2610005347
  • Open Access
  • Article

Transforming Learning through Productive Failure: A Multi-Dimensional Framework for Fairness in Education

  • Jing Liu 1,   
  • Junjie Yan 1,   
  • Xiaonan Luo 1,   
  • Wanni Chen 2,   
  • Xueliang Li 3, *

Received: 08 Apr 2026 | Revised: 12 Jul 2026 | Accepted: 20 Aug 2026 | Published: 30 Sep 2026

Abstract

High-stakes testing and standardized instruction have long dominated education environments. They often place students with learning difficulties on learning tracks with low-cognitive tasks and low classroom participation. This trend further aggravates educational inequality. Research on fairness in education has gradually shifted its focus from resource allocation to the quality of learning processes. However, most current classroom practices still adopt an “error-prevention instruction” approach oriented toward reducing mistakes and maintaining accuracy rates. This approach limits learners’ deep cognitive development. Against this background, this study takes the theory of productive-failure instruction (Productive Failure, PF) as its core. It re-examines the transition of students with learning difficulties from the perspective of fairness in education. It combines literature analysis, conceptual analysis, and theoretical deduction to systematically explore the internal mechanisms through which productive-failure instruction promotes the cognitive development of students with learning difficulties. This study holds that students with learning difficulties are not a group with fixed ability deficits. They are a group of “developmental differences” that long stay in environments with low-quality cognitive engagement. Compared with traditional error-prevention instruction, productive-failure instruction works through three mechanisms: cognitive conflict, delayed instruction, and knowledge reconstruction. It promotes learners’ deep conceptual understanding and transfer of learning. On this basis, this study further proposes a four-dimensional classroom transformation model of productive failure for fairness in education. The model reshapes the classroom learning ecology. It provides new theoretical explanations and practical pathways for the transition of students with learning difficulties and the practice of fairness in education.

References 

  • 1.

    Agasisti, T., Avvisati, F., Borgonovi, F., & Longobardi, S. (2018). Academic resilience: What schools and countries do to help disadvantaged students succeed in PISA (OECD Education Working Papers, No. 167). OECD Publishing. https://doi.org/10.1787/e22490ac-en.

  • 2.

    Allen-Platt, C., Gerstner, C.-C., Boruch, R., & Ruby, A. (2021). Toward a science of failure analysis: A narrative review. Review of Research in Education, 45(1), 223–252. https://doi.org/10.3102/0091732x20985074.

  • 3.

    Ashman, G., Kalyuga, S., & Sweller, J. (2020). Problem-solving or explicit instruction: Which should go first when element interactivity is high? Educational Psychology Review, 32(1), 229–247. https://doi.org/10.1007/s10648-019-09500-5.

  • 4.

    Au, W. (2007). High-stakes testing and curricular control: A qualitative metasynthesis. Educational Researcher, 36(5), 258–267. https://doi.org/10.3102/0013189x07306523.

  • 5.

    Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295x.84.2.191.

  • 6.

    Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2024). Generative AI can harm learning (SSRN Scholarly Paper No. 4895486). SSRN. https://doi.org/10.2139/ssrn.4895486.

  • 7.

    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.

  • 8.

    Borman, G. D., Stringfield, S. C., & Slavin, R. E. (2001). Title I: Compensatory education at the crossroads. Routledge.

  • 9.

    Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007.

  • 10.

    Chen, Y., Chui, H., Huang, Y., & King, R. B. (2024). Adaptation and validation of the Chinese version of fear of failure in learning scale. Journal of Psychoeducational Assessment, 42(7), 866–881. https://doi.org/10.1177/07342829241257867.

  • 11.

    De Bortoli, L., Underwood, C., & Thomson, S. (2023). PISA 2022. Reporting Australia’s results. Volume I: Student performance and equity in education. Australian Council for Educational Research. https://doi.org/10.37517/978-1-74286-725-0.

  • 12.

    Dee, T. S., & Jacob, B. (2011). The impact of No Child Left Behind on student achievement. Journal of Policy Analysis and Management, 30(3), 418–446. https://doi.org/10.1002/pam.20586.

  • 13.

