An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark
Since the Bologna Process was adopted, continuous assessment has been a cornerstone in the curriculum of most of the courses in the different degrees offered by the Spanish Universities. Continuous assessment plays an important role in both students’ and lecturers’ academic lives. In this study, we...
| Main Authors: | , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
MDPI
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/14068 |
| _version_ | 1789406338209546240 |
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| author | Morales Giraldo, María Encarnación Salmerón Cerdán, Antonio Maldonado González, Ana Devaki Masegosa Arredondo, Andrés Ramón Rumí Rodríguez, Rafael |
| author_facet | Morales Giraldo, María Encarnación Salmerón Cerdán, Antonio Maldonado González, Ana Devaki Masegosa Arredondo, Andrés Ramón Rumí Rodríguez, Rafael |
| author_sort | Morales Giraldo, María Encarnación |
| collection | DSpace |
| description | Since the Bologna Process was adopted, continuous assessment has been a cornerstone in the curriculum of most of the courses in the different degrees offered by the Spanish Universities. Continuous assessment plays an important role in both students’ and lecturers’ academic lives. In this study, we analyze the effect of the continuous assessment on the performance of the students in their final exams in courses of Statistics at the University of Almería. Specifically, we study if the performance of a student in the continuous assessment determines the score obtained in the final exam of the course in such a way that this score can be predicted in advance using the continuous assessment performance as an explanatory variable. After using and comparing some powerful statistical procedures, such as linear, quantile and logistic regression, artificial neural networks and Bayesian networks, we conclude that, while the fact that a student passes or fails the final exam can be properly predicted, a more detailed forecast about the grade obtained is not possible. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-14068 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-140682023-04-12T19:36:45Z An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark Morales Giraldo, María Encarnación Salmerón Cerdán, Antonio Maldonado González, Ana Devaki Masegosa Arredondo, Andrés Ramón Rumí Rodríguez, Rafael continuous assessment Bayesian networks artificial neural networks classification Since the Bologna Process was adopted, continuous assessment has been a cornerstone in the curriculum of most of the courses in the different degrees offered by the Spanish Universities. Continuous assessment plays an important role in both students’ and lecturers’ academic lives. In this study, we analyze the effect of the continuous assessment on the performance of the students in their final exams in courses of Statistics at the University of Almería. Specifically, we study if the performance of a student in the continuous assessment determines the score obtained in the final exam of the course in such a way that this score can be predicted in advance using the continuous assessment performance as an explanatory variable. After using and comparing some powerful statistical procedures, such as linear, quantile and logistic regression, artificial neural networks and Bayesian networks, we conclude that, while the fact that a student passes or fails the final exam can be properly predicted, a more detailed forecast about the grade obtained is not possible. 2022-11-03T11:07:47Z 2022-11-03T11:07:47Z 2022-10-27 info:eu-repo/semantics/article 2227-7390 http://hdl.handle.net/10835/14068 10.3390/math10213994 en https://www.mdpi.com/2227-7390/10/21/3994 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | continuous assessment Bayesian networks artificial neural networks classification Morales Giraldo, María Encarnación Salmerón Cerdán, Antonio Maldonado González, Ana Devaki Masegosa Arredondo, Andrés Ramón Rumí Rodríguez, Rafael An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark |
| title | An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark |
| title_full | An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark |
| title_fullStr | An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark |
| title_full_unstemmed | An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark |
| title_short | An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark |
| title_sort | empirical analysis of the impact of continuous assessment on the final exam mark |
| topic | continuous assessment Bayesian networks artificial neural networks classification |
| url | http://hdl.handle.net/10835/14068 |
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