Course complexity in engineering education using E-learner's affective-state prediction

Abstract

PurposeAffective states in learning have gained immense attention in education. The precise affective-states prediction can increase the learning gain by adapting targeted interventions that can adjust the changes in individual affective states of students. Several techniques are devised for predicting the affective states considering audio, video and biosensors. Still, the system that relies on analyzing audio and video cannot certify anonymity and is subjected to privacy problems.Design/methodology/approachA new strategy, termed rider squirrel search algorithm-based deep long short-term memory (RiderSSA-based deep LSTM) is devised for affective-state prediction. The deep LSTM training is done by the proposed RiderSSA. Here, RiderSSA-based deep LSTM effectively predicts the affective states like confusion, engagement, frustration, anger, happiness, disgust, boredom, surprise and so on. In addition, the learning styles are predicted based on the extracted features using rider neural network (RideNN), for which the Felder–Silverman learning-style model (FSLSM) is considered. Here, the RideNN classifies the learners. Finally, the course ID, student ID, affective state, learning style, exam score and course completion are taken as output data to determine the correlative study.FindingsThe proposed RiderSSA-based deep LSTM provided enhanced efficiency with elevated accuracy of 0.962 and the highest correlation of 0.406.Originality/valueThe proposed method based on affective prediction obtained maximal accuracy and the highest correlation. Thus, the method can be applied to the course recommendation system based on affect prediction.

A Survey and Implementation of Machine Learning Algorithms for Customer Churn Prediction

“…Neural Networks: Deep learning techniques, specifically neural networks that can learn complex patterns and relationships from large volumes of data, can be used for customer churn prediction. [16], [17], [18] 4. Ensemble Methods: The integrated system combines multiple models using techniques such as packaging, promotion, and configuration to increase the accuracy of loss estimation.…”