Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS
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 furthe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/24/9148 |
| _version_ | 1797543910076055552 |
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| author | Germán Moltó Diana M. Naranjo J. Damian Segrelles |
| author_facet | Germán Moltó Diana M. Naranjo J. Damian Segrelles |
| author_sort | Germán Moltó |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:52:09Z |
| format | Article |
| id | doaj.art-4412585381cf43ea8296bedc2e7f10b7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T13:52:09Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4412585381cf43ea8296bedc2e7f10b72023-11-21T01:57:48ZengMDPI AGApplied Sciences2076-34172020-12-011024914810.3390/app10249148Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWSGermán Moltó0Diana M. Naranjo1J. Damian Segrelles2Instituto de Instrumentación para Imagen Molecular (I3M), Centro mixto CSIC—Universitat Politècnica de València, 46022 Camino de Vera s/n, SpainInstituto de Instrumentación para Imagen Molecular (I3M), Centro mixto CSIC—Universitat Politècnica de València, 46022 Camino de Vera s/n, SpainInstituto de Instrumentación para Imagen Molecular (I3M), Centro mixto CSIC—Universitat Politècnica de València, 46022 Camino de Vera s/n, SpainCloud 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.https://www.mdpi.com/2076-3417/10/24/9148learning analyticscloud computing |
| spellingShingle | Germán Moltó Diana M. Naranjo J. Damian Segrelles Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS Applied Sciences learning analytics cloud computing |
| title | Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS |
| title_full | Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS |
| title_fullStr | Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS |
| title_full_unstemmed | Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS |
| title_short | Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS |
| title_sort | insights from learning analytics for hands on cloud computing labs in aws |
| topic | learning analytics cloud computing |
| url | https://www.mdpi.com/2076-3417/10/24/9148 |
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