Spatio-temporal processes are ubiquitous and prevalent across disciplines. Understanding the mechanisms underlying processes and integrating this information into models is of great interest, as it can improve forecasting accuracy and align with scientific motivation. Examples of such models include Partial Differential Equation (PDE) Models or…

In the era of artificial intelligence (AI), big data (BD), and digital transformation (DT), analytics students should gain the ability to solve business problems by integrating various methods. This teaching brief illustrates how two such methods--Bayesian analysis and Markov chains--can be combined to enhance student learning using the Analytics…

This study examined whether instructional humor (IH) was not just another type of seductive detail when covariates such as humor pre-disposition, prior-knowledge, and working memory capacity were controlled. Participants were students (N = 228) from universities who were randomly assigned two stimuli conditions in the classic experimental design.…

The ability of automatically modeling learners' personalities is an important step in building adaptive learning environments. Several studies showed that knowing the personality of each learner can make the learning interaction with the provided learning contents and activities within learning systems more effective. However, the traditional…

How do we teach and learn with our students about data literacy, at the same time as Biesta (2015) calls for an emphasis on 'subjectification' i.e. 'the coming into presence of unique individual beings'? (Good Education in an Age of Measurement: Ethics, Politics, Democracy. Routledge) Our response to these challenges and the datafication of higher…

Prior research has shown that, under certain conditions, Human-Agent (H-A) alignment exists to a stronger degree than that found in Human-Human (H-H) communication. In an H-H Second Language (L2) setting, evidence of alignment has been linked to learning and teaching strategy. We present a novel analysis of H-A and H-H L2 learner dialogues using…

The volume of data that can be captured and stored from students' everyday interactions with digital environments allows for the creation of models of student knowledge, skills, and attributes unobtrusively. However, models and techniques for transforming these data into information that is useful for educators have not been established. This…

Bayesian Knowledge Tracing (BKT) is a commonly used approach for student modeling, and Long Short Term Memory (LSTM) is a versatile model that can be applied to a wide range of tasks, such as language translation. In this work, we directly compared three models: BKT, its variant Intervention-BKT (IBKT), and LSTM, on two types of student modeling…

Purpose: The purpose of this paper is to explore the concept of metacompetencies in entrepreneurship education through students' expressions of metacompetencies in their learning processes, aiming to provide assistance embedding metacompetencies in entrepreneurship education. Design/methodology/approach: The empirical study is based on qualitative…

The changes of learning environments and the advancement of learning theories have increasingly demanded for feedback that can describe learning progress trajectories. Effective assessment should be able to evaluate how learners acquire knowledge and develop problem solving skills. Additionally, it should identify what issues these learners have…

In recent years, an increasing number of analyses in Learning Analytics and Educational Data Mining (EDM) have adopted a "Discovery with Models" approach, where an existing model is used as a key component in a new EDM/analytics analysis. This article presents a theoretical discussion on the emergence of discovery with models, its…