How Much is Enough? Formative Assessment Dynamics

Quantifying the Relationship Between Intermediate Quiz Performance and Final Exam Scores

Authors

DOI:

https://doi.org/10.18608/jla.2025.8753

Keywords:

assessment, formative assessment, learning analytics, learning outcomes, outcomes prediction, research paper

Abstract

While the educational value of formative assessment is widely acknowledged, the precise amount needed to effectively predict student performance on summative assessments remains unclear. This study investigates the relationship between intermediate formative assessment performance and final exam scores, addressing the critical question of how much assessment is needed for accurate prediction. Using a large dataset encompassing over 20,000 student enrollments across 127 course runs of 15 online biomedical sciences courses, we examined the correlation between intermediate assessment scores and final exam performance. Our results show that after completing about 40% of the formative assessments in a course, student scores demonstrate a strong correlation (Pearson r > 0.7) with their final exam scores. The correlation after taking additional formative assessments reaches a maximum of approximately 0.75. This finding was consistent across different course types and lengths, suggesting that the relative amount of assessment taken, rather than the absolute number, is key. Surprisingly, we found that random sampling of assessments was even more predictive than chronological sampling, suggesting that the proportion of questions used, relative to the total number of assessment questions, is more important than their specific sequence. These findings contribute to a deeper understanding of the predictive capabilities of formative assessment, and enable educators to identify at-risk students earlier, optimize assessment design, and develop more efficient and targeted interventions.

References

Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In S. Dawson, C. Haythornthwaite, S. Buckingham Shum, D. Gašević, & R. Ferguson (Eds.), LAK ’12: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267–270). ACM Press. https://doi.org/10.1145/2330601.2330666

Bennett, R. E. (2011). Formative assessment: A critical review. Assessment in Education: Principles, Policy & Practice, 18(1), 5–25. https://doi.org/10.1080/0969594x.2010.513678

Black, P., Harrison, C., Lee, C., Marshall, B., & Wiliam, D. (2003). Assessment for learning: Putting it into practice. Open University Press. https://oro.open.ac.uk/24157/

Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102

Bloom, B. S. (1968). Learning for mastery. Regional Education Laboratory for the Carolinas and Virginia. https://files.eric.ed.gov/fulltext/ED053419.pdf

Bulut, O., Gorgun, G., Yildirim-Erbasli, S. N., Wongvorachan, T., Daniels, L. M., Gao, Y., Lai, K. W., & Shin, J. (2023). Standing on the shoulders of giants: Online formative assessments as the foundation for predictive learning analytics models. British Journal of Educational Technology, 54(1), 19–39. https://doi.org/10.1111/bjet.13276

Butler, A. C. (2018). Multiple-choice testing in education: Are the best practices for assessment also good for learning? Journal of Applied Research in Memory and Cognition, 7(3), 323–331. https://doi.org/10.1016/j.jarmac.2018.07.002

Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3): 354–380. https://doi.org/10.1037/0033-2909.132.3.354

Chi, M., VanLehn, K., Litman, D., & Jordan, P. (2011). Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies. User Modeling and User-Adapted Interaction, 21(1–2), 137–180. https://doi.org/10.1007/s11257-010-9093-1

Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695. https://doi.org/10.1080/13562517.2013.827653

Dawson, S., Gašević, D., Siemens, G., & Joksimović, S. (2014). Current state and future trends: A citation network analysis of the learning analytics field. In M. Pistilli, J. Willis, D. Koch, K. Arnold, S. Teasley, & A. Pardo (Eds.), LAK ’14: Proceedings of the fourth international conference on learning analytics and knowledge (pp. 231–240). ACM Press. https://doi.org/10.1145/2567574.2567585

Einig, S. (2013). Supporting students’ learning: The use of formative online assessments. Accounting Education, 22(5), 425–444. https://doi.org/10.1080/09639284.2013.803868

