Mapping Internal and External Factors Behind Students’ Mathematics Learning Difficulties: A Qualitative Diagnostic Study at the Lower Secondary Level

Mapping Internal and External Factors Behind Students’ Mathematics Learning Difficulties: A Qualitative Diagnostic Study at the Lower Secondary Level

Authors

  • Maria Editha Bela Sekolah Tinggi Keguruan dan Ilmu Pendidikan Citra Bakti

DOI:

https://doi.org/10.64780/jole.v1i2.71

Keywords:

External factors, learning difficulties, mathematics education, qualitative analysis, student engagement

Abstract

Background: learning at the lower secondary level often presents persistent challenges for students, particularly when conceptual understanding is weak and learning environments are not supportive. Learning difficulties in mathematics cannot be viewed solely as cognitive problems, but rather as complex phenomena shaped by the interaction of internal learner characteristics and external educational contexts. Understanding these interacting factors is essential for designing instructional practices that respond to students’ actual learning needs.

Aims: This study aims to map and analyze the internal and external factors that contribute to students’ difficulties in learning mathematics, with a focus on how these factors influence students’ engagement, conceptual understanding, and ability to apply basic mathematical operations.

Method: A qualitative descriptive approach was employed. Data were collected through classroom observations, semi-structured interviews with students and teachers, documentation, and diagnostic tests. Thirteen lower secondary students were purposively selected to represent varying levels of learning difficulty. Data analysis followed systematic stages of reduction, display, and interpretation to identify recurring patterns across data sources.

Results: The findings indicate that students’ learning difficulties are influenced by internal factors such as low learning motivation, limited mastery of basic mathematical concepts, poor concentration, and reluctance to ask questions. External factors include minimal parental support, unconducive learning environments, and learning habits that prioritize non-academic activities. Diagnostic test results confirmed that these factors are closely associated with students’ weak performance in mathematical problem solving.

Conclusion: The study highlights that mathematics learning difficulties emerge from the interaction between personal learning dispositions and environmental conditions rather than from instructional content alone. These findings emphasize the need for instructional strategies that strengthen foundational understanding, foster active student engagement, and create supportive learning environments both at school and at home. Teachers are encouraged to adopt more responsive and contextualized pedagogical approaches, while parents and schools should collaborate to support students’ learning routines. By addressing both internal and external dimensions of learning difficulty, mathematics instruction can become more inclusive, effective, and aligned with students’ real learning experiences.

References

Abukhousa, E. (2025). Reflect, Reason, Apply: Enhancing Learning and Cognitive Engagement in Maths and Statistics. Int. Conf. High. Educ. Adv., 1040–1048. https://doi.org/10.4995/HEAd25.2025.20068

Ahmed Alnaim, F., & Sakız, H. (2025). Pedagogical components in the inclusion of students with mathematical learning difficulties in mathematics classes. International Journal of Inclusive Education, 29(5), 721–740. https://doi.org/10.1080/13603116.2023.2216697

Annuš, N., & Kmeť, T. (2024). Learn with M.E.—Let Us Boost Personalized Learning in K-12 Math Education! Education Sciences, 14(7). https://doi.org/10.3390/educsci14070773

Arias Valencia, M. M. (2022). Principles, scope, and limitations of the methodological triangulation. 40(2). http://www.scielo.org.co/scielo.php?pid=S0120-53072022000200003&script=sci_arttext

Bakker, A., Cai, J., & Zenger, L. (2021). Future themes of mathematics education research: An international survey before and during the pandemic. Educational Studies in Mathematics, 107(1), 1–24. https://doi.org/10.1007/s10649-021-10049-w

Berkovich, I., & Grinshtain, Y. (2023). A Review of Rigor and Ethics in Qualitative Educational Administration, Management, and Leadership Research Articles Published in 1999-2018. Leadership and Policy in Schools, 22(3), 549–564. https://doi.org/10.1080/15700763.2021.1931349

Bingham, A. J. (2023). From Data Management to Actionable Findings: A Five-Phase Process of Qualitative Data Analysis. International Journal of Qualitative Methods, 22, 16094069231183620. https://doi.org/10.1177/16094069231183620

Dignath, C., Rimm-Kaufman, S., Van Ewijk, R., & Kunter, M. (2022). Teachers’ Beliefs About Inclusive Education and Insights on What Contributes to Those Beliefs: A Meta-analytical Study. Educational Psychology Review, 34(4), 2609–2660. https://doi.org/10.1007/s10648-022-09695-0

Fitrah, M., Sofroniou, A., Setiawan, C., Widihastuti, W., Yarmanetti, N., Jaya, M. P. S., Panuntun, J. G., Arfaton, A., Beteno, S., & Susianti, I. (2025). The Impact of Integrated Project-Based Learning and Flipped Classroom on Students’ Computational Thinking Skills: Embedded Mixed Methods. Education Sciences, 15(4). https://doi.org/10.3390/educsci15040448

Iwuanyanwu, P. N. (2021). Contemporary Problems of Teaching and Learning in Mathematics Education. 13(2), 23–35.

