Main Article Content

Abstract

Formative feedback is an important aspect in mathematics teaching since it helps students to build mathematical thinking, enhance conceptual knowledge, and work toward better learning results. Although it is crucial, a consensus on the most effective feedback practices and research instruments to be used in the mathematics setting is limited. Numerous research studies implement various tools without a clear reference to mathematical capabilities, which leads to inappropriate research results and difficulties in the classroom. The research was based on a systematic literature review (SLR) to investigate the current application of different research instruments to measure formative feedback in mathematics education and its effect on student learning outcomes. Using ProQuest, Scopus, and EBSCOhost as the sources of data collection on the guidance of PRISMA protocols, key search words that were used were formative feedback and mathematics learning outcomes. The number of selected articles was 21, which was analyzed with the help of thematic and descriptive approaches. The review found six broad types of tools to provide formative feedback, namely, student feedback tools, gamification frameworks, structured frameworks, classroom artifacts, digital tools, and observation protocols. Student feedback tools and digital tools were most used among them as they also indicate a transition to interactive and personalized learning environments. These tools were often associated with six major learning outcomes, such as conceptual understanding, engagement, self-regulation, critical thinking, emotional responses, and motivation, with conceptual understanding and engagement being the most affected. Overall, the results suggest that formative feedback is an essential tool in improving mathematics learning yet also indicates the gap in the correspondence between the instruments of feedback and the specific mathematical skills. The study requires additional research to determine better links and create more productive and evidence-based feedback practices.

Keywords

mathematics formative feedback formative assessment PRISMA systematic literature review

Article Details

How to Cite
Mohd Zaini, N. F. M., Mohamed, N. A., Norddin, N. I., Mohamed, N. H., & Mohamed, N. F. (2025). FORMATIVE FEEDBACK IN MATHEMATICS EDUCATION: A SYSTEMATIC REVIEW OF RESEARCH INSTRUMENTS AND LEARNING OUTCOMES. Jurnal Penelitian Pembelajaran Matematika Sekolah (JP2MS), 9(3), 365–380. https://doi.org/10.33369/jp2ms.9.3.365-380