Ferguson, R., Khosravi, H., Kovanović, V., Viberg, O., Aggarwal, A., Brinkhuis, M., Buckingham Shum, S., Chen, L. K., Drachsler, H., Guerrero, V. A., Hanses, M., Hayward, C., Hicks, B., Jivet, I., Kitto, K., Kizilcec, R., Lodge, J. M., Manly, C. A., Matz, R. L., … Yan, V. X. (2023). Aligning the goals of learning analytics with its research scholarship: An open peer commentary approach. Journal of Learning Analytics, 10(2), 14–50. https://doi.org/10.18608/jla.2023.8197

Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5–6), 304–317. https://doi.org/10.1504/ijtel.2012.051816

Foster, E., & Siddle, R. (2020). The effectiveness of learning analytics for identifying at-risk students in higher education. Assessment and Evaluation in Higher Education, 45(6), 842–854. https://doi.org/10.1080/02602938.2019.1682118

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x

Gikandi, J. W., Morrow, D., & Davis, N. E. (2011). Online formative assessment in higher education: A review of the literature. Computers & Education, 57(4), 2333–2351. https://doi.org/10.1016/j.compedu.2011.06.004

Glick, D., Cohen, A., Festinger, E., Xu, D., Li, Q., & Warschauer, M. (2019). Predicting success, preventing failure. In D. Ifenthaler, D.-K. Mah, & J. Y.-K. Yau (Eds.), Utilizing learning analytics to support study success (pp. 249–273). Springer Cham. https://doi.org/10.1007/978-3-319-64792-0_14

Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Journal of Educational Technology & Society, 15(3), 42–57. https://www.jstor.org/stable/jeductechsoci.15.3.42

Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge. https://www.routledge.com/Visible-Learning-A-Synthesis-of-Over-800-Meta-Analyses-Relating-to-Achievement/Hattie/p/book/9780415476188

Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487

Hrastinski, S. (2019). What do we mean by blended learning? TechTrends, 63(5), 564–569. https://doi.org/10.1007/s11528-019-00375-5

Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2015). NMC horizon report: 2015 higher education edition. The New Media Consortium. https://library.educause.edu/resources/2015/2/2015-horizon-report

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. https://doi.org/10.1037/0033-2909.119.2.254

Knight, S., Buckingham Shum, S., & Littleton, K. (2014). Epistemology, assessment, pedagogy: Where learning meets analytics in the middle space. Journal of Learning Analytics, 1(2), 23–47. https://doi.org/10.18608/jla.2014.12.3

Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40. https://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-and-education

Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588–599. https://doi.org/10.1016/j.compedu.2009.09.008

McLaughlin, T., & Yan, Z. (2017). Diverse delivery methods and strong psychological benefits: A review of online formative assessment. Journal of Computer Assisted Learning, 33(6), 562–574. https://doi.org/10.1111/jcal.12200

Moskal, P. D., Dziuban, C. D., & Picciano, A. G. (Eds.). (2023). Data analytics and adaptive learning. Routledge. https://doi.org/10.4324/9781003244271

Motz, B. A., Bergner, Y., Brooks, C. A., Gladden, A., Gray, G., Lang, C., Li, W., Marmolejo-Ramos, F., & Quick, J. D. (2023). LAK of direction: Misalignment between the goals of learning analytics and its research scholarship. Journal of Learning Analytics, 10(2), 1–13. https://doi.org/10.18608/jla.2023.7913

Nicol, D. (2007). E‐assessment by design: Using multiple‐choice tests to good effect. Journal of Further and Higher Education, 31(1), 53–64. https://doi.org/10.1080/03098770601167922

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

OECD. (2005). Formative assessment: Improving learning in secondary classrooms. OECD Publishing, Paris. https://doi.org/10.1787/9789264007413-en

Rohrer, D. (2012). Interleaving helps students distinguish among similar concepts. Educational Psychology Review, 24(3), 355–367. https://doi.org/10.1007/S10648-012-9201-3

Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18(2), 119–144. https://doi.org/10.1007/bf00117714

Scriven, M. (1966). The methodology of evaluation. Social Science Education Consortium. https://eric.ed.gov/?id=ED014001

Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851

Siemens, G. (2012). Learning analytics: Envisioning a research discipline and a domain of practice. In S. Dawson, C. Haythornthwaite, S. Buckingham Shum, D. Gašević, & R. Ferguson (Eds.), LAK ’12: Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 4–8). ACM Press. https://doi.org/10.1145/2330601.2330605

Sortwell, A., Trimble, K., Ferraz, R., Geelan, D. R., Hine, G., Ramirez-Campillo, R., Carter-Thuiller, B., Gkintoni, E., & Xuan, Q. (2024). A systematic review of meta-analyses on the impact of formative assessment on K–12 students’ learning: Toward sustainable quality education. Sustainability, 16(17), Article 7826. https://doi.org/10.3390/su16177826

Spector, J. M., Ifenthaler, D., Sampson, D., Yang, L., Mukama, E., Warusavitarana, A., Dona, K. L., Eichhorn, K., Fluck, A., Huang, R., Bridges, S., Lu, J., Ren, Y., Gui, X., Deneen, C. C., San Diego, J., & Gibson, D. C. (2016). Technology enhanced formative assessment for 21st century learning. Journal of Educational Technology & Society, 19(3), 58–71. https://www.jstor.org/stable/jeductechsoci.19.3.58

Springer, L., Stanne, M. E., & Donovan, S. S. (1999). Effects of small-group learning on undergraduates in science, mathematics, engineering, and technology: A meta-analysis. Review of Educational Research, 69(1), 21–51. https://doi.org/10.3102/00346543069001021

Tempelaar, D. T., Heck, A., Cuypers, H., van der Kooij, H., & van de Vrie, E. (2013). Formative assessment and learning analytics. In D. Suthers, K. Verbert, E. Duval, & X. Ochoa (Eds.), LAK ’13: Proceedings of the third international conference on learning analytics and knowledge (pp. 205–209). ACM Press. https://doi.org/10.1145/2460296.2460337

Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157–167. https://doi.org/10.1016/j.chb.2014.05.038

Topping, K. J. (2005). Trends in peer learning. Educational Psychology, 25(6), 631–645. https://doi.org/10.1080/01443410500345172

van der Vleuten, C. P. M., & Schuwirth, L. W. T. (2005). Assessing professional competence: From methods to programmes. Medical Education, 39(3), 309–317. https://doi.org/10.1111/j.1365-2929.2005.02094.x

West, D., Heath, D., & Huijser, H. (2016). Let’s talk learning analytics: A framework for implementation in relation to student retention. Online Learning, 20(2). https://doi.org/10.24059/olj.v20i2.792

Wiliam, D. (2011). Embedded formative assessment. Solution Tree Press.

Wolff, A., Zdrahal, Z., Nikolov, A., & Pantucek, M. (2013). Improving retention: Predicting at-risk students by analysing clicking behaviour in a virtual learning environment. In D. Suthers, K. Verbert, E. Duval, & X. Ochoa (Eds.), LAK ’13: Proceedings of the third international conference on learning analytics and knowledge (pp. 145–149). ACM Press. https://doi.org/10.1145/2460296.2460324

Yeh, S. S. (2010). Understanding and addressing the achievement gap through individualized instruction and formative assessment. Assessment in Education: Principles, Policy & Practice, 17(2), 169–182. https://doi.org/10.1080/09695941003694466

Downloads

Published

2025-07-04

How to Cite

Parker, M. J., Bunch, M., & Pike, A. (2025). How Much is Enough? Formative Assessment Dynamics: Quantifying the Relationship Between Intermediate Quiz Performance and Final Exam Scores. Journal of Learning Analytics, 12(2), 196-210. https://doi.org/10.18608/jla.2025.8753