Function Art
Understanding the Artistic Transformation of Mathematical Functions through Learning Analytics
DOI:
https://doi.org/10.18608/jla.2026.9037Keywords:
function art, learning analytics, STEAM education, mathematical function, student profiling, research paperAbstract
This study explores how students across Grades 8 to 12 engage with mathematical functions in creative, visual ways through function art—an innovative STEAM-based educational approach. Grounded in the Trends in International Mathematics and Science Study (TIMSS) framework and employing a Design-Based Research methodology, the project involved 400 students from the Philippines who created digital artworks using GeoGebra. To uncover learner profiles, a person-centred clustering method—hierarchical clustering on principal components—was applied to variables representing the number and types of functions used. The results revealed three distinct student profiles: Repetitivists (high function quantity, low diversity), Simplists (low quantity and diversity), and Multifunctionists (high diversity, low quantity). Further analysis showed meaningful associations between cluster membership, grade level, and function strategies. Qualitative evaluation using TIMSS cognitive domains—Knowing, Applying, and Reasoning—highlighted that students’ use of mathematical strategies and precision in transformations varied widely, often independently of the quantity or diversity of functions used. These findings suggest that function art, when analyzed through learning analytics, provides a rich lens for understanding students’ mathematical thinking and offers valuable insights for tailoring interdisciplinary instruction in STEAM education.
References
Adak, A. K. (2021). The investigation on the reasoning and motivation: An assessment of the case of the students. Journal of Research in Humanities and Social Science, 9(2), 30–39.
Aguerrebere, C., He, H., Kwet, M., Laakso, M.-J., Lang, C., Marconi, C., Price-Dennis, D., & Zhang, H. (2022). Global perspectives on learning analytics in K-12 education. In C. Lang, G. Siemens, & A. F. Wise (Eds.), The handbook of learning analytics (2nd ed., pp. 223–231). SOLAR. https://doi.org/10.18608/hla22.022
Aldenderfer, M. S., & Blashfield, R. K. (1984). Cluster Analysis. Sage Publications.
Alhadad, S. S. J. (2018). Visualizing data to support judgement, inference, and decision making in learning analytics: Insights from cognitive psychology and visualization science. Journal of Learning Analytics, 5(2), 60–85. https://doi.org/10.18608/jla.2018.52.5
Antonenko, P. D., Toy, S., & Niederhauser, D. S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60(3), 383–398. https://doi.org/10.1007/s11423-012-9235-8
Arias-Alfonso, A. F., & Franco, C. A. (2021). The creative act in the dialogue between art and Mathematics. Mathematics, 9(13), Article 1517. https://doi.org/10.3390/math9131517
Asendorpf, J. B. (2002). The puzzle of personality types. European Journal of Personality, 16(1_suppl), S1–S5. https://doi.org/10.1002/per.446
Audigier, V., Husson, F., & Josse, J. (2016). A principal component method to impute missing values for mixed data. Advances in Data Analysis and Classification, 10(1), 5–26. https://doi.org/10.1007/s11634-014-0195-1
Avila, C. L. (2013). Graphing art revisited: The evolution of a good idea. Ohio Journal of School Mathematics, 67.
Barry, M. (2021). The graph menagerie: An exploration of the intersection of math, biology, and art (Publication No. 1432) [Undergraduate thesis, Western Washington University]. MABEL.
Bautista, G., Jr., Malacapo, J., Gallos-Cronberg, F., Kasti, H., Dana-Picard, N., & Lavicza, Z. (2025). Function arts: Linking mathematics, technology, and visual arts. School Science and Mathematics. https://doi.org/10.1111/ssm.18373
Bautista, G., Jr., Prodromou, T., Raynes, J., Gonzales, A., & Lavicza, Z. (2024). Function art: Analyzing popular functions and strategies. In E. Faggiano, A. Clark-Wilson, M. Tabach, & H.-G. Weigand (Eds.), Proceedings of the 17th ERME Topic Conference MEDA4 (pp. 65–72). University of Bari Aldo Moro.
