Volume 14 (2025) Download Cover Page

The effectiveness of statistical learning tasks based on Excel software in developing statistical thinking skills related to the labor market among students of the Applied College

Article Number: e2025089  |  Published Online: March 2025  |  DOI: 10.22521/edupij.2025.14.89

Bandar Marzoog Almutairi

Abstract

Background/purpose. This study addresses the lack of statistical thinking skills among Applied College students, a key requirement for professional success. Traditional statistics education focuses on procedural and computational methods rather than conceptual understanding, leading to misconceptions and difficulties in data organization, summarization, and interpretation. The research aligns with Umm Al-Qura University's Program Transformation Project, emphasizing workforce readiness by enhancing statistics courses. The study's primary goal was to design and implement Excel-based statistical learning tasks to improve students' statistical thinking skills, particularly in the Banking and Finance diploma program. The study aimed to equip students with essential data-handling skills for professional environments by integrating practical applications.

Materials/methods. This study utilized a mixed-methods approach, combining qualitative and quantitative techniques. The qualitative aspect focused on designing Excel-based statistical learning tasks and identifying essential statistical thinking skills, while the quantitative component employed a quasi-experimental design to evaluate task effectiveness. 

Results. The study demonstrated a significant improvement in statistical thinking skills among the experimental group using Excel-based tasks. It confirmed their effectiveness in developing key skills. It emphasized their importance in the job market, particularly for banking and finance diploma students, highlighting their role in enhancing future professional competencies.

Conclusion. The study concluded that Excel-based statistical learning tasks effectively develop students' statistical thinking skills. These skills are essential for success in the job market, particularly for students in banking and finance diploma programs.

Keywords: Statistical learning, task design for learning, statistical thinking skills in the labor market

References

Abdel Hamid,A.M.(2006). "The effectiveness of using the practical experiments approach in developing achievement, statistical thinking, and retention of learning statistics among second-year preparatory school students," in The Sixth Scientific Conference - Contemporary Approaches to Developing Mathematics Teaching and Learning, Benha University.

Agustan, S. (2016). Usage of Contextual Approach to Increase Student’s Understanding in Learning Mathematics. IOSR Journal of Mathematics12(6), 118–122.‏

Ali, A. A.(2011). The effectiveness of using the constructivist learning model in developing statistical thinking skills, achievement, and the persistence of the learning effect in statistics among students of colleges of education. Journal of Reading and Knowledge, no. 112, pp. 46–79.

Al-Rawahy, M. O. (2017). The effect of Quest Web in developing statistical thinking skills among tenth-grade students in the Sultanate of Oman. Journal of Educational and Psychological Studies, vol. 11, no. 3, pp. 617-644.

Altaylar, B. & Kazak,S.(2021). The Effect of Realistic Mathematics Education on Sixth Grade Students' Statistical Thinking‏. Acta Didactica Napocensia, vol. 14, no. 1, pp. 76-90.

Ariwinanda, V., Zubainur, C. M., & Sofyan, H. (2022). Statistical Reasoning Ability of Banda Aceh City High School Students. In Eighth Southeast Asia Design Research (SEA-DR) & the Second Science, Technology, Education, Arts, Culture, and Humanity (STEACH) International Conference (SEADR-STEACH 2021) (pp. 259-263). Atlantis Press.‏

Artzt, A. F., & Armour-Thomas, E. (2001). Mathematics teaching as problem-solving: A framework for studying teacher Metacognition underlying instructional practice in mathematics. In Metacognition in learning and instruction: Theory, research, and practice (pp. 127–148). Dordrecht: Springer Netherlands.‏

Baligar, P., Joshi, G., Velankar, Y. P., & Bhadri, G. N. (2017). Improving Data Analysis Skills in First Year Undergraduate Engineering Students: A Constructive Approach. https://doi.org/10.1109/WEEF.2017.8467093

Chan, S. W., Ismail, Z., & Sumintono, B. (2016). A framework for assessing high school students' statistical reasoning. Plos one11(11), e0163846.‏ doi:10.1371/journal.pone.0163846

