A Deep Learning Model Using CNN & LSTM to Forecast Student Learning Outcomes in Learning Management System

Abstract

The administration, monitoring, and reporting of educational activities are being done more and more with Learning Management Systems (LMSs). Blackboard is one such extensively utilized LMS at universities all over the world. This is because it may be used to match learning material pieces, student-teacher and student-student interactions, and assessment activities to predetermined objectives and student learning outcomes. This study aimed to determine the predictive power of various KPIs obtained from students' Blackboard interactions in order to forecast students' learning outcomes. Deep learning algorithms to forecast academic achievement were looked at as part of a mixed-methods study design. The degree of linear relationship between these factors and measures of student performance was ascertained by correlational tests. Out of the four models that were assessed, the CNN-LSTM predictive model proved to be the most effective because it combined long short-term memory with convolutional neural networks. The primary inference made from this data is that the CNN-LSTM technique might result in solutions that maximize and enhance how universities use the Blackboard LMS.