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   Learning analytics at “small” scale: Exploring a complexity-grounded model for assessment automation  
   
نویسنده Goggins Sean ,Xing Wanli ,Chen Xin ,Chen Bodong ,Wadholm Bob
منبع journal of universal computer science - 2015 - دوره : 21 - شماره : 1 - صفحه:66 -92
چکیده    This study proposes a process-oriented,automatic,formative assessment model for small group learning based on complex systems theory using a small dataset from a technology-mediated,synchronous mathematics learning environment. we first conceptualize small group learning as a complex system and explain how group dynamics and interaction can be modeled via theoretically grounded,simple rules. these rules are then operationalized to build temporally-embodied measures,where varying weights are assigned to the same measures according to their significance during different time stages based on the golden ratio concept. this theory-based measure construction method in combination with a correlation-based feature subset selection algorithm reduces data dimensionality,making a complex system more understandable for people. further,because the discipline of education often generates small datasets,a tree-augmented naïve bayes classifier was coded to develop an assessment model,which achieves the highest accuracy (95.8%) as compared to baseline models. finally,we describe a web-based tool that visualizes time-series activities,assesses small group learning automatically,and also offers actionable intelligence for teachers to provide real-time support and intervention to students. the fundamental contribution of this paper is that it makes complex,small group behavior visible to teachers in a learning context quickly. theoretical and methodological implications for technology mediated small group learning and learning analytics as a whole are then discussed.
کلیدواژه Assessment; Complex systems; Learning analytics; Small group learning
آدرس University of Missouri, School of Information Science & Learning Technologies, USA, University of Missouri, School of Information Science & Learning Technologies, USA, Purdue University, School of Engineering Education, USA, University of Minnesota, Department of Curriculum and Instruction, USA, university of missouri, school of information science and learning technologies, USA
 
     
   
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