Bautista, G., Jr., Prodromou, T., & Lavicza, Z. (2024). Function art as a teaching tool in STEAM education. 15th International Congress on Mathematical Education. https://www.researchgate.net/publication/382452429_FUNCTION_ART_AS_A_TEACHING_TOOL_IN_STEAM_EDUCATION
Bautista, G., Jr., Soeharto, S., Emara, M., Lavicza, Z., & Sabitzer, B. (2025). Exploring gender differences in mathematical fluency and flexibility in function art. International Journal of Mathematical Education in Science and Technology. https://doi.org/10.1080/0020739X.2025.2574945
Bautista, G., Jr., Tejera, M., Dana-Picard, T., & Lavicza, Z. (2023). Using DGS in investigating synchronised registers of representations of extrema problems. International Journal for Technology in Mathematics Education, 13(3), 163–170. https://doi.org/10.1564/tme_v30.3.7
Bergman, L. R. (2000). The application of a person-oriented approach: Types and clusters. In L. R. Bergman, R. B. Cairns, L.-G. Nilsson, & L. Nystedt (Eds.), Developmental science and the holistic approach (pp. 147–164). Routledge.
Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1), 205–220. https://www.learntechlib.org/p/156318/
Bevan, B., Peppler, K., Rosin, M., Scarff, L., Soep, E., & Wong, J. (2020). Purposeful pursuits: Leveraging the epistemic practices of the arts and sciences. In A. J. Stewart, M. P. Mueller, D. J. Tippins (Eds.), Converting STEM into STEAM programs: Methods and examples from and for education (pp. 21–38). Springer Nature. https://doi.org/10.1007/978-3-030-25101-7_3
Black, L. (2011). Art projects to further the understanding of algebra [Master’s thesis, California State University Channel Islands]. ScholarWorks. https://scholarworks.calstate.edu/downloads/r494vk97f
Blanton, M., Isler-Baykal, I., Stroud, R., Stephens, A., Knuth, E., & Gardiner, A. M. (2019). Growth in children’s understanding of generalizing and representing mathematical structure and relationships. Educational Studies in Mathematics, 102, 193–219. https://doi.org/10.1007/s10649-019-09894-7
Boaler, J. (2015). Mathematical mindsets: Unleashing students’ potential through creative math, inspiring messages and innovative teaching. Jossey-Bass.
Bouchet, F., Harley, J. M., Trevors, G. J., & Azevedo, R. (2013). Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. Journal of Educational Data Mining, 5(1), 104–146. https://doi.org/10.5281/ZENODO.3554613
Buckingham Shum, S., & Ferguson, R. (2012). Social learning analytics. Journal of Educational Technology & Society, 15(3), 3–26. https://www.jstor.org/stable/jeductechsoci.15.3.3
Calderón-Tena, C. O. (2016). Mathematical development: The role of broad cognitive processes. Educational Psychology in Practice, 32(2), 107–121. https://doi.org/10.1080/02667363.2015.1114468
Caspari-Sadeghi, S. (2022). Applying learning analytics in online environments: Measuring learners’ engagement unobtrusively. Frontiers in Education, 7, Article 840947. https://doi.org/10.3389/feduc.2022.840947
Cho, P., & Moore-Russo, D. (2014). How students come to understand the domain and range for the graphs of functions. Proceedings of the Joint Meeting of PME 38 and PME-NA, 36(2), 281–288.
