SSRN Author: Kshitij SharmaKshitij Sharma SSRN Content
http://www.ssrn.com/author=2587109
http://www.ssrn.com/rss/en-usFri, 01 Dec 2017 04:30:38 GMTeditor@ssrn.com (Editor)Fri, 01 Dec 2017 04:30:38 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: A New Lens for Looking at MOOC Data to Predict Student PerformanceWe present two novel methods to predict students' grades using their action time series in Massive Open Online Courses (MOOCs). The main motivation behind this contribution comes from three main differences in the methods used in previous research. First, the methods used to analyze time series often aggregate the data, which discards the effect of the previous actions on the present actions. Second, most of the previous research has a common assumption that the actions are distributed homogeneously in time, which might or might not be true for students in MOOCs. Third, the methods used to predict students' grades are often based on linear regressions and correlations, which assume a normal distribution for the data generation processes, which might not be true in all cases. To highlight the first two differences we propose to use Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) models. To deal with the third difference, we propose to use the Extreme Values Theory ...
http://www.ssrn.com/abstract=3055818
http://www.ssrn.com/1647549.htmlThu, 30 Nov 2017 17:10:10 GMTREVISION: A New Lens for Looking at MOOC Data to Predict Student PerformanceWe present two novel methods to predict students' grades using their action time series in Massive Open Online Courses (MOOCs). The main motivation behind this contribution comes from three main differences in the methods used in previous research. First, the methods used to analyze time series often aggregate the data, which discards the effect of the previous actions on the present actions. Second, most of the previous research has a common assumption that the actions are distributed homogeneously in time, which might or might not be true for students in MOOCs. Third, the methods used to predict students' grades are often based on linear regressions and correlations, which assume a normal distribution for the data generation processes, which might not be true in all cases. To highlight the first two differences we propose to use Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) models. To deal with the third difference, we propose to use the Extreme Values Theory ...
http://www.ssrn.com/abstract=3055818
http://www.ssrn.com/1635484.htmlThu, 19 Oct 2017 17:39:50 GMTREVISION: An Application of Extreme Value Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-Tracking DataThe statistics used in education research are based on central trends such as the mean or standard deviation, discarding outliers. This paper adopts another viewpoint that has emerged in Statistics, called the Extreme Value Theory (EVT). EVT claims that the bulk of the normal distribution is mostly comprised of uninteresting variations while the most extreme values convey more information. We applied EVT to eye-tracking data collected during online collaborative problem solving with the aim of predicting the quality of collaboration. We compare our previous approach, based on central trends, with an EVT approach focused on extreme episodes of collaboration. The latter occurred to provide a better prediction of the quality of collaboration.
http://www.ssrn.com/abstract=2831445
http://www.ssrn.com/1573625.htmlMon, 13 Mar 2017 13:23:34 GMTREVISION: An Application of Extreme Values Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-Tracking DataThe statistics used in education research are based on central trends such as the mean or standard deviation, discarding outliers. This paper adopts another viewpoint that has emerged in Statistics, called the Extreme Value Theory (EVT). EVT claims that the bulk of the normal distribution is mostly comprised of uninteresting variations while the most extreme values convey more information. We applied EVT to eye-tracking data collected during online collaborative problem solving with the aim of predicting the quality of collaboration. We compare our previous approach, based on central trends, with an EVT approach focused on extreme episodes of collaboration. The latter occurred to provide a better prediction of the quality of collaboration.
http://www.ssrn.com/abstract=2831445
http://www.ssrn.com/1573242.htmlSat, 11 Mar 2017 07:41:10 GMT