Using Learning Analytics for Preserving Academic Integrity
This paper presents the results of integrating learning analytics into the assessment process to enhance academic integrity in the e-learning environment. The goal of this research is to evaluate the computational-based approach to academic integrity. The machine-learning based framework learns stud...
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Athabasca University Press
2017
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/426a49954056401f9c1435b4afc2e031 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | This paper presents the results of integrating learning analytics into the assessment process to enhance academic integrity in the e-learning environment. The goal of this research is to evaluate the computational-based approach to academic integrity. The machine-learning based framework learns students’ patterns of language use from data, providing an accessible and non-invasive validation of student identities and student-produced content. To assess the performance of the proposed approach, we conducted a series of experiments using written assignments of graduate students. The proposed method yielded a mean accuracy of 93%, exceeding the baseline of human performance that yielded a mean accuracy rate of 12%. The results suggest a promising potential for developing automated tools that promote accountability and simplify the provision of academic integrity in the e-learning environment.
|
|---|