Clements, D. H., & Battista, M. T. (1992). Geometry and spatial reasoning. In D. A. Grouws (Ed.), Handbook of research on mathematics teaching and learning (pp. 420–464). Macmillan. https://doi.org/10.1108/978-1-60752-874-620251022
Cobo Rodríguez, G., García-Solórzano, D., Santamaría, E., & Morán Moreno, J. A. (2011). Modeling students’ activity in online discussion forums: A strategy based on time series and agglomerative hierarchical clustering. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, & J. Stamper (Eds.), Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011) (pp. 61–70). Eindhoven University of Technology. https://www.researchgate.net/publication/221570398_Modeling_Students'_Activity_in_Online_Discussion_Forums_A_Strategy_based_on_Time_Series_and_Agglomerative_Hierarchical_Clustering
Confrey, J., & Smith, E. (1995). Splitting, covariation, and their role in the development of exponential functions. Journal for Research in Mathematics Education, 26(1), 66–86. https://doi.org/10.2307/749228
Coskun, S. (2011). A multiple case study investigating the effects of technology on students’ visual and non-visual thinking preferences: Comparing paper-pencil and dynamic software based strategies of algebra word problems (Publication No. 1912) [Doctoral dissertation, University of Central Florida]. STARS. https://stars.library.ucf.edu/etd/1912
Costantino, T. (2018). STEAM by another name: Transdisciplinary practice in art and design education. Arts Education Policy Review, 119(2), 100–106. https://doi.org/10.1080/10632913.2017.1292973
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.
De Sousa, R. G., Peres, S. M., Fantinato, M., & Reijers, H. A. (2021). Concept drift detection and localization in process mining: An integrated and efficient approach enabled by trace clustering. In Proceedings of the 36th Annual ACM Symposium on Applied Computing (pp. 364–373). ACM Press. https://doi.org/10.1145/3412841.3441918
Dietiker, L. (2015). What mathematics education can learn from art: The assumptions, values, and vision of mathematics education. Journal of Education, 195(1), 1–10. https://www.jstor.org/stable/44510604
Ding, C., & He, X. (2004). K-means clustering via principal component analysis. In Proceedings of the twenty-first international conference on machine learning (ICML ’04) (p. 29). ACM Press. https://doi.org/10.1145/1015330.1015408
Disher, F. (1995). Graphing art. The Mathematics Teacher, 88(2), 124–128. https://www.jstor.org/stable/27969231
Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychologist, 46(1), 6–25. https://doi.org/10.1080/00461520.2011.538645
Emara, M., Schwab, S., Alnahdi, G., & Gerdenitsch, C. (2025). The relationship between students’ personality traits, attention state, and use of regulatory strategies during emergent distance learning. BMC Psychology, 13(1), Article 118. https://doi.org/10.1186/s40359-025-02451-3
Everitt, B. S. (1979). Unresolved problems in cluster analysis. Biometrics, 35(1), 169–181. https://doi.org/10.2307/2529943
Fenyvesi, K., Brownell, C., Burnard, P., Sinha, P., Olivier, W., Steyn, C., & Lavicza, Z. (2019). Mathematics and art connections expressed in artworks by South African students. In S. Wuppuluri & D. Wu (Eds.), On art and science: Tango of an eternally inseparable duo (pp. 291–312). Springer International Publishing. https://doi.org/10.1007/978-3-030-27577-8_19
Freedman, K. (2003). The importance of student artistic production to teaching visual culture. Art Education, 56(2), 38–43.
Gardner, J., & Brooks, C. (2018). Student success prediction in MOOCs. User Modeling and User-Adapted Interaction, 28(2), 127–203. https://doi.org/10.1007/s11257-018-9203-z
Gavrilas, L., & Kotsis, K. T. (2025). Integrating learning theories and innovative pedagogies in STEM education: A comprehensive review. Eurasian Journal of Science and Environmental Education, 5(1), 11–17.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (Eds.). (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.