Ciabattari, T., Lowney, K. S., Monson, R. A., Senter, M. S., & Chin, J. (2018). Linking sociology majors to labor market success. Teaching Sociology46(3), 191-207.‏ https://doi.org/10.1177/0092055X18760691

Coolican, H. (2017). Research methods and statistics in psychology. Psychology Press.‏ https://doi.org/10.4324/9780203769836

DelMas, R. C. (2002). Statistical literacy, reasoning, and thinking: A commentary. Journal of Statistics Education10(2).‏ https://doi.org/10.1080/10691898.2002.11910674

Ducharme, A., Smith, C. P., & King, B. (2022). Pre-Service Teachers' Discourse Moves During Whole Class Mathematical Discussions: An Analysis and Proposed Framework. Journal of Mathematics Education at Teachers College13(2).‏

Fatimah, A. T., Wahyudin, W., & Prabawanto, S. (2020). The role of agricultural contextual knowledge on the mathematical understanding of vocational students. In Journal of Physics: Conference Series (Vol. 1521, No. 3, p. 032020). IOP Publishing.‏ doi:10.1088/1742-6596/1521/3/032020.

Farnsworth, D. L. (2022). Transforming data in a first course in statistics. International Journal of Mathematical Education in Science and Technology, 54, 1146–1152. https://doi.org/10.1080/0020739X.2022.2086499.

Garfield, J. B. (2003). Assessing statistical reasoning. Statistics Education Research Journal2(1), 22–38.‏

Garfield, J., & Ben-Zvi, D. (2004). Research on statistical literacy, reasoning, and thinking: Issues, challenges, and implications. In The challenge of developing statistical literacy, reasoning and thinking (pp. 397–409). Dordrecht: Springer Netherlands.‏

Garfield, J., Hogg, B., Schau, C., & Whittinghill, D. (2002). First courses in statistical science: The status of educational reform efforts. Journal of Statistics Education10(2).‏ https://doi.org/10.1080/10691898.2002.11910665

George, N. R., & Kumah, M. S. Enhancing Senior Secondary Students’ Performance In Statistical Charts Using Microsoft Excel Spreadsheet Software Package In Rivers State Nigeria.International Journal of Scientific and Research Publications, vol. 11, no. 12, pp. 427–432, 2021. http://dx.doi.org/10.29322/IJSRP.11.12.2021.p12061

Ghasempour, Z., Bakar, N., & Jahanshahloo, G. R. (2013). Innovation in teaching and learning through problem-posing tasks and metacognitive strategies. International journal of pedagogical innovations1(01).‏

Glasnovic Gracin, D. (2018). Requirements in mathematics textbooks: a five-dimensional analysis of textbook exercises and examples. International journal of mathematical education in science and technology49(7), 1003-1024.‏ https://doi.org/10.1080/0020739X.2018.1431849

Glasnovic Gracin, D. (2018). Requirements in mathematics textbooks: a five-dimensional analysis of textbook exercises and examples. International journal of mathematical education in science and technology49(7), 1003-1024.‏ https://doi.org/10.1080/0020739X.2018.1431849

Gün, Ö., & Taş, F. (2021). An Evaluation of Mathematical Tasks Designed by Pre-Service Teachers Within the Framework of Task Design Principles. International Journal for Mathematics Teaching and Learning22(2), 17-32.‏

Harris, J. (2023). Statistical Success: Three-Year Analysis of Student Performance and Student Insights from a First-Year College Statistics Course. Advances in Social Sciences Research Journal, 10(5), 103–121. https://doi.org/10.14738/assrj.105.14687

Hershbein, B., Macaluso, C., & Yeh, C. (2018, July). Labor market concentration and the demand for skills. In IDSC of IZA Workshop: Matching Workers and Jobs Online (pp. 2–6).‏

Human Capacity Development Program. (2021). Human Capacity Development Program Media Document. [Online]. Available: https://na.vision2030.gov.sa/ar/v2030/vrps/hcdp /.

