Investigation of Web-based teaching and learning by boosting algorithms

The analysis of teaching and learning in distance education is an active research topic in recent years. We propose a new method by introducing a machine learning algorithm called boosting to investigate this problem. The boosting algorithm can also be treated as a data mining method, trying to infer from a large amount of training data the essential factors and their relations which influence the students´ academic successes. Based on the trained model it is possible to predict students´ academic successes and assist them to adjust their learning behaviors. More importantly, these findings are of great importance to academic administrators and instructional developers to improve the teaching modes and online courseware design.