Pedagogical Research

• Afriyani, D., Sa’dijah, C., Subanji, & Muksar, M. (2018). Characteristics of students’ mathematical understanding in solving multiple representation task based on SOLO taxonomy. International Electronic Journal of Mathematics Education, 13(3), 281-287. https://doi.org/10.12973/iejme/3920
• Agustin, R. D., Sa’dijah, C., Susiswo, & Sukoriyanto. (2024). Mathematical semantics representation obstacles of preservice mathematics teachers to solve geometry problems. Pegem Journal of Education and Instruction, 14(3), 376-392. https://doi.org/10.47750/pegegog.14.03.35
• Ahmed, S. K. (2024). The pillars of trustworthiness in qualitative research. Journal of Medicine, Surgery, and Public Health, 2, Article 100051. https://doi.org/10.1016/j.glmedi.2024.100051
• Alifiani, Subanji, Permadi, H., & Irawati, S. (2023). Metacognitive intervention: Can it solve suspension of sense-making in integration problem-solving? Mathematics Teaching Research Journal, 15(1), 44-66. https://files.eric.ed.gov/fulltext/EJ1391470.pdf
• Aniswita, A., Sepriyanti, N., & Medika, G. H. (2024). Unravelling undergraduate mathematics students’ understanding of derivatives. JRAMathEdu: Journal of Research and Advances in Mathematics Education, 9(4), 262-272. https://doi.org/10.23917/jramathedu.v9i4.11267
• Bajracharya, R. R., Sealey, V. L., & Thompson, J. R. (2023). Student understanding of the sign of negative definite integrals in mathematics and physics. International Journal of Research in Undergraduate Mathematics Education, 9, 62-91. https://doi.org/10.1007/s40753-022-00202-y
• Barana, A., Marchisio, M., & Sacchet, M. (2021). Interactive feedback for learning mathematics in a digital learning environment. Education Sciences, 11(6), Article 279. https://doi.org/10.3390/educsci11060279
• Barbieri, C. A., Miller-Cotto, D., Clerjuste, S. N., & Chawla, K. (2023). A meta-analysis of the worked examples effect on mathematics performance. Educational Psychology Review, 35, Article 11. https://doi.org/10.1007/s10648-023-09745-1
• Birt, L., Scott, S., Cavers, D., Campbell, C., & Walter, F. (2016). Member checking: A tool to enhance trustworthiness or merely a nod to validation? Qualitative Health Research, 26(13), 1802-1811. https://doi.org/10.1177/1049732316654870
• Biza, I., González-Martín, A. S., & Pinto, A. (2022). ‘Scaffolding’ or ‘filtering’: A review of studies on the diverse roles of calculus courses for students, professionals and teachers. International Journal of Research in Undergraduate Mathematics Education, 8(2), 389-418. https://doi.org/10.1007/s40753-022-00180-1
• Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328-352. https://doi.org/10.1080/14780887.2020.1769238
• Byrne, D. (2022). A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Quality & Quantity, 56(3), 1391-1412. https://doi.org/10.1007/s11135-021-01182-y
• Chiu, T. K. F. (2024). A classification tool to foster self-regulated learning with generative artificial intelligence by applying self-determination theory: A case of ChatGPT. Educational Technology Research and Development, 72, 2401-2416. https://doi.org/10.1007/s11423-024-10366-w
• Chytas, C., van Borkulo, S. P., Drijvers, P., Barendsen, E., & Tolboom, J. L. J. (2024). Computational thinking in secondary mathematics education with GeoGebra: Insights from an intervention in calculus lessons. Digital Experiences in Mathematics Education, 10(2), 228-259. https://doi.org/10.1007/s40751-024-00141-0
• Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.
• Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024). Impact of AI assistance on student agency. Computers & Education, 210, Article 104967. https://doi.org/10.1016/j.compedu.2023.104967
• Di Martino, P., Gregorio, F., & Iannone, P. (2023). The transition from school to university in mathematics education research: New trends and ideas from a systematic literature review. Educational Studies in Mathematics, 113(1), 7-34. https://doi.org/10.1007/s10649-022-10194-w
• Double, K. S., McGrane, J. A., & Hopfenbeck, T. N. (2020). The impact of peer assessment on academic performance: A meta-analysis. Educational Psychology Review, 32, 481-509. https://doi.org/10.1007/s10648-019-09510-3
• Duval, R. (2017). Understanding the mathematical way of thinking: The registers of semiotic representations. Springer. https://doi.org/10.1007/978-3-319-56910-9
• Fonger, N. L. (2019). Meaningfulness in representational fluency: An analytic lens for students’ creations, interpretations, and connections. The Journal of Mathematical Behavior, 54, Article 100678. https://doi.org/10.1016/j.jmathb.2018.10.003
• Gandolfi, A. (2025). GPT-4 in education: Evaluating aptness, reliability, and loss of coherence in solving calculus problems and grading submissions. International Journal of Artificial Intelligence in Education, 35(1), 367-397. https://doi.org/10.1007/s40593-024-00403-3
• Guan, R., Raković, M., Chen, G., & Gašević, D. (2025). How educational chatbots support self-regulated learning? A systematic review of the literature. Education and Information Technologies, 30, 4493-4518. https://doi.org/10.1007/s10639-024-12881-y
• Halák, J. (2022). Mathematics embodied: Merleau-Ponty on geometry and algebra as fields of motor enaction. Synthese, 200, Article 34. https://doi.org/10.1007/s11229-022-03526-z
• Hwang, G.-J., & Tu, Y.-F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), Article 584. https://doi.org/10.3390/math9060584
• Jin, S.-H., Im, K., Yoo, M., Roll, I., & Seo, K. (2023). Supporting students’ self-regulated learning in online learning using artificial intelligence applications. International Journal of Educational Technology in Higher Education, 20, Article 37. https://doi.org/10.1186/s41239-023-00406-5
• Kholid, M. N., Sa’dijah, C., Hidayanto, E., & Permadi, H. (2022). Students’ reflective thinking pattern changes and characteristics of problem solving. Reflective Practice, 23(3), 319-341. https://doi.org/10.1080/14623943.2021.2025353
• Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131. Medical Teacher, 42(8), 846-854. https://doi.org/10.1080/0142159X.2020.1755030
• Korstjens, I., & Moser, A. (2018). Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. European Journal of General Practice, 24(1), 120-124. https://doi.org/10.1080/13814788.2017.1375092
• Li, S., Shen, Y., Jiao, X., & Cai, S. (2022). Using augmented reality to enhance students’ representational fluency of linear functions in middle schools. Mathematics, 10(10), Article 1718. https://doi.org/10.3390/math10101718
• Miller, E. M., Porter, J. E., & Barbagallo, M. S. (2023). Simplifying qualitative case study research methodology: A step-by-step guide using a palliative care example. The Qualitative Report, 28(8), 2363-2379. https://doi.org/10.46743/2160-3715/2023.6478
• Mtisi, S. (2022). The qualitative case study research strategy as applied on a rural enterprise development doctoral research project. International Journal of Qualitative Methods, 21, Article 16094069221145849. https://doi.org/10.1177/16094069221145849
• Murniasih, T. R., Sa’dijah, C., Muksar, M., & Susiswo. (2020). Fraction sense: An analysis of preservice mathematics teachers’ cognitive obstacles. Center for Educational Policy Studies Journal, 10(2), 27-47. https://doi.org/10.26529/cepsj.742
• Nilsen, H. K., & Knutsen, K. H. (2023). First-year engineering students’ interpretations of differentials and definite integrals. International Journal of Research in Undergraduate Mathematics Education, 9(1), 173-200. https://doi.org/10.1007/s40753-022-00208-6
• Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16, Article 1609406917733847. https://doi.org/10.1177/1609406917733847
• Nurrahmawati, Sa’dijah, C., Sudirman, & Muksar, M. (2021). Assessing students’ errors in mathematical translation: From symbolic to verbal and graphic representations. International Journal of Evaluation and Research in Education, 10(1), 115-125. https://doi.org/10.11591/ijere.v10i1.20819
• Nuryanis, Nusantara, T., Murtadho, N., & Faizah, S. (2026). AI-based scaffolding and conceptual understanding: Evidence from Indonesian students using PLS-SEM. International Journal of Evaluation and Research in Education, 15(2), 1060-1078. https://doi.org/10.11591/ijere.v15i2.38450
• O’Connor, C., & Joffe, H. (2020). Intercoder reliability in qualitative research: Debates and practical guidelines. International Journal of Qualitative Methods, 19, Article 1609406919899220. https://doi.org/10.1177/1609406919899220
• Pepin, B., Buchholtz, N., & Salinas-Hernández, U. (2025). A scoping survey of ChatGPT in mathematics education. Digital Experiences in Mathematics Education, 11(1), 9-41. https://doi.org/10.1007/s40751-025-00172-1
• Sammallahti, E., Finell, J., Jonsson, B., & Korhonen, J. (2023). A meta-analysis of math anxiety interventions. Journal of Numerical Cognition, 9(2), 346-362. https://doi.org/10.5964/jnc.8401
• Schoenherr, J., & Schukajlow, S. (2024). Characterizing external visualization in mathematics education research: A scoping review. ZDM – Mathematics Education, 56, 73-85. https://doi.org/10.1007/s11858-023-01494-3
• Serhan, D., & Welcome, N. (2024). Integrating ChatGPT in the calculus classroom: Student perceptions. International Journal of Technology in Education and Science, 8(2), 325-335. https://doi.org/10.46328/ijtes.559
• Shvarts, A., Alberto, R., Bakker, A., Doorman, M., & Drijvers, P. (2021). Embodied instrumentation in learning mathematics as the genesis of a body-artifact functional system. Educational Studies in Mathematics, 107(3), 447-469. https://doi.org/10.1007/s10649-021-10053-0
• Sneed, S., Nguyen, C. H. P., & Eubank, C. L. (2020). An introduction to case study methodology: Single case and multiple case approaches. International Journal of Adult Education and Technology, 11(4), 1-11. https://doi.org/10.4018/IJAET.2020100101
• Tall, D. O. (2008). The transition to formal thinking in mathematics. Mathematics Education Research Journal, 20(2), 5-24. https://doi.org/10.1007/BF03217474
• Thompson, P. W., & Harel, G. (2021). Ideas foundational to calculus learning and their links to students’ difficulties. ZDM – Mathematics Education, 53(3), 507-519. https://doi.org/10.1007/s11858-021-01270-1
• 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), Article 1019. https://doi.org/10.3390/educsci14091019
• 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
• Way, J., & Ginns, P. (2024). Embodied learning in early mathematics education: Translating research into principles to inform teaching. Education Sciences, 14(7), Article 696. https://doi.org/10.3390/educsci14070696
• Wulan, E. R., Subanji, & Muksar, M. (2021). Metacognitive failure in constructing proof and how to scaffold it. Al-Jabar: Jurnal Pendidikan Matematika, 12(2), 295-314. https://doi.org/10.24042/ajpm.v12i2.9590
• Yan, Z., Lao, H., Panadero, E., Fernández-Castilla, B., Yang, L., & Yang, M. (2022). Effects of self-assessment and peer-assessment interventions on academic performance: A meta-analysis. Educational Research Review, 37, Article 100484. https://doi.org/10.1016/j.edurev.2022.100484
• Yang, Y., Maeda, Y., & Gentry, M. (2024). The relationship between mathematics self-efficacy and mathematics achievement: Multilevel analysis with NAEP 2019. Large-scale Assessments in Education, 12, Article 16. https://doi.org/10.1186/s40536-024-00204-z
• Yoon, H., Hwang, J., Lee, K., Roh, K. H., & Kwon, O. N. (2024). Students’ use of generative artificial intelligence for proving mathematical statements. ZDM – Mathematics Education, 56(7), 1531-1551. https://doi.org/10.1007/s11858-024-01629-0
• Zakariya, Y. F. (2022). Improving students’ mathematics self-efficacy: A systematic review of intervention studies. Frontiers in Psychology, 13, Article 986622. https://doi.org/10.3389/fpsyg.2022.986622
• Zhuang, Y. (2025). Lessons from using ChatGPT in calculus: Insights from two contrasting cases. Journal of Formative Design in Learning, 9(1), 25-35. https://doi.org/10.1007/s41686-025-00098-2

