Analysis And Prediction Of Online Learning Behavior Based On Blackboard Platform

In recent years,with the development of Internet and communication technology,web-based online learning attracts many researchers and learners with the characteristics of flexibility and openness.Although the learner-centered network breaks the limits of space and realizes the autonomous learning and personalized learning,the temporal separation of online learning limits the direct and timely information exchange between teachers and students.Since teachers cannot observe the learning behavior characteristics of learners directly,they are unable to provide learners with targeted guidance and help.The use of learning analysis technology provide a viable way to improve the quality of online teaching and online learning effect,to predict the online learning effect of students and to determine the key factors affecting the performance of learners.With the development of online learning,learning analysis technology has become the main content of educational technology research in the future.Based on the behavioral science theory,combined with the Blackboard network course,with "online psychology introduction" the network curriculum learning behavior as the research object,according to the model of learning behavior related to network,the online learning behavior is divided into five categories and can be used as learning for feature classification.The five types of behavior were analyzed through the statistical method,we determined the data variables related to the academic achievement,constructed the prediction model of the final grade,and used two logistic regression analysis to predict the reliability of the model.This paper is divided into six parts: the first part is the introduction elaborating the paper research background,the research significance,the research present situation,the research content,the research method.The second part,the theoretical basis and related concepts.The concept of behavioral science,the concept of online learning behavior and learning analysis techniques are briefly summarized.Third,the introduction of network courses and data acquisition.According to the network learning behavior model,according to the Blackboard network course,referring to the learner specification model and the object meta data specification,the quantitative parameters of the behavioral characteristics are selected.The fourth part,online learning behavior analysis.According to the data variables of the network behavior characteristics,the online learning behavior is analyzed and the learning behavior related to the learning effect is initially determined.The fifth part is the establishment and verification of the performance prediction model.First,this part inquires from a statistical point of learning behavior and learning effect data related variables,then according to the learner's final results establishes the regression model,finally analyses selected 60 as the critical value of regression and predicts accuracy verification results using two logic.The sixth part is the conclusion and suggestion.The study results showed that correlated data variables were 13,29% degrees model to explain the eventual establishment of the amount of independent variables online duration,test scores and questions hits,blogging is significant,two logistic regression equation to predict the total accurate rate is 78.1%.Finally,some suggestions are put forward according to the conclusion.This study provides suggestions for teachers to know the online learning situation of learners in a timely manner and help teachers to design and develop the curriculum and resources,arrange and organize the network classroom teaching activities,and make the evaluation standard of the network courses.