基于自动聚类和集成学习的网络教学形成性评价方法

The Formative Assessment for Online Teaching Based on Auto-classifying and Ensemble Learning

Cloud computing platform supported by big data has triggered the significant revolution of education， generating all kinds of open courses of online teaching. These open courses can guide the educatees’ study and meet the educatees’ individual requirements by using free online learning mode， stimulating the educatees’ study enthusiasm. For open courses of online learning， it is a critical problem to give the accurate evaluation according the educatees’ individual characteristics. To address the issue， this paper proposes a formative assessment by utilizing the numerous online learning data of educatees’ learning activities on open courses. Firstly， taking the cognitive hierarchy as the classification standard of online learning， the educatees’ knowledge applicable level is classified by k-means clustering analysis. Then to improve the efficiency of data processing and accelerate the evaluation， the data feature is extracted by using three-layer automatic encoder based on neural network to reduce the data dimension and obtain the key features. After feature extraction， the dimension-reduced data in training set is utilized to train the individual machine learner. Then the integrated learning mechanism is introduced to synthesize the learning result of every individual machine learner， making the evaluation results more accurate. In this paper， the data of online learning activity of junior middle school students on specific knowledge points is collected to construct the training data and test data sets. The effectiveness of the proposed method is verifies by comparing the formative assessment results with the experts’ manual evaluation on the test data set.