Smart Detection of Low Engagement in Students using Artificial Intelligence and Behavioral Data

Abstract

This work aims to develop a basic artificial intelligence program that can identify the class's most indolent student. The purpose of the program is to examine specific behaviors that demonstrate a lack of effort. These include missing a lot of classes, arriving late, especially by more than twenty minutes, turning in assignments late, and receiving poor grades. The program evaluates each of these factors for each student and determines which ones fit all the criteria for being lazy. We start by entering each student's information. Their name, number of absences, tardiness on various days, final grade, and whether or not they turned in their work on time are all included in this. This makes it easier to evaluate each student's performance in relation to the class average. It determines whether the student meets each condition using straightforward checks, also known as Boolean logic, such as "true" or "false." A student is labeled lazy if they have missed more than four classes, been late more than twenty minutes at least once, failed to turn in assignments on time, and received a grade below the class average. The program creates a list of the lazy students after evaluating every one of them. It selects the student with the lowest score from this list. The term "laziest" is then applied to that student. The program indicates that no lazy student was found if no student satisfies every requirement. This prevents students from being unfairly judged if they only missed one or two things.