Application of k Means Clustering algorithm for prediction of Students Academic Performance
Lagos State University（拉各斯州立大学）
O. J. Oyelade, O. O. Oladipupo, I. C. Obagbuwa
备注：
IEEE format, International Journal of Computer Science and Information Security, IJCSIS January 2010, ISSN 1947 5500, http://sites.google.com/site/ijcsis/
发表信息：
International Journal of Computer Science and Information Security, IJCSIS, Vol. 7, No. 1, pp. 292-295, January 2010, USA
The ability to monitor the progress of students academic performance is a critical issue to the academic community of higher learning. A system for analyzing students results based on cluster analysis and uses standard statistical algorithms to arrange their scores data according to the level of their performance is described. In this paper, we also implemented k mean clustering algorithm for analyzing students result data. The model was combined with the deterministic model to analyze the students results of a private Institution in Nigeria which is a good benchmark to monitor the progression of academic performance of students in higher Institution for the purpose of making an effective decision by the academic planners.
Research on Education Big Data for Students Academic Performance Analysis based on Machine Learning
LaFetra College of Education, University of La Verne（拉文大学拉菲特教育学院）；Steinhardt School of Culture, Education, and Human Development, New York University（纽约大学斯坦哈特文化、教育与人类发展学院）；Hunan University of Humanities, Science and Technology（湖南人文科技学院）
Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students
University of Pennsylvania（宾夕法尼亚大学）；Kyushu University（九州大学）；Sorbonne University（索邦大学）；Ateneo de Manila University（雅典耀大学）；LIP6（巴黎第六大学信息学实验室）；ait.kyushu-u.ac.jp（九州大学先进信息技术研究所）
Evaluation of Machine Learning Models in Student Academic Performance Prediction
Dept. of Computer Engineering General Sir John Kotelawala Defence University（计算机工程系 加普尔·约翰·科特拉瓦拉国防大学）；Dept. of Computer Science Sri Lanka Institute of Information Technology（计算机科学系 斯里兰卡信息技术研究所）
Explainable AI and Machine Learning for Exam-based Student Evaluation: Causal and Predictive Analysis of Socio-academic and Economic Factors
Institute of Information Technology Jahangirnagar University（贾汗吉尔纳加尔大学信息科技学院）；University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Cefalo Dhaka Bangladesh（达卡 Bangladesh 的 Cefalo）
Which CS1 Students Will Fail? Identifying Digital Markers from Learning Analytics in Computer Systems and Architecture Using Weighted Academic Momentum and Interaction Logs
University of Zambia（赞比亚大学）；DataLab Research Group, University of Zambia（赞比亚大学数据实验室研究组）
Protecting Student Mental Health with a Context-Aware Machine Learning Framework for Stress Monitoring
Dept. of Computer Science, George Mason University, Virginia, USA（计算机科学系，乔治·马歇尔大学，弗吉尼亚州，美国）；Dept. of Computer Science and Engineering, University of Rajshahi, Bangladesh（计算机科学与工程系，拉贾沙希大学，孟加拉国）；Dept. of Computer Science and Engineering, Green University of Bangladesh, Bangladesh（计算机科学与工程系，孟加拉国绿色大学，孟加拉国）
Impact, Causation and Prediction of Socio-Academic and Economic Factors in Exam-centric Student Evaluation Measures using Machine Learning and Causal Analysis
Institute of Information Technology（信息科技研究所）；Jahangirnagar University（贾汗吉尔纳加尔大学）；University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；Erik Jonsson School of Engineering and Computer Science（埃里克·乔纳森工程与计算机科学学院）；University of Texas（德克萨斯大学）
Transformative Influence of LLM and AI Tools in Student Social Media Engagement: Analyzing Personalization, Communication Efficiency, and Collaborative Learning
