One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models' excessive dependence on the low-level…

Early Warning Systems (EWS) in higher education accommodate student counsellors by identifying at-risk students and allow them to intervene in a timely manner to prevent student dropout. This study evaluates an EWS that shares student-specific risk information with student counsellors, which was implemented at a large Dutch university. A…

Whether spanking is detrimental for social-emotional (SE) development remains controversial, mostly due to disputes around the internal and external validity of existing evidence. This study examined the effect of spanking on the SE development of Bhutanese children, using a national, longitudinal sample (N = 1377; M[subscript age] = 50.5 months…

Automated Essay Scoring (AES) technologies provide innovative solutions to score the written essays with a much shorter time span and at a fraction of the current cost. Traditionally, AES emphasized the importance of capturing the "coherence" of writing because abundant evidence indicated the connection between coherence and the overall…

Understanding the response process used by test takers when responding to multiple-choice questions (MCQs) is particularly important in evaluating the validity of score interpretations. Previous authors have recommended eye-tracking technology as a useful approach for collecting data on the processes test taker's use to respond to test questions.…

Futures studies offer a framework of ideas and guidelines that allow us to develop more productive images of the future and ways of working with it. Despite several efforts to translate this approach to different educational contexts, it is still a field under development. The main objective of this article is to present and discuss the latest…

The student profile has become an important component of education systems. Many systems objectives, as e-recommendation, e-orientation, e-recruitment and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context and several studies have been carried out either for…

The goodness-of-fit model, which proposes that developmental outcomes result from combinations of environmental and children's factors, has contributed substantially to the recognition of person × environment processes. However, which pattern of person × environment interactions characterizes this model remains unclear, making it difficult to test…

Past research has demonstrated that the reasons underlying students' decisions to engage in academic tasks (i.e., achievement goals) are associated with the experience of test anxiety. Empirical investigations focused on the association between achievement goals and test anxiety have historically been guided by the dichotomous, trichotomous, and…

The study investigated the joint contribution of the self-regulated learning (SRL) and individual differences approaches to the prediction of university students' grade point average (GPA) obtained at three separate time points throughout their degree (3 years). We assessed cognitive (i.e., previous academic performance, cognitive ability, and…

One of the challenges of discussing validity arguments for machine scoring of essays centers on the absence of a commonly held definition and theory of good writing. At best, the algorithms attempt to measure select attributes of writing and calibrate them against human ratings with the goal of accurate prediction of scores for new essays.…

Cognitive scientists have previously shown that students' perceptions of their learning and performance on assessments often do not match reality. This process of self-assessing performance is a component of metacognition, which also includes the practice of thinking about one's knowledge and identifying and implementing strategies to improve…

Accurately forecasting course enrollment rates in higher education is of great concern in order to minimize unnecessary administrative costs as well as burden to both students and faculty. This research aimed to first recreate course enrollment predictions based on a conditional probability analysis using student data from San Diego State…

Predicting student performance in an academic institution is important for detecting at-risk students and administering early-intervention strategies. We propose a new grade prediction model that considers three factors: temporal dynamics of prior courses across previous semesters, short-term performance consistency, and relative performance…

The purpose of this study is to investigate how the school budgeting process factors into the financial stability of a district, that ultimately impacts academic achievement. Best practices as defined by methods that allow for greater transparency, cost effectiveness, and overall success in the passing of school budgets. School funding is an…