The ELI Method and Vector Learning Among Engineering Students

Abstract

Introduction: Learning vectors in engineering requires overcoming conceptual and representational difficulties. The ELI method integrates planning, cooperation, technology, and metacognition to promote understanding, argumentation, and the transfer of knowledge to professional contexts. Materials and methods: A quantitative, quasi-experimental pretest–posttest design is presented using adjusted scores for 60 civil engineering students, divided into control and experimental groups of 30 students each. The design includes eight 90-minute sessions over four weeks, comparing conventional teaching with the ELI method. Weighted scores on a 0–10 scale, along with absolute and normalized gains, are examined using descriptive statistics, Student’s t-tests, Cohen’s d, and regression models, controlling for pretest scores, attendance, and age. Results and discussion: In the simulated dataset of 60 university students, the groups showed nearly equivalent baseline performance. The experimental group achieved a mean posttest score of 7.28, compared with 6.20 in the control group, with a statistically significant difference and an effect size of 1.02. Mean gains were 2.14 and 1.11 points, respectively. The difference favoring the ELI method persisted after adjustment for baseline performance, attendance, and age. This pattern is conceptually consistent with previous research on active learning and is compatible with the instructional hypothesis. The discussion relates these findings to the activation of prior knowledge, the coordination of representations, cooperation, and metacognition, integrated into the teaching and learning of vectors. Conclusions: The ELI method was associated with greater gains in vector learning. Future research should use larger samples, strengthen controls, and assess retention and transfer.