References

  1. Ayalon, M., & Wilkie, K. J. (2020). Investigating peer-assessment strategies for mathematics pre-service teacher learning on formative assessment. J Math Teacher Educ, 24, 399–426. https://doi.org/10.1007/s10857-020-09465-1
  2. Badshah, A., Ghani, A., Daud, A., Jalal, A., Bilal, M., & Crowcroft, J. (2023). Towards Smart Education through Internet of Things: A Survey. ACM Computing Surveys, 56(2), 1–33. https://doi.org/10.1145/3610401
  3. Balaskas, S., Zotos, C., Koutroumani, M., & Rigou, M. (2023). Effectiveness of GBL in the engagement, motivation, and satisfaction of 6th grade pupils: A Kahoot! Approach. Education Sciences, 13(12), 1214. https://doi.org/10.3390/educsci13121214
  4. Barana, A., Marchisio, M., & Sacchet, M. (2021). Interactive feedback for learning mathematics in a digital learning environment. Education Sciences, 11(6), 279. https://doi.org/10.3390/educsci11060279
  5. Chang, D., Hwang, G.-J., Chang, S.-C., & Wang, S.-Y. (2021). Promoting students’ cross-disciplinary performance and higher order thinking: A peer assessment-facilitated STEM approach in a mathematics course. Educational Technology Research and Development, 69(6), 3281–3306. https://doi.org/10.1007/s11423-021-10062-z
  6. Chihodzi, B., Mwakapenda, W., & Ngulube, B. (2023). Ticks and crosses in primary mathematics assessments: What purpose do they serve? Pythagoras, 44(1). https://doi.org/10.4102/pythagoras.v44i1.647
  7. Chu, H.-C., Chen, J.-M., Kuo, F.-R., & Yang, S.-M. (2021). Development of an Adaptive Game-Based Diagnostic and Remedial Learning System Based on the Concept-Effect Model for Improving Learning Achievements in Mathematics. Educational Technology & Society, 24(4), 36–53.
  8. Clincy, M., Melzer, K., Schaaf, G., Eichhorn, A., & Verné, N. (2022). Inside the “sandbox”: The effects of unlimited practice for summative online-tests. International Journal of Emerging Technologies in Learning, 17(23), 115–127. https://doi.org/10.3991/ijet.v17i23.35939
  9. Divjak, B., Žugec, P., & Pažur Aničić, K. (2022). E-assessment in mathematics in higher education: a student perspective. International Journal of Mathematical Education in Science and Technology, 1–23. https://doi.org/10.1080/0020739x.2022.2117659
  10. Erika, B., & Torulf, P. (2023). The effect of a formative assessment practice on student achievement in mathematics. Frontiers in Education, 8(23). https://doi.org/10.3389/feduc.2023.1101192
  11. Ferrer, J., Ringer, A., Saville, K., A Parris, M., & Kashi, K. (2020). Students’ motivation and engagement in higher education: the importance of attitude to online learning. Higher Education, 83(2), 317–338. https://doi.org/10.1007/s10734-020-00657-5
  12. Fuentes-Riffo, K., Salcedo-Lagos, P., Sanhueza-Campos, C., Pinacho-Davidson, P., Friz-Carillo, M., Kotz-Grabole, G., & Espejo-Burkart, F. (2023). The influence of gamification on high school students’ motivation in geometry lessons. Sustainability, 15(21). https://doi.org/10.3390/su152115615
  13. Fyfe, E. R., & Brown, S. A. (2020). This is easy, you can do it! Feedback during mathematics problem solving is more beneficial when students expect to succeed. Instructional Science, 48(1), 23–44. https://doi.org/10.1007/s11251-019-09501-5
  14. Hsu, Y.-P., Meyen, E. L., & Lee, Y.-J. (n.d.). Understanding Emotional Analytics for Student Engagement. In Advances in Higher Education and Professional Development (pp. 70–102). IGI Global. https://doi.org/10.4018/978-1-5225-5769-2.ch004
  15. Iannone, P., & Vondrová, N. (2023). The novelty effect on assessment interventions: A qualitative replication study of oral performance assessment in undergraduate mathematics. International Journal of Science and Mathematics Education, 22, 375-397. https://doi.org/10.1007/s10763-023-10368-9
  16. Ibragimov, G. I., & Kalimullina, A. A. (2021). Descriptors derived from feedback on teaching mathematics in school. Eurasia Journal of Mathematics, Science and Technology Education, 17(10). https://doi.org/10.29333/ejmste/11185
  17. Juma, Z. O., Owino, I., & Obiero, M. (2022). Delivery of Integral calculus at Maseno University: Is STACK really playing an Integral part? International Journal of Emerging Technologies in Learning (IJET), 17(23), 64–68. https://doi.org/10.3991/ijet.v17i23.36621
  18. Kültür, Y. Z., & Kutlu, M. O. (2021). The effect of formative assessment on high school students' mathematics achievement and attitudes. Journal of Pedagogical Research, 5(4), 155-171. https://doi.org/10.33902/JPR.2021474302
  19. Lee, H., Chung, H. Q., Zhang, Y., Abedi, J., & Warschauer, M. (2020). The effectiveness and features of formative assessment in US K-12 education: A systematic review. Applied Measurement in Education, 33(2), 124–140. https://doi.org/10.1080/08957347.2020.1732383
  20. Lee, J., & Paul, N. (2023). A Review of Pedagogical Approaches for Improved Engagement and Learning Outcomes in Mathematics. Journal of Student Research, 12(3). https://doi.org/10.47611/jsrhs.v12i3.5021
  21. Li, M. (2024). Integrating Artificial Intelligence in Primary Mathematics Education: Investigating Internal and External Influences on Teacher Adoption. International Journal of Science and Mathematics Education, 23(5), 1283–1308. https://doi.org/10.1007/s10763-024-10515-w
  22. Lui, A. M., & Andrade, H. L. (2022). Inside the next black box: Examining students’ responses to teacher feedback in a formative assessment context. Frontiers in Education, 7, 751549. https://doi.org/10.3389/feduc.2022.751549
  23. Luo, Z., Abbasi, B. N., Yang, C., Li, J., & Sohail, A. (2024). A systematic review of evaluation and program planning strategies for technology integration in education: Insights for evidence-based practice. Education and Information Technologies, 29(16), 21133–21167. https://doi.org/10.1007/s10639-024-12707-x
  24. Mangwiro, C., & Machaba, F. (2022). Teacher questioning techniques to elicit learners’ mathematical thinking. The International Journal of Science, Mathematics and Technology Learning, 30(1), 51–66. https://doi.org/10.18848/2327-7971/cgp/v30i01/51-66
