A Solution to Manage the Full Life Cycle of Learning Analytics in a Learning Management System: AnalyTIC
Learning Analytics (LA) has a significant impact in learning and teaching processes. These processes can be improved using the available data retrieved from students' activity inside the virtual classrooms of a learning management system (LMS). This process requires the development of a tool th...
- Autores:
-
Simanca Herrera, Fredys Alberto
Crespo R.G.
Baena L.R.
Burgos D.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Universidad Cooperativa de Colombia
- Repositorio:
- Repositorio UCC
- Idioma:
- OAI Identifier:
- oai:repository.ucc.edu.co:20.500.12494/41746
- Acceso en línea:
- https://doi.org/10.15446/rsap.V19n6.60382
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046025388&doi=10.1590%2f1678-4162-9182&partnerID=40&md5=1000b22fa9e6bc5cebd0ce6a0e8d9d8b
https://hdl.handle.net/20.500.12494/41746
- Palabra clave:
- Computer aided instruction
E-learning
Education computing
Educational technology
Information management
Learning systems
Life cycle
Software testing
Development model
Learning analytics
Learning and teachings
Learning management system
On-line education
Personalized learning
personalized mentoring
Virtual Classroom
Students
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
| Summary: | Learning Analytics (LA) has a significant impact in learning and teaching processes. These processes can be improved using the available data retrieved from students' activity inside the virtual classrooms of a learning management system (LMS). This process requires the development of a tool that allows one to handle the retrieved information properly. This paper presents a solution to this need, in the form of a development model and actual implementation of an LA tool. Four phases (Explanation, Diagnosis, Prediction and Prescription) are implemented in the tool, allowing a teacher to track students' activity in a virtual classroom via the Sakai LMS. It also allows for the identification of users who face challenges in their academic process and the initiation of personalised mentoring by the teacher or tutor. The use of the tool was tested on groups of students in an algorithms course in the periods 2017-1, 2017-2, 2018-1 and 2018-2, with a total of 90 students - in parallel with the control groups in the same periods that totalled 95 students - obtaining superior averages in the test groups versus the control groups, which evidenced the functionality and utility of the software. © 2013 IEEE. |
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