Prediction Model on Student Performance based on Internal Assessment using Deep Learning

Abstract

Educational Data Mining plays a crucial role in identifying academically weak students of an institute and helps to develop different recommendation system for them. Students from three colleges of Assam, India were considered in our research which their records were run on deep learning using sequential neural model and adam optimization method. The paper compared other classification methods such as Artificial Immune Recognition System v2.0 and Adaboost, to find out the prediction of the results of the students. The highest classification rate was 95.34% produced by the deep learning techniques. The Precision, Recall, F-Score, Accuracy, and Kappa Statistics Performance were calculated as a statistics decisions to find the best classification methods. The dataset used in this paper was 10140 student records. Directing the student for their future plan comes from discovering the hidden patterns by using Data Mining techniques.

Monitoring of the Education Quality and Implementing of Individual Learning: Demonstration of Approaches and Educational Data Mining Algorithms

“…On the other hand, the p+ vs. p-imbalance results that binary classifier will be trained on a larger amount N+ of data corresponding to features of dominant class, and, as consequence, on the strongly unbalanced samples p+>>p-, the minority class of academic performance is virtually ignored by forecasting procedure. These effects can be seen from Table 1 where are demonstrated the results of forecast of 10 different types of classifiers trained by using Weka 3.8 [10] on the samples of 100 students derived from 6 experimental unbalanced dataset which, as we are sure [7,[11][12][13][14][15], have reasonable pre-processing quality.…”

Redesign Presentation Technique: Challenges to Encourage Learning Motivation

“…The impact is that many of them are not serious about preparing for presentations. This weakness is an accumulation of environmental factors such as institutions, lecture, and learning environments [5].…”

Internal and External QA in HE: LA Tools and Self-Evaluation Report Preparation

“…LA tools are a possible way to ensure quality and improved efficiency, which is crucial for many HEIs [7]. Many HEIs worldwide [3,8,9,10,11,12,13,14,15,16] have already used LA tools to track data for institution's work, curriculum, teachers, students to improve the quality of learning, student retention, enhance student performance, deliver early interventions and immediate feedback and make significant progress in improving the learning processes. Many of these tools are developed for the needs of students, teachers and managers of institutions and provide them with improved indicators to measure the effectiveness of teaching methods, learners' engagement in the LMS, and the effectiveness of learning process using technology [5].…”