Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence
Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/10045/131521
Título: | Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence |
---|---|
Autor/es: | Rico-Juan, Juan Ramón | Sánchez-Cartagena, Víctor M. | Valero-Mas, Jose J. | Gallego, Antonio-Javier |
Grupo/s de investigación o GITE: | Reconocimiento de Formas e Inteligencia Artificial | Transducens |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Student profile identification | Online Judge systems | Multi-Instance Learning | eXplainable Artificial Intelligence | Machine Learning |
Fecha de publicación: | 23-ene-2023 |
Editor: | IEEE |
Cita bibliográfica: | IEEE Transactions on Learning Technologies. 2023, 16(6): 955-969. https://doi.org/10.1109/TLT.2023.3239110 |
Resumen: | Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an educational context such information may be deemed insufficient, it would be beneficial for both the student and the instructor to receive additional feedback about the overall development of the task. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor. More precisely, we consider the use of learning-based schemes—particularly, Multi-Instance Learning and classical Machine Learning formulations—to model student behaviour. Besides, Explainable Artificial Intelligence is contemplated to provide human-understandable feedback. The proposal has been evaluated considering a case of study comprising 2,500 submissions from roughly 90 different students from a programming-related course in a Computer Science degree. The results obtained validate the proposal: the model is capable of significantly predicting the user outcome (either passing or failing the assignment) solely based on the behavioural pattern inferred by the submissions provided to the OJ. Moreover, the proposal is able to identify prone-to-fail student groups and profiles as well as other relevant information, which eventually serves as feedback to both the student and the instructor. |
Patrocinador/es: | This work has been partially funded by the “Programa Redes-I3CE de investigacion en docencia universitaria del Instituto de Ciencias de la Educacion (REDES-I3CE-2020-5069)” of the University of Alicante. The third author is supported by grant APOSTD/2020/256 from “Programa I+D+I de la Generalitat Valenciana”. |
URI: | http://hdl.handle.net/10045/131521 |
ISSN: | 1939-1382 |
DOI: | 10.1109/TLT.2023.3239110 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1109/TLT.2023.3239110 |
Aparece en las colecciones: | INV - TRANSDUCENS - Artículos de Revistas INV - GRFIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
Rico-Juan_etal_2023_IEEE-TLT_accepted.pdf | Accepted Manuscript (acceso abierto) | 6,83 MB | Adobe PDF | Abrir Vista previa |
Rico-Juan_etal_2023_IEEE-TLT_final.pdf | Versión final (acceso restringido) | 14,07 MB | Adobe PDF | Abrir Solicitar una copia |
Todos los documentos en RUA están protegidos por derechos de autor. Algunos derechos reservados.