Conceptual and procedural errors of pre-service elementary teachers in solving educational research statistics problems
DOI:
https://doi.org/10.29408/jel.v12i1.32327Keywords:
error analysis, learning difficulties, problem-solving, statistics education researchAbstract
Pre-service elementary teachers often make errors in educational research and problem-solving, highlighting the need to examine the nature and causes of these errors. This study employed a qualitative descriptive case study design to identify the types of errors made by Elementary School Teacher Education (ESTE) students in solving educational research statistics problems and to examine the factors contributing to these errors. The participants were 58 ESTE students enrolled in a statistics course at a private university in Indonesia. Data were collected through problem-solving tests, interviews, and classroom observations. The test instrument was used to assess students’ understanding of statistical concepts, while interviews and observations explored cognitive and contextual factors influencing error occurrence. The findings indicate that conceptual errors occurred in 17% of cases and procedural errors in 22%. Conceptual errors were primarily due to difficulties interpreting statistical software outputs, whereas procedural errors were associated with inaccuracies in applying formulas and performing calculations. These findings contribute to a clearer theoretical understanding of conceptual and procedural errors in statistics learning and highlight the need for instructional approaches that balance conceptual and procedural aspects in educational research statistics.
References
Abu-ghalyoun, O. (2021). Pre-service teachers’ difficulties in reasoning about sampling variability. Educational Studies in Mathematics, 108(3), 553–577. https://doi.org/10.1007/s10649-021-10067-8
Arifin, S., & Aprisal. (2020). Analisis tingkat pemahaman konsep statistika mahasiswa calon guru menggunakan two tier test berbasis online [Analysis of pre-service teachers’ conceptual understanding of statistics using an online two-tier test]. Delta: Jurnal Ilmiah Pendidikan Matematika, 8(2), 201–208. https://doi.org/10.31941/delta.v8i2.1059
Dodeen, H., & Alqawasmi, A. A. (2025). Exploring statistical anxiety, attitudes, and self-efficacy among social sciences students: The impact of gender, academic progression, and achievement. Educational Process: International Journal, 18, e2025437. https://doi.org/10.22521/edupij.2025.18.437
Ferreira, R. A., Rodríguez, C., Guzmán, B., Sepúlveda, F., & Peake, C. (2025). The interplay of working memory, vocabulary, and math anxiety in early mathematical learning. In Journal of Intelligence, 13(10), 1-20. https://doi.org/10.3390/jintelligence13100125
Hiebert, J. (2013). Conceptual and procedural knowledge: The case of mathematics. In Conceptual and Procedural Knowledge: The Case of Mathematics. https://doi.org/10.4324/9780203063538
Ikram, F. Z., & Rosidah. (2024). Kesalahan mahasiswa fakultas keguruan dan ilmu pendidikan dalam menggunakan SPSS [Errors of students in the faculty of teacher training and education in using SPSS]. Jurnal Media TIK: Jurnal Media Pendidikan Teknik Informatika dan Komputer, 7(2), 144–149. https://doi.org/10.59562/mediatik.v7i2.2819
Ismail, E. N., Ramadhani, D. A., Ramadhan, Anie, E. E., Akil, & Azis, A. (2025). Validitas alat ukur dalam evaluasi pembelajaran: Studi faktor yang mempengaruhi validitas alat ukur [Validity of measurement tools in learning evaluation: A study of factors affecting instrument validity]. Jurnal Kajian Ilmiah Interdisipliner, 9(5), 391–400. https://sejurnal.com/pub/index.php/jkii/article/view/7428
Lakshmanan, M. (2022). Common errors in using statistical tools and data presentation. In Introduction to Basics of Pharmacology and Toxicology: Volume 3: Experimental Pharmacology: Research Methodology and Biostatistics, 897–910. Springer Nature Singapore. https://doi.org/10.1007/978-981-19-5343-9_63