  25. Maričić, M., & Lavicza, Z. (2024). Enhancing student engagement through emerging technology integration in STEAM learning environments. Education and Information Technologies, 29(17), 23361–23389. https://doi.org/10.1007/s10639-024-12710-2
  26. Mejeh, M., Sarbach, L., & Hascher, T. (2024). Effects of adaptive feedback through a digital tool – a mixed-methods study on the course of self-regulated learning. Education and Information Technologies, 29(14), 1–43. https://doi.org/10.1007/s10639-024-12510-8
  27. Mokhithi, M., Campbell, A. L., Shock, J. P., & Padayachee, P. (2025). ‘I call it math therapy’: student narratives of growth, belonging and confidence in mathematical thinking workshops. International Journal of Mathematical Education in Science and Technology, 1–26. https://doi.org/10.1080/0020739x.2025.2564193
  28. Montenegro-Rueda, M., Fernández-Cerero, J., Mena-Guacas, A. F., & Reyes-Rebollo, M. M. (2023). Impact of Gamified Teaching on University Student Learning. Education Sciences, 13(5), 470. https://doi.org/10.3390/educsci13050470
  29. Morris, R., Perry, T., & Wardle, L. (2021). Formative assessment and feedback for learning in higher education: A systematic review. Review of Education, 9(3). https://doi.org/10.1002/rev3.3292
  30. Muhammad Sofwan, M., Tajularipin, S., Ahmad Fauzi, M. A., & Aida Suraya, M. Y. (2021). Implementation of oral questioning in assessing student learning in mathematics teaching in primary schools. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(5), 137–143. https://doi.org/10.17762/turcomat.v12i5.805
  31. Nurzatulshima, K., Zakiya, A., Saoud, A., & Zeinab, Z. (2021). Assessment practices of mathematics teachers in Oman. Turkish Journal of Computer and Mathematics Education, 12(14), 4217-4224.
  32. Pekrun, R. (2024). Control-Value Theory: From Achievement Emotion to a General Theory of Human Emotions. Educational Psychology Review, 36(3). https://doi.org/10.1007/s10648-024-09909-7
  33. Piñero Charlo, J. C., Noriega Bustelo, R., Canto López, M. del C., & Costado Dios, M. T. (2022). Influence of the algorithmization process on the mathematical competence: A case study of trainee teachers assessing ABN- and CBC-instructed schoolchildren by gamification. Mathematics, 10(16), 3021. https://doi.org/10.3390/math10163021
  34. Procopio, M., Fernández-Cézar, R., Fernandes-Procopio, L., & Yánez-Araque, B. (2024). Neuroscience-Based Information and Communication Technologies Development in Elementary School Mathematics through Games: A Case Study Evaluation. Education Sciences, 14(3), 213. https://doi.org/10.3390/educsci14030213
  35. Rakes, C. R., Wesneski, A., & Laws, R. (2023). Building mathematics learning through inquiry using student-generated data: Lessons learned from Plan-Do-Study-Act cycles. Education Sciences, 13(9), 919. https://doi.org/10.3390/educsci13090919
  36. Rojas, E., & Benakli, N. (2020). Mathematical Literacy and Critical Thinking. In Teaching College-Level Disciplinary Literacy (pp. 197–226). Springer International Publishing. https://doi.org/10.1007/978-3-030-39804-0_8
  37. Rumanová, L., Vallo, D., & Záhorská, J. (2020). The impact of formative assessment on results of secondary school pupils in mathematics: One case of schools in Slovakia. TEM Journal, 9(3), 1200–1207. https://doi.org/10.18421/tem93-47
  38. Rulida, L. Jr. S. (2025). Student Engagement in Blended Mathematics Learning: The Role of Perceived Teaching Performance and Critical Thinking Skills. Asian Journal of Education and Social Studies, 51(5), 573–582. https://doi.org/10.9734/ajess/2025/v51i51941
  39. Safadi, R., & Hawa, N. (2023). Learning from erroneous examples in the mathematics classroom: Do students with different naïve ideas benefit equally? Instructional Science, 52, 277-308. https://doi.org/10.1007/s11251-023-09648-2
  40. Schindler, M., & Bakker, A. (2020). Affective field during collaborative problem posing and problem solving: a case study. Educational Studies in Mathematics, 105(3), 303–324. https://doi.org/10.1007/s10649-020-09973-0
  41. Shahzad, M. F., Xu, S., & Zahid, H. (2024). Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education. Education and Information Technologies, 30(3), 3691–3716. https://doi.org/10.1007/s10639-024-12949-9
  42. Sulistyani, D., Subekti, E. E., & Wardana, M. Y. S. (2021). Students’ learning difficulties review from mathematics problem-solving ability in third-grade elementary school. Indonesian Journal of Educational Research and Review, 4(2), 345–351. https://doi.org/10.23887/ijerr.v4i2.30310
  43. Téllez, N. R., Villela, P. R., & Bautista, R. B. (2024). Evaluating ChatGPT-generated linear algebra formative assessments. International Journal of Interactive Multimedia and Artificial Intelligence, 8(5), 75. https://doi.org/10.9781/ijimai.2024.02.004
  44. Torres-Peña, R. C., Peña-González, D., Chacuto-López, E., Ariza, E. A., & Vergara, D. (2024). Updating Calculus Teaching with AI: A Classroom Experience. Education Sciences, 14(9), 1019. https://doi.org/10.3390/educsci14091019
  45. Urrutia, F., & Araya, R. (2023). Automatically detecting incoherent written math answers of fourth-graders. Systems, 11(7), 353. https://doi.org/10.3390/systems11070353
  46. Vaughan, M., & Uribe, S. N. (2024). Re-examining our feedback model: strategies for enhancing student learning and cultivating feedback literacy through formative assessments. Assessment & Evaluation in Higher Education, 49(5), 711–723. https://doi.org/10.1080/02602938.2024.2323468
  47. Yang, J., Chen, Y., & Wang, Y. (2025). Exploring the Interplay of Motivation, Engagement and Critical Thinking Among EFL Learners: Evidence From Structural Equation Modelling. European Journal of Education, 60(3). https://doi.org/10.1111/ejed.70187
  48. Zhang, F., Wang, X., & Zhang, X. (2024). Applications of deep learning method of artificial intelligence in education. Education and Information Technologies, 30(2), 1563–1587. https://doi.org/10.1007/s10639-024-12883-w