Abstract
Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.
Highlights
The use of technology in education is getting more and more intensive day by day, and it is becoming a necessity for effective and permanent learning to be updated
Focusing on RQ1 (How accurate is the predictive model in the whole institution after four semesters of available data?), the Gradual At-Risk (GAR) model has improved significantly compared to the results presented in [3], by increasing the size of the datasets and selecting the best algorithm and training set
The ongoing pandemic pointed out the deficiencies that we currently have in education [66] and the need to improve our learning processes and environments
Summary
The use of technology in education is getting more and more intensive day by day, and it is becoming a necessity for effective and permanent learning to be updated. In the field of Artificial Intelligence (AI), the technology has moved effectively in education to a different dimension with a significant leap [1]. It is well-known that students benefit in material access, and in monitoring their processes, evaluating their learning, and monitoring their performance, through intelligent tutoring systems (ITS). One of these ITS is developed in the LIS (Learning Intelligent System) project [2], which aims to assist students in their educational processes.
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