Implementing a Technology-Integrated Problem-Based Learning (TIPBL) Strategy in a Higher Education Mathematics Classroom  

Authors

DOI:

https://doi.org/10.46328/ijemst.5730

Keywords:

Technology-Integrated Problem-Based Learning, academic grit, mathematical mindset, technology perception, teacher education

Abstract

This study examined the influence of Technology-Integrated Problem-Based Learning (TIPBL) on engagement, academic grit, mathematical mindset, and perceptions of technology among first-year Bachelor of Secondary Education major in Mathematics (BSEd-Mathematics) students at a state university in the Philippines. Using a collaborative action research design utilizing modified explanatory sequential mixed-methods approach, quantitative data were collected via validated scales (Academic Grit Scale, Mathematical Mindset Scale, and Perception of Technology in Teaching and Learning Scale), while qualitative insights were derived from post-intervention interviews. Results indicated high levels of academic grit and growth-oriented mathematical mindset, with technology perceived positively for teaching and learning. Qualitative themes highlighted collaborative learning, technology efficacy, and resilience, though concerns about over-reliance on technology and access disparities emerged. Further, the qualitative data revealed that students perceived TIPBL as engaging, collaborative, and motivating. They described increased participation, stronger perseverance, a more open mindset toward solving problems, and positive attitudes toward the integration of technology. The findings suggest TIPBL develops perseverance, mindset development, and critical thinking, but equitable implementation and balanced technology integration are essential. Recommendations include scaffolding self-regulation, addressing digital inequities, and promoting collaborative problem-solving. It is also recommended that educators incorporate TIPBL strategies in their instruction This study contributes to literature on technology-enhanced pedagogies and their psychological impacts in teacher education.

 

References

Abrahamson, D., & Trninic, D. (2015). Bringing forth mathematical concepts: Signifying sensorimotor enactment in fields of promoted action. ZDM Mathematics Education, 47(2), 295–306. https://doi.org/10.1007/s11858-014-0620-0

Alakrash, H. M., & Razak, N. A. (2021). Technology-based language learning: Investigation of digital technology and digital literacy. Sustainability, 13(21), 12304. https://doi.org/10.3390/su132112304

Attard, C. (2018). Engagement with mathematics: What does it mean and what does it look like? Australian Primary Mathematics Classroom, 23(1), 3–7.

Ball, D. L., Thames, M. H., & Phelps, G. (2008). Content knowledge for teaching: What makes it special? Journal of Teacher Education, 59(5), 389–407. https://doi.org/10.1177/0022487108324554

Bernardo, A. B. I., Ganotice, F. A., & King, R. B. (2018). Motivation gap and achievement gap between public and private high schools in the Philippines. The Asia-Pacific Education Researcher, 27(5), 415–429. https://doi.org/10.1007/s40299-018-0400-7

Bernardo, A. B., Cordel, M. O., Lapinid, M. R., Teves, J. M. M., Yap, S. A., & Chua, U. C. (2022). Contrasting profiles of low-performing mathematics students in public and private schools in the Philippines: Insights from machine learning. Frontiers in Psychology, 13, 778174. https://doi.org/10.3389/fpsyg.2022.778174

Bicer, A., Perihan, C., & Lee, Y. (2021). A meta-analysis: The effects of problem-based learning on students' critical thinking skills. Science & Education, 30(4), 967–994. https://doi.org/10.1007/s11191-021-00212-3

Boaler, J. (2016). Mathematical mindsets: Unleashing students' potential through creative math, inspiring messages and innovative teaching. Jossey-Bass.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Carr, N. (2014). The glass cage: Automation and us. W. W. Norton.

