Artigue, M. (2002). Learning mathematics in a CAS environment: The genesis of a reflection about instrumentation and the dialectics between technical and conceptual work. International Journal of Computers for Mathematical Learning, 7(3), 245–274. https://doi.org/10.1023/A:1022103903080

Article  Google Scholar 

Balt, K., & Buteau, C. (2020, September 4). Using programming for pure/applied mathematics investigation: Mandelbrot set and running in the rain illustrations [Video]. YouTube. https://youtu.be/irTlCE-eXhc

Barabé, G., & Proulx, J. (2017). Révolutionner l’enseignement des mathématiques: Le projet visionnaire de Seymour Papert. For the Learning of Mathematics, 37(2), 25–30.

Google Scholar 

Barba, L. A. (2016, March 5). Computational thinking: I do not think it means what you think it means. Lorena A. Barba Group. http://lorenabarba.com/blog/computational-thinking-i-do-not-think-it-means-what-you-think-it-means

Benakli, N., Kostadinov, B., Satyanarayana, A., & Singh, S. (2017) Introducing computational thinking through hands-on projects using R with applications to calculus, probability and data analysis. International Journal of Mathematical Education in Science and Technology, 48(3), 393–427, https://doi.org/10.1080/0020739X.2016.1254296

Article  Google Scholar 

Bitter, C., & Loney, E. (2015, August). Deeper learning: Improving student outcomes for college, career, and civic life. Education Policy Center at American Institutes for Research. https://www.air.org/sites/default/files/2021-06/Deeper-Learning-EPC-Brief-August-2015.pdf

Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Proceedings of the American Educational Research Association (AERA) annual conference. http://web.media.mit.edu/~kbrennan/files/Brennan_Resnick_AERA2012_CT.pdf

Broley, L., Caron, F., & Saint-Aubin, Y. (2018). Levels of programming in mathematical research and university mathematics education. International Journal of Research in Undergraduate Mathematics Education, 4(1), 38–55. https://doi.org/10.1007/s40753-017-0066-1

Article  Google Scholar 

Broley, L., Buteau, C., & Sardella, J. (2023). When preservice and inservice teachers join forces: A collaborative way to support the enactment of new coding curricula in mathematics classrooms. Journal of Pedagogical Research, 7(2), 21-40.

Google Scholar 

Broley, L., Buteau, C., & Muller, E. (2017). (Legitimate peripheral) computational thinking in mathematics. In T. Dooley & G. Gueudet (Eds.), Proceedings of the Congress of European Society for Research in Mathematics Education (CERME) (pp. 2515–2522). https://hal.science/hal-01946353/document

Broley, L., Ablorh, E., Buteau, C., Mgombelo, J., & Muller, E. (2022a). Effective orchestration features of a project-based approach to learning programming for mathematics investigation. In M. Trigueros, B. Barquero, R. Hochmuth, & J. Peters (Eds.), Proceedings of INDRUM 2022 Fourth Conference of the International Network for Didactic Research in University Mathematics (pp. 592–601). University of Hannover and INDRUM. https://uca.hal.science/INDRUM2022/hal-04027009v1

Broley, L., Ablorh, E., Buteau, C., Mgombelo, J., & Muller, E. (2022b). Effectiveness of a project-based approach to integrating computing in mathematics. In S. Smith Karunakaran & A. Higgins (Eds.), Proceedings of the 2022 Annual Conference on Research in Undergraduate Mathematics Education (pp. 72–80). http://sigmaa.maa.org/rume/RUME24.pdf

Broley, L., Buteau, C., Levay, D., Marshall, N., Muller, E., & Sardella, J. (2022). Students facing and handling challenges in programming-based mathematics inquiry projects. Proceedings of the 2022 Annual Conference on Research in Undergraduate Mathematics Education (pp. 63–71). Boston, MA.

Buteau, C., & Muller, E. (2017). Assessment in undergraduate programming-based mathematics courses. Digital Experiences in Mathematics Education, 3(2), 97–114. https://doi.org/10.1007/s40751-016-0026-4

Buteau, C., Muller, E., & Marshall, N. (2015). When a university mathematics department adopted core mathematics courses of an unintentionally constructionist nature: Really? Digital Experiences in Mathematics Education, 1(2–3), 133–155. https://doi.org/10.1007/s40751-015-0009-x

Article  Google Scholar 

Buteau, C., Muller, E., Marshall, N., Sacristán, A., & Mgombelo, J. (2016). Undergraduate mathematics students appropriating programming as a tool for modelling, simulation, and visualization: A case study. Digital Experiences in Mathematics Education, 2(2), 142–166. https://doi.org/10.1007/s40751-016-0017-5

Article  Google Scholar 

Buteau, C., Gueudet, G., Muller, E., Mgombelo, J., & Sacristán, A. (2019). University students turning computer programming into an instrument for “authentic” mathematical work. International Journal of Mathematical Education in Science and Technology, 51(7), 1020–1041. https://doi.org/10.1080/0020739X.2019.1648892

