Analyzing Students’ Academic Performance Based on Fuzzy Inference System
Source abstract
Abstract
Evaluating students’ knowledge and competencies to achieve the desired learning goals is one of the most important stages of the teaching process. The purpose of this study is to create a dataset consisting of programming questions and determine the level of these questions according to the Bloom taxonomy and the weight of each concept they contain, by taking expert opinion. The student’s score, question difficulty, and complexity levels are considered to determine the extent to which the student has learned a concept. A total of 96 students participated in this study, 51 in the experimental group and 45 in the control group. Random design for a pre-test–post-test control group was used to measure the students’ learning performance and self-efficacy regarding programming. While the experimental group students were given detailed feedback on how much they learned a concept, the control group students were only informed about the total score they received from the exam. The learning performance and self-efficacy perception regarding programming were analyzed using the paired samples t-test. Results show that the learning performance and self-efficacy perception regarding programming of the experimental group students improved significantly compared to the control group.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Educational Assessment and Pedagogy · Teaching and Learning Programming · Intelligent Tutoring Systems and Adaptive Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, Dokuz Eylul University, 35160 Izmir, Türkiye
References (33)
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