Chai, C. S., Koh, J. H. L., & Tsai, C. C. (2019). Examining preservice teachers' perceived knowledge of TPACK and cyberwellness through structural equation modeling. Australasian Journal of Educational Technology, 35(1), 119–133. https://doi.org/10.14742/ajet.4021

Chen, C. H., Yang, Y. C., & Hsiao, H. S. (2020). The effects of technology-integrated classroom instruction on K-12 students' mathematics achievement: A meta-analysis. Educational Technology Research and Development, 68(5), 2495–2523. https://doi.org/10.1007/s11423-020-09787-0

Clark, K. N., & Malecki, C. K. (2019). Academic grit scale: Psychometric properties and associations with achievement and life satisfaction. Journal of School Psychology, 72, 49–66. https://doi.org/10.1016/j.jsp.2018.12.001

Claro, S., Paunesku, D., & Dweck, C. S. (2016). Growth mindset tempers the effects of poverty on academic achievement. Proceedings of the National Academy of Sciences, 113(31), 8664–8668. https://doi.org/10.1073/pnas.1608207113

Cohen, E. G. (1994). Designing groupwork: Strategies for the heterogeneous classroom (2nd ed.). Teachers College Press.

Credé, M. (2018). What shall we do about grit? A critical review of what we know and what we don't know. Educational Researcher, 47(9), 606–611. https://doi.org/10.3102/0013189X18801322

Credé, M., Tynan, M. C., & Harms, P. D. (2017). Much ado about grit: A meta-analytic synthesis of the grit literature. Journal of Personality and Social Psychology, 113(3), 492–511. https://doi.org/10.1037/pspp0000102

Desimone, L. M. (2009). Improving impact studies of teachers' professional development: Toward better conceptualizations and measures. Educational Researcher, 38(3), 181–199. https://doi.org/10.3102/0013189X08331140

Dowker, A., Sarkar, A., & Looi, C. Y. (2016). Mathematics anxiety: What have we learned in 60 years? Frontiers in Psychology, 7, 508. https://doi.org/10.3389/fpsyg.2016.00508

Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101. https://doi.org/10.1037/0022-3514.92.6.1087

Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.

Eskreis-Winkler, L., Duckworth, A. L., Shulman, E. P., & Beal, S. (2014). The grit effect: Predicting retention in the military, the workplace, school and marriage. Frontiers in Psychology, 5, 36. https://doi.org/10.3389/fpsyg.2014.00036

Fleming, N. D. (2001). Teaching and learning styles: VARK strategies. Christchurch, New Zealand: Neil Fleming.

Furrer, C., & Skinner, E. (2003). Sense of relatedness as a factor in children’s academic engagement and performance. Journal of Educational Psychology, 95(1), 148–162. https://doi.org/10.1037/0022-0663.95.1.148

Geiger, V., Muir, T., & Lamb, J. (2020). Video-stimulated recall as a catalyst for teacher professional learning. Journal of Mathematics Teacher Education, 23(1), 35–55. https://doi.org/10.1007/s10857-018-9403-9

Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.

Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487

Haynes, S. N., Richard, D. C., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 7(3), 238–247. https://doi.org/10.1037/1040-3590.7.3.238

Heitin, L. (2016). Digital distraction: Student resistance in the math classroom. Education Week.

Higgins, S., Xiao, Z., & Katsipataki, M. (2019). The impact of digital technology on learning: A summary for the Education Endowment Foundation. Durham University.

Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266. https://doi.org/10.1023/B:EDPR.0000034022.16470.f3

Hmelo-Silver, C. E., Chinn, C. A., Chan, C. K. K., & O’Donnell, A. M. (2013). The international handbook of collaborative learning. Routledge.

Hwang, G. J., Lai, C. L., & Wang, S. Y. (2020). Seamless flipped learning: A mobile technology-enhanced flipped classroom with effective learning strategies. Journal of Computers in Education, 2(4), 449–473. https://doi.org/10.1007/s40692-015-0043-0

Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory and cooperative learning. Educational Researcher, 38(5), 365–379. https://doi.org/10.3102/0013189X09339057

Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2016). NMC Horizon Report: 2016 Higher Education Edition. The New Media Consortium.

Jonassen, D. H. (2011). Learning to solve problems: A handbook for designing problem-solving learning environments. Routledge.

Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424. https://doi.org/10.1080/07370000802212669

Kay, R., MacDonald, T., & DiGiuseppe, M. (2017). A comparison of lecture-based, active, and flipped classroom teaching approaches in higher education. Journal of Computing in Higher Education, 29(3), 1–20. https://doi.org/10.1007/s12528-017-9150-4

King, R. B., & McInerney, D. M. (2016). Family goals and motivational regulation among college students. Psychology in the Schools, 53(5), 461–477. https://doi.org/10.1002/pits.21917

Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation. American Psychologist, 57(9), 705–717. https://doi.org/10.1037/0003-066X.57.9.705

Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5–44. https://doi.org/10.1257/jel.52.1.5

Martin, A. J. (2013). Academic buoyancy and academic resilience: Exploring ‘everyday’ and ‘classic’ resilience in the face of academic adversity. School Psychology International, 34(5), 488–500. https://doi.org/10.1177/0143034312472759

Martin, A. J., & Marsh, H. W. (2006). Academic resilience and its psychological and educational correlates: A construct validity approach. Psychology in the Schools, 43(3), 267–281. https://doi.org/10.1002/pits.20149

Mayer, R. E. (2014). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 43–71). Cambridge University Press.

Megawanti, P., Suparman, S., & Kusumawardani, D. (2024). Mathematical mindset scale: Development and validation for Indonesian education students. Journal of Educational and Learning Studies, 7(1), 45–59. https://doi.org/10.32698/0234

Mertler, C. A. (2019). Action research: Improving schools and empowering educators (6th ed.). SAGE.

Middleton, J. A., Jansen, A., & Goldin, G. A. (2017). The complexities of mathematical motivation. In J. Cai (Ed.), Compendium for research in mathematics education (pp. 667–699). NCTM.

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x

OECD. (2019). PISA 2018 results (Volume I): What students know and can do. OECD Publishing. https://doi.org/10.1787/5f07c754-en

Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 422. https://doi.org/10.3389/fpsyg.2017.00422

Papastergiou, M. (2009). Digital game-based learning in high school computer science education: Impact on educational effectiveness and student motivation. Computers & Education, 52(1), 1–12. https://doi.org/10.1016/j.compedu.2008.06.004

Pape, S. J., & Tchoshanov, M. A. (2001). The role of representation(s) in developing mathematical understanding. Theory Into Practice, 40(2), 118–127. https://doi.org/10.1207/s15430421tip4002_6

Papert, S. (1987). Computer criticism vs. technocentric thinking. Educational Researcher, 16(1), 22–30. https://doi.org/10.3102/0013189X016001022

Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9

Plano Clark, V. L., & Ivankova, N. V. (2016). Mixed methods research: A guide to the field. SAGE.

Plass, J. L., Homer, B. D., & Kinzer, C. K. (2015). Foundations of game-based learning. Educational Psychologist, 50(4), 258–283. https://doi.org/10.1080/00461520.2015.1122533

Polya, G. (1945). How to solve it. Princeton University Press.

Postman, N. (1992). Technopoly: The surrender of culture to technology. Vintage.

Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002

Rittle-Johnson, B., Schneider, M., & Star, J. R. (2015). Not a one-way street: Bidirectional relations between procedural and conceptual knowledge of mathematics. Educational Psychology Review, 27(4), 587–597. https://doi.org/10.1007/s10648-015-9302-x

Robinson, L., Cotten, S. R., Ono, H., Quan-Haase, A., Mesch, G., Chen, W., Schulz, J., Hale, T. M., & Stern, M. J. (2015). Digital inequalities and why they matter. Information, Communication & Society, 18(5), 569–582. https://doi.org/10.1080/1369118X.2015.1012532

Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020

Savery, J. R. (2015). Overview of problem-based learning: Definitions and distinctions. In A. Walker, H. Leary, C. Hmelo-Silver, & P. A. Ertmer (Eds.), Essential readings in problem-based learning (pp. 5–15). Purdue University Press.

