@article {BIA0001170,
title={An Innovative approach of Progressive Feedback via Artificial Neural Networks},
author={Singh S., Jokhan A., Sharma B., Lal S.},
journal={Journal of Machine Learning Technologies},
year={2011}, url={http://bioinfopublication.org/viewhtml.php?artid=BIA0001170,
publisher={Bioinfo Publications}

Please cite as:

Singh, S., Jokhan, A., Sharma, B., & Lal, S. (2011). An Innovative approach of Progressive Feedback via Artificial Neural Networks. Journal of Machine Learning Technologies, 2(2), 64-71.

This paper highlights the importance of Machine Learning (ML) as an e-planning tool to enhance learning and improve student performances. The ML algorithms can be deployed to intelligently examine the interactions and the activity reports in a Learning Management System (Moodle) to diagnose each students academic progression. In this study, we group the behavior of students of an online course using the Self Organizing Map. The ML algorithm uses data obtained from the logs of Moodle to obtain a prediction map that permits rating each student’s ability to pass a course throughout the semester. Such swift e-planning mechanisms can be immensely helpful in identifying weak performances so that the coordinators, sponsors, parents and even the flagged students can take corrective measures, early in the semester. Within the scope of this work, the predictive attributes are further investigated and compared to reveal the degree of effectiveness of various activities in the online course.

keywords: {Self-organizing map, e-learning, e-planning, artificial neural network, k-means clustering, computers and education}

URL: http://repository.usp.ac.fj/4960/