Modelling experts' behavior with e-valUAM to measure computer science skills

Molins-Ruano, P.; Rodríguez, Pilar; Atrio, S.; Sacha, G. M.

doi:10.1016/j.chb.2016.03.044

Cited by 20 publications

(6 citation statements)

References 25 publications

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“…As a solution to limit test item exposure, a large question bank has been suggested. This solution is unfeasible in most cases, since most of the CAT models already require more items than comparable linear testing [ 43 ].…”

Section: 1 Backgroundmentioning

confidence: 99%

Influence of COVID-19 confinement on students’ performance in higher education

et al. 2020

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This study analyzes the effects of COVID-19 confinement on the autonomous learning performance of students in higher education. Using a field experiment with 458 students from three different subjects at Universidad Autó noma de Madrid (Spain), we study the differences in assessments by dividing students into two groups. The first group (control) corresponds to academic years 2017/2018 and 2018/2019. The second group (experimental) corresponds to students from 2019/2020, which is the group of students that had their faceto-face activities interrupted because of the confinement. The results show that there is a significant positive effect of the COVID-19 confinement on students' performance. This effect is also significant in activities that did not change their format when performed after the confinement. We find that this effect is significant both in subjects that increased the number of assessment activities and subjects that did not change the student workload. Additionally, an analysis of students' learning strategies before confinement shows that students did not study on a continuous basis. Based on these results, we conclude that COVID-19 confinement changed students' learning strategies to a more continuous habit, improving their efficiency. For these reasons, better scores in students' assessment are expected due to COVID-19 confinement that can be explained by an improvement in their learning performance.

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Section: 1 Backgroundmentioning

confidence: 99%

Influence of COVID-19 confinement on students’ performance in higher education

et al. 2020

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“…Over the last 60 years, marketing researchers (Fishbein, 1963;Mitchell & Olson, 1981;Ho & Dempsey, 2010;Molins-Ruano et al, 2016) have analyzed consumer behavior to understand the factors influencing consumer opinions towards a product/service. The theory on consumer attitudinal change suggests that negative attitude towards a product can be altered.…”

Section: Introductionmentioning

confidence: 99%

Group behavior in social media: Antecedents of initial trust formation

Shareef

Kapoor

Mukerji

et al. 2020

Computers in Human Behavior

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This study investigates the influence of antecedents of user trust amongst peers on social media platforms that they apply without sufficient cognitive judgment. Based on extensive literature review and analysis of social theories, we propose a theoretical framework for social media trust (SMT). Based on the SMT model, we conduct empirical analysis using structural equation modeling to verify the cause-effect relationships hypothesized in this study. The findings reveal that initial formation of SMT relies on five predictors-fulfilled expectations, predictability, familiarity, monitor, and norms. These findings have significant theoretical and practical implications. The study finds that peers are likely to invest blind faith in the content shared on social media groups without subjecting it to verification. It also identifies the threat of biased 2 peers, who spread irresponsible content with predetermined motives to influence members of certain social media groups. Policymakers can use insights from this study to highlight the ills of non-verification, and its potential to cause harm at the very extreme.

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“…All tests carried out by the students were registered by the platform, including numerical parameters such as the score, date and hour, and were used by the AI algorithms to predict the performance of all students [26][27][28].…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment

et al. 2021

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Distance learning has been adopted as a very extended model during COVID-19-related confinement. It is also a methodology that can be applied in environments where people do not have easy access to schools. In this study, we automatically classify students as a function of their performance and we describe the best self-learning methodologies in distance learning, which will be useful both in confinement or for people with difficult access to schools. Due to the different learning scenarios provided by the different confinement conditions in the COVID-19 pandemic, we have performed the classification considering data before, during, and after COVID-19 confinement. Using a field experiment of 396 students, we have described the temporal evolution of students during all courses from 2016/2017 to 2020/2021. We have found that data obtained in the last month before the final exam of the subject include the most relevant information for a correct detection of students at risk of failure. On the other hand, students who obtain high scores are much easier to identify. Finally, we have concluded that the distance learning applied in COVID-19 confinement changed not only teaching strategies but also students’ strategies when learning autonomously.

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Modelling experts' behavior with e-valUAM to measure computer science skills

Cited by 20 publications

References 25 publications

Influence of COVID-19 confinement on students’ performance in higher education

Influence of COVID-19 confinement on students’ performance in higher education

Group behavior in social media: Antecedents of initial trust formation

Artificial Intelligence to Counterweight the Effect of COVID-19 on Learning in a Sustainable Environment

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