Analyzing Content Structure and Moodle Milestone to Classify Student Learning Behavior in a Basic Desktop Tools Course

This paper analyzes the content structure and Moodle milestone to classify the students' learning behavior for a basic desktop-tools on-line virtual course. The data collection phase is completed for a Learning Analytics (LA) process as a first step; by using the generated interactions among students, and with learning resources, assessments, and so on. A first exploratory data analysis study is also done with the extracted indicators (or features) of all interactions to classify them in five traits. A multidimensional parameter reduction has been implemented based on Principal Component Analysis (PCA), an example of it is also given.

[1]  Rebeca P. Díaz Redondo,et al.  Is interpersonal participation relevant to pass? , 2016, TEEM.

[2]  Maren Scheffel,et al.  Quality Indicators for Learning Analytics , 2014, J. Educ. Technol. Soc..

[3]  Francisco J. García-Peñalvo,et al.  Analyzing navigation logs in MOOC: a case study , 2016, TEEM.

[4]  George Siemens,et al.  Learning Analytics , 2013 .

[5]  Carlos Delgado Kloos,et al.  Evaluation of a learning analytics application for open edX platform , 2017, Comput. Sci. Inf. Syst..

[6]  G. Lavanya Devi,et al.  Attribute subset selection by mixed weighting mean classification method , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).