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Ensuring equity in education is a goal for sustainable development. Among the factors that hinder equity, socioeconomic status (SES) has the highest impact on learning Mathematics. This paper addresses the issue of equity at the secondary school level by proposing an approach based on adopting automatic formative assessment (AFA). Carefully designed mathematical activities with interactive feedback were experimented with a sample of 299 students of grade 8 for a school year. A control group of 257 students learned the same topics using traditional methodologies. Part of the sample belonged to low SES. The learning achievement was assessed through pre-and post-tests to understand if the adoption of AFA impacted learning and whether the results depended on the students’ SES. The results show a positive effect of the experimentation (effect size: 0.42). Moreover, the effect size of the experimentation restricted to the low-SES group is high (0.77). In the treatment group, the results do not depend on SES, while in the control group, they do, suggesting that AFA is an equitable approach while traditional instruction risks perpetuating inequalities.


Automatic Assessment Digital Learning Environment Equity Formative Assessment Socioeconomic Status

Article Details

How to Cite
Barana, A., & Marchisio Conte, M. (2024). Promoting socioeconomic equity through automatic formative assessment. Journal on Mathematics Education, 15(1), 227–252.


  1. Aleven, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2016). Instruction Based on Adaptive Learning Technologies. In R. E. Mayer & P. A. Alexander (Eds.), Handbook of Research on Learning and Instruction (2nd ed., pp. 522–560). Routledge.
  2. Angrist, J. D., & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist’s companion. Princeton university press.
  3. Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. L. (2006). Measuring cognitive and psychological engagement: Validation of the Student Engagement Instrument. Journal of School Psychology, 44(5), 427–445.
  4. Araya, R., Gormaz, R., Bahamondez, M., Aguirre, C., Calfucura, P., Jaure, P., & Laborda, C. (2015). ICT Supported Learning Rises Math Achievement in Low Socio Economic Status Schools. In G. Conole, T. Klobučar, C. Rensing, J. Konert, & E. Lavoué (Eds.), Design for Teaching and Learning in a Networked World (Vol. 9307, pp. 383–388). Springer International Publishing.
  5. Barana, A. (2021). From Formulas to Functions through Geometry: A Path to Understanding Algebraic Computations. European Journal of Investigation in Health, Psychology and Education, 11(4), 1485–1502.
  6. Barana, A. (2022). Understanding linear functions in an interactive digital learning environment. In U. T. Jankvist, R. Elicer, A. Clark-Wilson, H. G. Weigand, & M. Thomsen (Eds.), Proceedings of the 15th international conference on technology in mathematics teaching (ICTMT 15) (pp. 255–262). Danish School of Education, Aarhus University.
  7. Barana, A., & Marchisio, M. (2020). An interactive learning environment to empower engagement in Mathematics. Interaction Design and Architecture(s) Journal - IxD&A, 45, 302–321.
  8. Barana, A., Marchisio, M., & Sacchet, M. (2019). Advantages of Using Automatic Formative Assessment for Learning Mathematics. In S. Draaijer, D. Joosten-ten Brinke, & E. Ras (Eds.), Technology Enhanced Assessment (Vol. 1014, pp. 180–198). Springer.
  9. Barana, A., Marchisio, M., & Sacchet, M. (2021). Interactive Feedback for Learning Mathematics in a Digital Learning Environment. Education Sciences, 11(6), 279.
  10. Baya’a, N. F. (1990). Mathematics anxiety, mathematics achievement, gender, and socio‐economic status among Arab secondary students in Israel. International Journal of Mathematical Education in Science and Technology, 21(2), 319–324.
  11. Black, P., & Wiliam, D. (2009). Developing the theory of formative assessment. Educational Assessment, Evaluation and Accountability, 21(1), 5–31.
  12. Boaler, J. (2008). Promoting ‘relational equity’ and high mathematics achievement through an innovative mixed‐ability approach. British Educational Research Journal, 34(2), 167–194.
  13. Brancaccio, A., Marchisio, M., Palumbo, C., Pardini, C., Patrucco, A., & Zich, R. (2015). Problem Posing and Solving: Strategic Italian Key Action to Enhance Teaching and Learning Mathematics and Informatics in the High School. Proceedings of 2015 IEEE 39th Annual Computer Software and Applications Conference, 845–850.
