Bridging Theory and Practice: A Literature Review on Learning Trajectories in Statistical Literacy Instruction
DOI:
https://doi.org/10.31980/plusminus.v5i1.2521Keywords:
Statistical Literacy, Prospective Teacher Education, Statistical Instruction, Literasi Statistik, Learning Trajectory, Pendidikan Calon Guru, Pembelajaran Statistik, Systematic Literature ReviewAbstract
Literasi statistik merupakan keterampilan penting, terutama bagi calon guru yang akan mengajarkan konsep-konsep statistik kepada siswa. Namun, banyak calon guru yang kesulitan dalam memahami dan menerapkan konsep statistik secara efektif. Penelitian ini bertujuan mengkaji secara sistematis temuan-temuan terdahulu mengenai penggunaan Learning Trajectory (LT) dalam pembelajaran literasi statistik bagi calon guru, guna merancang kerangka pembelajaran yang lebih terstruktur dan aplikatif. Metode yang digunakan adalah Systematic Literature Review (SLR) berdasarkan model Xiao & Watson, yang mencakup tiga tahap: perencanaan, pelaksanaan, dan pelaporan tinjauan. Artikel diperoleh dari database Education Resource Information Centre (ERIC), melalui proses seleksi bertahap yang terdiri dari penyaringan kualitas, penyaringan kelayakan, dan penyaringan relevansi, hingga diperoleh 7 artikel akhir dari 31 artikel awal. Teknik analisis yang digunakan adalah thematic coding untuk mengidentifikasi pola dan kesenjangan dalam penelitian sebelumnya. Hasil menunjukkan bahwa learning trajectory berpotensi besar dalam meningkatkan pemahaman statistik calon guru dengan menyelaraskan pembelajaran secara progresif, meskipun integrasi konteks lokal dan fokus eksplisit pada calon guru masih jarang ditemukan. Kesimpulannya, learning trajectory yang dirancang secara kontekstual dapat menjadi jembatan antara teori dan praktik dalam pembelajaran literasi statistik untuk calon guru.
Statistical literacy is an essential skill, particularly for prospective teachers who will be responsible for teaching statistical concepts to students. However, many prospective teachers struggle to understand and effectively apply these concepts. This study aims to systematically examine previous findings on the use of Learning Trajectories (LT) in statistical literacy instruction for prospective teachers, to design a more structured and applicable instructional framework. The method employed is a Systematic Literature Review (SLR) based on the Xiao & Watson model, which consists of three stages: planning, conducting, and reporting the review. Articles were sourced from the Education Resource Information Centre (ERIC) database through a multi-step selection process involving quality screening, eligibility screening, and relevancy screening, resulting in a final sample of 7 articles out of an initial 31. Thematic coding was used to identify patterns and gaps in prior research. The results indicate that learning trajectories hold significant potential to enhance prospective teachers’ statistical understanding by supporting progressive learning; however, the integration of local contexts and a specific focus on prospective teachers remain limited. In conclusion, contextually designed learning trajectories can serve as a bridge between theory and practice in statistical literacy instruction for prospective teachers.
References
Afriansyah, E. A., & Arwadi, F. (2021). Learning Trajectory of Quadrilateral Applying Realistic Mathematics Education: Origami-Based Tasks. Mathematics Teaching Research Journal, 13(4), 42-78.
Almašiová, A., Kohútová, K., & Fričová, J. (2021). The level of statistical literacy in future teachers and the use of available technology with the aim to increase it. In AIP Conference Proceedings (Vol. 2343). American Institute of Physics Inc.