Jones, G. A., Thornton, C. A., Langrall, C. W., Mooney, E. S., Perry, B., & Putt, I. J. (2000). A framework for characterizing children's statistical thinking. Mathematical thinking and learning2(4), 269–307.‏ https://doi.org/10.1207/S15327833MTL0204_3

Kesumawati, N. (2020). Students’ Ability of Statistical Reasoning in Descriptive Statistics Problem Solving.‏in Proceedings of the International Conference on Mathematics and Islam (ICMIs 2018). DOI: 10.5220/0008524905300536

Lee, C. B., Tang, H., Sam, K. M., & Xiong, G. (2018). Spreadsheet proficiency: Which spreadsheet skills are important? Journal of Information Technology Management29(3).‏

Mahato, T. K. (2023). Biostatistics: Simple, practical way to use Microsoft Excel to find mean, median, mode, standard deviation, and correlation. World Journal of Biology Pharmacy and Health Sciences13(3), 150-155.‏ https://doi.org/10.30574/wjbphs.2023.13.3.0128

Meitrilova, A., & Putri, R. I. I. (2020). Learning design using PMRI to teach central tendency materials. In Journal of Physics: Conference Series (Vol. 1470, No. 1, p. 012086). IOP Publishing.‏ doi:10.1088/1742-6596/1470/1/012086

Mooney, E. S. (2002). A framework for characterizing middle school students' statistical thinking. Mathematical Thinking and Learning4(1), 23–63.‏

Musa, A. M. & Al-Jabr S. M.(2022). The effect of using problem-solving strategies in developing statistical thinking among sixth-grade students. Al-Quds Open University Journal for Educational and Psychological Research and Studies, vol. 13, no. 38, pp. 63-73.

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 methods16(1), 1609406917733847.‏

Nur, P., & Rambe, N. H. (2023). Kegiatan Pembelajaran dengan Memanfaatkan Aplikasi Microsoft Excel. Kitabah: Jurnal Pendidikan Sosial Humaniora1(2), 63-80.‏

Paredes, S., Cáceres, M. J., Diego-Mantecón, J. M., Blanco, T. F., & Chamoso, J. M. (2020). Creating realistic mathematics tasks involving authenticity, cognitive domains, and openness characteristics: A study with pre-service teachers: Sustainability12(22), 9656.‏

Parrish, C. W., & Bryd, K. O. (2022). Cognitively Demanding Tasks: Supporting Students and Teachers during Engagement and Implementation. International Electronic Journal of Mathematics Education17(1).‏ https://doi.org/10.29333/iejme/11475

Perry, J., Lundie, D., & Golder, G. (2019). Metacognition in schools: What does the literature suggest about the effectiveness of teaching Metacognition in schools? Educational Review71(4), 483-500.‏ http://dx.doi.org/10.1080/00131911.2018.1441127

Radmehr, F. (2023). Toward a theoretical framework for task design in mathematics education.‏ Journal on Mathematics Education, vol. 14, no. 2, pp. 189–204. http://doi.org/10.22342/jme.v14i2.pp189-204

Rahmah, D. A., & Setianingsih, R. (2020). ANALYSIS OF STATISTICAL REASONING IN SOLVING NON-ROUTINE PROBLEMS. Jurnal Ilmiah Pendidikan Matematika Volume9(1).‏

Rosidah, R., & Ikram, F. Z. (2021). The measure of central tendency: undergraduate students' error in decision-making perspective. International Journal of Education14(1), 39-47.‏ doi: 10.17509/ije.v14i1.29408 .

Ruiz, D., Hijón‐Neira, R., & Howard, E. (2024). Sustainable Enhancement of Statistical Literacy: Utilizing Data Analysis and Text Mining to Identify Statistical Issues. https://doi.org/10.20944/preprints202408.0491.v1.