Buteau, C., Muller, E., Mgombelo, J., Sacristán, A., & Driese, K. (2020). Instrumental genesis stages of programming for mathematical work. Digital Experience in Mathematics Education, 6(3), 367–390. https://doi.org/10.1007/s40751-020-00060-w

Article  Google Scholar 

Buteau, C., Muller, E., & Ralph, B. (2015, June). Integration of programming in the undergraduate math program at Brock University. Proceedings of math+coding symposium. https://ctuniversitymath.ca/wp-content/uploads/2023/10/buteaumullerralph-codingmathproceedings-final.pdf

Buteau, C., Muller, E., Mgombelo, J., & Sacristán, A. (2018). Computational thinking in university mathematics education: A theoretical framework. In A. Weinberg, C. Rasmussen, J. Rabin, M. Wawro, & S. Brown (Eds.), Proceedings of Research in Undergraduate Mathematics Education conference (pp. 1171–1179). http://sigmaa.maa.org/rume/RUME21.pdf

Buteau, C., Sacristán, A.I., & Muller, E. (2019). Roles and Demands in Constructionist Teaching of Computational Thinking in University Mathematics. Constructivist Foundations, 14(3): 294-309.

Buteau, C., Muller, E., Mgombelo, J., Sacristán, A., Santacruz Rodríguez, M., & Gueudet, G. (2023). Instrumental orchestration of using programming for authentic mathematics investigation projects. In A. Clark-Wilson, O. Robutti, & N. Sinclair (Eds.), The mathematics teacher in the digital era (2nd ed., pp. 289–322). Springer. https://doi.org/10.1007/978-3-031-05254-5_11

Cansu, F. K., & Cansu, S. K. (2019). An Overview of Computational Thinking. International Journal of Computer Science Education in Schools, 3(1), 17–30. https://doi.org/10.21585/ijcses.v3i1.53

Cook, L. S., Smagorinsky, P., Fry, P. G., Konopak, B., & Moore, C. (2002). Problems in developing a constructivist approach to teaching: One teacher’s transition from teacher preparation to teaching. The Elementary School Journal, 102(5), 389–413. https://doi.org/10.1086/499710

Article  Google Scholar 

Creswell, J. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE.

Darling-Hammond, L., & Rustique-Forrester, E. (2005). The consequences of student testing for teaching and teacher quality. Yearbook of the National Society for the Study of Education, 107(14), 289−319. https://doi.org/10.1111/j.1744-7984.2005.00034.x

Article  Google Scholar 

Duckworth, A. L., & Yeager, D. S. (2015). Measurement matters: Assessing personal qualities other than cognitive ability for educational purposes. Educational Researcher, 44(4), 237-251.

Eom, S. B., & Ashill, N. (2016). The determinants of students’ perceived learning outcomes and satisfaction in university online education: An update. Decision Sciences: Journal of Innovative Education, 14(2), 185−215. https://doi.org/10.1111/dsji.12097

Article  Google Scholar 

Farkas, G. (2003). Cognitive skills and noncognitive traits and behaviors in stratification processes. Annual Review of Sociology, 29(1), 541-562.

Gilbert, M. C., Musu-Gillette, L. E., Woolley, M. E., Karabenick, S. A., Strutchens, M. E., & Martin, W. G. (2014). Student perceptions of the classroom environment: Relations to motivation and achievement in mathematics. Learning Environments Research, 17, 287–304. https://doi.org/10.1007/s10984-013-9151-9

Article  Google Scholar 

Gueudet, G., Buteau, C., Muller, E., Mgombelo, J., Sacristán, A. I., & Santacruz Rodriguez, M. (2022). Development and evolution of instrumented schemes: A case study of learning programming for mathematical investigations. Educational Studies in Mathematics, 110, 353−377. https://doi.org/10.1007/s10649-021-10133-1

Article  Google Scholar 

Gueudet, G., Buteau, C., Broley, L., Mgombelo, J., Muller, E., Sacristán, A. I., & Santacruz Rodriguez, M. (2023). Learning programming for mathematical investigations: An instrumental and community of practice approach. Research in Mathematics Education. https://doi.org/10.1080/14794802.2023.2239195

Article  Google Scholar 

Gueudet, G., Buteau, C., Muller, E., Mgombelo, J., & Sacristán, A. (2020). Programming as an artefact: What do we learn about university students’ activity? In T. Hausberger, M. Bosch, & F. Chellougui (Eds.), Proceedings of INDRUM 2020: Third conference of the International Network for Didactic Research in University Mathematics (pp. 443−452). https://hal.science/INDRUM2020/public/INDRUM2020_Proceedings.pdf

Guin, D., Ruthven, K., & Trouche, L. (2005). The didactical challenge of symbolic calculators: Turning a computational device into a mathematical instrument. Springer.