Layn, M. R., Arsyad, R. Bin, Mulyono, Sira’a, Y., & Kadtabalubun, C. (2023). Analisis kesalahan menyelesaikan soal statistika dan pengolahan data ditinjau dari kemampuan mahasiswa Universitas Muhammadiyah Sorong [Analysis of errors in solving statistical problems and data processing: a review of the abilities of students at Muhammadiyah University of Sorong]. KAMBIK: Journal of Mathematics Education, 1(2), 43–53. https://doi.org/10.33506/jme.v1i2.3068
Legesse, M. Y., Kakoma, L., & Ejigu, T. (2020). Analyzing the effects of mathematical discourse-based instruction on eleventh-grade students’ procedural and conceptual understanding of probability and statistics. Studies in Educational Evaluation, 67, 100918. https://doi.org/10.1016/j.stueduc.2020.100918
Leng, N., & Meng, C. (2023). Making sense of students’ errors in solving problems related to measures of dispersion. International Journal of Evaluation and Research in Education (IJERE), 12(2), 924–940. https://doi.org/10.11591/ijere.v12i2.24580
Lenz, K., Reinhold, F., & Wittmann, G. (2024). Transitions between conceptual and procedural knowledge profiles. Patterns in understanding fractions and indicators for individual differences. Learning and Individual Differences, 116(102548), 1–12. https://doi.org/10.1016/j.lindif.2024.102548
Lian, L. H., Yew, W. T., & Meng, C. C. (2022). Assessing lower secondary school students’ common errors in statistics. Pertanika Journal of Social Sciences and Humanities, 30(3), 1427–1450. https://doi.org/10.47836/pjssh.30.3.26
Mendes, R. A., Loxton, N. J., Stuart, J., Donnell, A. W. O., & Stainer, M. J. (2024). Statistics anxiety or statistics fear? A reinforcement sensitivity theory perspective on psychology students’ statistics anxiety, attitudes, and self-efficacy. European Journal of Psychology of Education, 39(3), 2461–2480. https://doi.org/10.1007/s10212-024-00802-z
Miles, M. B., Huberman, A. M., & Saldana, J. (2014). Qualitative data analysis: A methods sourcebook (3 ed.). https://www.metodos.work/wp-content/uploads/2024/01/Qualitative-Data-Analysis.pdf
Nisa, R. K., & Dahlan, J. A. (2025). Analyzing students’ errors on the topic of statistics using Watson’s criteria. Pi-Radian: Journal of Mathematics Education. Pi-Radian: Journal of Mathematics Education, 3(1), 61–76. https://doi.org/10.63214/piradian.v3i1.pp61-76
Nooijen, C. C. A. Van, Koning, B. B. De, Bramer, W. M., Isahakyan, A., Asoodar, M., Kok, E., Merrienboer, J. J. G. Van, & Paas, F. (2024). A cognitive load theory approach to understanding expert–novice differences: scaffolding and working memory limitations. In Educational Psychology Review, 36(1). Springer US. https://doi.org/10.1007/s10648-024-09848-3
Ouwehand, K., Lespiau, F., Tricot, A., & Paas, F. (2025). Cognitive load theory: Emerging trends and innovations. Education Sciences, 15, 458. https://doi.org/10.3390/educsci15040458
Pallauta, J. D., Arteaga, P., & Garzón-Guerrero, J. A. (2021). Secondary school students’ construction and interpretation of statistical tables. Mathematics, 9(24). https://doi.org/10.3390/math9243197
Parks, J., & Yeh, D. D. (2021). How to Lie with Statistics and Figures. Surgical Infections, 22(6), 611–619. https://doi.org/10.1089/sur.2021.065
Prameshti, N. L., Darmawan, P., & Dejarlo, J. O. (2024). Analysis of students’ errors in solving statistics problems based on Newman’s Error theory : a study on high school students. Polyhedron International Journal in Mathematics Education, 2(11), 56–63. https://doi.org/10.59965/pijme.v2i2.150
Pujiarti, T., Mahdin, M., & Ilham, I. (2024). Analisis kemampuan pemahaman konsep pada mata kuliah dasar-dasar statistik mahasiswa PGSD STKIP Yapis Dompu [Analysis of conceptual understanding in basic statistics courses for PGSD students at STKIP Yapis Dompu]. JagoMIPA: Jurnal Pendidikan Matematika dan IPA, 4(2), 345–351. https://doi.org/10.53299/jagomipa.v4i2.600
Radke, S. C., Krishnamoorthy, R., Ma, J. Y., & Kelton, M. L. (2023). “Your truth isn’t the truth”: Data activities and informal inferential reasoning. In The Journal of Mathematical Behavior, 69, 1–17. Elsevier Science. https://doi.org/10.1016/j.jmathb.2023.101053
Rohimah, S. M. (2024). Statistika penelitian pendidikan (analisis manual dan IBM SPSS) [Educational research statistics: Manual and IBM SPSS analysis]. Rajawali Press.
