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

Abstract

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.

Artificial intelligence in a modernizing science and technology education: a textual narrative synthesis in the COVID-19 era

“…Also, if these assessment tasks can cater individual differences of learners and if these assessment tasks are suited for individual needs of the students. With the use of an AI called e-valUAM, Subirats et al [14] were able to analyze students' data and applied an adaptive test. This system gathered data from students who studied using a Learning Management System (LMS), either Google Classroom or Moodle can take a formative test from there.…”

Artificial Intelligence in Higher Education during and After the COVID-19 Pandemic: Need, Transition and Transformation

“…COVID-19 had an effect on the adoption of Google Meet as COVID-19 increases the various types of fear: family confinement situation, fear of educational failure and fear of losing social relationships are the most common types of threats that students and teachers/educators may face. Subirats et al [21] To classify students according to their performance, considering data before, during and after COVID-19 confinement.…”

“…The pandemic has precipitated monumental changes in higher education institutions, forcing them to shift from traditional face-to-face learning to the use of digital systems, including remote learning [7]. In response, the adoption of distance learning has become a widespread model to address the educational need and confinement associated with COVID-19, as this methodology is suitable for use in remote or hard-toreach environments [21]. On the other hand, Ashour, El-Refae and Zaitoun [22] reported that many universities have not been able to make strategic changes towards online teaching, but have shifted towards online development using the same curriculum and content as traditional teaching, i.e.…”

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

“…For example, to predict academic grades and dropout [29,30], used classification [31][32][33], used regression in higher education and high school datasets, respectively. Moreover, students' marks may be predicted using clustering as [34] or applying supervised machine learning algorithms as [35,36]. In other articles, it has been pointed out that those approximations can be also good indicators to predict the future marks of a student in soft skills subjects [37].…”