Predicting Student Performance in Interactive Online Question Pools Using Mouse Interaction Features. Modeling student learning and further predicting the performance is a wellestablished task in online learning and is crucial to personalized education by recommending different learning resources to different students based on their needs. Interactive online question pools (e.g., educational game platforms), an important component of online education, have become increasingly popular in recent years. However, most existing work on student performance prediction targets at online learning platforms with a wellstructured curriculum, predefined question order and accurate knowledge tags provided by domain experts. It remains unclear how to conduct student performance prediction in interactive online question pools without such wellorganized question orders or knowledge tags by experts. In this paper, we propose a novel approach to boost student performance prediction in interactive online question pools by further considering student interaction features and the similarity between questions. Specifically, we introduce new features (e.g., think time, first attempt, and first draganddrop) based on student mouse movement trajectories to delineate studentsu0027 problemsolving details. In addition, heterogeneous information network is applied to integrating studentsu0027 historical problemsolving information on similar questions, enhancing student performance predictions on a new question. We evaluate the proposed approach on the dataset from a realworld interactive question pool using four typical machine learning models.

Leveraging Neural Network for Online Learning Performance Prediction and Learning Suggestion Learning performance analysis is such a research field that draws much attention from researchers though it has just been emerged in recent years. On the one hand, analyzing learning behaviors can help learners to choose their learning methods and allocate their study time in a more appropriate way. On the other hand, learning analysis can provide valuable feedbacks for teachers and administrators to improve teaching efficiency and quality. This paper studies and analyzes more than 640,000 learning data from the MOOC platform edX. A treebased model along with an information gain measure is applied to identify the usefulness of data features. A backpropagation neural network model is further adopted to train data and achieve a prediction model of learning performance. In addition, a genetic algorithm calculates learning score conditions and return feedbacks as suggestions to learners. Experiment results demonstrate the effectiveness of the utilization of the methods in the predication of online learning performance.