (2014). The power of anticipated feedback: effect on studentsʼ achievement goals and achievement emotions. Learning and Instruction 29, 115-124. https://doi.org/10.1016/j.learninstruc.2013.09.002
- Rawlings, A. M., Tapola, A., & Niemivirta, M. (2017). Predictive effects of temperament on motivation. International Journal of Educational Psychology, 6(2), 148-182. https://doi.org/10.17583/ijep.2017.2414
- Reeve, J., Ryan, R., Deci, E. L., & Jang, H. (2009). Understanding and promoting autonomous self-regulation: A self-determination theory perspective. In D. H. Schunk & B. J. Zimmerman (Eds.) Motivation and self-regulated learning, theory, research and applications (pp. 223-244). Routledge.
- Renninger, K. A., & Hidi, S. (2011). Revisiting the conceptualization, measurement, and generation of interest. Educational psychologist, 46(3),168-184.
- Rosenthal-von der Pütten, A. M., Hastall, M. R., Köcher, S., Meske, C., Heinrich, T., Labrenz, F., & Ocklenburg, S. (2019). “Likes” as social rewards: their role in online social comparison and decisions to like other people’s selfies. Computers in Human Behavior, 92, 76-86.
- Ruzek, E. A., Hafen, C. A., Allen, J. P., Gregory, A., Mikami, A. Y., & Pianta, R. C. (2016). How teacher emotional support motivates students: the mediating roles of perceived relatedness, autonomy support and competence. Learning & Instruction, 42, 95-103.
- Ryan, R. M., & Deci, E. L. (2000a). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78. https://doi.org/10.1037110003-066X.55.1.68
- Ryan, R. M., & Deci, E. L. (2000b). Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions. Contemporary Educational Psychology, 25, 54-67. https://doi.org/10.1006/ceps.1999.1020
- Ryan, R. M., & Stiller, J. (1991). The social contexts of internalization: parent and teacher influences on autonomy, motivation and learning. In M.L. Maehr & P.L. Pintrich (Eds.) Advances in Motivation and achievement, volume 7 (pp. 115-149). JAI Press Inc.
- Ryan, R. M., Stiller, J., & Lynch, J.H. (1994). Representations of relationships to teachers, parents, and friends as predictors of academic motivation and self-esteem. The Journal of Early Adolescence, 14(2), 226-249. https://doi.org/10.1177/027243169401400207
- Schenke, K., Ruzek, E., Lam, A. C., Karabenik, S. A., & Eccles, J. S. (2018). To the means and beyond: understanding variation in students’ perceptions of teacher emotional support. Learning and Instruction, 55, 13-21.
- Skinner, E., & Edge, K. (2002). Self-determination, coping, and development. In E. L. Deci & R. M. Ryan (Eds.) Handbook of self-determination research (pp. 297-337). The University of Rochester Press.
- Tempelaar, D., Rienties, B., Mittelmeier, J., & Nguyen, Q. (2018). Student profiling in a dispositional learning analytics application using formative assessment. Computers in Human Behavior, 78, 408-420. https://doi.org/10.1016/j.chb.2017.08.010
- Tofighi, D., & Enders, C. K. (2007). Identifying the Correct Number of Classes in a Growth Mixture Model. In G.R. Hancock (Eds.) Advances in Latent Variable Mixture Models (pp. 317-341). Information Age.
- UCLA (2019). Latent class analysis, MPlus data analyses examples. Retrieved from https://stats.idre.ucla.edu/mplus/dae/latent-class-analysis/
- Wang, M., & Peck, S.C. (2013). Adolescent educational success and mental health vary across school engagement profiles. Developmental Psychology, 49(7), 1266-1276.
- Wentzel, K. R. (2009). Students’ relationships with teachers as motivational contexts. In K. R. Wentzel, & A. Wigfield (Eds.) Handbook of Motivation at School (pp. 301-322). Routledge.
- Wiliam, D. (2011). What is assessment for learning? Studies in Educational Evaluation, 37, 3-14.
- Williams, G. A., & Kibowski, F. (2016). Latent class analysis and latent profile analysis. In L. A. Jason & D. S. Glenwick (Eds.) Handbook of methodological approaches to community-based research: qualitative, quantitative, and mixed methods (pp.143-152). Oxford University Press.