Andriatna, R., & Kurniawati, I. (2021). Analisis Level Literasi Statistik Mahasiswa Calon Guru Matematika. Jurnal Pendidikan Matematika Dan Matematika, 5(2), 619–632. https://doi.org/10.36526/tr.v%vi%i.1497
Arnold, P., Confrey, J., Jones, R. S., Lee, H. S., & Pfannkuch, M. (2018). Statistics Learning Trajectories (Ben-Zvi, Ed.; pp. 295–326). Springer International Publishing. https://doi.org/10.1007/978-3-319-66195-7_9
Ben-Zvi, D. (2020). Data handling and statistics teaching and learning. In S. Lerman (Ed.), Encyclopedia of Mathematics Education (2nd Edition, pp. 177–180). Springer. https://doi.org/10.1007/978-3-030-15789-0
Bilgin, E. A. (2021). Developing a mobile application to improve the levels of statistical literacy among graduate students. International Journal of Education and Literacy Studies, 9(4), 113–122. https://doi.org/10.7575/aiac.ijels.v.9n.4p.113
Borremans, L. F. N., Koomen, H. M. Y., & Spilt, J. L. (2024). Fostering Teacher–student Relationship-Building Competence: A Three-Year Learning Trajectory for Initial Pre-Primary and Primary Teacher Education. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1349532
Budgett, S., & Rose, D. (2017). Developing statistical literacy in the final school year. Statistics Education Research Journal, 16(1), 139–162. https://doi.org/https://doi.org/10.52041/serj.v16i1.221
Büscher, C. (2022). Design principles for developing statistical literacy on middle schools. Statistics Education Research Journal, 21(1). https://doi.org/10.52041/serj.v21i1.80
Callingham, R., & Watson, J. M. (2017). The development of statistical literacy at school. Statistics Education Research Journal, 16(1), 181–201. https://doi.org/https://doi.org/10.52041/serj.v16i1.223
Chance, B., Tintle, N., Reynolds, S., Patel, A., Chan, K., & Leader, S. (2023). Student performance in curricula centered simulation-based inference. Statistics Education Research Journal, 21(3), 2–24. https://doi.org/10.52041/serj.v21i3.6
Daro, P., Mosher, F. A., & Corcoran, T. B. (2011). Learning Trajectories in Mathematics: A Foundation for Standards, Curriculum, Assessment, and Instruction. https://doi.org/10.12698/cpre.2011.rr68
Gal, I. (2002). Adults’ statistical literacy: meanings, components, and responsibilities. International Statistical Review, 70(1), 1–25. https://doi.org/10.1111/j.1751-5823.2002.tb00336.x
Gal, I. (2019). Understanding statistical literacy: About knowledge of contexts and models. Actas Del Tercer Congreso Internacional Virtual de Educación Estadística, 1–15. https://www.ugr.es/~fqm126/civeest/ponencias/gal.pdf
Gravemeijer, K., & Cobb, P. (2006). Design research from a learning design perspective. In Akker, Jan van den., Gravemeijer, Koeno., McKenney, Susan., & Nieeven, Nienke. (Ed.), Educational Design Research (pp. 29–63). Routledge. https://doi.org/10.4324/9780203088364-12
Hassad, R. A. (2011). Constructivist and Behaviorist Approaches: Development and Initial Evaluation of a Teaching Practice Scale for Introductory Statistics at the College Level. Numeracy, 4(2). https://doi.org/10.5038/1936-4660.4.2.7
Iqrima, Zulkarnain, I., & Kamaliyah. (2023). Soal Matematika dalam Materi Statistika Berbasis Etnomatematika untuk Mengukur Literasi Matematis Siswa. Plusminus: Jurnal Pendidikan Matematika, 3(1), 39-50. https://doi.org/10.31980/plusminus.v3i1.1221
Jayanti, R., & Cesaria, A. (2024). Pengaruh kemampuan literasi numerasi dan dukungan orang tua terhadap hasil belajar matematika soal cerita di sekolah dasar. Jurnal Inovasi Pembelajaran Matematika: PowerMathEdu, 3(2), 137-148. https://doi.org/10.31980/pme.v3i2.1441
Kim, E. M., Nabors Oláh, L., & Peters, S. (2020). A Learning Progression for Constructing and Interpreting Data Display. ETS Research Report Series, 2020(1), 1–27. https://doi.org/10.1002/ets2.12285
Llinares, A. Z. (2022). Prospective Teachers’ Use of Conceptual Advances of Learning Trajectories to Develop Their Teaching Competence in the Context of Pattern Generalization. Mathematics, 10(12), 1974. https://doi.org/10.3390/math10121974