    Dee, T. S., Jacob, B., & Schwartz, N. L. (2013). The effects of NCLB on school resources and practices. Educational Evaluation and Policy Analysis, 35(2), 252–279. https://doi.org/10.3102/0162373712467080.

  • 14.

    Desimone, L. M. (2009). Improving impact studies of teachers’ professional development: Toward better conceptualizations and measures. Educational Researcher, 38(3), 181–199. https://doi.org/10.3102/0013189x08331140.

  • 15.

    Dong, Z., Liu, H., & Zheng, X. (2021). The influence of teacher-student proximity, teacher feedback, and near-seated peer groups on classroom engagement: An agent-based modeling approach. PLoS ONE, 16(1), e0244935. https://doi.org/10.1371/journal.pone.0244935.

  • 16.

    Fang, J., Brown, G. T. L., & Hamilton, R. (2023). Changes in Chinese students’ academic emotions after examinations: Pride in success, shame in failure, and self-loathing in comparison. British Journal of Educational Psychology, 93(1), 245–261. https://doi.org/10.1111/bjep.12552.

  • 17.

    Florian, L., & Black-Hawkins, K. (2011). Exploring inclusive pedagogy. British Educational Research Journal, 37(5), 813–828. https://doi.org/10.1080/01411926.2010.501096.

  • 18.

    Furtak, E. M., Seidel, T., Iverson, H., & Briggs, D. C. (2012). Experimental and quasi-experimental studies of inquiry-based science teaching. Review of Educational Research, 82(3), 300–329. https://doi.org/10.3102/0034654312457206.

  • 19.

    Gracia-Calandín, J., & Tamarit-López, I. (2021). Education as a common good from the capability approach. Journal of Philosophy of Education, 55(4–5), 817–828. https://doi.org/10.1111/1467-9752.12575.

  • 20.

    Hadjar, A., & Naumann, I. K. (2025). The digital divide. sozialpolitik.ch, 2025(2). https://doi.org/10.18753/2297-8224-8576.

  • 21.

    Hanushek, E. A. (2003). The failure of input-based schooling policies. The Economic Journal, 113(485), F64–F98. https://doi.org/10.1111/1468-0297.00099.

  • 22.

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

  • 23.

    Henningsen, M., & Stein, M. K. (1997). Mathematical tasks and student cognition: Classroom-based factors that support and inhibit high-level mathematical thinking and reasoning. Journal for Research in Mathematics Education, 28(5), 524–549. https://doi.org/10.2307/749690.

  • 24.

    Ho, D. G., Sa’adi, M., He, D., & Hoon, C. Y. (2023). Silence over the wire: Student verbal participation and the virtual classroom in the digital era. Asia Pacific Education Review, 24(4), 599–615. https://doi.org/10.1007/s12564-023-09834-4.

  • 25.

    Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10(1–2), 18–26. https://doi.org/10.1007/s13162-020-00161-0.

  • 26.

    Jennings, J. L., & Lauen, D. L. (2016). Accountability, inequality, and achievement: The effects of the No Child Left Behind Act on multiple measures of student learning. RSF: The Russell Sage Foundation Journal of the Social Sciences, 2(5), 220–241. https://doi.org/10.7758/rsf.2016.2.5.11.

  • 27.

    Jin, S., Fang, G., Cheung, K. C., & Sit, P. S. (2022). Factors associated with academic resilience in disadvantaged students: An analysis based on the PISA 2015 B-S-J-G (China) sample. Frontiers in Psychology, 13, 846466. https://doi.org/10.3389/fpsyg.2022.846466.

  • 28.

    Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424. https://doi.org/10.1080/07370000802212669.

  • 29.

    Kapur, M. (2014). Productive failure in learning math. Cognitive Science, 38(5), 1008–1022. https://doi.org/10.1111/cogs.12107.

  • 30.

    Kapur, M. (2015). Learning from productive failure. Learning: Research and Practice, 1(1), 51–65. https://doi.org/10.1080/23735082.2015.1002195.

  • 31.

    Khalil, M., & Er, E. (2023). Will ChatGPT get you caught? Rethinking of plagiarism detection. Center for Open Science. https://doi.org/10.35542/osf.io/fnh48.

