Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
|
Daily Overview |
| Session | ||
24 SES 03 A JS: Joint Paper Session - NW 11 and NW 24
Joint Paper Session | ||
    | ||
24. Mathematics Education Research
Paper Action Research Report: Developing Students' Skills in Making Conclusions When Solving Complex Problems Through Scaffolding NIS Kyzylorda, Kazakhstan Presenting Author:Development of Students’ Skills in Making Conclusions When Solving Complex Mathematical Problems through Scaffolding In contemporary mathematics education, priorities have shifted from routine reproduction of algorithms toward the development of learners’ reasoning, analytical thinking, and problem-solving skills. Students in mathematics classes are now expected not only to perform calculations but also to interpret results, justify methods, and construct coherent conclusions based on evidence (Boaler, 2016). Complex mathematical tasks, particularly those involving exponential and logarithmic functions, require students to analyze conditions, select appropriate strategies, and synthesize final interpretations. However, many 10th-grade students experience difficulties in connecting procedural steps with meaningful conclusions and in explaining the logic of their solutions (Anghileri, 2006). Thus, mathematics teachers play a critical role in designing structured learning environments that scaffold students’ reasoning processes, gradually leading them to independent conclusion-making. This study aims to explore how scaffolding strategies in mathematics lessons can enhance students’ ability to formulate justified conclusions when solving complex problems, focusing on guided questioning, collaborative learning, and gradual transfer of responsibility. The main research problem addressed in this study is: This study focuses on the following research questions:
Literature Review Conclusion-making in mathematics involves a set of competencies that enable students to interpret results, connect representations, and justify reasoning (Boaler, 2016). Fostering these competencies aligns with modern educational frameworks emphasizing higher-order thinking, problem solving, and metacognitive reflection (Mercer & Littleton, 2007). Scaffolding, rooted in Vygotsky’s concept of the Zone of Proximal Development, provides temporary and adaptive support that guides learners toward independent performance (Vygotsky, 1978). Research demonstrates that scaffolding in mathematics promotes deeper conceptual understanding and the ability to articulate conclusions rather than merely execute procedures (Anghileri, 2006). Scholarly literature identifies several effective strategies for developing mathematical reasoning: guided questioning, dialogic interaction, collaborative problem solving, and gradual transfer of responsibility (van de Pol et al., 2010). Guided questioning helps students focus on key relationships within a task, while collaborative work encourages verbalization of thought processes and peer explanation. Scaffolded tasks lead learners step by step from supported analysis to independent justification. A growing body of research confirms that combining these strategies results in significant improvements in students’ engagement, critical thinking, and ability to draw evidence-based conclusions (Boaler, 2016; Anghileri, 2006; Mercer & Littleton, 2007). Methodology, Methods, Research Instruments or Sources Used Methodology This study employed a single-group pre-test/post-test design to investigate the development of conclusion-making skills in 24 Grade 10 students (ages 15–16) during an eight-week intervention focused on the unit “Exponential and Logarithmic Functions.” During weeks one and two, students participated in guided problem analysis using structured questions aimed at identifying conditions, strategy choice, and interpretation of results. Weeks three to five involved small-group collaborative tasks where learners solved interdisciplinary problems from biology, physics, and economics and presented reasoned conclusions. Weeks six and seven included scaffolded individual tasks in which students independently selected methods, justified steps, and formulated written conclusions. The final week focused on presentations and reflection. Data collection included pre- and post-tests measuring interpretation of results, justification of methods, and quality of conclusions; classroom observations; student questionnaires; and evaluation of written solutions using a rubric. Quantitative data were analyzed with paired comparisons, and qualitative data were examined through thematic analysis to identify patterns of reasoning, collaboration, and independence. Conclusions, Expected Outcomes or Findings Results and Discussion 1. How does structured scaffolding affect students’ ability to formulate mathematical conclusions? The findings reveal that structured scaffolding substantially improved students’ conclusion-making skills. Comparison of pre- and post-test results showed significant progress: interpretation of results increased by 32%, justification of methods by 29%, and quality of written conclusions by 37%. Guided prompts helped students connect intermediate steps with final answers and express reasoning using appropriate mathematical language (van de Pol et al., 2010). 