Large Language Models (LLMs) are rapidly gaining attention across the open-source and commercial fields, bolstered by their constantly growing capabilities. While such models have a vast array of applications, their integration into higher education--as supportive tools for lecturers--has been largely unexplored. Exploring this area entails…

Programming demands a variety of cognitive skills, and mastering these competencies is essential for success in computer science education. The importance of formative feedback is well acknowledged in programming education, and thus, a diverse range of techniques has been proposed to generate and enhance formative feedback for programming…

Understanding students' multi-party epistemic and topic based-dialogue contributions, or how students present knowledge in group-based chat interactions during collaborative game-based learning, offers valuable insights into group dynamics and learning processes. However, manually annotating these contributions is labor-intensive and challenging.…

The current insertion of Machine Learning (ML) in our everyday life demonstrates the importance of introducing the teaching of a basic understanding of ML already in school. Accompanying this trend arises the need to assess the students' learning of ML, yet so far only a few assessment models have been proposed, most of them rather simple, based…

The recent appearance of generative artificial intelligence (AI) platforms has been seen by many as disruptive for education. In this paper I attempt to locate the source of tension between educational goals and new information technologies including AI. I argue that this tension arises from new conceptions of epistemic agency that are…

The rapid advancement of artificial intelligence (AI) has given rise to sophisticated language models that excel in understanding and generating human-like text. With the capacity to process vast amounts of information, these models effectively tackle problems across diverse domains. In this paper, we present a comparative analysis of prominent AI…

In this paper, we examine whether early warning signals from accounting courses (such as early engagement and early formative performance) are predictive of first-year progression outcomes, and whether this data is more predictive than personal data (such as gender and prior achievement). Using a machine learning approach, results from a sample of…

In the ever-evolving landscape of education, you have most likely experienced a significant expansion in your teaching responsibilities. Your role may have stretched to encompass being proficient in various technology platforms, nurturing the social and emotional learning of your students, and adjusting to amplified documentation requirements.…

Purpose: Many studies using machine learning (ML) in speech, language, and hearing sciences rely upon cross-validations with single data splitting. This study's first purpose is to provide quantitative evidence that would incentivize researchers to instead use the more robust data splitting method of nested k-fold cross-validation. The second…

The research seeks to delve into and comprehend the attitudes of university students regarding artificial intelligence (AI) and to identify potential factors influencing these attitudes. The research employs a descriptive research design with a quantitative approach. A sample of 240 university students, including both males and females, was…

Student opinions for a course are important to educators and administrators, regardless of the type of the course or the institution. Reading and manually analyzing open-ended feedback becomes infeasible for massive volumes of comments at institution level or online forums. In this paper, we collected and pre-processed a large number of course…

This paper charts a forward-looking roadmap for open universities, drawing upon their historical evolution and current practices. It advocates a shift toward a universally accessible, personalized education system. At the heart of this proposed advancement lies the customization of learning paths and experiences, where individualized advising and…

GPT-3 is a neuronal language model that performs tasks such as classification, question-answering and text summarization. Although chatbots like BlenderBot-3 work well in a conversational sense, and GPT-3 can assist experts in evaluating questions, they are quantifiably worse than real teachers in several pedagogical dimensions. We present the…

The purpose of this qualitative study was to learn how digital-native college students perceive of cheating and plagiarism. Today's students grew up with high-speed internet, smartphones, and instant access to information. Their learning environment was greatly altered during the COVID-19 pandemic, shifting many from in-person to online learning.…

Automated decision-making is becoming increasingly common in the public sector. As a result, political institutions recommend the presence of humans in these decision-making processes as a safeguard against potentially erroneous or biased algorithmic decisions. However, the scientific literature on human-in-the-loop performance is not conclusive…