学习者在线学习行为投入新探索——基于点、线、面方法的评估模型构建

A New Exploration of Learners’Online Learning Behavior Investment-Construction of Application Evaluation Model Based on Point,Line and Area Method

作为线上学习质量的重要凭据,学习者的在线学习投入备受关注。纵观已有的在线学习投入研究,有基于行为数据的分析研判,也有结合认知、情感和社会因素的综合评判,但仍有诸多未解之谜,需进一步深入持续地分析探讨。从当前网络信息获取及应用的有效方式来看,大量的行为数据是在线学习投入质量分析的重要信息内容。从学习行为的外显性和内隐性两个属性对学习者在线学习信息进行分类探索,有助于综合考虑影响学习质量的行为、认知和情感等因素。在此分类基础上,结合点、线、面研究方法各自的特点,先后通过百分等级量表、单维和多维Rasch分析技术的应用,分别构建了针对学习者在线学习行为投入强度、效度及精准度评估的模型。最后通过准确获取在线学习的过程信息、全面掌握线上学习的质量结果,以及主动反思在线学习参与的实效性等应用验证了评估模型的有效性。

As an important evidence of online learning quality,learners’online learning engagement attracts much attention.A comprehensive review of existing studies on online learning engagement includes analysis and judgment based on behavioral data and comprehensive evaluation based on cognitive,emotional and social factors.However,there are still many unsolved mysteries,which need further continuous and in-depth analysis and discussion.According to the effective ways of obtaining and applying network information,a large amount of behavioral data is the important information content of online learning investment quality analysis.This paper explores the classification of online learning information from the two attributes of explicit and implicit learning behaviors,which is helpful to consider the behavioral,cognitive and affective factors that affect the quality of learning.On the basis of this classification,combined with the characteristics of point,line and area research methods,the evaluation models for the intensity,validity and accuracy of learners’online learning behavior investment were constructed by using percentage scale,single and multidimensional Rasch analysis techniques successively.Finally,through accurate access to online learning process information,a comprehensive grasp of online learning quality results,as well as active reflection on the effectiveness of online learning participation and other aspects of the application of the validation of the effectiveness of the evaluation model.