  • 32.

    Kieser, F., Tschisgale, P., Rauh, S., Bai, X., Maus, H., Petersen, S., Stede, M., Neumann, K., & Wulff, P. (2024). David vs. Goliath: Comparing conventional machine learning and a large language model for assessing students’ concept use in a physics problem. Frontiers in Artificial Intelligence, 7, 1408817. https://doi.org/10.3389/frai.2024.1408817.

  • 33.

    Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. https://doi.org/10.1207/s15326985ep4102_1.

  • 34.

    Klein, E. D. (2020). School accountability. In Oxford research encyclopedia of education. Oxford University Press. https://doi.org/10.1093/acrefore/9780190264093.013.677.

  • 35.

    Koedel, C., & Rockoff, J. E. (2015). Value-added modeling: A review. Economics of Education Review, 47, 180–195. https://doi.org/10.1016/j.econedurev.2015.01.006.

  • 36.

    Kozanitis, A., & Nenciovici, L. (2023). Effect of active learning versus traditional lecturing on the learning achievement of college students in humanities and social sciences: A meta-analysis. Higher Education, 86(6), 1377–1394. https://doi.org/10.1007/s10734-022-00977-8.

  • 37.

    Lazonder, A. W., & Harmsen, R. (2016). Meta-analysis of inquiry-based learning. Review of Educational Research, 86(3), 681–718. https://doi.org/10.3102/0034654315627366.

  • 38.

    Lee, H.-Y., Chen, P.-H., Wang, W.-S., Huang, Y.-M., & Wu, T.-T. (2024). Empowering ChatGPT with guidance mechanism in blended learning: Effect of self-regulated learning, higher-order thinking skills, and knowledge construction. International Journal of Educational Technology in Higher Education, 21(1), 27. https://doi.org/10.1186/s41239-024-00447-4.

  • 39.

    Lee, J.-A., & Kim, C.-J. (2019). Teaching and learning science in authoritative classrooms: Teachers’ power and students’ approval in Korean elementary classrooms. Research in Science Education, 49(5), 1367–1393. https://doi.org/10.1007/s11165-017-9659-6.

  • 40.

    Li, H., Liu, J., Zhang, D., & Liu, H. (2021). Examining the relationships between cognitive activation, self-efficacy, socioeconomic status, and achievement in mathematics: A multi-level analysis. British Journal of Educational Psychology, 91(1), 101–126. https://doi.org/10.1111/bjep.12351.

  • 41.

    Loibl, K., Roll, I., & Rummel, N. (2017). Towards a theory of when and how problem solving followed by instruction supports learning. Educational Psychology Review, 29(4), 693–715. https://doi.org/10.1007/s10648-016-9379-x.

  • 42.

    Loibl, K., Tillema, M., Rummel, N., & van Gog, T. (2020). The effect of contrasting cases during problem solving prior to and after instruction. Instructional Science, 48(2), 115–136. https://doi.org/10.1007/s11251-020-09504-7.

  • 43.

    Mao, M., Zang, L., & Zhang, H. (2020). The effects of parental absence on children development: Evidence from left-behind children in China. International Journal of Environmental Research and Public Health, 17(18), 6770. https://doi.org/10.3390/ijerph17186770.

  • 44.

    Masten, A. S. (2001). Ordinary magic: Resilience processes in development. American Psychologist, 56(3), 227–238. https://doi.org/10.1037/0003-066x.56.3.227.

  • 45.

    McCaffrey, D. F., Lockwood, J. R., Koretz, D., Louis, T. A., & Hamilton, L. (2004). Models for value-added modeling of teacher effects. Journal of Educational and Behavioral Statistics, 29(1), 67–101. https://doi.org/10.3102/10769986029001067.

  • 46.

    Mesquita, B., & Karasawa, M. (2004). Self-conscious emotions as dynamic cultural processes. Psychological Inquiry, 15(2), 161–166. https://www.jstor.org/stable/20447222.

  • 47.

    Metcalfe, J. (2017). Learning from errors. Annual Review of Psychology, 68(1), 465–489. https://doi.org/10.1146/annurev-psych-010416-044022.

