Persona:
Romero Martínez, Sonia Janeth

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0000-0001-8330-6694
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Romero Martínez
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Sonia Janeth
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2020 - 20212
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Autor: Bravo-Agapito, Javier ×

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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Attitudes towards technology among distance education students: Validation of an explanatory model
    (Universidad Complutense, Madrid, 2020) Romero Martínez, Sonia Janeth; Ordoñez Camacho, Xavier Giovanni; Guillén Gámez, Francisco David; Bravo-Agapito, Javier; https://orcid.org/0000-0002-8153-5706; https://orcid.org/0000-0001-6470-526X; https://orcid.org/0000-0002-3516-7367
    Attitudes toward technology are preconceived notions that affect the teaching-learning process and the academic-professional performance of students, in particular those who use technology. This investigation has two objectives: to test the measuring properties (reliability, factorial structure) of an instrument that measures attitudes and to propose and validate a model that hypothesizes digital competence and frequency of use of technologies have a positive impact on attitudes. The sample included 1,251 students of the Madrid Open University in a nonexperimental, explanatory study using structural equation methodology. The results indicated adequate psychometric properties for the test and good adjustment of the proposed model (χ² = 163.91, df = 37, p < .001) allowing for further exploration of the relationship between use, skill, and attitudes in the distance education context and improving the properties of measuring instruments proposed in Spanish.
  • Miniatura
    Publicación
    Early prediction of undergraduate Student's academic performance in completely online learning: A five-year study
    (ELSEVIER, 2021) Bravo-Agapito, Javier; Romero Martínez, Sonia Janeth; Pamplona, Sonia; https://orcid.org/0000-0002-3516-7367 View this author’s ORCID profile
    This decade, e-learning systems provide more interactivity to instructors and students than traditional systemsand make possible a completely online (CO) education. However, instructors could not warn if a CO student is engaged or not in the course, and they could not predict his or her academic performance in courses. This work provides a collection of models (exploratory factor analysis, multiple linear regressions, cluster analysis, and correlation) to early predict the academic performance of students. These models are constructed using Moodle interaction data, characteristics, and grades of 802 undergraduate students from a CO university. The models result indicated that the major contribution to the prediction of the academic student performance is made by four factors: Access, Questionnaire, Task, and Age. Access factor is composed by variables related to accesses of students in Moodle, including visits to forums and glossaries. Questionnaire factor summarizes variables related to visits and attempts in questionnaires. Task factor is composed of variables related to consulted and submitted tasks. The Age factor contains the student age. Also, it is remarkable that Age was identified as a negative predictor of the performance of students, indicating that the student performance is inversely proportional to age. In addition, cluster analysis found five groups and sustained that number of interactions with Moodle are closely related to performance of students.