Integrating Problem-Based Learning and Deep Learning to Enhance Mathematical Reasoning among Junior High School Students: A One-Group Pretest-Posttest Quasi-Experimental Study
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| Title: | Integrating Problem-Based Learning and Deep Learning to Enhance Mathematical Reasoning among Junior High School Students: A One-Group Pretest-Posttest Quasi-Experimental Study |
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| Language: | English |
| Authors: | Torang Siregar, Mhel Cedric D. Bendo (ORCID |
| Source: | Online Submission. 2026. |
| Peer Reviewed: | N |
| Page Count: | 22 |
| Publication Date: | 2026 |
| Document Type: | Reports - Research |
| Education Level: | Junior High Schools Middle Schools Secondary Education Elementary Education Grade 8 |
| Descriptors: | Mathematical Logic, Junior High School Students, Problem Based Learning, Artificial Intelligence, Grade 8, Foreign Countries, Mathematics Skills |
| Geographic Terms: | Indonesia |
| 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 < 0.001, 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. |
| Abstractor: | As Provided |
| Entry Date: | 20260101 |
| Accession Number: | ED682629 |
| Database: | ERIC |
| 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 < 0.001, 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. |
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