SIMON: Supporting at-risk students through evidence-based personalized guidance
DOI:
https://doi.org/10.56433/bw25p144Keywords:
predictive model, academic achievement, Feedback, targeted intervention, engagementAbstract
This study examines the SIMON (Study skills and Interest MONitor) platform, developed at Ghent University (UGent), which supports bachelor’s students by addressing three key questions: What am I capable of? What can I do to succeed? Am I making progress? SIMON integrates predictive models, targeted interventions, and continuous monitoring to enhance academic trajectories.
At entry, SIMON provides evidence-based insights into students’ likelihood of success using assessments of cognitive and non-cognitive factors such as mathematics, reading comprehension, motivation, and test anxiety. These predictions, grounded in historical data, help students set realistic expectations, which research links to better outcomes. Building on this, faculty advisors enter tailored recommendations and remediation actions into the platform, connecting students to appropriate support resources. Progress monitoring is facilitated through visualizations and clear metrics, providing transparency in the face of strict academic progress regulations in Flanders.
This study analyzes SIMON’s effectiveness through predictive validity of success probabilities, the impact of faculty interventions, and student engagement with the platform. The dataset includes anonymized records from over 30,000 bachelor’s students. Preliminary results show a strong correlation between SIMON predictions and actual performance, as well as the platform’s utility in identifying at-risk students and supporting timely interventions.
Findings highlight the importance of combining data-driven insights with human guidance to foster proactive student engagement. While SIMON effectively predicts outcomes and offers actionable advice, its impact depends on students’ willingness to engage and implement recommendations.
In conclusion, SIMON demonstrates how universities can enhance academic success and study progress through continuous, personalized guidance. This integrated approach underscores the potential of combining advanced predictive analytics with supportive human interaction to improve higher education outcomes.
References
Brown, D. (2002). Introduction to Theories of Career Development and Choice: Origins, Evolution, and Current Efforts. In D. Brown (Ed.), Career choice and development (4th ed.). John Wiley.
Carver, C. S., & Scheier, M. F. (1990). Origins and functions of positive and negative affect: A control-process view. Psychological review, 97(1), 19. doi:10.1037/0033-295X.97.1.19
Cassady, J. (2004). The influence of cognitive test anxiety across the learning–testing cycle. Learning and Instruction, 14(6), 569-592. doi:10.1016/j.learninstruc.2004.09.002
Feldman, D. C., & Whitcomb, K. M. (2005). The effects of framing vocational choices on young adults' sets of career options. Career Development International, 10(1), 7-25. doi:10.1108/13620430510577600
Fonteyne, L., De Fruyt, F., Dewulf, N., Duyck, W., Erauw, K., Goeminne, K., … Rosseel, Y. (2015). Basic mathematics test predicts statistics achievement and overall first year academic success. EUROPEAN JOURNAL OF PSYCHOLOGY OF EDUCATION, 30(1), 95–118. https://doi.org/10.1007/s10212-014-0230-9
Fonteyne, L., Wille, B., Duyck, W., & De Fruyt, F. (2016). Exploring vocational and academic fields of study: development and validation of the Flemish SIMON Interest Inventory (SIMON-I). International Journal for Educational and Vocational Guidance, 17, 233-262.
Fonteyne, L., Duyck, W., & De Fruyt, F. (2017). Program-specific prediction of academic achievement on the basis of cognitive and non-cognitive factors. Learning and Individual Differences, 56, 34-48. doi: 10.1016/j.lindif.2017.05.003
Fonteyne, L. (2022). SIMON biedt handvatten voor studiekeuze en -succes. TH&MA (DEN HAAG), 2022(4), 14–18
Gourlay, L. (2015). Posthuman texts: nonhuman actors, mediators and the digital university. Social Semiotics, 25(4), 484-500.
Groenez, S., Van den Brande, I., & Nicaise, I. (2003). Cijferboek sociale ongelijkheid in het Vlaamse onderwijs. Retrieved from http://informatieportaalssl.be/archiefloopbanen/rapporten/LOA-rapport_10.pdf .
Harrison, C. J., Konings, K. D., Molyneux, A., Schuwirth, L. W. T., Wass, V., & van der Vleuten, C. P. M. (2013). Web-based feedback after summative assessment: how do students engage? Medical education, 47(7), 734-744. doi:10.1111/medu.12209
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81- 112. doi:10.3102/003465430298487
Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Psychological Assessment Resources.
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Computers & Education, 104, 18–33. https://doi.org/10.1016/j.compedu.2016.10.001
Krieshok, T. S., Black, M. D., & McKay, R. A. (2009). Career decision making: The limits of rationality and the abundance of non-conscious processes. Journal of Vocational Behavior, 75(3), 275-290. doi:10.1016/j.jvb.2009.04.006
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling (Vol. 26, p. 13). Springer.
Lemhöfer, K., & Broersma, M. (2012). Introducing LexTALE: a quick and valid lexical test for advanced learners of English. Behavior research methods, 44, 325-343. doi:10.3758/s13428-011-0146-0
Majuri, J., Koivisto, J., & Hamari, J. (2018). Gamification of education and learning: A review of empirical literature. GamiFIN, 11-19.
Nicol, D. J., & Macfarlane‐Dick, D. (2006). Formative assessment and self‐regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090
Nicholson, L., Putwain, D., Connors, L., & Hornby-Atkinson, P. (2013). The key to successful achievement as an undergraduate student: confidence and realistic expectations? Studies in Higher Education, 38(2), 285-298.
Owen, S., & Froman, R. (1988). Development of a college academic self-efficacy scale. Paper presented at the National Council on Measurement in Education, New Orleans, LA.
Petrovic, N., & Milosevic, I. (2024). Looking for Answers: A Scoping Review of Academic Help-Seeking in Digital Higher Education Research (2019–2024). Education Sciences, 15(9), 1095. https://doi.org/10.3390/educsci15091095
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838
Sailer, M., & Homner, L. (2020). The gamification of learning: A meta-analysis. Educational psychology review, 32(1), 77-112.
Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460-475. doi:10.1006/ceps.1994.1033
SweSAT. (2011). Swedish Scholastic Aptitude Test. Retrieved from http://www.edusci.umu.se/english/swesat/
Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2020). Learning analytics and interventions: A review of studies on the impact of data-driven personalization in higher education. Computers in Human Behavior, 108, 106340. https://doi.org/10.1016/j.chb.2020.106340Tinto, V. (2017). Through the eyes of students. Journal of College Student Retention: Research, Theory & Practice, 19(3), 254–269. https://doi.org/10.1177/1521025115621917
Vansteenkiste, M., Sierens, E., Soenens, B., Luyckx, K., & Lens, W. (2009). Motivational profiles from a self-determination perspective: The quality of motivation matters. Journal of Educational Psychology, 101(3), 671-688. doi:10.1037/a0015083
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Lot Fonteyne, Simon Acke
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Journal of Perspectives in Applied Academic Practice has made best effort to ensure accuracy of the contents of this journal, however makes no claims to the authenticity and completeness of the articles published. Authors are responsible for ensuring copyright clearance for any images, tables etc which are supplied from an outside source.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