Lapidot-Lefler, N. (2025). Teacher responsiveness in inclusive education: A participatory study of pedagogical practice, well-being, and sustainability. 17(7), 2919.

Li, M., & Li, B. (2024). Unravelling the dynamics of technology integration in mathematics education: A structural equation modelling analysis of TPACK components. Education and Information Technologies, 29(17), 23687–23715. https://doi.org/10.1007/s10639-024-12805-w

López, S. A., Hetz, I. L., López Maldonado, E., Sanhueza, C. Z., Vejar, F. H., & Olivares, H. (2022). School engagement in students from a Mapuche intercultural high school: A qualitative study. Ciencias Psicologicas, 16(1). https://doi.org/10.22235/cp.v16i1.2514

Magnone, K. Q., & Yezierski, E. J. (2024). Beyond Convenience: A Case and Method for Purposive Sampling in Chemistry Teacher Professional Development Research. Journal of Chemical Education, 101(3), 718–726. https://doi.org/10.1021/acs.jchemed.3c00217

Marks, R., Foster, C., Barclay, N., Barnes, A., & Treacy, P. (2021). A comparative synthesis of UK mathematics education research: What are we talking about and do we align with international discourse? Research in Mathematics Education, 23(1), 39–62. https://doi.org/10.1080/14794802.2020.1725612

Morgan, H. (2024). Using triangulation and crystallization to make qualitative studies trustworthy and rigorous. 29(7), 1844–1856.

Nicmanis, M. (2024). Reflexive Content Analysis: An Approach to Qualitative Data Analysis, Reduction, and Description. International Journal of Qualitative Methods, 23, 16094069241236603. https://doi.org/10.1177/16094069241236603

Olivares, D. (2024). A Socio-Constructivist Perspective on Problem-Solving Approaches in Mathematics: Perceptions of Future Primary Education Teachers. International Journal of Learning, Teaching and Educational Research, 23(9), 220–241. https://doi.org/10.26803/ijlter.23.9.12

Prathibha, K. N., Upadhyaya, G., Jagadeesha, B., & Tantry, R. (2024). A Novel Evaluation on the Impact of Modern Pedagogical Tools for Improving the Learning Outcomes of Engineering Mathematics. Proc. - Int. Conf. Adv. Comput., Commun. Appl. Informatics, ACCAI. Proceedings - 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2024. https://doi.org/10.1109/ACCAI61061.2024.10601972

Rath, A. (2025). Leveraging ChatGPT to support terminology learning in oral anatomy: A mixed-methods study among linguistically diverse dental students. BMC Medical Education, 25(1), 1425. https://doi.org/10.1186/s12909-025-07968-0

Rycroft‐Smith, L., & Stylianides, A. J. (2022). What makes a good educational research summary? A comparative judgement study of mathematics teachers’ and mathematics education researchers’ views. Review of Education, 10(1), e3338. https://doi.org/10.1002/rev3.3338

Samuel, A., & Merkebu, J. (2025). Exploring Sampling Strategies to Maximize Qualitative Research Studies in Adult Education. Adult Learning, 10451595251349183. https://doi.org/10.1177/10451595251349183

Stone, L. A., Benoit, L., Martin, A., & Hafler, J. (2023). Barriers to identifying learning disabilities: A qualitative study of clinicians and educators. 23(6), 1166–1174.

Vale, I., & Barbosa, A. (2023). Active learning strategies for an effective mathematics teaching and learning. European Journal of Science and Mathematics Education, 11(3), 573–588. https://doi.org/10.30935/scimath/13135

Zhang, N., Ke, F., Dai, C.-P., Southerland, S. A., & Yuan, X. (2025). Seeking to support preservice teachers’ responsive teaching: Leveraging artificial intelligence-supported virtual simulation. British Journal of Educational Technology, 56(3), 1148–1169. https://doi.org/10.1111/bjet.13522

Zin, N. A. M., & Mahmud, M. S. (2024). Perceptions of Malaysian University Mathematics Instructors of the Challenges they Face in Implementing Effective Distance Learning. International Journal of Learning, Teaching and Educational Research, 23(5), 158–179. https://doi.org/10.26803/ijlter.23.5.9

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Published

2025-06-28

How to Cite

Editha Bela, M. (2025). Mapping Internal and External Factors Behind Students’ Mathematics Learning Difficulties: A Qualitative Diagnostic Study at the Lower Secondary Level . Journal of Literacy Education, 1(2), 70–80. https://doi.org/10.64780/jole.v1i2.71
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