APA

Nursit, I., Nusantara, T., & Hidayanto, E. (2026). Addressing symbolic obstacles in undergraduate calculus through artificial intelligence scaffolding. Pedagogical Research, 11(4), em0295. https://doi.org/10.29333/pr/19631

Vancouver

Nursit I, Nusantara T, Hidayanto E. Addressing symbolic obstacles in undergraduate calculus through artificial intelligence scaffolding. PEDAGOGICAL RES. 2026;11(4):em0295. https://doi.org/10.29333/pr/19631

AMA

Nursit I, Nusantara T, Hidayanto E. Addressing symbolic obstacles in undergraduate calculus through artificial intelligence scaffolding. PEDAGOGICAL RES. 2026;11(4), em0295. https://doi.org/10.29333/pr/19631

Chicago

Nursit, Isbadar, Toto Nusantara, and Erry Hidayanto. "Addressing symbolic obstacles in undergraduate calculus through artificial intelligence scaffolding". Pedagogical Research 2026 11 no. 4 (2026): em0295. https://doi.org/10.29333/pr/19631

Harvard

Nursit, I., Nusantara, T., and Hidayanto, E. (2026). Addressing symbolic obstacles in undergraduate calculus through artificial intelligence scaffolding. Pedagogical Research, 11(4), em0295. https://doi.org/10.29333/pr/19631

MLA

Nursit, Isbadar et al. "Addressing symbolic obstacles in undergraduate calculus through artificial intelligence scaffolding". Pedagogical Research, vol. 11, no. 4, 2026, em0295. https://doi.org/10.29333/pr/19631