Exploratory Learning Environments (ELEs) are open-ended software in which students build scientific models and examine properties of the models by running them and analyzing the results (Amershi and Conati, Intelligent tutoring systems. LNCS. Springer, Heidelberg, 463–472, 2006); Chen (Instr Sci, 23(1–3):183–220, 1995); (Cocea et al., 2008). ELEs are generally used in classes too large for teachers to monitor all students and provide assistance when needed (Gal et al., 2008). They are also becoming increasingly prevalent in developing countries where access to teachers and other educational resources is limited (Pawar et al., 2007). Thus, there is a need to develop tools of support for teachers’ understanding of students’ activities. This chapter presents methods for addressing these needs. It presents an efficient algorithm for intelligently recognizing students’ activities, and novel visualization methods for presenting these activities to teachers. Our empirical analysis is based on an ELE for teaching chemistry that is used by thousands of students in colleges and high schools in several countries (Yaron et al., Science, 328(5978), 584–585, 2010).
|Title of host publication||Educational Data Mining|
|Number of pages||39|
|Publication status||Published - 7 Nov 2013|