Assessing the Relevance of Information Sources for Modelling Student Performance in a Higher Mathematics Education Course
In recent years, most educational institutions have integrated digital technologies into their teaching–learning processes. Learning Management Systems (LMS) have gained increasing popularity, particularly in higher education, due to their ability to manage teacher–student interactions. These system...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Education Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7102/13/6/555 |
| _version_ | 1797595204717379584 |
|---|---|
| author | Adrián Pérez-Suay Ricardo Ferrís-Castell Steven Van Vaerenbergh Ana B. Pascual-Venteo |
| author_facet | Adrián Pérez-Suay Ricardo Ferrís-Castell Steven Van Vaerenbergh Ana B. Pascual-Venteo |
| author_sort | Adrián Pérez-Suay |
| collection | DOAJ |
| description | In recent years, most educational institutions have integrated digital technologies into their teaching–learning processes. Learning Management Systems (LMS) have gained increasing popularity, particularly in higher education, due to their ability to manage teacher–student interactions. These systems store valuable information which describes students’ behaviour throughout a course. These data can be utilised to construct statistical models that represent learner behaviour within an online LMS platform. In this study, we aim to compare different sources of information and, more ambitiously, to provide insights into which source of information is most valuable for inferring student performance. The considered sets of information come from (i) the Moodle LMS; (ii) socio-economic data about students acquired from a survey; and (iii) subject marks achieved throughout the course. To determine the relevance of the incorporated information, we use artificial intelligence (AI) methods, and we report the importance measures of four state-of-the-art methods. Our findings indicate that the selected methodology is suitable for making inferences about student performance while also shedding light on model decisions through explainability. |
| first_indexed | 2024-03-11T02:33:38Z |
| format | Article |
| id | doaj.art-5a4fe7e765b644af9df7e5a8ebcbe122 |
| institution | Directory Open Access Journal |
| issn | 2227-7102 |
| language | English |
| last_indexed | 2024-03-11T02:33:38Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj.art-5a4fe7e765b644af9df7e5a8ebcbe1222023-11-18T10:05:57ZengMDPI AGEducation Sciences2227-71022023-05-0113655510.3390/educsci13060555Assessing the Relevance of Information Sources for Modelling Student Performance in a Higher Mathematics Education CourseAdrián Pérez-Suay0Ricardo Ferrís-Castell1Steven Van Vaerenbergh2Ana B. Pascual-Venteo3Departament de Didàctica de la Matemàtica, Universitat de València, Av. Tarongers 4, 46022 València, SpainDepartament d’Informàtica, Universitat de València, Avinguda de l’Universitat, 46100 Burjassot, SpainDepartamento de Matemáticas, Estadística y Computación, Universidad de Cantabria, Av. de los Castros 48, 39005 Santander, SpainLaboratori de Processat d’Imatges, Universitat de València, Catedràtic Agustín Escardino Benlloch, 9, 46980 Paterna, SpainIn recent years, most educational institutions have integrated digital technologies into their teaching–learning processes. Learning Management Systems (LMS) have gained increasing popularity, particularly in higher education, due to their ability to manage teacher–student interactions. These systems store valuable information which describes students’ behaviour throughout a course. These data can be utilised to construct statistical models that represent learner behaviour within an online LMS platform. In this study, we aim to compare different sources of information and, more ambitiously, to provide insights into which source of information is most valuable for inferring student performance. The considered sets of information come from (i) the Moodle LMS; (ii) socio-economic data about students acquired from a survey; and (iii) subject marks achieved throughout the course. To determine the relevance of the incorporated information, we use artificial intelligence (AI) methods, and we report the importance measures of four state-of-the-art methods. Our findings indicate that the selected methodology is suitable for making inferences about student performance while also shedding light on model decisions through explainability.https://www.mdpi.com/2227-7102/13/6/555student performancelearning management systemsmathematics educationartificial intelligence |
| spellingShingle | Adrián Pérez-Suay Ricardo Ferrís-Castell Steven Van Vaerenbergh Ana B. Pascual-Venteo Assessing the Relevance of Information Sources for Modelling Student Performance in a Higher Mathematics Education Course Education Sciences student performance learning management systems mathematics education artificial intelligence |
| title | Assessing the Relevance of Information Sources for Modelling Student Performance in a Higher Mathematics Education Course |
| title_full | Assessing the Relevance of Information Sources for Modelling Student Performance in a Higher Mathematics Education Course |
| title_fullStr | Assessing the Relevance of Information Sources for Modelling Student Performance in a Higher Mathematics Education Course |
| title_full_unstemmed | Assessing the Relevance of Information Sources for Modelling Student Performance in a Higher Mathematics Education Course |
| title_short | Assessing the Relevance of Information Sources for Modelling Student Performance in a Higher Mathematics Education Course |
| title_sort | assessing the relevance of information sources for modelling student performance in a higher mathematics education course |
| topic | student performance learning management systems mathematics education artificial intelligence |
| url | https://www.mdpi.com/2227-7102/13/6/555 |
| work_keys_str_mv | AT adrianperezsuay assessingtherelevanceofinformationsourcesformodellingstudentperformanceinahighermathematicseducationcourse AT ricardoferriscastell assessingtherelevanceofinformationsourcesformodellingstudentperformanceinahighermathematicseducationcourse AT stevenvanvaerenbergh assessingtherelevanceofinformationsourcesformodellingstudentperformanceinahighermathematicseducationcourse AT anabpascualventeo assessingtherelevanceofinformationsourcesformodellingstudentperformanceinahighermathematicseducationcourse |