  • 48.

    Meyer, A., Kleinknecht, M., & Richter, D. (2023). What makes online professional development effective? The effect of quality characteristics on teachers’ satisfaction and changes in their professional practices. Computers & Education, 200, 104805. https://doi.org/10.1016/j.compedu.2023.104805.

  • 49.

    Mitra, D. L., & Serriere, S. C. (2012). Student voice in elementary school reform. American Educational Research Journal, 49(4), 743–774. https://doi.org/10.3102/0002831212443079.

  • 50.

    Morisano, D., Hirsh, J. B., Peterson, J. B., Pihl, R. O., & Shore, B. M. (2010). Setting, elaborating, and reflecting on personal goals improves academic performance. Journal of Applied Psychology, 95(2), 255–264. https://doi.org/10.1037/a0018478.

  • 51.

    Morris, R., Perry, T., & Wardle, L. (2021). Formative assessment and feedback for learning in higher education: A systematic review. Review of Education, 9(3), e3292. https://doi.org/10.1002/rev3.3292.

  • 52.

    Mu, J., Bayrak, A., & Ufer, S. (2022). Conceptualizing and measuring instructional quality in mathematics education: A systematic literature review. Frontiers in Education, 7, 994739. https://doi.org/10.3389/feduc.2022.994739.

  • 53.

    Nachtigall, V., Serova, K., & Rummel, N. (2020). When failure fails to be productive: Probing the effectiveness of productive failure for learning beyond STEM domains. Instructional Science, 48(6), 651–697. https://doi.org/10.1007/s11251-020-09525-2.

  • 54.

    Nehyba, J., Juhaňák, L., & Cigán, J. (2023). Effects of seating arrangement on students’ interaction in group reflective practice. The Journal of Experimental Education, 91(2), 249–277. https://doi.org/10.1080/00220973.2021.1954865.

  • 55.

    Nieuwenhuis, J., & Hooimeijer, P. (2016). The association between neighbourhoods and educational achievement, a systematic review and meta-analysis. Journal of Housing and the Built Environment, 31(2), 321–347. https://doi.org/10.1007/s10901-015-9460-7.

  • 56.

    Novak, A. M., & Treagust, D. F. (2022). Supporting the development of scientific understanding when constructing an evolving explanation. Disciplinary and Interdisciplinary Science Education Research, 4(1), 2. https://doi.org/10.1186/s43031-021-00043-w.

  • 57.

    Organisation for Economic Co-operation and Development. (2012). Equity and quality in education: Supporting disadvantaged students and schools. OECD Publishing. https://doi.org/10.1787/9789264130852-en.

  • 58.

    Ouyang, F., & Zhang, L. (2024). AI-driven learning analytics applications and tools in computer-supported collaborative learning: A systematic review. Educational Research Review, 44, 100616. https://doi.org/10.1016/j.edurev.2024.100616.

  • 59.

    Panadero, E., Jonsson, A., & Botella, J. (2017). Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses. Educational Research Review, 22, 74–98. https://doi.org/10.1016/j.edurev.2017.08.004.

  • 60.

    Pelánek, R., Effenberger, T., & Jarušek, P. (2024). Personalized recommendations for learning activities in online environments: A modular rule-based approach. User Modeling and User-Adapted Interaction, 34(4), 1399–1430. https://doi.org/10.1007/s11257-024-09396-z.

  • 61.

    Polesel, J., Rice, S., & Dulfer, N. (2014). The impact of high-stakes testing on curriculum and pedagogy: A teacher perspective from Australia. Journal of Education Policy, 29(5), 640–657. https://doi.org/10.1080/02680939.2013.865082.

  • 62.

    Pounder, J. S. (2018). Measuring student success: A value-added approach. In The future of higher education in the Middle East and Africa (pp. 205–210). Springer. https://doi.org/10.1007/978-3-319-64656-5_17.

  • 63.

    Ranalli, J., Link, S., & Chukharev-Hudilainen, E. (2017). Automated writing evaluation for formative assessment of second language writing: Investigating the accuracy and usefulness of feedback as part of argument-based validation. Educational Psychology, 37(1), 8–25. https://doi.org/10.1080/01443410.2015.1136407.

