Evaluationing The Online Learners’ Learning Effects Based On Their Learning Behavior Data

Although online learning systems can effectively help learners to learn anytime and anywhere,individual differences such as learning preference,learning style and cognitive ability would lead to various online learning behaviors,as well as different learning effects.Many existing researches about how different learning behavours would influence learning effects still focused on the impacts that controlled factors may produce on the learning effects,few of them have noticed that those controlled factors may be a reflection of the behaviour features occurred during their learning processes,and can be represented by the key features that are recognized and extracted from the basic feature space which is used to describe the learning behaviours when learners are using the online platforms.In order to build a feasible feature space for the learning behaviours to describe individual differences,an analysis about how online learning behaviour may generate various online learning patterns that may influence learning effects is conducted based on a SPOC platform.The research reveals that by using learning style as the classification index,different learning patterns regarding different kinds of learning resources were adopted by different LS biased learners,and the difference between two kinds of learners was also appeared in their final learning effects and period learning effects.The research suggested that,the feature-space construction should be tightly connected with the users’ choices of the media kinds of the learning resources;besides that,the general intelligence theory of the knowledge integration suggested that the applying of the self-reflected information also should be considered in the feature space as one of the features that can be used to label a learning behaviour.Based on the multi-faceted learning behaviours that were collected from a prototype learning system in which a ‘C programming’ course is embedded,this paper proposed a learning effect regression/prediction model named FLR based on naive Bayesian model,and applied particle swarm algorithm to optimize the goal function;FLR not only significantly improved the prediction accuracy of the final learning effects compared with the linear regression with multi-variable,but also capable of explain the cause-result relations among learning behaviours and their learning effects(including learning effects in different learning phase).