Home > Events > 2017 > PhD Thesis defense: Analyzing the Behavior of Students Regarding Learning Activities, Badges, and Academic Dishonesty in MOOC Environments
PhD Thesis defense: Analyzing the Behavior of Students Regarding Learning Activities, Badges, and Academic Dishonesty in MOOC Environments
31 May 2017 - 11:00am to 1:00pm
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Speaker(s): 
José Antonio Ruipérez-Valiente, PhD Student, IMDEA Networks Institute and University Carlos III of Madrid
Location: 

Sala Audiovisuales 3.1.S08 - Building Rey Pastor (Library), Universidad Carlos III de Madrid, Avda. Universidad, 30, 28911 Leganes – Madrid

José Antonio Ruipérez-ValienteThe 'big data' scene has brought new improvement opportunities to most products and services, including education. Web-based learning has become very widespread over the last decade, which in conjunction with the MOOC phenomenon, it has enabled the collection of large and rich data samples regarding the interaction of students with these educational online environments. We have detected different areas in the literature that still need improvement and more research studies. Particularly, in the context of MOOC and SPOC, where we focus our data analysis on the platforms Khan Academy, Open edX and Coursera. More specifically, we are going to work towards learning analytics visualization dashboards, carrying out an evaluation of these visual analytics tools. Additionally, we will delve into the activity and behavior of students with regular and optional activities, badges and their online academically dishonest conduct. The analysis of activity and behavior of students is divided first in exploratory analysis providing descriptive and inferential statistics, like correlations and group comparisons, as well as numerous visualizations that facilitate conveying understandable information. Second, we apply clustering analysis to find different profiles of students for different purposes e.g., to analyze potential adaptation of learning experiences and pedagogical implications. Third, we also provide three machine learning models, two of them to predict learning outcomes (learning gains and certificate accomplishment) and one to classify submissions as illicit or not. We also use these models to discuss about the importance of variables. Finally, we discuss our results in terms of the motivation of students, student profiling, instructional design, potential actuators and the evaluation of visual analytics dashboards providing different recommendations to improve future educational experiments.

 

About José Antonio Ruipérez-Valiente

Prior to his incorporation to IMDEA Networks, José Antonio worked in the private sector, as a Programmer for Accenture Technology Solutions, as well as in academia, having joined University Carlos III of Madrid (UC3M) as a Research Assistant.

In 2010 he obtained his B.Sc. in Telecommunications Systems from the Catholique University of San Antonio (Universidad Católica de San Antonio) (Murcia, Spain). In 2013 he received his M.Sc. in Telecommunications Engineering from UC3M. He has received the Outstanding Graduate of the Year Award (ranked 1st in the class) upon the completion of each of the degrees. His master’s thesis also received an additional award delivered by Accenture. He has already finished the M.Sc. in Telematics Engineering at UC3M and now he is devoting his work towards achieving the Ph.D.

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The thesis defense will be conducted in English

PhD Thesis Advisor: Dr. Pedro Jose Muñoz Merino, University Carlos III of Madrid
University: University Carlos III of Madrid, Spain
Doctoral Program: Telematics Engineering