Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Abstract

Learning Management System (LMS) is an application software that is used in automation, delivery, administration, tracking, and reporting of courses and programs in educational sector. The LMS which exploits machine learning (ML) has the ability of accessing user data and exploit it for improving the learning experience. The recently developed artificial intelligence (AI) and ML models helps to accomplish effective performance monitoring for LMS. Among the different processes involved in ML based LMS, feature selection and classification processes find beneficial. In this motivation, this study introduces Glowwormbased Feature Selection with Machine Learning Enabled Performance Monitoring (GSO-MFWELM) technique for LMS. The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS. The proposed GSO-MFWELM technique involves GSO-based feature selection technique to select the optimal features. Besides, Weighted Extreme Learning Machine (WELM) model is applied for classification process whereas the parameters involved in WELM model are optimally fine-tuned with the help of Mayfly Optimization (MFO) algorithm. The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance. The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects. The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

Ship Lock Electromagnetically Remote Fault Diagnosis Mode Based on Machine Learning

“…The learning rule of neural network is an algorithm to modify the weight, which aims to obtain appropriate mapping function or other system performance. Teachers' free learning rules use adaptive learning methods to enable nodes to selectively accept different features in the input space, showing their unique performance [6]. For a multilayer feedforward network with at least one hidden layer, as long as there are enough hidden layer neurons, it can achieve any approximation of any function of interest with any accuracy; In other words, it can save almost all the information in the sample as knowledge to the trained network.…”