Automated monitoring of human–computer interaction for assessing teachers’ digital competence based on LMS data extraction
The fast-paced evolution of technology has compelled the digitalization of education, requiring educators to interact with computers and develop digital competencies relevant to the teaching–learning process. This need has prompted various organizations to define frameworks for assessing digital com...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Ramon Llull (URL) |
| Repositorio: | DAU Arxiu Digital de la Universitat Ramon Llull |
| OAI Identifier: | oai:dau.url.edu:20.500.14342/6089 |
| Acceso en línea: | http://hdl.handle.net/20.500.14342/6089 https://doi.org/10.3390/s24113326 |
| Access Level: | acceso abierto |
| Palabra clave: | Automation Human-computer interaction Digital literacy Digital competence 21st century skills Teacher evaluation 004 378 62 68 |
| Sumario: | The fast-paced evolution of technology has compelled the digitalization of education, requiring educators to interact with computers and develop digital competencies relevant to the teaching–learning process. This need has prompted various organizations to define frameworks for assessing digital competency emphasizing teachers’ interaction with computer technologies in education. Different authors have presented assessment methods for teachers’ digital competence based on the video analysis of recorded classes using sensors such as cameras, microphones, or electroencephalograms. The main limitation of these solutions is the large number of resources they require, making it difficult to assess large numbers of teachers in resource-constrained environments. This article proposes the automation of teachers’ digital competence evaluation process based on monitoring metrics obtained from teachers’ interaction with a Learning Management System (LMS). Based on the Digital Competence Framework for Educators (DigCompEdu), indicators were defined and extracted that allow automatic measurement of a teacher’s competency level. A tool was designed and implemented to conduct a successful proof of concept capable of automating the evaluation process of all university faculty, including 987 lecturers from different fields of knowledge. Results obtained allow for drawing conclusions on technological adoption according to the teacher’s profile and planning educational actions to improve these competencies. |
|---|