2. How do guided questioning, collaborative tasks, and gradual release support interpretation and justification? Classroom observations demonstrated high engagement during collaborative problem solving. Students actively discussed strategy selection, compared solutions, and defended conclusions before peers. Participation in group tasks enhanced learners’ ability to synthesize information and confidently justify evidence-based results (Mercer & Littleton, 2007). Scaffolded individual tasks supported autonomy, enabling students to design solution paths and construct coherent explanations (Anghileri, 2006). 3. What challenges do students face and how can scaffolding help? Questionnaire data and analysis of written work indicated that some students remained dependent on teacher prompts and experienced difficulty managing time in complex tasks. Lower-achieving learners were sometimes passive in groups. However, structured guidance—clear roles, step-by-step descriptors, and fading support—helped overcome these barriers and promoted more balanced participation and self-regulation (Boaler, 2016). Conclusion This study highlights that scaffolding effectively develops students’ skills in making conclusions when solving complex mathematical problems. Incorporating guided questioning, collaborative learning, and gradual transfer of responsibility supports independent reasoning and evidence-based justification. The findings emphasize the value of scaffolded approaches in modern mathematics education to move learners from procedural performance toward meaningful analytical thinking. References •Anghileri, J. (2006). Scaffolding practices that enhance mathematics learning. Journal of Mathematics Teacher Education, 9(1), 33-52. •Boaler, J. (2016). Mathematical Mindsets: Unleashing Students' Potential through Creative Math, Inspiring Messages, and Innovative Teaching. Jossey-Bass. •Mercer, N., & Littleton, K. (2007). Dialogue and the Development of Children's Thinking: A Sociocultural Approach. Routledge. •Tomlinson, C. A. (2014). The Differentiated Classroom: Responding to the Needs of All Learners. ASCD. •van de Pol, J., Volman, M., & Beishuizen, J. (2010). Scaffolding in teacher-student interaction: A decade of research. Educational Psychology Review, 22(3), 271-296. •Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Harvard University Press. •Webb, N. M. (2009). The teacher's role in promoting collaborative dialogue in the classroom. British Journal of Educational Psychology, 79(1), 1-28. •Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89-100. 24. Mathematics Education Research
Paper Mathematics Resilience, Digital Literacy and Mental Health Status: A Structural Equation Model on Students' Mathematics Achievement University of Southeastern Philippines, Philippines Presenting Author:The global transition toward flexible and blended learning frameworks in higher education has necessitated a radical shift in how complex subjects like mathematics are taught and mastered. While these digital landscapes ensure academic continuity, they simultaneously introduce significant barriers, including reduced interpersonal interaction, heavy reliance on digital platforms, and heightened cognitive and emotional stress. In this context, mathematics resilience emerges as a vital construct, representing the learner's ability to persist through adversity and navigate the complexities of mathematical tasks (Cassidy, 2016). However, the effectiveness of this resilience is heavily dependent on a student’s digital literacy; despite being perceived as "tech-savvy," many learners still struggle to utilize technology as a functional tool for deep mathematical engagement (Taja-on, 2023). Consequently, there is an urgent need to investigate the interplay between mathematics resilience, digital proficiency, and student well-being to ensure that modern instructional modalities foster growth rather than academic anxiety. Methodology, Methods, Research Instruments or Sources Used Research Design This study is quantitative in nature and made used of descriptive-correlational and causal comparative research designs. Descriptive research helps identify the attributes of a particular phenomenon based on an observation or the exploration of correlation between two or more variables (Creswell, 2002). Thus, the study examined the relationship of academic resilience, digital literacy, and mental health status towards mathematics achievement. Moreover, this study focused on fitting the data to hypothesized models of academic resilience, digital literacy, and mental health status and mathematics achievement of students. Hence, causal comparative design will be employed to examine how the independent variable affects the dependent variable and involves cause and effect relationships (Williams, 2011). Respodents and Sampling Technique This study utilized a stratified sampling technique to select 279 first-year College of Education students from the University of Southeastern Philippines (USeP) who were enrolled in the Mathematics in the Modern World course for SY 2024-2025. By dividing the population into 19 academic programs across the CEd and CTET colleges and selecting 15 students per stratum, the researchers ensured a representative sample that exceeds the minimum requirement of 250 participants recommended for robust Structural Equation Modeling (SEM) (Boomsma, 2000). To maintain the integrity of the sample, the study specifically included only regular students within these programs, excluding those who were cross-enrolled from other departments. Instruments To gather