INTEGRATING PROBLEM-BASED LEARNING AND DEEP LEARNING TO ENHANCE MATHEMATICAL REASONING AMONG JUNIOR HIGH SCHOOL STUDENTS: A ONE-GROUP PRETEST–PPOSTTEST QUASI-EXPERIMENTAL STUDY

Abstract Mathematical reasoning is a fundamental competency in contemporary mathematics education, yet many junior high school students continue to experience difficulties in constructing logical arguments, connecting mathematical concepts, and solving complex problems. This study examined changes in junior high school students' mathematical reasoning following the implementation of Problem-Based Learning (PBL) integrated with a Deep Learning approach. A quantitative one-group pretest–posttest quasi-experimental design was employed involving 30 Grade VIII students from a public junior high school in North Sumatra, Indonesia. The instructional intervention was implemented across four mathematics learning sessions on Systems of Linear Equations in Two Variables to promote meaningful learning, reflective thinking, and active problem solving. Data were collected using a validated mathematical reasoning test administered before and after the intervention. Descriptive statistics, the Kolmogorov–Smirnov normality test, paired-sample t-test, and N-Gain analysis were used. The findings revealed a statistically significant improvement in students' mathematical reasoning following the instructional intervention, with mean scores increasing from 41.73 in the pretest to 79.40 in the posttest (t(29) = 18.063, p < .001, Cohen's d = 3.30). Because the study employed a one-group pretest–posttest design, these findings indicate substantial within-group improvement following the intervention rather than definitive evidence of causal effectiveness. The mean N-Gain score of 0.64 indicated a moderate level of improvement. The results suggest that integrating PBL with a Deep Learning approach is associated with improvements in mathematical reasoning. The study contributes to innovative mathematics instruction and offers practical implications for improving teaching and learning in education. Keywords: Problem-based learning; deep learning; mathematical reasoning; mathematics education

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Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-01
DOI
https://doi.org/10.5281/zenodo.22212645
Primary Topic
Mathematics Education and Pedagogy
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article
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article

INTEGRATING PROBLEM-BASED LEARNING AND DEEP LEARNING TO ENHANCE MATHEMATICAL REASONING AMONG JUNIOR HIGH SCHOOL STUDENTS: A ONE-GROUP PRETEST–PPOSTTEST QUASI-EXPERIMENTAL STUDY

Torang Siregar, Mhel Cedric D. Bendo
Zenodo (CERN European Organization for Nuclear Research)
Mathematics Education and Pedagogy
article

INTEGRATING PROBLEM-BASED LEARNING AND DEEP LEARNING TO ENHANCE MATHEMATICAL REASONING AMONG JUNIOR HIGH SCHOOL STUDENTS: A ONE-GROUP PRETEST–PPOSTTEST QUASI-EXPERIMENTAL STUDY

Torang Siregar, Mhel Cedric D. Bendo
article en

Abstract

Abstract Mathematical reasoning is a fundamental competency in contemporary mathematics education, yet many junior high school students continue to experience difficulties in constructing logical arguments, connecting mathematical concepts, and solving complex problems. This study examined changes in junior high school students' mathematical reasoning following the implementation of Problem-Based Learning (PBL) integrated with a Deep Learning approach. A quantitative one-group pretest–posttest quasi-experimental design was employed involving 30 Grade VIII students from a public junior high school in North Sumatra, Indonesia. The instructional intervention was implemented across four mathematics learning sessions on Systems of Linear Equations in Two Variables to promote meaningful learning, reflective thinking, and active problem solving. Data were collected using a validated mathematical reasoning test administered before and after the intervention. Descriptive statistics, the Kolmogorov–Smirnov normality test, paired-sample t-test, and N-Gain analysis were used. The findings revealed a statistically significant improvement in students' mathematical reasoning following the instructional intervention, with mean scores increasing from 41.73 in the pretest to 79.40 in the posttest (t(29) = 18.063, p < .001, Cohen's d = 3.30). Because the study employed a one-group pretest–posttest design, these findings indicate substantial within-group improvement following the intervention rather than definitive evidence of causal effectiveness. The mean N-Gain score of 0.64 indicated a moderate level of improvement. The results suggest that integrating PBL with a Deep Learning approach is associated with improvements in mathematical reasoning. The study contributes to innovative mathematics instruction and offers practical implications for improving teaching and learning in education. Keywords: Problem-based learning; deep learning; mathematical reasoning; mathematics education

Zenodo (CERN European Organization for Nuclear Research)
Polytechnic University of the Philippines (PH), Universitas Islam Negeri Syekh Ali Hasan Ahmad Addary Padangsidimpuan
Quality Education
Openalex Percentile: Top 5%
Mathematics Education and Pedagogy
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