  • 64.

    Roll, I., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2011). Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learning and Instruction, 21(2), 267–280. https://doi.org/10.1016/j.learninstruc.2010.07.004.

  • 65.

    Roorda, D. L., Koomen, H. M. Y., Spilt, J. L., & Oort, F. J. (2011). The influence of affective teacher–student relationships on students’ school engagement and achievement. Review of Educational Research, 81(4), 493–529. https://doi.org/10.3102/0034654311421793.

  • 66.

    Ross, A., & Andreas, J. (2024). Toward in-context teaching: Adapting examples to students’ misconceptions. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Vol. 1, pp. 13283–13310). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-long.718.

  • 67.

    Sandsør, A. M. J., Zachrisson, H. D., Karoly, L. A., & Dearing, E. (2023). The widening achievement gap between rich and poor in a Nordic country. Educational Researcher, 52(4), 195–205. https://doi.org/10.3102/0013189x221142596.

  • 68.

    Schneider, B. (2024). Three challenges in implementing multimodal learning analytics in real-world learning environments. Learning: Research and Practice, 10(1), 103–112. https://doi.org/10.1080/23735082.2023.2270611.

  • 69.

    Schwartz, D. L., Chase, C. C., Oppezzo, M. A., & Chin, D. B. (2011). Practicing versus inventing with contrasting cases: The effects of telling first on learning and transfer. Journal of Educational Psychology, 103(4), 759–775. https://doi.org/10.1037/a0025140.

  • 70.

    Shabrina, P., Mostafavi, B., Abdelshiheed, M., Chi, M., & Barnes, T. (2024). Investigating the impact of backward strategy learning in a logic tutor: Aiding subgoal learning towards improved problem solving. International Journal of Artificial Intelligence in Education, 34(3), 825–861. https://doi.org/10.1007/s40593-023-00338-1.

  • 71.

    Simon, P. D., Fryer, L. K., & Nakao, K. (2024). Assessing class participation in physical and virtual spaces: Current approaches and issues. Frontiers in Education, 8, 1306568. https://doi.org/10.3389/feduc.2023.1306568.

  • 72.

    Simonsmeier, B. A., Flaig, M., Deiglmayr, A., Schalk, L., & Schneider, M. (2022). Domain-specific prior knowledge and learning: A meta-analysis. Educational Psychologist, 57(1), 31–54. https://doi.org/10.1080/00461520.2021.1939700.

  • 73.

    Sinha, T., & Kapur, M. (2021). When problem solving followed by instruction works: Evidence for productive failure. Review of Educational Research, 91(5), 761–798. https://doi.org/10.3102/00346543211019105.

  • 74.

    Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. https://doi.org/10.3102/00346543075003417.

  • 75.

    Smidekova, Z., Janik, M., Minarikova, E., & Holmqvist, K. (2020). Teachers’ gaze over space and time in a real-world classroom. Journal of Eye Movement Research, 13(4), 1. https://doi.org/10.16910/jemr.13.4.1.

  • 76.

    Strello, A., Strietholt, R., Steinmann, I., & Siepmann, C. (2021). Early tracking and different types of inequalities in achievement: Difference-in-differences evidence from 20 years of large-scale assessments. Educational Assessment, Evaluation and Accountability, 33(1), 139–167. https://doi.org/10.1007/s11092-020-09346-4.

  • 77.

    Sum, E. S. W., & Kwon, O. N. (2020). Classroom talk and the legacy of Confucian culture in mathematics classroom. Teaching and Teacher Education, 88, 102964. https://doi.org/10.1016/j.tate.2019.102964.

  • 78.

    Sun, Y., Zhu, X., Zhou, X., Yao, B., Zhang, K., Wang, D., Chen, J., & He, L. (2024). Who changed the destiny of rural students, and how? Unpacking ICT-mediated remote education in rural China. arXiv. https://arxiv.org/abs/2401.13799.

  • 79.

    Tai, J., Ajjawi, R., Boud, D., Dawson, P., & Panadero, E. (2018). Developing evaluative judgement: Enabling students to make decisions about the quality of work. Higher Education, 76(3), 467–481. https://doi.org/10.1007/s10734-017-0220-3.