Halverson, E., & Sawyer, K. (2022). Learning in and through the arts. Journal of the Learning Sciences, 31(1), 1–13. https://doi.org/10.1080/10508406.2022.2029127
Henriksen, D. (2017). Creating STEAM with design thinking: Beyond STEM and arts integration. STEAM, 3(1), Article 11. https://doi.org/10.5642/steam.20170301.11
Henriksen, D., Creely, E., Henderson, M., & Mishra, P. (2021). Creativity and technology in teaching and learning: A literature review of the uneasy space of implementation. Educational Technology Research and Development, 69(4), 2091–2108. https://doi.org/10.1007/s11423-020-09912-z
Hohenwarter, M., Hohenwarter, J., Kreis, Y., & Lavicza, Z. (2008). Teaching and learning calculus with free dynamic mathematics software GeoGebra. In 11th International Congress on Mathematical Education (ICME 11). https://www.researchgate.net/publication/228869636_Teaching_and_Learning_Calculus_with_Free_Dynamic_Mathematics_Software_GeoGebra
Ifenthaler, D., & Yau, J. Y.-K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68(4), 1961–1990. https://doi.org/10.1007/s11423-020-09788-z
Khalil, M., & Ebner, M. (2017). Clustering patterns of engagement in Massive Open Online Courses (MOOCs): The use of learning analytics to reveal student categories. Journal of Computing in Higher Education, 29(1), 114–132. https://doi.org/10.1007/s12528-016-9126-9
Kieran, C., & Drijvers, P. (2006). The co-emergence of machine techniques, paper-and-pencil techniques, and theoretical reflection: A study of CAS use in secondary school algebra. International Journal of Computers for Mathematical Learning, 11(2), 205–263.
Kim, B., & Kim, J. (2016). Development and validation of evaluation indicators for teaching competency in STEAM education in Korea. Eurasia Journal of Mathematics, Science and Technology Education, 12(7), 1909–1924. https://doi.org/10.12973/eurasia.2016.1537a
Knight, S., Wise, A. F., & Chen, B. (2017). Time for change: Why learning analytics needs temporal analysis. Journal of Learning Analytics, 4(3), 7–17. https://doi.org/10.18608/jla.2017.43.2
Komatsu, K., & Jones, K. (2020). Interplay between paper-and-pencil activity and dynamic-geometry-environment use during generalisation and proving. Digital Experiences in Mathematics Education, 6(2), 123–143. https://doi.org/10.1007/s40751-020-00067-3
Kovanović, V., Joksimović, S., Poquet, O., Hennis, T., De Vries, P., Hatala, M., Dawson, S., Siemens, G., & Gašević, D. (2019). Examining communities of inquiry in Massive Open Online Courses: The role of study strategies. The Internet and Higher Education, 40, 20–43. https://doi.org/10.1016/j.iheduc.2018.09.001
Kovanovic, V., Mazziotti, C., & Lodge, J. (2021). Learning analytics for primary and secondary schools. Journal of Learning Analytics, 8(2), 1–5. https://doi.org/10.18608/jla.2021.7543
Krippendorff, K. (2019). Content analysis: An introduction to its methodology (4th ed.). Sage Publications.
Land, M. H. (2013). Full STEAM ahead: The benefits of integrating the arts into STEM. Procedia Computer Science, 20, 547–552. https://doi.org/10.1016/j.procs.2013.09.317
Landau, S., Morven, L., Everitt, B. S., & Stahl, D. (2011). Cluster analysis (5th ed.). John Wiley & Sons.
