Analyzing The Students’ Academic Performance By Learning And Doing Technology

Abstract

Predicting students’ performance is a very important task in any educational system. Therefore, to predict the learner’s behavior towards studies many techniques are used like clustering, classification, regression. Technology could be used to measure student performance by utilizing computer-based assessment platforms that collect and analyze data from students' interactions. Those platforms allow for in-depth collection and analysis of data, providing insights into individual student strengths and weaknesses. By correlating this data with common student success metrics, potential trends can be identified. Additionally, technology-enhanced education systems capture a rich array of data as students interact with them, which can be used to predict student performance. In this paper, student’s performance prediction model and new features are introduced that have a great influence on student’s academic achievement. student absence days in class and parents’ involvement in the learning process. In this paper, considerable attention is on the punctuality of students and the effect of participation of parents in the learning process. This category of features is concerned with the learner’s interaction with the e–learning management system. Those are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning.  The researcher intentioned to find out there is a significant impact on technology education learning by doing. Thus, analyzing and processing these data carefully could give us useful information about the students’ knowledge and the relationship between them and the academic tasks.