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Paper Title : STUDENT ACADEMIC PERFORMANCE PREDICTION

ISSN : 2395-1303
Year of Publication : 2022
10.5281/zenodo.7295209

Authors: Mr. K. Kumara Swamy, P. Sreekala, N. Manasvi, S. Aishwarya

         


Abstract
On most modern university campuses, digital data trails from many sources that cover various facets of student life are kept on a regular basis. However, it is still difficult to I combine these data to get a comprehensive picture of a student, (ii) utilise these data to predict academic achievement effectively, and (iii) use such predictions to encourage positive engagement of students with the university. In this project, a model called Augmented Education (AugmentED) is suggested as a first solution to this issue. In our work, (1) the first step is to run an experiment based on a real-world campus dataset of college students (N = 156) that compiles multisource behavioural data including both inside- and outside-the-classroom behaviour as well as online and offline learning. In particular, metrics measuring the linear and nonlinear behavioural changes (e.g., regularity and stability) of campus lifestyles are estimated, and features representing dynamic changes in temporal lifestyle patterns are extracted by the use of long short-term memory in order to gain in-depth insight into the features leading to excellent or poor performance (LSTM). (2) Next, classification algorithms based on machine learning are created to forecast academic success. (3) Visualized feedback is aimed to help students (particularly at-risk students) communicate with the university more effectively and strike a balance between their personal and academic lives. The results of the studies demonstrate how well the AugmentED model can forecast students' academic achievement.

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Keywords
— STUDENT ACADEMIC PERFORMANCE PREDICTION