Safarini, T. D. (2019). Developing students’ collaboration skills through project-based learning in statistics. In Journal of Physics: Conference Series (Vol. 1265, No. 1, p. 012011). IOP Publishing.‏ doi:10.1088/1742-6596/1265/1/012011

Saidi, S. S., & Siew, N. M. (2019). Assessing Students' Understanding of the Measures of Central Tendency and Attitude towards Statistics in Rural Secondary Schools. International Electronic Journal of Mathematics Education14(1), 73–86.‏ https://doi.org/10.12973/iejme/3968

Schneider, W., & Artelt, C. (2010). Metacognition and mathematics education. ZDM42, 149-161.‏ DOI 10.1007/s11858-010-0240-2

Schoenfeld, A. H. (2019). What makes for powerful classrooms, and how can we support teachers in creating them? It is a story of research and practice, productively intertwined. Compendium for early career researchers in mathematics education, 495.‏ https://doi.org/10.1007/978-3-030-15636-7

Shaughnessy, J. M. (2007). Research on statistics' reasoning and learning. Second handbook of research on mathematics teaching and learning, 957–1009.‏ https://doi.org/10.14935/jssej.35.139

Shi, Y. (2005). Building math confidence in classroom learning using Microsoft Excel. Online Journal for Workforce Education and Development1(2), 5.‏

Shilo, A., & Kramarski, B. (2019). Mathematical-metacognitive discourse: How can it be developed among teachers and their students? Empirical evidence from a videotaped lesson and two case studies. ZDM51, 625-640.‏ https://doi.org/10.1007/s11858-018-01016-6

Stein, M. K., Grover, B. W., & Henningsen, M. (1996). Building student capacity for mathematical thinking and reasoning: An analysis of mathematical tasks used in reform classrooms. American Educational Research Journal33(2), 455-488.‏

Sulistyani, N. (2019). Error analysis in solving inferential statistics problems for psychology students. In Journal of Physics: Conference Series (Vol. 1180, No. 1, p. 012006). IOP Publishing.‏ doi:10.1088/1742-6596/1180/1/012006

Verhoeven, P. (2006, July). Statistics education in the Netherlands and Flanders: An outline of introductory courses at Universities and Colleges. In ICOTS-7 Conference Proceedings (pp. 60115-2828).‏

Vos, P. (2020). Task contexts in Dutch mathematics education. National reflections on the Netherlands didactics of mathematics: Teaching and learning in the context of realistic mathematics education, 31–53.‏

Wahab, A., Mahmud, A., & Tiro, M. A. (2018). The effectiveness of a learning module for statistical literacy. The New Educational Review53(1), 187-199.‏ DOI: 10.15804/tner.2018.53.3.16

Watson, J., & Callingham, R. (2003). Statistical literacy: A complex hierarchical construct. Statistics Education Research Journal2(2), 3–46.‏

Widjaja, W. (2013). The Use of Contextual Problems to Support Mathematical Learning. Indonesian Mathematical Society Journal on Mathematics Education4(2), 157–168.‏

Wijaya, A., van den Heuvel-Panhuizen, M., & Doorman, M. (2015). Opportunity-to-learn context-based tasks provided by mathematics textbooks. Educational Studies in Mathematics89, 41-65.‏ DOI 10.1007/s10649-015-9595-1

Wilson, N. S., & Bai, H. (2010). The relationships and impact of teachers' metacognitive knowledge and pedagogical understandings of Metacognition. Metacognition and learning5, 269–288.‏ DOI 10.1007/s11409-010-9062-4

Zepeda, C. D., Hlutkowsky, C. O., Partika, A. C., & Nokes-Malach, T. J. (2019). Identifying teachers' support of Metacognition through classroom talk and its relation to growth in conceptual learning. Journal of Educational Psychology111(3), 522.‏ https://dx.doi.org/10.1037/edu0000300

Announcement

EDUPIJ Citation Metrics

EDUPIJ News!

► Educational Process International Journal has changed to publish in article number order instead of in page range order beginning with Volume 14 (2025).