Lee, B. (2002). Name, arts, mathematics, and technology. In Meeting of ICTM2, Greece. https://users.math.uoc.gr/~ictm2/Proceedings/pap280.pdf
Lee, J. E., Chan, J. Y. C., Botelho, A., & Ottmar, E. (2022). Does slow and steady win the race? Clustering patterns of students’ behaviors in an interactive online mathematics game. Educational Technology Research and Development, 70(5), 1575–1599. https://doi.org/10.1007/s11423-022-10138-4
Lee, Y.-S., Park, Y. S., & Taylan, D. (2011). A cognitive diagnostic modeling of attribute mastery in Massachusetts, Minnesota, and the U.S. national sample using the TIMSS 2007. International Journal of Testing, 11(2), 144–177. https://doi.org/10.1080/15305058.2010.534571
Leinhardt, G., Zaslavsky, O., & Stein, M. K. (1990). Functions, graphs, and graphing: Tasks, learning, and teaching. Review of Educational Research, 60(1), 1–64. https://www.jstor.org/stable/1170224
Lemaire, P., & Reder, L. (1999). What affects strategy selection in arithmetic? The example of parity and five effects on product verification. Memory & Cognition, 27(2), 364–382. https://doi.org/10.3758/BF03211420
Lepp, A., Li, J., Barkley, J. E., & Salehi-Esfahani, S. (2015). Exploring the relationships between college students’ cell phone use, personality, and leisure. Computers in Human Behavior, 43, 210–219. https://doi.org/10.1016/j.chb.2014.11.006
Liu, H. (2015). Comparing Welch’s ANOVA, a Kruskal-Wallis test and traditional ANOVA in case of heterogeneity of variance (Publication No. 3985) [Master’s thesis, Virginia Commonwealth University]. VCU Scholars Compass. https://doi.org/10.25772/BWFP-YE95
Marshall, J. (2014). Transdisciplinarity and art integration: Toward a new understanding of art-based learning across the curriculum. Studies in Art Education, 55(2), 104–127. https://doi.org/10.1080/00393541.2014.11518922
Marton, F., & Säljö, R. (1976). On qualitative differences in learning: I—Outcome and process. British Journal of Educational Psychology, 46(1), 4–11. https://doi.org/10.1111/j.2044-8279.1976.tb02980.x
McKenney, S., & Reeves, T. C. (2013). Systematic review of design-based research progress: Is a little knowledge a dangerous thing? Educational Researcher, 42(2), 97–100. https://doi.org/10.3102/0013189X12463781
Mejias, S., Thompson, N., Sedas, R. M., Rosin, M., Soep, E., Peppler, K., Roche, J., Wong, J., Hurley, M., Bell, P., & Bevan, B. (2021). The trouble with STEAM and why we use it anyway. Science Education, 105(2), 209–223. https://doi.org/10.1002/sce.21605
Moon, J., Yeo, S., Banihashem, S. K., & Noroozi, O. (2024). Using multimodal learning analytics as a formative assessment tool: Exploring collaborative dynamics in mathematics teacher education. Journal of Computer Assisted Learning, 40(6), 2753–2771. https://doi.org/10.1111/jcal.13028
Mullis, I. V. S., & Martin, M. O. (Eds.). (2017). TIMSS 2019 assessment frameworks. TIMSS & PIRLS International Study Center. http://timssandpirls.bc.edu/timss2019/frameworks/
Mullis, I. V. S., & Martin, M. O. (2019). PIRLS 2021 assessment frameworks. TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College and International Association for the Evaluation of Educational Achievement.
Mullis, I. V. S., Martin, M. O., Foy, P., Kelly, D. L., & Fishbein, B. (Eds.). (2020). TIMSS 2019 international results in mathematics and science. TIMSS & PIRLS International Study Center. https://timssandpirls.bc.edu/timss2019/international-results/
Mullis, I. V. S., Martin, M. O., & von Davier, M. (Eds.). (2021). TIMSS 2023 assessment framework. TIMSS & PIRLS International Study Center. https://timssandpirls.bc.edu/timss2023/frameworks/index.html
National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics.
Oviatt, S., Grafsgaard, J., Chen, L., & Ochoa, X. (2018). Multimodal learning analytics: Assessing learners’ mental state during the process of learning. In The handbook of multimodal-multisensor interfaces: Signal processing, architectures, and detection of emotion and cognition–Volume 2 (pp. 331–374). Association for Computing Machinery and Morgan & Claypool. https://doi.org/10.1145/3107990.3108003
Papert, S. (1980). Mindstorm: Children, computers, and powerful ideas. Basic Books.
Papert, S., & Harel, C. (1991). Situating constructionism. In Constructionism. Ablex Publishing. https://web.media.mit.edu/~calla/web_comunidad/Reading-En/situating_constructionism.pdf
Patton, M. Q. (2014). Qualitative research & evaluation methods (4th ed.). Sage Publications.