Guo, J.-P., Lv, S., Wang, S.-C., Wei, S.-M., Guo, Y.-R., & Yang, L.-Y. (2023). Reciprocal modeling of university students’ perceptions of the learning environment, engagement, and learning outcome: A longitudinal study. Learning and Instruction, 83, Article 101692. https://doi.org/10.1016/j.learninstruc.2022.101692

Hannula, M. S., Di Martino, P., Pantziara, M., Zhang, Q., Morselli, F., Heyd-Metzuyanim, E., Lutovac, S., Kaasila, R., Middleton, J. A., Jansen, A., & Goldin, G. A. (2016). Attitudes, beliefs, motivation and identity in mathematics education: An overview of the field and future directions. Springer. https://doi.org/10.1007/978-3-319-32811-9

Article  Google Scholar 

Heckman, J. J., Stixrud, J., & Urzua, S. (2006). The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. Journal of Labor Economics, 24(3), 411-482.

Hidayatullah, A., & Csíkos, C. (2024). The role of students’ beliefs, parents’ educational level, and the mediating role of attitude and motivation in students’ mathematics achievement. The Asia-Pacific Education Researcher, 33(2), 253–262. https://doi.org/10.1007/s40299-023-00724-2

Article  Google Scholar 

Hoadley, C. (2012). What is a community of practice and how can we support it? In D. H. Jonassen & S. M. Land (Eds.), Theoretical foundations of learning environments (2nd ed., pp. 287−300). Routledge.

Hoyles, C., & Noss, R. (2015, June 19−21). Revisiting programming to enhance mathematics learning [Paper presentation]. Math + Coding Symposium, London, ON, Canada. http://www.researchideas.ca/coding/

Jacques, L. A. (2017). What does project-based learning (PBL) look like in the mathematics classroom? American Journal of Educational Research, 5(4), 428–433. https://pubs.sciepub.com/education/5/4/11/

Kafai, Y. B., & Burke, Q. (2013). Computer programming goes back to school. Phi Delta Kappan, 95(1), 61–65. https://doi.org/10.1177/003172171309500111

Article  Google Scholar 

Kallia, M., van Borkulo, S. P., Drijvers, P., Barendsen, E., & Tolboom, J. (2021). Characterising computational thinking in mathematics education: a literature-informed Delphi study. Research in mathematics education, 23(2), 159-187.

Google Scholar 

Kilpatrick, W. H. (1921). Dangers and difficulties of the project method and how to overcome them—A symposium. Teachers College Record, 22(4), 283–288. https://doi.org/10.1177/016146812102200402

Article  Google Scholar 

King, D., Varsavsky, C., Belward, S. & Matthews, K. (2017). Investigating students’ perceptions of graduate learning outcomes in mathematics. International Journal of Mathematical Education in Science and Technology, 48(Sup1), S67–S80. https://doi.org/10.1080/0020739X.2017.1352044

Article  Google Scholar 

Lahdenperä, J. & Nieminen, J. H. (2020). "How does a mathematician fit in? A mixed-methods analysis of university students’ sense of belonging in mathematics." International Journal of Research in Undergraduate Mathematics Education 6.3 (2020): 475–494.

Laurillard, D. (2002). Rethinking university teaching: A conversational framework for the effective use of learning technologies (2nd ed.). Routledge.

Lave, J. (1993). Situated learning in communities of practice. In L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 63–82). American Psychological Association.

Google Scholar 

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press. https://doi.org/10.1017/CBO9780511815355

Article  Google Scholar 

Lave, J., & Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge University Press.

Google Scholar 

Lazarides, R., & Rubach, C. (2017). Instructional characteristics in mathematics classrooms: Relationships to achievement goal orientation and student engagement. Mathematics Education Research Journal, 29, 201–217. https://doi.org/10.1007/s13394-017-0196-4

Article  Google Scholar 

Lee, J., & Shute, V. J. (2010). Personal and social-contextual factors in K–12 academic performance: An integrative perspective on student learning. Educational Psychologist, 45(3), 185-202.

Leron, U., & Dubinsky, E. (1995). An abstract algebra story. American Mathematical Monthly, 102(3), 227–242. https://doi.org/10.1080/00029890.1995.11990563

Article  Google Scholar 

Lim, K. H., & Selden, A. (2009, September). Mathematical habits of mind. In S. L. Swars, D. W. Stinson, & S. Lemons-Smith (Eds.), Proceedings of the thirty-first annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 1576−1583). https://www.pmena.org/pmenaproceedings/PMENA%2031%202009%20Proceedings.pdf

Lockwood, E., & Mørken, K. (2021). A call for research that explores relationships between computing and mathematical thinking and activity in RUME. International Journal of Research in Undergraduate Mathematics Education, 7, 404−416. https://doi.org/10.1007/s40753-020-00129-2

Article  Google Scholar 

Ludwig, P., Tongen, A., Walton, B. (2018). Two project-based strategies in an interdisciplinary mathematical modeling in biology course. Problems, Resources, and Issues in Mathematics Undergraduate Studies, 28(4), 300–317. https://doi.org/10.1080/10511970.2016.1246495

Article  Google Scholar 

Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51–61. https://doi.org/10.1016/j.chb.2014.09.012

Article  Google Scholar 

Mascaró, M., Sacristán, A. I., & Rufino, M. (2016). For the love of statistics: Appreciating and learning to apply experimental analysis and statistics through computer programming activities. Teaching Mathematics and its Applications: An International Journal of the IMA, 35(2), 74–87. https://doi.org/10.1093/teamat/hrw006

Article