Student performance evaluation using data mining techniques for engineering education

In this research work, we are implementing a student performance evaluation model using Mamdani Fuzzy Inference System (FIS) and Neuro Fuzzy system and comparing the results with classical averaging method for Network Analysis (NA) course studied by third semester Electronics and Communication Engineering students. This work explains the designing of scoring rubrics using Bloom's levels as the criteria of assessment for NA course. Also at initial stages of learning how students' strengths and weaknesses can be identified using rubrics and develop critical thinking skills. The five inputs identify, understand, apply, analyze and design/create are five levels of learning as per Bloom's Taxonomy. Fuzzy rules are applied and the evaluated results are expressed in both crisp and linguistic variables and compared with classical aggregate scores. © 2018 Advances in Science, Technology and Engineering Systems. All rights reserved.