the necessary data for the structural model, this study utilized four distinct instruments: the Academic Resilience Scale (Simbulas, 2018) to measure students' psychological persistence, the Digital Literacy in Higher Education Questionnaire (Miranda, Isaias & Pifano, 2018) to evaluate technical competencies, the Depression Anxiety Stress Scale-42 (DASS-21) to assess mental health status, and a 45-item multiple choice Mathematics Achievement tesrt to quantify academic performance. To ensure the scientific rigor of the findings, these adapted instruments underwent a rigorous process of content validity by subject matter experts and reliability testing to confirm that they consistently and accurately measure the specific constructs within the Philippine higher education context. Conclusions, Expected Outcomes or Findings Conclusion (up to 300 words) The regression result weights on the effect measured variables to latent variables showed that mathematics resilience and digital literacy significantly predict mathematics achievement since the computed p-values of the two (2) latent variables are greater than the critical value which is .05. Since mathematics resilience has high correlation, and digital literacy has low correlation, it implies that mathematics resilience is more important than digital literacy comparably based on beta weights between 2.16 and -0.87. To determine if the model is a good model or not, the criterion of each model fit indeces were considered. The result revealed that the model satisfied with the p-value, which is greater than 0.05, Root Mean Square of Error Approximation (RMSEA) is less than 0.05, P-close is greater than 0.05. Moreover, in the following indices satisfy the criteria to have a model fit namely: Chi-Square/Degrees of Freedom (CMIN/DF) is lesser than 2, Normed Fit Index (NFI) is greater than 0.95, Tucker-Lewis Index (TLI) is greater than 0.95, Comparative Fit Index (CFI) is greater than 0.95 and Goodness of Fit Index (GFI) is greater 0.95. Based on the findings of the study, mathematic resilience and digital literacy are the strong determinants of mathematics achievement. Thus, the best fitting structural model is composed of mathematics resilience and digital literacy for mathematics achievement. The institution should institutionalize mathematical resilience by integrating growth-mindset interventions and grit-based problem-solving strategies directly into the Mathematics in the Modern World curriculum, shifting the focus from mere rote memorization to psychological persistence. Concurrently, the university must prioritize a robust digital literacy program that goes beyond basic software use; by providing targeted training in technical data analysis and digital collaborative tools. References Bernardo, A. B. I. (2021). Socioeconomic status moderates the relationship between growth mindset and learning in mathematics and science: Evidence from PISA 2018 Philippine data. International Journal of School & Educational Psychology, 9(3), 208–222. https://doi.org/10.1080/21683603.2020.1832635 Boomsma, A. (2000). Reporting analyses of covariance structures. Structural equation modeling, 7(3), 461-483. https://doi.org/10.1207/S15328007SEM0703_6 Cassidy, S. (2015). Resilience building in students: The role of academic self- efficacy. Frontiers in Psychology, 6, 1781. https://doi.org/10.3389/fpsyg.2015.01781 Creswell, J. W. (2002). Research design: Qualitative, quantitative and mixed method approaches. (2nd ed.). Thousand Oaks, CA: Sage. Davis, F. (1985). A Technology Acceptance Model for Empirically Testing New End-User Information Systems. Eccles J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motivation (pp. 75–146). San Francisco, CA: W. H. Freeman. Ma, X., Li, Y., & Jiang, Y. (2020). The impact of online platforms on student engagement and achievement in mathematics: A meta-analysis. Journal of Educational Technology & Society, 23(3), 69-82 Middleton, M. J., & Midgley, C. (1997). Avoiding the demonstration of lack of ability: An underexplored aspect of goal theory. Journal of Educational Psychology, 89(4), 710–718. https://doi.org/10.1037/0022-0663.89.4.710 Miranda, P., Isaias, P., & Pifano, S. (2018). Digital literacy in higher education: A survey on students’ self-assessment. In Learning and Collaboration Technologies. Learning and Teaching: 5th International Conference, LCT 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, July 15-20, 2018, Proceedings, Part II 5 (pp. 71-87). Springer International Publishing. https://doi.org/10.1007/978-3-319-91152-6_6 OECD (2021), OECD Economic Outlook, Volume 2021 Issue 2, OECD Publishing, Paris, https://doi.org/10.1787/66c5ac2c-en. Simbulas, L. S. (2018). Aptitude, resilience, and teacher attributes of learners: A structural model on mathematics achievement. Unpublished Dissertation. Bukidnon State University. Taja-on, E. (2023). Digital literacy on mathematical performance of college students in the course mathematics in the modern world. School of Education Research Journal, 4(1), 1-10. https://doi.org/10.5281/zenodo.10435709 Tria, J. Z. (2020). The COVID-19 pandemic through the lens of education in the Philippines: The new normal. International Journal of Pedagogical Development and Lifelong Learning, 1(1), ep2001. https://doi.org/10.30935/ijpdll/8311 Williams, C. (2011). Research methods. Journal of Business & Economic Research (JBER), 5(3). https://doi.org/10.19030/jber.v5i3.2532 | ||