Deep Network for the Iterative Estimations of Students' Cognitive Skills

The effective educational systems aimed at improving Cognitive Skills (CS) both in and out of institutions. Such system relies on timely prediction of CS during students' activities in intuitions. Meanwhile, literature is saturated with the number of approaches which have used study schedules, biological and environmental factors to predict CS. However, the loopholes in prior studies have become the main source of inspiration for the current attempt. In this study, we propose a Bayesian Neural Network which predicts CS by iterative manipulations of CS under the profound influence of Student's Basic Attributes. Initially, the study classifies the Basic Attributes into three factors (1.age group, 2. gender, and 3. parent's cohabitation status) which have multiple layers. Furthermore, the technique splits the range of CS into 20 periodic outcome variables (with a period of 0.5). Eventually, the network iteratively estimates each outcome of CS by feed-forward process through Basic Attributes layers. We have reviewed the performance of the proposed network by using a students' score dataset. The results have illustrated that the current technique obtained significant prediction accuracy in terms of accuracy measures.