基于TOPSIS多属性决策方法的MOOCs用户协作学习效果评价研究  

Evaluation of MOOCs User Collaborative Learning Effect——Based on TOPSIS Multiple Attribute Decision

作　　者：徐光[1,2] 刘鲁川[1] 

机构地区：[1]山东财经大学管理科学与工程学院,山东济南250014 [2]齐鲁师范学院经济与管理学院,山东济南250200

出　　处：《山东财经大学学报》2016年第6期95-103,共9页Journal of Shandong University of Finance and Economics

基　　金：国家自然科学基金项目"社会化阅读服务用户持续使用的行为机理研究"(71373144);教育部人文社会科学研究规划基金项目"移动阅读系统用户持续使用的理论模型:基于情感视角的实证研究"(12YJA860010);山东省统计科研课题"互联网时代高校MOOCS教学效果评价指标体系研究"(KT15179)

摘　　要：文章从MOOCs用户体验角度,以协作学习理论为研究视角,提出了MOOCs协作学习效果评价指标体系,并应用该指标体系于实践,采用逼近理想点法(TOPSIS)多属性决策方法对齐鲁师范学院6个MOOCs网络班级的协作学习效果进行了评价。该指标体系以发展性评价理论和群体协作学习理论为基础,采用过程性评价与总结性评价、定性评价与定量评价相结合的评价方案,着重评价协作学习过程中的群体情感表现,不仅丰富了MOOCs评价的理论基础,也提供了一整套的群体协作学习评价方案及实施步骤;同时,还拓展了TOPSIS方法的应用领域,把TOPSIS方法引入MOOCs学习效果评价研究中,尝试为解决MOOCs高辍学率问题提供新的方向。From the perspective of MOOCs user experience and collaborative learning theory, this paper puts forward a MOOCs collaborative learning effect evaluation index system and evaluates, by adotping this system and TOPSIS multiple attribute decision method, the collaborative learning effects of the six MOOCs network classes from Qilu Normal University. Based on development evaluation theory and group collaborative learning theory, this index system combines process evaluation vs summative evaluation, qualitative evaluation vs quantitative evaluation, and focuses on evaluating the group emotional expression in collaborative learning process. This study enriches the theoretical basis of MOOCs evaluation, provides a set of evaluation program and implementation steps for group collaborative learning, extends the application field of TOPSIS method, introduces the TOPSIS method into the study of MOOCs learning effect evaluation, and tries to provide a new direction for solving the problem of high MOOCs dropout rate.

关 键 词：MOOCs 评价指标体系 协作学习 TOPSIS多属性决策 

分 类 号：G434[文化科学—教育学]