Quadrant Analysis of Learner’s Performance in Quantitative Aptitude and Coding
Surya Pavan Kumar Gudla, N V Murali Krishna Raja, Karri Chandra Sekhar, Sandeep Kumar, B. Ravi Kumar, C P Pavan Kumar Hota · 2025
With the aim of improving learning for varied learners in various situations, abstract educational data mining (EDM) entails utilising methodologies, tools, and research to extract significant insights from educational data repositories. The primary objective of this research is to examine the quantitative aptitude and coding performance of 366 engineering undergraduate students. A convenience and random sampling technique was used to gather the dataset, and preprocessing was then performed to guarantee data completeness. Descriptive statistics demonstrate that students do well overall in both areas, with slightly more variability in Quantitative Aptitude scores than in Coding, where performance is more constant. Based on how well the students performed in these two areas, a quadrant analysis was used to classify the students into four groups: Excellent, Good, Average, and Poor. The results of this analysis demonstrated the differences in the way students developed in the two skill areas, highlighting the significance of tailored educational interventions. Quantitative aptitude and coding had a weakly positive link, according to the correlation study, indicating that these abilities are largely independent of one another. The study highlights that in order to help pupils in these areas, specific instructional practices are required. It is advised to connect technology with pedagogy through the Metacognition-aided Technological Pedagogical Content Knowledge (TPACK) framework. This will improve student outcomes in programming courses and enhance their metacognition. The results offer insightful information on the patterns of student performance that can guide the creation of instructional interventions that are more successful.