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Student engagement is a key construct for learning and teaching. While most of the literature explored the student engagement analysis on computer-based settings, this paper extends that focus to classroom instruction. To best examine…
Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of…
Performance in introductory courses, particularly physics, is often crucial for student success in STEM majors and can impact an individual's tendency to persist in their chosen field. To enhance students' individual learning experiences,…
An increasing number of studies in physics education research use social network analysis to quantify interactions among students. These studies typically gather data through online surveys using one of two different survey formats:…
Machine Learning (ML) and Artificial Intelligence (AI) are powering the applications we use, the decisions we make, and the decisions made about us. We have seen numerous examples of non-equitable outcomes, from facial recognition…
With the increase of distance learning, in general, and e-learning, in particular, having a system capable of determining the engagement of students is of primordial importance, and one of the biggest challenges, both for teachers,…
Research in student knowledge and learning of science has typically focused on explaining conceptual change. Recent research, however, documents the great degree to which student thinking is dynamic and context-sensitive, implicitly calling…
Computer science's increased recognition as a prominent field of study has attracted students with diverse academic backgrounds. This has significantly increased the already high failure rates in introductory courses. To address this…
This study examines whether including more contextual information in data analysis could improve our ability to identify the relation between students' online learning behavior and overall performance in an introductory physics course. We…
In this study, we explored the extent to which problems and instructional strategies affect social cohesion and interactions for information seeking in physics classrooms. Three sections of a mechanics physics course taught at a Chilean…
The role of student experiences in physics beyond the classroom which support their development has been the subject of exciting research in recent years. Results, typically from small studies at single institutions, have illustrated that…
While predictive models are increasingly common in higher education, causal evidence regarding the interventions they trigger remains rare. This study evaluates an AI-guided student support system at a large university using doubly robust…
Students' beliefs about the extent to which meaningful others, including their peers, recognize them as a strong science student are correlated with their persistence in science courses and careers. Yet, prior work has found a gender bias…
Physics education researchers have developed many evidence-based instructional strategies to enhance physics students' conceptual learning. These strategies have historically been tested using assessments such as the Force Concept Inventory…
Research suggests that interacting with more peers about physics course material is correlated with higher student performance. Some studies, however, have demonstrated that different topics of peer interactions may correlate with their…
The research presented in this thesis was motivated by the need to improve introductory physics courses. Introductory physics courses are generally the first courses in which students learn to create models to solve complex problems.…
Network analysis is an important tool in understanding the behavior of complex systems of interacting entities. However, due to the limitations of data gathering technologies, some interactions might be missing from the network model. This…
Networks of social interactions are the substrate upon which civilizations are built. Often, we create new bonds with people that we like or feel that our relationships are damaged through the intervention of third parties. Despite their…
The growing field of machine learning (ML) and artificial intelligence (AI) presents a unique and unexplored case within persistence research, meaning it is unclear how past findings from engineering will apply to this developing field. We…
The empirical study of network dynamics has been limited by the lack of longitudinal data. Here we introduce a quantitative indicator of link persistence to explore the correlations between the structure of a mobile phone network and the…