Improvement of Academic Analytics Processes Through the Identification of the Main Variables Affecting Early Dropout of First-Year Students in Technical Degrees. A Case Study
The field of research on the phenomenon of university dropout and the factors that promote it is of the utmost relevance, especially in the current context of the Covid-19 pandemic. Students who have started degrees in the last two years have completed their university studies in periods of lockdown...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universidad Internacional de La Rioja (UNIR)
2025-01-01
|
| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.ijimai.org/journal/bibcite/reference/3330 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841560801239891968 |
|---|---|
| author | A. Llauró David Fonseca E. Villegas M. Aláez S. Romero |
| author_facet | A. Llauró David Fonseca E. Villegas M. Aláez S. Romero |
| author_sort | A. Llauró |
| collection | DOAJ |
| description | The field of research on the phenomenon of university dropout and the factors that promote it is of the utmost relevance, especially in the current context of the Covid-19 pandemic. Students who have started degrees in the last two years have completed their university studies in periods of lockdown and unlike traditional education, this has often involved taking online classes. In this scenario, the students' motivation and the way they are able to cope with the difficulties of the first year of a university course are very relevant, especially in technical degrees. Previous studies show that a large number of undergraduate students drop out prematurely. In order to act to reduce dropout rates, schools, especially technical schools, should be able to map the entry profile of students and identify the factors that promote early dropout. This paper focuses on identifying, categorizing and evaluating a number of indicators according to the perception of tutors and the field of study, based on the application of quantitative and qualitative techniques. The results support the approach taken, as they show how tutors can identify students at risk of dropping out at the beginning of the course and act proactively to monitor and motivate them. |
| format | Article |
| id | doaj-art-bcc6b33e243748658962598481ce199c |
| institution | Kabale University |
| issn | 1989-1660 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj-art-bcc6b33e243748658962598481ce199c2025-01-03T15:20:36ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16602025-01-01919210310.9781/ijimai.2023.06.002ijimai.2023.06.002Improvement of Academic Analytics Processes Through the Identification of the Main Variables Affecting Early Dropout of First-Year Students in Technical Degrees. A Case StudyA. LlauróDavid FonsecaE. VillegasM. AláezS. RomeroThe field of research on the phenomenon of university dropout and the factors that promote it is of the utmost relevance, especially in the current context of the Covid-19 pandemic. Students who have started degrees in the last two years have completed their university studies in periods of lockdown and unlike traditional education, this has often involved taking online classes. In this scenario, the students' motivation and the way they are able to cope with the difficulties of the first year of a university course are very relevant, especially in technical degrees. Previous studies show that a large number of undergraduate students drop out prematurely. In order to act to reduce dropout rates, schools, especially technical schools, should be able to map the entry profile of students and identify the factors that promote early dropout. This paper focuses on identifying, categorizing and evaluating a number of indicators according to the perception of tutors and the field of study, based on the application of quantitative and qualitative techniques. The results support the approach taken, as they show how tutors can identify students at risk of dropping out at the beginning of the course and act proactively to monitor and motivate them.https://www.ijimai.org/journal/bibcite/reference/3330academic analyticsdropoutstudents interactionlearning analyticspredictionintelligent tutoring systems |
| spellingShingle | A. Llauró David Fonseca E. Villegas M. Aláez S. Romero Improvement of Academic Analytics Processes Through the Identification of the Main Variables Affecting Early Dropout of First-Year Students in Technical Degrees. A Case Study International Journal of Interactive Multimedia and Artificial Intelligence academic analytics dropout students interaction learning analytics prediction intelligent tutoring systems |
| title | Improvement of Academic Analytics Processes Through the Identification of the Main Variables Affecting Early Dropout of First-Year Students in Technical Degrees. A Case Study |
| title_full | Improvement of Academic Analytics Processes Through the Identification of the Main Variables Affecting Early Dropout of First-Year Students in Technical Degrees. A Case Study |
| title_fullStr | Improvement of Academic Analytics Processes Through the Identification of the Main Variables Affecting Early Dropout of First-Year Students in Technical Degrees. A Case Study |
| title_full_unstemmed | Improvement of Academic Analytics Processes Through the Identification of the Main Variables Affecting Early Dropout of First-Year Students in Technical Degrees. A Case Study |
| title_short | Improvement of Academic Analytics Processes Through the Identification of the Main Variables Affecting Early Dropout of First-Year Students in Technical Degrees. A Case Study |
| title_sort | improvement of academic analytics processes through the identification of the main variables affecting early dropout of first year students in technical degrees a case study |
| topic | academic analytics dropout students interaction learning analytics prediction intelligent tutoring systems |
| url | https://www.ijimai.org/journal/bibcite/reference/3330 |
| work_keys_str_mv | AT allauro improvementofacademicanalyticsprocessesthroughtheidentificationofthemainvariablesaffectingearlydropoutoffirstyearstudentsintechnicaldegreesacasestudy AT davidfonseca improvementofacademicanalyticsprocessesthroughtheidentificationofthemainvariablesaffectingearlydropoutoffirstyearstudentsintechnicaldegreesacasestudy AT evillegas improvementofacademicanalyticsprocessesthroughtheidentificationofthemainvariablesaffectingearlydropoutoffirstyearstudentsintechnicaldegreesacasestudy AT malaez improvementofacademicanalyticsprocessesthroughtheidentificationofthemainvariablesaffectingearlydropoutoffirstyearstudentsintechnicaldegreesacasestudy AT sromero improvementofacademicanalyticsprocessesthroughtheidentificationofthemainvariablesaffectingearlydropoutoffirstyearstudentsintechnicaldegreesacasestudy |