Analyzing Content Structure and Moodle Milestone to Classify Student Learning Behavior in a Basic Desktop Tools Course
Date
2017-10-18
Authors
Ros, S.
Lázaro, J. C.
Robles-Gómez, A.
Caminero, A. C.
Tobarra, L.
Pastor, R.
Journal Title
Journal ISSN
Volume Title
Publisher
ACM
Abstract
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 gen-erated 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 interac-tions 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.
Description
Keywords
Learning Analytics (LA), Indicators, Principal Component Analysis (PCA), Moodle, Ex-ploratory Data Analysis (EDA)
Citation
Ros, S., Lázaro, J. C., Robles-Gómez, A., Caminero, A. C., Tobarra, L., & Pastor, R. (2017). Analyzing Content Structure and Moodle Milestone to Classify Student Learning Behavior in a Basic Desktop Tools Course. In J. M. Dodero, M. S. Ibarra Sáiz, & I. Ruiz Rube (Eds.), Fifth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’17) (Cádiz, Spain, October 18-20, 2017) (pp. Article 42). New York, NY, USA: ACM. doi:10.1145/3144826.3145392