  14. Campodifiori, E., Figura, E., Monica, P., & Ricci, R. (2010). Un indicatore di status socio-economico-culturale degli allievi della quinta primaria in Italia. INVALSI Working Paper Series, 2010(2), 1–25.
  15. Cascella, C. (2020). Intersectional effects of Socioeconomic status, phase and gender on Mathematics achievement. Educational Studies, 46(4), 476–496.
  16. Cohen, J. (1969). Statistical power analysis for the behavioral sciences. Academic Press.
  17. Coleman, J. S. (1966). Equality of educational opportunity (No. 0E-36001). U. S. Government Printing Office.
  18. Cook, D. L. (1962). The Hawthorne Effect in Educational Research. The Phi Delta Kappan, 44(2).
  19. Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed). Sage Publications.
  20. Cusi, A., Malara, N. A., & Navarra, G. (2011). Early Algebra: Theoretical Issues and Educational Strategies for Bringing the Teachers to Promote a Linguistic and Metacognitive approach to it. In J. Kai & E. Knuth, Early Algebraization: Cognitive, Curricular, and Instructional Perspectives (pp. 483–510). Springer.
  21. Elbers, E., & de Haan, M. (2005). The construction of word meaning in a multicultural classroom. Mediational tools in peer collaboration during mathematics lessons. European Journal of Psychology of Education, 20(1), 45–59.
  22. Fahlgren, M., Brunström, M., Dilling, F., Kristinsdóttir, B., Pinkernell, G., & Weigand, H.-G. (2021). Technology-rich assessment in mathematics. In A. Clark-Wilson, A. Donevska-Todorova, E. Faggiano, J. Trgalová, & H.-G. Weigand (Eds.), Mathematics Education in the Digital Age: Learning, Practice and Theory (pp. 69–83). Routledge.
  23. Field, S., Kuczera, M., & Pont, B. (2007). No more failures: Ten steps to equity in education. OECD.
  24. Gaona, J., Reguant, M., Valdivia, I., Vásquez, M., & Sancho-Vinuesa, T. (2018). Feedback by automatic assessment systems used in mathematics homework in the engineering field. Computer Applications in Engineering Education, 26(4), 994–1007.
  25. Gates, P. (2014). Equity and Access in Mathematics Education. In S. Lerman (Ed.), Encyclopedia of Mathematics Education (pp. 217–221). Springer Netherlands.
  26. Giusti, S., Gui, M., Micheli, M., & Parma, A. (2015). Gli effetti degli investimenti in tecnologie digitali nelle scuole del Mezzogiorno (Vol. 33). Collana Materiali Uval.
  27. Gutstein, E. (2006). Reading and writing the world with mathematics: Toward a pedagogy for social justice. Routledge.
  28. Heritage, M., & Wylie, C. (2018). Reaping the benefits of assessment for learning: Achievement, identity, and equity. ZDM, 50(4), 729–741.
  29. Hoogland, K., & Tout, D. (2018). Computer-based assessment of mathematics into the twenty-first century: Pressures and tensions. ZDM, 50(4), 675–686.
  30. Huang, X., Craig, S. D., Xie, J., Graesser, A., & Hu, X. (2016). Intelligent tutoring systems work as a math gap reducer in 6th grade after-school program. Learning and Individual Differences, 47, 258–265.
  31. Lerman, S. (2014). Socioeconomic Class in Mathematics Education. In S. Lerman (Ed.), Encyclopedia of Mathematics Education (pp. 553–558). Springer Netherlands.
  32. McConney, A., & Perry, L. B. (2010). Socioeconomic status, self-efficacy, and mathematics achievement in Australia: A secondary analysis. Educational Research for Policy and Practice, 9(2), 77–91.
  33. MIUR. (2012). Indicazioni Nazionali per il curricolo della scuola dell’infanzia e del primo ciclo d’istruzione.
  34. Ng, C., Bartlett, B., & Elliott, S. N. (2018). Empowering engagement: Creating learning opportunities for students from challenging backgrounds. Springer.
  35. Nortvedt, G. A., & Buchholtz, N. (2018). Assessment in mathematics education: Responding to issues regarding methodology, policy, and equity. ZDM, 50(4), 555–570.
