Artificial Intelligence Assisted Student Learning and Performance Analysis using Instructor Evaluation Model

Abstract

Student's proficiency in a subject may be gauged through the use of Learning Management Systems (LMS) (LMSs). Using a scoring algorithm to calculate the percentage of each student's attentiveness in the specific class, this system aims to increase the use of artificial intelligence approaches. The graph provided by this tool may be used to measure how well a student understands the material being taught. Data mining tools are increasingly being used in higher education to help students and administrators better understand and resolve educational and administrative difficulties. The majority of educational mining research focuses on replicating students' performance rather than instructors' performance. The course assessment questionnaire is a standard instrument for evaluating teachers' performance based on students' perceptions. There are a number of different ways for creating classification techniques in this system, from decision trees to support vector machines to machine learning techniques, including differential evolution. Comparing their answers to an actual course assessment question based on its accuracy, retention and sensitivity. Students' online conduct has become more accessible because to the use of Learning Management Systems (LMSs) in educational institutions. These statistics have been utilized by several studies to predict student outcomes. A wide range of subjects and predictive characteristics collected from the LMS make it difficult to draw generalized statements about the processes that control student performance. To get started let's take a look at some of the most recent research' theoretical foundations and common predictions in the field of learning analytics. A total of 4,989 students took 17 blended courses with Moodle LMS at the same school, and both multi-level and conventional regressions are utilized to examine the impact of LMS predictor factors and between-course evaluation grades on student performance. Despite the fact that they were gathered inside a single institution, our studies demonstrate that the outcomes of predictive modelling varied greatly between courses. As a result, the predictive models' range of motion is constrained. The data from LMS also shows that early intervention and intermediate assessment grades have little (if any) relevance from LMS data.