Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS

Autor: Diana M. Naranjo, J. Damian Segrelles, Germán Moltó
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Computer science
Population
Learning analytics
Cloud computing
02 engineering and technology
lcsh:Technology
lcsh:Chemistry
0202 electrical engineering
electronic engineering
information engineering
CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL
ComputingMilieux_COMPUTERSANDEDUCATION
Gender analysis
General Materials Science
education
Instrumentation
lcsh:QH301-705.5
Dropout (neural networks)
Fluid Flow and Transfer Processes
learning analytics
Data processing
education.field_of_study
business.industry
lcsh:T
Process Chemistry and Technology
05 social sciences
cloud computing
General Engineering
050301 education
Data science
lcsh:QC1-999
Computer Science Applications
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
020201 artificial intelligence & image processing
Learning Management
Tracking (education)
business
lcsh:Engineering (General). Civil engineering (General)
0503 education
lcsh:Physics
Zdroj: Applied Sciences, Vol 10, Iss 9148, p 9148 (2020)
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname
Applied Sciences
Volume 10
Issue 24
RUA. Repositorio Institucional de la Universidad de Alicante
Universidad de Alicante (UA)
ISSN: 2076-3417
Popis: [EN] Cloud computing instruction requires hands-on experience with a myriad of distributed computing services from a public cloud provider. Tracking the progress of the students, especially for online courses, requires one to automatically gather evidence and produce learning analytics in order to further determine the behavior and performance of students. With this aim, this paper describes the experience from an online course in cloud computing with Amazon Web Services on the creation of an open-source data processing tool to systematically obtain learning analytics related to the hands-on activities carried out throughout the course. These data, combined with the data obtained from the learning management system, have allowed the better characterization of the behavior of students in the course. Insights from a population of more than 420 online students through three academic years have been assessed, the dataset has been released for increased reproducibility. The results corroborate that course length has an impact on online students dropout. In addition, a gender analysis pointed out that there are no statistically significant differences in the final marks between genders, but women show an increased degree of commitment with the activities planned in the course.
This research was funded by the Spanish "Ministerio de Economia, Industria y Competitividad through grant number TIN2016-79951-R (BigCLOE)", the "Vicerrectorado de Estudios, Calidad y Acreditacion" of the Universitat Politecnica de Valencia (UPV) to develop the PIME B29 and PIME/19-20/166, and by the Conselleria d'Innovacio, Universitat, Ciencia i Societat Digital for the project "CloudSTEM" with reference number AICO/2019/313.
Databáze: OpenAIRE
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