  36. OECD. (2020). PISA 2018 Results (Volume II): Where All Students Can Succeed. OECD.
  37. Osadebe, P. U., & Oghomena, D.-E. (2018). Assessment of Gender, Location and Socio-Economic Status on Students’ Performance in Senior Secondary Certificate Examination in Mathematics. International Education Studies, 11(8), 98.
  38. Page, M. S. (2002). Technology-Enriched Classrooms: Effects on Students of Low Socioeconomic Status. Journal of Research on Technology in Education, 34(4), 389–409.
  39. Pellegrini, M., Vivanet, G., & Trinchero, R. (2018). Gli indici di effect size nella ricerca educativa. Analisi comparativa e significatività pratica. Educational, Cultural and Psychological Studies, 18, 275–309.
  40. Rohn, D. (2013). Equity in Education: The Relationship Between Race, Class, and Gender in Mathematics for Diverse Learners. Urban Education Research & Policy Annuals, 1(1), 13–22.
  41. Sacchet, M. (2022). Ten Tips for Successful Creation of Contextualized Problems for Secondary School Students with Maple. Maple Transactions, 2(1).
  42. Sangwin, C. (2015). Computer Aided Assessment of Mathematics Using STACK. In S. J. Cho (Ed.), Selected Regular Lectures from the 12th International Congress on Mathematical Education (pp. 695–713). Springer.
  43. Sangwin, C., Makar, K., Cazes, C., Lee, A., & Wong, K. L. (2010). Micro-level Automatic Assessment Supported by Digital Technologies. In C. Hoyles & J.-B. Lagrange (Eds.), Mathematics education and technology: Rethinking the terrain: The 17th ICMI study (Vol. 13, pp. 227–250). Springer.
  44. Scherer, R., & Siddiq, F. (2019). The relation between students’ socioeconomic status and ICT literacy: Findings from a meta-analysis. Computers & Education, 138, 13–32.
  45. Semana, S., & Santos, L. (2018). Self-regulation capacity of middle school students in mathematics. ZDM, 50(4), 743–755.
  46. Stacey, K., & Wiliam, D. (2013). Technology and Assessment in Mathematics. In M. A. Clements (Ed.), Third International Handbook of Mathematics Education (Vol. 27, pp. 721–751). Springer.
  47. Suppes, P., Liang, T., Macken, E. E., & Flickinger, D. P. (2014). Positive technological and negative pre-test-score effects in a four-year assessment of low socioeconomic status K-8 student learning in computer-based Math and Language Arts courses. Computers & Education, 71, 23–32.
  48. Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, 48(6), 1273–1296.
  49. United Nations. (2016). Transforming our world: The 2030 agenda for sustainable development.
  50. United Nations. (2022). The Sustainable Development Goals Report 2022.
  51. Valli Jayanthi, S., Balakrishnan, S., Lim Siok Ching, A., Aaqilah Abdul Latiff, N., & Nasirudeen, A. M. A. (2014). Factors Contributing to Academic Performance of Students in a Tertiary Institution in Singapore. American Journal of Educational Research, 2(9), 752–758.
  52. Wang, L., Li, X., & Li, N. (2014). Socio-economic status and mathematics achievement in China: A review. ZDM, 46(7), 1051–1060.
  53. Wiliam, D. (2006). Formative Assessment: Getting the Focus Right. Educational Assessment, 11(3–4), 283–289.
  54. Wright, P. (2016). Social justice in the mathematics classroom. London Review of Education, 14(2), 104–118.
  55. Yang Hansen, K., & Strietholt, R. (2018). Does schooling actually perpetuate educational inequality in mathematics performance? A validity question on the measures of opportunity to learn in PISA. ZDM, 50(4), 643–658.
  56. Yerushalmy, M., Nagari-Haddif, G., & Olsher, S. (2017). Design of tasks for online assessment that supports understanding of students’ conceptions. ZDM, 49(5), 701–716.
  57. Zhu, Y. (2018). Equity in Mathematics Education: What Did TIMSS and PISA Tell Us in the Last Two Decades? In G. Kaiser, H. Forgasz, M. Graven, A. Kuzniak, E. Simmt, & B. Xu (Eds.), Invited Lectures from the 13th International Congress on Mathematical Education (pp. 769–786). Springer International Publishing.