Schoenfeld, A. H. (1992). Learning to think mathematically: Problem solving, metacognition, and sense-making in mathematics. In D. Grouws (Ed.), Handbook of research on mathematics teaching and learning (pp. 334–370). Macmillan.

SEAMEO INNOTECH. (2021). Education in Southeast Asia during COVID-19: Country practices and innovations. SEAMEO INNOTECH.

Selwyn, N. (2016). Education and technology: Key issues and debates. Bloomsbury.

Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189. https://doi.org/10.3102/0034654307313795

Sisk, V. F., Burgoyne, A. P., Sun, J., Butler, J. L., & Macnamara, B. N. (2018). To what extent and under which circumstances are growth mind-sets important to academic achievement? Two meta-analyses. Psychological Science, 29(4), 549–571. https://doi.org/10.1177/0956797617739704

Slavin, R. E. (2014). Cooperative learning and academic achievement: Why does groupwork work? Anales de Psicología, 30(3), 785–791. https://doi.org/10.6018/analesps.30.3.201201

Tomlinson, C. A. (2014). The differentiated classroom: Responding to the needs of all learners (2nd ed.). ASCD.

Tourón, J., Martín, D., Navarro, E., Pradas, S., & Íñigo, V. (2018). Validación de constructo de un instrumento para medir la competencia digital docente de los profesores (CDD). Revista Española de Pedagogía, 76(269), 25–54. https://doi.org/10.22550/REP76-1-2018-02

Trilling, B., & Fadel, C. (2009). 21st century skills: Learning for life in our times. Jossey-Bass.

Van Deursen, A. J. A. M., & Van Dijk, J. A. G. M. (2019). The first-level digital divide shifts from inequalities in physical access to inequalities in material access. New Media & Society, 21(2), 354–375. https://doi.org/10.1177/1461444818797082

Van Dijk, J. (2020). The digital divide. Polity Press.

Veenman, M. V. J., Van Hout-Wolters, B. H. A. M., & Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations. Metacognition and Learning, 1(1), 3–14. https://doi.org/10.1007/s11409-006-6893-0

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

Warschauer, M. (2004). Technology and social inclusion: Rethinking the digital divide. MIT Press.

Webb, N. M., Franke, M. L., Ing, M., Turrou, A. C., Johnson, N. C., & Zimmerman, J. (2019). Teacher practices that promote productive dialogue and learning in mathematics classrooms. International Journal of Educational Research, 97, 176–186. https://doi.org/10.1016/j.ijer.2017.07.009

Winegar, L., & Abbott, S. (2022). Digital literacy and the disinformation divide. Journal of Media Literacy Education, 14(1), 1–14. https://doi.org/10.23860/JMLE-2022-14-1-1

Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Erlbaum.

Wolters, C. A. (2003). Regulation of motivation: Evaluating an underemphasized aspect of self-regulated learning. Educational Psychologist, 38(4), 189–205. https://doi.org/10.1207/S15326985EP3804_1

Yeager, D. S., & Dweck, C. S. (2012). Mindsets that promote resilience: When students believe that personal characteristics can be developed. Educational Psychologist, 47(4), 302–314. https://doi.org/10.1080/00461520.2012.722805

Yeager, D. S., & Dweck, C. S. (2020). What can be learned from growth mindset controversies? American Psychologist, 75(9), 1269–1284. https://doi.org/10.1037/amp0000794

Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., ... & Dweck, C. S. (2019). A national experiment reveals where a growth mindset improves achievement. Nature, 573(7774), 364–369. https://doi.org/10.1038/s41586-019-1466-y

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

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2026-03-27

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Implementing a Technology-Integrated Problem-Based Learning (TIPBL) Strategy in a Higher Education Mathematics Classroom  . (2026). International Journal of Education in Mathematics, Science and Technology, 14(3), 841-874. https://doi.org/10.46328/ijemst.5730