相关论文: Who will dropout from university? Academic risk pr…
Machine learning models are often used to make predictions about admissions process outcomes, such as for colleges or jobs. However, such decision processes differ substantially from the conventional machine learning paradigm. Because…
Predictive models for identifying at-risk students early can help teaching staff direct resources to better support them, but there is a growing concern about the fairness of algorithmic systems in education. Predictive models may…
Challenge Theory (Shye & Haber 2015; 2020) has demonstrated that a newly devised challenge index (CI) attributable to every binary choice problem predicts the popularity of the bold option, the one of lower probability to gain a higher…
Various strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for…
Understanding the formation of social ties requires disentangling the roles of individual traits and local network structure. We analyse signed social relationships among 3,395 students using an interpretable machine learning model -- the…
Predicting risk profiles of individuals in networks (e.g.~susceptibility to a particular disease, or likelihood of smoking) is challenging for a variety of reasons. For one, `local' features (such as an individual's demographic information)…
Shapley value has recently become a popular way to explain the predictions of complex and simple machine learning models. This paper is discusses the factors that influence Shapley value. In particular, we explore the relationship between…
University dropout remains a persistent challenge in higher education systems, yet causal evidence on the mechanisms triggering early disengagement is limited. This study estimates the causal effect of first-time academic failure on…
The use of large language model (LLM)-based AI chatbots among college students has increased rapidly, yet little is known about how individual psychological attributes shape students' interaction patterns with these technologies. This…
Happiness underlines the intuitive constructs of a specified population based on positive psychological outcomes. It is the cornerstone of the cognitive skills and exploring university student's happiness has been the essence of the…
The complex nature of artificial neural networks raises concerns on their reliability, trustworthiness, and fairness in real-world scenarios. The Shapley value -- a solution concept from game theory -- is one of the most popular explanation…
Not everyone who enrolls in college will leave with a certificate or degree, but the number of people who drop out or take a break is much higher than experts previously believed. In December 2013, there were 29 million people with some…
Student performance prediction is one of the most important subjects in educational data mining. As a modern technology, machine learning offers powerful capabilities in feature extraction and data modeling, providing essential support for…
The lack of an engaging pedagogy and the highly competitive atmosphere in introductory science courses tend to discourage students from pursuing science, technology, engineering, and mathematics (STEM) majors. Once in a STEM field, academic…
Having observed low success rates among first-year university students in both Belgium and France, we develop prediction models in this paper in order to identify, at the earliest possible stage, those students who are at risk of failing at…
With the growing popularity of wearable devices, the ability to utilize physiological data collected from these devices to predict the wearer's mental state such as mood and stress suggests great clinical applications, yet such a task is…
The successful application of machine learning (ML) methods becomes increasingly dependent on their interpretability or explainability. Designing explainable ML systems is instrumental to ensuring transparency of automated decision-making…
The predictive normalized maximum likelihood (pNML) approach has recently been proposed as the min-max optimal solution to the batch learning problem where both the training set and the test data feature are individuals, known sequences.…
The ability to recognize weakness of students and solving any problem may confront them in timely fashion is always a target of all educational institutions. This study was designed to explore how can predictive and statistical analysis…
The aim of this study was to predict university students' learning performance using different sources of data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources:…