An Empirical Analysis of the Impact of Continuous Assessment on the Final Exam Mark

Abstract

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.

Implementation of Continuous-Assessment on Postgraduate Program

“…Information collection can be done in multiple ways. There are four assessment terms known, namely traditional assessment, assessment, performance assessment, and authentic assessment, with a simple description as shown in Table 6 (Bakhru & Mehta, 2020;González-Campos et al, 2018;Morales et al, 2022;Sridharan, 2016). Table 6.…”

“…Students should have started designing small projects in every lecture to complete their final assignment (Carrillo et al, 2019;Gillis & Krull, 2020;Mohanty et al, 2021;Varghese et al, 2019). Based on the researcher's observations, it is often the case that final terms are only thought about before the study ends, and it seems that this is one of the sources of the length of the study completion process (Day et al, 2018;Morales et al, 2022).…”

Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education

“…Topics addressed in this Special Issue include data mining, machine learning, learning analytics, prediction methods, pattern recognition, decision analysis, probabilistic reasoning, fuzzy systems, student or patient modelling, adaptive systems, collaborative systems, recommendation systems, experimental design, and empirical study cases. Specifically, there are twenty rigorously reviewed papers included in this Special Issue, with eleven specializing in the field of medicine and health [1][2][3][4][5][6][7][8][9][10][11] and nine specializing in the field of education [12][13][14][15][16][17][18][19][20]. In total, fifteen papers (43% of the received) were rejected for publication.…”

“…In the field of education, work is mainly oriented towards improving teaching [12][13][14] or learning [18] and predicting educational performance [15][16][17]20] or drop-out rates [19].…”

“…The experimental results show an 80% success rate from the two first tasks (of six), increasing to 90% after the first four, in predicting the final grade. Moreover, the proposal of [16] compared different statistical and artificial intelligence techniques to determine if the performance of a student in the continuous assessment determines the score obtained in the final exam of the course. Those techniques are based on linear, quantile, and logistic regression; artificial neural networks; and Bayesian networks.…”