A Granularity-Based Intelligent Tutoring System for Zooarchaeology

Subirats, Laia; Pérez, Leopoldo; Hernández, Constantino Criado; Fort, Santi; Sacha, G. M.

doi:10.3390/app9224960

Cited by 4 publications

(4 citation statements)

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“…Since the early 1980s, researchers have developed intelligent tutoring systems looking for the introduction of the artificial intelligence in education [39]. Subirats et al [40] introduce a tutoring system that uses three different granularities to help students classify animals from bone fragments in zooarchaeology. The 3406 bone remains, which have 64 attributes, were obtained from an excavation of the Middle Paleolithic.…”

Section: A Review Of the Contributions In This Special Issuementioning

confidence: 99%

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et al. 2020

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Artificial intelligence applied to the educational field has a vast potential, especially after the effects worldwide of the COVID-19 pandemic. Online or blended educational modes are needed to respond to the health situation we are living in. The tutorial effort is higher than in the traditional face-to-face approach. Thus, educational systems are claiming smarter learning technologies that do not pretend to substitute the faculty but make their teaching activities easy. This Special Issue is oriented to present a collection of papers of original advances in educational applications and services propelled by artificial intelligence, big data, machine learning, and deep learning.

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Section: A Review Of the Contributions In This Special Issuementioning

confidence: 99%

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et al. 2020

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“…In this context, the term emergency remote learning (ERL) was coined [ 10 ]. Fortunately, due to the interest in distance education that already appeared years before the pandemic, many e-learning platforms were available and widely used during the confinement [ 11 ], including many advanced tools such as intelligent tutoring systems (ITS) [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence

et al. 2023

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This study provides the profiles and success predictions of students considering data before, during, and after the COVID-19 pandemic. Using a field experiment of 396 students and more than 7400 instances, we have analyzed students’ performance considering the temporal distribution of autonomous learning during courses from 2016/2017 to 2020/2021. After applying unsupervised learning, results show 3 main profiles from the clusters obtained in the simulations: students who work continuously, those who do it in the last-minute, and those with a low performance in the whole autonomous learning. We have found that the highest success ratio is related to students that work in a continuous basis. However, last-minute working is not necessarily linked to failure. We have also found that students’ marks can be predicted successfully taking into account the whole data sets. However, predictions are worse when removing data from the month before the final exam. These predictions are useful to prevent students’ wrong learning strategies, and to detect malpractices such as copying. We have done all these analyses taking into account the effect of the COVID-19 pandemic, founding that students worked in a more continuous basis in the confinement. This effect was still present one year after. Finally, We have also included an analysis of the techniques that could be more effective to keep in a future non-pandemic scenario the good habits that were detected in the confinement.

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“…In this context, the term emergency remote learning (ERL) has been coined [10]. Fortunately, due to the interest in distance education in the last years, several e-learning tools were available by that time and widely used [11], including also intelligent tutoring systems (ITS) [12].…”

Section: Introductionmentioning

confidence: 99%

Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence

Subirats¹,

Corral²,

Perez-Ruiz³

et al. 2021

Preprint

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This study provides the profiles of students and a regression prediction of marks considering data before, during, and after the COVID-19 pandemic. Using a field experiment of 396 students and more than 7400 instances, we have studied the differences in performance considering the temporal distribution of autonomous learning during courses from 2016/2017 to 2020/2021. After applying unsupervised learning, results show 3 profiles of students: continuous students, last-minute students, intense and last-minute students. We have found that the highest success ratio is related to students that work in a continuous basis. However, last minute working is not necessary linked to failure. After applying regression, results show that the mark of the students can be predicted successfully and that the most relevant values are the mean mark in self-evaluation obtained the month before the final exam, the mean mark two months before, the number of attempts two months before and the number of attempts the month before. Results are a little worse but still acceptable if the prediction wants to be made a month before the final exam. This regression is useful to prevent students' wrong learning strategies, and (more effective) to detect malpractices such as copying. We have done all these analysis taking into account the effect of the COVID-19 pandemic, including also a discussion about which factors will be extended in time and which ones are transitory and only due to the confinement.

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A Granularity-Based Intelligent Tutoring System for Zooarchaeology

Cited by 4 publications

References 35 publications

Smart Learning

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Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence

Temporal analysis of academic performance in higher education before, during and after COVID-19 confinement using artificial intelligence

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