As the development of online learning is growing, a large amount of log data on student
activity is available and accumulated in Learning Management Systems (LMS). This …

Identifying at-risk students as soon as possible is a challenge in educational institutions.
Decreasing the time lag between identification and real at-risk state may significantly reduce …

Artificial intelligence has impacted education in recent years. Datafication of education has
allowed developing automated methods to detect patterns in extensive collections of …

This work discusses a nudging intervention mechanism combined with an artificial
intelligence (AI) system for early detection of learners' risk of failing or dropping out. Different …

With the onset of online education via technology-enhanced learning platforms, large
amount of educational data is being generated in the form of logs, clickstreams …

Course dropout is a concern in online higher education, mainly in first-year courses when
different factors negatively influence the learners' engagement leading to an unsuccessful …

We present the findings of a pilot plan of active learning implemented in introductory physics
in a Chilean public university. The model is research based as it considered a literature …

Information and Communication Technologies have modified the traditional Higher
education institutions and how the teaching-learning process is conducted. Nowadays, most …

Background: Foundational courses in engineering curricula (FECs) are critical to student
success in engineering but tend to consist of large lecture-style learning environments at …

The systematic review on the ways to identify at-risk students is still limited. Hence, the
purpose of this paper is to review the related papers so as to show a clear picture on the way …