Algorithmic bias in education
In this paper, we review algorithmic bias in education, discussing the causes of that bias and
reviewing the empirical literature on the specific ways that algorithmic bias is known to have …
reviewing the empirical literature on the specific ways that algorithmic bias is known to have …
How do the existing fairness metrics and unfairness mitigation algorithms contribute to ethical learning analytics?
With the widespread use of learning analytics (LA), ethical concerns about fairness have
been raised. Research shows that LA models may be biased against students of certain …
been raised. Research shows that LA models may be biased against students of certain …
Algorithmic fairness in education
Data-driven predictive models are increasingly used in education to support students,
instructors, and administrators, which has raised concerns about the fairness of their …
instructors, and administrators, which has raised concerns about the fairness of their …
Interpretable dropout prediction: towards XAI-based personalized intervention
Student drop-out is one of the most burning issues in STEM higher education, which induces
considerable social and economic costs. Using machine learning tools for the early …
considerable social and economic costs. Using machine learning tools for the early …
Artificial intelligence and new technologies in inclusive education for minority students: a systematic review
Artificial intelligence (AI) and new technologies are having a pervasive impact on modern
societies and communities. Given the potential of these new technologies to transform the …
societies and communities. Given the potential of these new technologies to transform the …
Using machine learning to predict student retention from socio-demographic characteristics and app-based engagement metrics
Student attrition poses a major challenge to academic institutions, funding bodies and
students. With the rise of Big Data and predictive analytics, a growing body of work in higher …
students. With the rise of Big Data and predictive analytics, a growing body of work in higher …
Leveraging class balancing techniques to alleviate algorithmic bias for predictive tasks in education
Predictive modeling is a core technique used in tackling various tasks in learning analytics
research, eg, classifying educational forum posts, predicting learning performance, and …
research, eg, classifying educational forum posts, predicting learning performance, and …
Automatic short math answer grading via in-context meta-learning
Automatic short answer grading is an important research direction in the exploration of how
to use artificial intelligence (AI)-based tools to improve education. Current state-of-the-art …
to use artificial intelligence (AI)-based tools to improve education. Current state-of-the-art …
Advancing equity and inclusion in educational practices with AI‐powered educational decision support systems (AI‐EDSS)
A key goal of educational institutions around the world is to provide inclusive, equitable
quality education and lifelong learning opportunities for all learners. Achieving this requires …
quality education and lifelong learning opportunities for all learners. Achieving this requires …
Cross-institutional transfer learning for educational models: Implications for model performance, fairness, and equity
Modern machine learning increasingly supports paradigms that are multi-institutional (using
data from multiple institutions during training) or cross-institutional (using models from …
data from multiple institutions during training) or cross-institutional (using models from …