Using email messages to improve learning. A behavioral economics perspective

Miguel Angel Vallejo, Laura Vallejo-Slocker

Resumen


Motivation is the main factor that increases success in education and has special importance in distance learning. The use of personalized email messages has been shown to be useful for increasing retention and engagement. The aim of this study was to evaluate the effect of motivational emails on exam scores: 922 psychology students were randomly assigned to message or no-message groups. The message group received weekly email messages based on behavioral economic principles. There were no differences between groups in exam scores. A decision-tree analysis showed that the message group with a history of academic success improved their exam scores when compared with the no-message group. Exam scores are related to having a history of academic success and to the perceived relationship between knowledge and the score obtained. Furthermore, the usefulness of this experience and the opinions of students are discussed.


Texto completo:

PDF (English)

Referencias


Case, P., & Elliot, B. (1997). Attrition and retention in distance learning programs, problems strategies, problems and solutions. Open Praxis, 1, 30-33.

Deci, E., & Ryan, R. (1985). Intrinsic motivation and self-determination in human behavior. Plenum Press.

Dweck, C.S. (1999). Self-theories: Their role in motivation, personality and development. Psychology Press.

Dweck, C.S. (2006). Mindset. Random House.

Huett, J. B., Kalinowski, K. E., Moller, L., & Huett, K. C. (2008). Improving the Motivation and Retention of Online Students through the Use of ARCS-Based E-Mails. American Journal of Distance Education, 22(3), 159–176. https://doi.org/10.1080/08923640802224451

Hume, S., O'Reilly, F., Groot, B., Chande, R., Sanders, M., Hollingsworth, A., Meer, J.T., Barnes, J., Booth, S., Kozman, E., & Soon, X-Z. (2018). Improving engagement and attainment in maths and English courses: insights from behavioural research. Department for Education. UK Government.

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/738028/Improving_engagement_and_attainment_in_maths_and_English-courses.pdf

Inkelaar, T., & Simpson, O. (2015). Challenging the ‘distance education deficit’ through ‘motivational emails.’ Open Learning: The Journal of Open and Distance Learning, 30(2), 152–163. https://doi.org/10.1080/02680513.2015.1055718

Juergens, C. M. (2016). Study habits as predictors of exam scores. Psychology and Education: An Interdisciplinary Journal, 53(3–4), 36–47.

Karlan, D., & Wood, D. H. (2017). The effect of effectiveness: Donor response to aid effectiveness in a direct mail fundraising experiment. Journal of Behavioral and Experimental Economics, 66, 1–8. https://doi.org/10.1016/j.socec.2016.05.005

Kauffman, D. E., & Husman, J. (2004). Effects of Time Perspective on Student Motivation: Introduction to a Special Issue. Educational Psychology Review, 16(1), 1–7.

https://doi.org/10.1023/B:EDPR.0000012342.37854.58

Keller, J. M. (1987). Development and Use of the ARCS Model of Motivational Design. Journal of Instructional Development, 10(3), 2–10. https://www.jstor.org/stable/30221294

Lizzio, A., & Wilson, K. (2013). First-year students’ appraisal of assessment tasks: Implications for efficacy, engagement and performance. Assessment & Evaluation in Higher Education, 38(4), 389–406. https://doi.org/10.1080/02602938.2011.637156

Motz, B. A., Mallon, M. G., & Quick, J. D. (2021). Automated Educative Nudges to Reduce Missed Assignments in College. IEEE Transactions on Learning Technologies, 14(2), 189–200. https://doi.org/10.1109/TLT.2021.3064613

Niederdeppe, J., Avery, R. J., Kellogg, M. D., & Mathios, A. (2017). Mixed messages, mixed outcomes: Exposure to direct-to-consumer advertising for statin drugs is associated with more frequent visits to fast food restaurants and exercise. Health Communication, 32(7), 845–856. https://doi.org/10.1080/10410236.2016.1177903

Önder, E., & Uyar, S. (2017). CHAID Analysis to Determine Socioeconomic Variables That Explain Students’ Academic Success. Universal Journal of Educational Research, 5(4), 608–619. https://doi.org/10.13189/ujer.2017.050410

Onwuegbuzie, A. J., & Collins, K. M. (2010). An innovative method for stress and coping researchers for analyzing themes in mixed research: Introducing chi-square automatic interaction detection (CHAID). In K. M. Collins, A. J. Onwuegbuzie, & Q. G. Jio (Eds.), Toward a broader understanding of stress and coping: Mixed methods approaches (pp. 287-301). Information Age.

Pintrich, P., & Schunk, D. (1996). Motivation in education: Theory, research, and applications. Prentice-Hall.

Rau, P.-L. P., Gao, Q., & Wu, L.-M. (2008). Using Mobile Communication Technology in High School Education: Motivation, Pressure, and Learning Performance. Computers & Education, 50(1), 1–22. https://doi.org/10.1016/j.compedu.2006.03.008

Rokach, L., & Maimon, O. (2015). Data mining with decision trees: Theory and applications. 2nd ed. World Scientific Publishing.

Schaap, L., Schmidt, H. G., & Verkoeijen, P. P. J. L. (2012). Assessing Knowledge Growth in a Psychology Curriculum: Which Students Improve Most? Assessment & Evaluation in Higher Education, 37(7), 875–887. https://doi.org/10.1080/02602938.2011.581747

Schwerdtfeger, A. R., Schmitz, C., & Warken, M. (2012). Using text messages to bridge the intention-behavior gap? A pilot study on the use of text message reminders to increase objectively assessed physical activity in daily life. Frontiers in Psychology, 3. https://doi.org/10.3389/fpsyg.2012.00270

Simpson, O. (2013). Supporting students for success in online and distance education. Routledge.

Smith, B. O., White, D. R., Kuzyk, P. C., & Tierney, J. E. (2018). Improved Grade Outcomes with an E-Mailed “Grade Nudge.” Journal of Economic Education, 49(1), 1–7. https://doi.org/10.1080/00220485.2017.1397570

Snipes, J., Fancsali, C., & Stoker, G. (2012). Student academic mindset intervention: A review of the current landscape. Report released by the Stuoski Fonudation. https://www.impaqint.com/sites/default/files/project-reports/impaq%20student%20academic%20mindset%20interventions%20report%20august%202012_0.pdf Accessed 15 November 2019.

Valle, A., Cabanach, R. G., Nunez, J. C., Gonzalez-Pienda, J., Rodriguez, S., & Pineiro, I. (2003). Cognitive, Motivational, and Volitional Dimensions of Learning: An Empirical Test of a Hypothetical Model. Research in Higher Education, 44(5), 557–580. https://www.jstor.org/stable/40197322

Vandamme, J.-P., Meskens, N., & Superby, J.-F. (2007). Predicting academic performance by data mining methods. Education Economics, 15(4), 405-419. https://doi.org/10.1080/09645290701409939

Wlodkowski, R.J., & Ginsberg, M.B. (1995). Diversity and motivation: Culturally responsive teaching. Jossey-Bass.




DOI: http://dx.doi.org/10.54988/cv.2026.2.1689

Enlaces refback

  • No hay ningún enlace refback.


Campus Virtuales

ISSN: 2255-1514

www.revistacampusvirtuales.es

campusvirtuales@uajournals.com