Romero, E. P. J., Laguerta, M., & Andrade, R. (2023). Perceived statistics self-efficacy, research anxiety, and research confidence of mathematics pre-service teachers in one state university in the Philippines. Jurnal Pendidikan Progresif, 13(2), 708–722. https://doi.org/10.23960/jpp.v13.i2.202343
Sardareh, S. A., Brown, G., & Denny, P. (2025). Statistical software usability for novice research students in the social sciences: An eye-tracking study. Journal of Statistics and Data Science Education, 00(0), 1–25. https://doi.org/10.1080/26939169.2025.2497550
Shamsuddin, M., Mahlan, S. B., Alias, F. A., Hamat, M., & Mohamed, S. A. (2021). Analysis of student error in statistical subject: A case study for online learning. International Journal of Academic Research in Progressive Education and Development, 10(3), 73–83. https://doi.org/10.6007/IJARPED/v10-i3/10714
Shimizu, Y., & Kang, H. (2025). Research on classroom practice and students ’ errors in mathematics education: A scoping review of recent developments for 2018-2023. ZDM Mathematics Education, 57, 695–710. https://doi.org/10.1007/s11858-025-01704-0
Silva, P. N., & Sarnecka, B. W. (2025). What do your students struggle with? A survey of statistics instructors. Journal of Statistics and Data Science Education, 00(0), 1–12. https://doi.org/10.1080/26939169.2025.2455560
Sugiyono. (2017). Metode penelitian kuantitatif, kualitatif, dan R&D [Quantitative, qualitative, and R&D research methods]. Alfabeta, CV. https://digi-lib.stekom.ac.id/assets/dokumen/ebook/feb_35efe6a47227d6031a75569c2f3f39d44fe2db43_1652079047.pdf
Thanheiser, E., & Mamolo, A. (2024). Introduction to the virtual special issue: Mathematics that underpins social issues. Journal of Mathematical Behavior, 75, 101176. https://doi.org/10.1016/j.jmathb.2024.101176
Uğraş, H. (2025). Research on mathematics anxiety in primary school: bibliometric analysis and evaluation of trends. Frontiers in Psychology, 16(1545556), 1–21. https://doi.org/10.3389/fpsyg.2025.1545556
Vetten, A. De, Keijzer, R., Schoonenboom, J., & Oers, B. Van. (2023). Pre-service primary school teachers’ knowledge during teaching informal statistical inference. Statistics Education Research Journal, 22(1), 1–16. https://doi.org/10.52041/serj.v22i2.424
Winarso, W., & Toheri, T. (2021). An analysis of students’ errors in learning mathematical problem solving: The perspective of David Kolb’s theory. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(1), 139–150. https://doi.org/10.16949/turkbilmat.753899
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Siti Maryam Rohimah, Sarah Anida Putri, Yullys Helsa
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with the Jurnal Elemen agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).
- Authors are able to enter into separate, additional contractual arrangements for the distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Jurnal Elemen is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License
