Elizabeth B. Cloude

Elizabeth B. Cloude

Marie Curie Research Fellow

Talks and presentations

Reframing thinking about and modeling learning through complex dynamical systems

April 10, 2024

Webinar, Society for Learning Analytics Research, remote

Abstract: Learning is a highly individual process of change that emerges from multiple interacting components (e.g. cognitive, social) that occur at varying levels (e.g., individual, group) and timescales (e.g. micro, meso, macro) in constantly changing environments. Due to its complexity, the theoretical assumptions that describe learning are difficult to computationally model, and many existing methodologies are limited by conventional statistics that do not adhere to these assumptions. In recent years, the learning analytics community has explored the potential of complex dynamical systems for modeling and analyzing learning processes. Complex dynamical systems (CDS) approach refers to theoretical views, largely from physics and biology, that preserve the complexity of learning and could be potentially useful in studying socio-/ technical-/ material-/ symbolic systems that learn.

Unveiling the Power of Affect during Learning

June 16, 2023

Webinar, Society for Learning Analytics Research, remote

Abstract: In the realm of education, affect has long been acknowledged as a significant factor that impacts learning. Represented by cognitive structures in the mind, affect is described as a mood, feeling, or emotion, which transmits information about the world we experience and compels us to act and make decisions. Research finds that an inability (or ability) to regulate affect (e.g., confusion or frustration) can greatly impact how an individual learns with educational technologies (e.g., intelligent tutoring systems, game-based learning environments, MOOCs). Yet, there are significant theoretical, methodological, and analytical challenges impeding our understanding on how to best identify (and intervene) if and when affect becomes detrimental during learning with educational technologies.

How Emotions Change during Learning with an Intelligent Tutoring System

August 01, 2020

Conference proceedings presentation, Cognitive Science Society, Virtual Conference

Abstract: Emotional experiences have a significant impact on learning about complex topics. Yet, challenges exist because emotions are typically operationalized as end products, excluding if, how, and when emotions change during learning and their relation to metacognition and performance with advanced learning technologies such as intelligent tutoring systems (ITSs). In this paper, we addressed these challenges by capturing and analyzing 117 college students’ concurrent and self-reported emotions at 3 time points during learning with MetaTutor, an ITS. Analyses revealed negative relationships between increases in boredom, metacognitive monitoring accuracy, and performance. We also found that if confusion persisted over time during learning, it was detrimental to performance. These findings provide implications for designing affect-sensitive ITSs which foster emotion-regulation and metacognitive monitoring based on changes in emotions during learning to optimize performance.