Meriyati, M., Shaulita, R., & Turnip, L. N. (2018). Problem Based Learning Strategy: The Impact on Mathematical Learning Outcomes Viewed From Anxiety Levels. Al-Jabar Jurnal Pendidikan Matematika, 9(2), 199–208. https://doi.org/10.24042/ajpm.v9i2.3719
Muñiz-Rodríguez, L., Rodríguez-Muñiz, L. J., & Alsina, Á. (2020). Deficits in the statistical and probabilistic literacy of citizens: Effects in a world in crisis. Mathematics, 8(11), 1–20. https://doi.org/10.3390/math8111872
Mutiakandi, N. M., & Sari, N. M. (2024). Literasi Matematis dan Self-Confidence pada Model Problem-Based Learning. Plusminus: Jurnal Pendidikan Matematika, 4(2), 369-384. https://doi.org/10.31980/plusminus.v4i2.1484
Odom, A. L., & Bell, C. V. (2017). Developing PK-12 preservice teachers’ skills for understanding data-driven instruction through inquiry learning. Journal of Statistics Education, 25(1), 29–37. https://doi.org/10.1080/10691898.2017.1288557
Schield, M. (2017). GAISE 2016 promotes statistical literacy. Statistical Education Research Journal, 16(1), 50–54. https://doi.org/https://doi.org/10.52041/serj.v16i1.214
Sharma, S. (2017). Definitions and models of statistical literacy: a literature review. Open Review of Educational Research, 4(1), 118–133. https://doi.org/10.1080/23265507.2017.1354313
Tarabant, C., & Wozniak, F. (2023). The teaching of statistical literacy in France: A curricular study. Thirteenth Congress of the European Society …. https://hal.science/hal-04413700/
Tiro, M. A. (2018). National Movement for Statistical Literacy in Indonesia: An Idea. Journal of Physics: Conference Series, 1028(1). https://doi.org/10.1088/1742-6596/1028/1/012216
Tishkovskaya, S., & Lancaster, G. A. (2010). Teaching Strategies to Promote Statistical Literacy: Review and Implementation. www.stat.auckland.ac.nz/~iase/
Ünal, D., Çığşar, B., Alam, D., Atalar, Ş. E., et al. (2023). Reading The World with Statistical Literacy: Results of An Emprical Study. Bilge International Journal of Science and Technology Research, 7(2), 198-203. https://doi.org/10.30516/bilgesci.1251429
Utari, R. S., Amalia, L., & Rohman. (2024). Developing a Local Instructional Theory using TPACK Framework to Support Students’ Collaborative Skills. AIP Conference Proceedings, 3052(1). https://doi.org/10.1063/5.0201052
Utari, R. S., Putri, R. I. I., Zulkardi, Z., & Hapizah, H. (2024). Designing Learning Trajectories to Support Prospective Teachers’ Statistical Literacy Skills. Mosharafa: Jurnal Pendidikan Matematika, 13(4), 877-894. https://doi.org/10.31980/mosharafa.v13i4.2546
van den Heuvel-Panhuizen, M., Drijvers, P., Education, M., Sciences, B., & Goffree, F. (2014). Realistic Mathematics Education. In Encyclopedia of Mathematics Education (pp. 521–532). https://doi.org/10.1007/978-94-007-4978-8
van Dijke-Droogers, M., Drijvers, P., & Bakker, A. (2022). Introducing Statistical Inference: Design of a Theoretically and Empirically Based Learning Trajectory. International Journal of Science and Mathematics Education, 20(8), 1743–1766. https://doi.org/10.1007/s10763-021-10208-8
Wahyuni, I., Suwarno, S., & Afdhila, D. (2024). Realistic Mathematics-Based E-Booklets to Improve Students' Mathematical Literacy Ability. Mosharafa: Jurnal Pendidikan Matematika, 13(1), 151-162. https://doi.org/10.31980/mosharafa.v13i1.1983
Wallman, K. K. (1993). Enhancing statistical literacy: enriching our society. Journal of the American Statistical Association, 88(421), 1–8. https://doi.org/10.1080/01621459.1993.10594283
Watson, J., & Smith, C. (2022). Statistics education at a time of global disruption and crises: A growing challenge for the curriculum, classroom, and beyond. Curriculum Perspectives, 42(2), 171–179. https://doi.org/10.1007/s41297-022-00167-7
Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Plusminus: Jurnal Pendidikan Matematika
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