Peppler, K., & Wohlwend, K. (2018). Theorizing the nexus of STEAM practice. Arts Education Policy Review, 119(2), 88–99. https://doi.org/10.1080/10632913.2017.1316331
Perales, F. J., & Aróstegui, J. L. (2024). The STEAM approach: Implementation and educational, social and economic consequences. Arts Education Policy Review, 125(2), 59–67. https://doi.org/10.1080/10632913.2021.1974997
Perignat, E., & Katz-Buonincontro, J. (2019). STEAM in practice and research: An integrative literature review. Thinking Skills and Creativity, 31, 31–43. https://doi.org/10.1145/3107990.3108003
Peters, A. (2024). Using the TIMSS curriculum model to develop a framework for coherence and its role in developing mathematical connections. Research in Mathematics Education, 26(2), 300–324. https://doi.org/10.1080/14794802.2024.2371026
Portaankorva-Koivisto, P., & Havinga, M. (2019). Integrative phenomena in visual arts and mathematics. Journal of Mathematics and the Arts, 13(1–2), 4–24. https://doi.org/10.1080/17513472.2018.1504269
Radford, L. (2006). Algebraic thinking and the generalization of patterns: A semiotic perspective. In S. Alatorre, J. L. Cortina, M. Sáiz, & A. Méndez (Eds.), Proceedings of the 28th Conference of the International Group for the Psychology of Mathematics Education, North American Chapter (Vol. 1, pp. 2–21). Universidad Pedagógica Nacional. https://www.researchgate.net/publication/239933692_Algebraic_thinking_and_the_generalization_of_patterns_A_semiotic_perspective
Rebholz, J. (2017). Alphabet soup. The Mathematics Teacher, 110(7), 534–540. https://doi.org/10.5951/mathteacher.110.7.0534
Schneider, M., & Stern, E. (2010). The developmental relations between conceptual and procedural knowledge: A multimethod approach. Developmental Psychology, 46(1), 178–192. https://doi.org/10.1037/a0016701
Schoevers, E. M., Leseman, P. P., & Kroesbergen, E. H. (2020). Enriching mathematics education with visual arts: Effects on elementary school students’ ability in geometry and visual arts. International Journal of Science and Mathematics Education, 18(8), 1613–1634. https://doi.org/10.1007/s10763-019-10018-z
Sharp, B. (2007). Making the most of digital imagery. The Mathematics Teacher, 100(9), 590–593. https://www.jstor.org/stable/27972345
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
Stewart, J., Redlin, L., & Watson, S. (2024). Precalculus: Mathematics for calculus (8th ed.). Cengage Learning.
Stylianou, D. A. (2002). On the interaction of visualization and analysis: The negotiation of a visual representation in expert problem solving. The Journal of Mathematical Behavior, 21(3), 303–317. https://doi.org/10.1016/S0732-3123(02)00131-1
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312. https://doi.org/10.1016/0959-4752(94)90003-5
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory in perspective. In J. Sweller, P. Ayres, & S. Kalyuga, Cognitive load theory (pp. 237–242). Springer New York. https://doi.org/10.1007/978-1-4419-8126-4_18
Sylviani, S., Permana, F. C., & Azizan, A. T. (2024). Enhancing mathematical interest through visual arts integration: A systematic literature review. International Journal of Education in Mathematics, Science and Technology, 12(5), 1217–1235. https://doi.org/10.46328/ijemst.4118
Thuneberg, H. M., Salmi, H. S., & Bogner, F. X. (2018). How creativity, autonomy and visual reasoning contribute to cognitive learning in a STEAM hands-on inquiry-based math module. Thinking Skills and Creativity, 29, 153–160. https://doi.org/10.1016/j.tsc.2018.07.003
Tinoca, L., Piedade, J., Santos, S., Pedro, A., & Gomes, S. (2022). Design-based research in the educational field: A systematic literature review. Education Sciences, 12(6), Article 410. https://doi.org/10.3390/educsci12060410