  • 80.

    Tan, C. Y. (2024). Socioeconomic status and student learning: Insights from an umbrella review. Educational Psychology Review, 36(4), 99. https://doi.org/10.1007/s10648-024-09929-3.

  • 81.

    Terrin, É., & Triventi, M. (2023). The effect of school tracking on student achievement and inequality: A meta-analysis. Review of Educational Research, 93(2), 236–274. https://doi.org/10.3102/00346543221100850.

  • 82.

    Tweed, R. G., & Lehman, D. R. (2002). Learning considered within a cultural context: Confucian and Socratic approaches. American Psychologist, 57(2), 89–99. https://doi.org/10.1037/0003-066x.57.2.89.

  • 83.

    van de Pol, J., Mercer, N., & Volman, M. (2019). Scaffolding student understanding in small-group work: Students’ uptake of teacher support in subsequent small-group interaction. Journal of the Learning Sciences, 28(2), 206–239. https://doi.org/10.1080/10508406.2018.1522258.

  • 84.

    van de Pol, J., Volman, M., & Beishuizen, J. (2010). Scaffolding in teacher–student interaction: A decade of research. Educational Psychology Review, 22(3), 271–296. https://doi.org/10.1007/s10648-010-9127-6.

  • 85.

    Vaughn, M. (2021). Student agency in the classroom: Honoring student voice in the curriculum. Teachers College Press.

  • 86.

    Villegas, A. M., & Lucas, T. (2002). Preparing culturally responsive teachers. Journal of Teacher Education, 53(1), 20–32. https://doi.org/10.1177/0022487102053001003.

  • 87.

    Wannarka, R., & Ruhl, K. (2008). Seating arrangements that promote positive academic and behavioural outcomes: A review of empirical research. Support for Learning, 23(2), 89–93. https://doi.org/10.1111/j.1467-9604.2008.00375.x.

  • 88.

    Xu, S., & Lu, C. (2022). Hybridization of curriculum integration and high-stakes testing: Chinese teachers’ experiences and struggles in handling competing institutional logics. Teaching and Teacher Education, 119, 103845. https://doi.org/10.1016/j.tate.2022.103845.

  • 89.

    Xu, W., Wu, Y., & Ouyang, F. (2023). Multimodal learning analytics of collaborative patterns during pair programming in higher education. International Journal of Educational Technology in Higher Education, 20(1), 3. https://doi.org/10.1186/s41239-022-00377-z.

  • 90.

    Yan, J. J., Luo, X. N., Zeng, Q. F., Zhang, H. L., & Wu, J. N. (2025). Investigation and strategy research on the causes of middle school students’ learning difficulties in the context of the leading country in education. Journal of Educational Technology and Innovation, 7(2), 49. https://doi.org/10.61414/0shg4n49.

  • 91.

    Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0.

  • 92.

    Zhang, H. L., Yan, J. J., Liu, J., Zou, Z. C., Zeng, Q. F., Jin, Y. L., & Wu, J. N. (2025). An empirical study on the reflective feedback model based on pedagogical agents to improve students’ problem-solving ability. Journal of Educational Technology and Innovation, 7(1), 66–75. https://doi.org/10.61414/tykx7317.

  • 93.

    Zhang, Y., Li, M., Chieh, Y., & Han, S. (2022). On a path to becoming more self-regulated: Reflective journals’ impact on Chinese English as a foreign language students’ self-regulated writing strategy use. Frontiers in Psychology, 13, 1042031. https://doi.org/10.3389/fpsyg.2022.1042031.

  • 94.

    Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2.

Share this article:
How to Cite
Liu, J.; Yan, J.; Luo, X.; Chen, W.; Li, X. Transforming Learning through Productive Failure: A Multi-Dimensional Framework for Fairness in Education. Journal of Educational Technology and Innovation 2026, 8 (3), 35–51. https://doi.org/10.61414/bsq6e774.
RIS
BibTex
Copyright & License
article copyright Image
Copyright (c) 2026 by the authors.
Article Metrics
9
Article Views
0
Citations