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 outcome or even dropping out from the university. The early identification of such potential at-risk learners is the key to intervening and trying to help them before…

Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision trade-off inherent in targeting students for…

Emphasis in school dropout literature has shifted from exploring wide-ranging causes of dropping out to soliciting a smaller number of predictive indicators to identify students at increased risk for dropping out. However, much of the past decade's Early Warning research excludes indicators that do not add to the predictive nature of the model…

Engineering design plays an important role in education. However, due to its open nature and complexity, providing timely support to students has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to help struggling students. It allows…

Objectives: There is a need to examine the utility of objective measures of attention-deficit/hyperactivity disorder (ADHD) symptoms in children. Objective measures of ADHD symptoms, such as physical activity, may provide a more nuanced assessment of behavior that can be used to augment traditional cross-informant ratings of ADHD symptoms by…

Identifying students at risk of failing a course has potential benefits, such as recommending the At-Risk students to various interventions that could improve pass rates. The challenges however, are firstly in measuring how effective these interventions are, i.e. measuring treatment effects, and secondly, to not only predict overall (average)…

Descartes expresses his opinion on the method very clear with the quote: "The whole secret of the method; starting from the circle and gradually going up the steps to the most complicated ". When it is thought that the knowledge of the absolute and unchanging truth in the positive sciences has not yet been reached, it should not be…

Student success and drop-out predictions have gained increased attention in recent years, connected to the hope that by identifying struggling students, it is possible to intervene and provide early help and design programs based on patterns discovered by the models. Though by now many models exist achieving remarkable accuracy-values, models…

Background/Context: Chronic absenteeism is a serious problem that has been linked to lower academic achievement, diminished socioemotional skills, and an increased likelihood of high school dropout (Allensworth et al., 2021; Gottfried, 2014). As a result, many schools have begun to embrace early warning systems (EWS) as a tool to identify and flag…

In 2016, Uruguay started gathering longitudinal student data to improve educational trajectories by putting in place an 'early alert' system. Underlying the system is the understanding that prior schooling predicts likelihood of grade repetition and grade repetition predicts later school dropout, while close follow-up can help prevent both…

By collecting longitudinal learner and learning data from a range of resources, predictive learning analytics (PLA) are used to identify learners who may not complete a course, typically described as being at risk. Mixed effects are observed as to how teachers perceive, use, and interpret PLA data, necessitating further research in this direction.…

Researchers investigated the impact of a professional learning intervention focused on teaching teachers to increase rigor, challenge, and engagement to reveal talent in low-income learners. The professional learning intervention's goal, to improve teachers' ability to recognize student ability and talent through use of proven high-level…

Students in the online learning who have other responsibilities of life such as work and family face attrition. Constructing a model of engagement with smallest granule of time has not been implemented widely, but implementing it is important as it allows to uncover more subtle patterns. We built a student engagement prediction model using 9…

This article is a revised version of the keynote presented at LAK '16 in Edinburgh. The article investigates some of the assumptions of learning analytics, notably those related to behaviourism. Building on the work of Ivan Pavlov, Herbert Simon, and James Gibson as ways of "learning as a machine," the article then develops two levels of…

Objective: This study examined the moderating and mediating effects of perceived social support on the association between precollege sexual assault (SA) and college-onset SA. Participants: A representative sample of 6,132 undergraduates. Methods: The PLUM procedure in SPSS was used to test the moderation model, with individual regressions…