Troup, J. D. S. (2015). Students’ development of geometric reasoning about the derivative of complex-valued functions (Publication No. 313) [Doctoral dissertation, University of Northern Colorado]. UNCOpen. https://digscholarship.unco.edu/dissertations/313/
Troup, J., Soto, H., & Kemp, A. (2024). Developing geometric reasoning of the relationship of the Cauchy Riemann equations and differentiation. International Journal of Research in Undergraduate Mathematics Education, 10(2), 607–641. https://doi.org/10.1007/s40753-023-00223-1
von Davier, M., Kennedy, A., Reynolds, K., Fishbein, B., Khorramdel, L., Aldrich, C., Bookbinder, A., Bezirhan, U., & Yin, L. (2024). TIMSS 2023 international results in mathematics and science. TIMSS & PIRLS International Study Center. https://doi.org/10.6017/lse.tpisc.timss.rs6460
Wahba, F. A. A., Tabieh, A. A., & Banat, S. Y. (2022). The power of STEAM activities in enhancing the level of metacognitive awareness of mathematics among students at the primary stage. Eurasia Journal of Mathematics, Science and Technology Education, 18(11), Article em2185. https://doi.org/10.29333/ejmste/12562
Whitehill, J., Mohan, K., Seaton, D., Rosen, Y., & Tingley, D. (2017). Delving deeper into MOOC student dropout prediction. arXiv. https://doi.org/10.48550/ARXIV.1702.06404
Wilkinson, L., & Friendly, M. (2009). The history of the cluster heat map. The American Statistician, 63(2), 179–184. https://doi.org/10.1198/tas.2009.0033
Wilkie, K. J. (2024). Creative thinking for learning algebra: Year 10 students’ problem solving and problem posing with quadratic figural patterns. Thinking Skills and Creativity, 52, Article 101550. https://doi.org/10.1016/j.tsc.2024.101550
Wu, X., Li, N., Wu, R., & Liu, H. (2025). Cognitive analysis and path construction of Chinese students’ mathematics cognitive process based on CDA. Scientific Reports, 15(1), Article 4397. https://doi.org/10.1038/s41598-025-89000-5
Xing, E. P., Ho, Q., Xie, P., & Wei, D. (2016). Strategies and principles of distributed machine learning on big data. Engineering, 2(2), 179–195. https://doi.org/10.1016/J.ENG.2016.02.008
Xu, Q., Ding, C., Liu, J., & Luo, B. (2015). PCA-guided search for K-means. Pattern Recognition Letters, 54, 50–55. https://doi.org/10.1016/j.patrec.2014.11.017
Yakman, G. (2008). STEAM education: An overview of creating a model of integrative education. In Pupils’ attitudes toward technology (PATT-19) conference: Research on technology, innovation, design & engineering teaching. https://www.researchgate.net/publication/327351326_STEAM_Education_an_overview_of_creating_a_model_of_integrative_education
Yerushalmy, M. (1997). Designing representations: Reasoning about functions of two variables. Journal for Research in Mathematics Education, 28(4), 431–466. https://doi.org/10.2307/749682
Zembat, I. O. (2008). Pre-service teachers’ use of different types of mathematical reasoning in paper-and-pencil versus technology-supported environments. International Journal of Mathematical Education in Science and Technology, 39(2), 143–160. https://doi.org/10.1080/00207390701828705
Zhai, X., & Pellegrino, J. W. (2023). Large-scale assessment in science education. In N. G. Lederman, D. L. Zeidler, & J. S. Lederman (Eds.), Handbook of research on science education (pp. 1045–1097). Routledge.
Zhang, C., & Jia, B. (2024). Enriching STEAM education with visual art: Education benefits, teaching examples, and trends. Discover Education, 3(1), Article 247. https://doi.org/10.1007/s44217-024-00354-w
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