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: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/21/3994 |
| _version_ | 1797467386055491584 |
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| author | María Morales Antonio Salmerón Ana D. Maldonado Andrés R. Masegosa Rafael Rumí |
| author_facet | María Morales Antonio Salmerón Ana D. Maldonado Andrés R. Masegosa Rafael Rumí |
| author_sort | María Morales |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T18:52:54Z |
| format | Article |
| id | doaj.art-6ecd16f0491e4f69ae40281b694ea2be |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T18:52:54Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-6ecd16f0491e4f69ae40281b694ea2be2023-11-24T05:43:15ZengMDPI AGMathematics2227-73902022-10-011021399410.3390/math10213994An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam MarkMaría Morales0Antonio Salmerón1Ana D. Maldonado2Andrés R. Masegosa3Rafael Rumí4Department of Mathematics, University of Almería, 04120 Almería, SpainDepartment of Mathematics, University of Almería, 04120 Almería, SpainDepartment of Mathematics, University of Almería, 04120 Almería, SpainDepartment of Computer Science, Aalborg University, 2450 Copenhagen SV, DenmarkDepartment of Mathematics, University of Almería, 04120 Almería, SpainSince 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.https://www.mdpi.com/2227-7390/10/21/3994continuous assessmentBayesian networksartificial neural networksclassification |
| spellingShingle | María Morales Antonio Salmerón Ana D. Maldonado Andrés R. Masegosa Rafael Rumí An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark Mathematics continuous assessment Bayesian networks artificial neural networks classification |
| 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 | https://www.mdpi.com/2227-7390/10/21/3994 |
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