TY - JOUR ID - 142228 TI - Academic progress monitoring through neural network JO - Big Data and Computing Visions JA - BDCV LA - en SN - 2783-4956 AU - Shukla, Ramri AU - Khalilian, Bardia AU - Partouvi, Sara AD - Department of Computer Science and Engineering, Amity University, Sector 125, Noida, Uttar Pradesh, India. AD - Department of Management and International Business (MIB), University of Auckland, New Zealand. AD - School of Management & Marketing, Taylor’s University, Malaysia. Y1 - 2021 PY - 2021 VL - 1 IS - 1 SP - 1 EP - 6 KW - Education KW - Neural Network KW - monitroing DO - 10.22105/bdcv.2021.142228 N2 - To lessen the impact of a low student success rate, it's critical to be able to identify students who are in danger of failing early on, so that more targeted remedial intervention may be implemented. Private colleges use a variety of techniques, including increased tuition, expanded laboratory access, and the formation of learning communities. The prompt identification of students in danger of failing a given programme is important to both the students and the institutions with which they are registered, as seen by the debate presented below. Students are classified using artificial neural networks and random forests in this article. A private higher education provider provided a dataset of 2000 students. Artificial neural networks were found to provide the best performing model, with an accuracy of 83.24% percent. UR - https://www.bidacv.com/article_142228.html L1 - https://www.bidacv.com/article_142228_8ac0005d65ffb1bf0553bb605b5aeba9.pdf ER -