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…

The purpose of this dissertation is to develop principles and strategies for and identify limitations of multisite cluster randomized trials in the context of partially and fully nested designs. In the first study, I develop principles of estimation, sampling variability, and inference for studies that leverage multisite designs within the context…

Based on his experience during the past two decades of research, teaching, and administration in a department composed of Accounting and Information Systems, the author makes several observations as well as some predictions for the next two decades based on trends that suggest accounting students need strong technology and analytics skills, as…

The computational model of school achievement represents a novel approach to theorizing school achievement, conceptualizing educational interventions as modifications to students' learning curves. By modeling the process and products of educational achievement simultaneously, this tool addresses several unresolved questions in educational…

Managers are increasingly being tasked with overseeing data-driven projects that incorporate prescriptive and predictive models. Furthermore, basic knowledge of the data analytics pipeline is a fundamental requirement in many modern organizations. Given the central importance of analytics in today's business environment, there is a growing demand…

The article is in the second phase of research is about "the big data architecture for pre-teacher preparation supply chain with prescriptive analytics of higher education in Thailand". The objectives of the study were (1) to study the pre-teacher preparation supply chain in Thailand, (2) to develop a model the big data system for the…

Researchers have argued that writing skills have significant implications for developing reading skills. A growing body of research has provided evidence that writing skills, in particular invented spelling, provide unique predictive information regarding future reading skills. This study examined which preschool early writing skills (i.e., name…