Emotions play a central role in shaping learning within digital environments. Although their effects may depend on how students’ emotional experiences manifest into concrete behaviors, the links between these dimensions remain underexplored. This study investigates the most common behaviors during episodes of boredom, confusion, frustration, and engaged concentration in an educational game, as well as associations with situational interest, self-efficacy, prior knowledge, and learning gains, using interaction logs and sensor-free affect detectors. Results show that boredom is linked to off-task roaming, both consistently associated with lower motivation and learning. In contrast, behaviors during engaged concentration, frustration, and especially confusion vary widely, shaped by motivational traits and prior knowledge and offering diverse associations with learning. Concrete regulatory responses in these states—such as systematizing findings with in-game tools, skimming domain content to resolve doubts, or testing hypotheses—are positively associated with learning and motivation, reflecting students’ ability to regulate emotions and address cognitive challenges. However, less constructive responses, such as aimless wandering, were tied to lower knowledge and motivation, underscoring the need for additional support. These findings extend existing affective theory by underscoring the importance of considering the behavioral dimension when analyzing students’ emotions in digital learning environments.

Introductory programming courses (CS1) bring together students with diverse prior experiences, which shape their use of learning resources, emotional responses, and academic performance. This study employs structural equation modeling, self-reported affective data, and multimodal interaction logs to investigate how prior programming knowledge affects affect, resource utilization, and outcomes in a CS1 course that features an automated assessment tool (AAT), instructional videos, and worked examples. Students who persisted with practice and advanced beyond basic tasks achieved the strongest outcomes, though they followed different emotional pathways. By contrast, relying solely on videos or worked examples did not significantly lead to success, and disengagement was generally tied to weaker performance. Novices often reported confusion and frustration; while these emotions sometimes hindered learning, they also drove deeper engagement with the AAT, improving outcomes for those who persisted. Experienced students, however, more often reported boredom, which consistently reduced practice and led to poorer outcomes. These findings underscore the need for adaptive support that balances challenge with guidance to sustain engagement and promote success for diverse learners in CS1.

The advancements in automatic language processing being ushered in by Large Language Models suggest enormous potential for better personalization during student learning. However, this potential can be best exploited if we know that LLMs are equally capable of interacting with students who speak or write in a range of different dialects. This case study uses systematically manipulated student essays, previously evaluated by human raters, to examine how ChatGPT responds to and addresses specific dialect differences. Results point to important concerns about the potential biases and limitations of both LLMs and humans when evaluating and providing feedback to students who use minoritized dialects. Addressing these concerns is critical for the field of learning analytics, as it seeks to ensure equity and asset-based approaches to learning analytics.

For more than a decade, a handful of theoretical models have shaped a substantial amount of the research related to students’ emotional experiences during learning. This research has been productive, but articulating the underlying implicit assumptions in existing theories and their implications in our empirical interpretations can help to better investigate the reciprocal relationships between learning and emotion, and subsequently, to develop better interventions. This paper expands upon the existing theoretical frameworks, increasing the types of questions we ask about affect dynamics. We do so within the context of Crystal Island, a virtual world that allows middle school students to investigate microbiology questions. Specifically, we use this data to examine and revise the assumptions that are implicit in these models and the methods we use to investigate them.

Student affect is widely recognized as a major influence on learning gains and engagement, which has led to the development of many automated affect detectors. However, in order to respond effectively to student affect, we must know how students interpret it. This study proposes a novel automated detector that models when students attribute their epistemic emotion to task difficulty. The goal is to use detectors like this one to better understand how to respond to students' affective states (in this case, boredom, confusion, frustration and nervousness). We then discuss the implications of this novel detector for real-time support in game-based learning environments.

Computer science (CS) education at the university level is often challenging, particularly for students with no prior programming experience. To help scaffold students' CS learning, instructors often utilize systems for automated assessment of programming assignments, where students can individually learn online using automatically generated feedback. However, despite the growing usage of these systems, learning outcomes are often mixed and not all students benefit equally from using these applications. In this study, we utilize Ordered Network Analysis (ONA) to examine data from a system for automated assessment of programming assignments and compare platform activity between novice students (N=110) achieving high (N=43) and low (N=67) scores on the final test of an introductory CS course. We identify and visualize differences in the activity patterns between the groups. High performing novice students tend to request feedback more often, while low performing students more often leave the assignment unsolved after experiencing an unsuccessful attempt. These findings show that Ordered Network Analysis can serve as a useful tool for understanding student behaviors, facilitating the design of targeted interventions that might support learners at key moments in their programming engagement towards task success.

During middle school, students' learning experiences begin to influence their future decisions about college enrollment and career selection. Prior research indicates that both knowledge gained and the disengagement and affect experienced during this period are predictors of these future outcomes. However, this past research has investigated affect, disengagement, and knowledge in an overall fashion – looking at the average manifestation of these constructs across all topics studied across a year of mathematics. It may be that some mathematics topics are more associated with these outcomes than others. In this study, we use data from middle school students interacting with a digital mathematics learning platform, to analyze the interplay of these features across different topic areas. Our findings show that mastering Functions is the most important predictor of both college enrollment and STEM career selection, while the importance of knowing other topic areas varies across the two outcomes. Furthermore, while subject knowledge tends to be the most relevant predictor for general college enrollment, affective states, especially confusion and engaged concentration, become more important for predicting STEM career selection.

In today's data-driven educational technologies, algorithms have a pivotal impact on student experiences and outcomes. Therefore, it is critical to take steps to minimize biases, to avoid perpetuating or exacerbating inequalities. In this paper, we investigate the degree to which algorithmic biases are present in two learning analytics models: knowledge estimates based on Bayesian Knowledge Tracing (BKT) and carelessness detectors. Using data from a learning platform used across the United States at scale, we explore algorithmic bias following three different approaches: 1) analyzing the performance of the models on every demographic group in the sample, 2) comparing performance across intersectional groups of these demographics, and 3) investigating whether the models trained using specific groups can be transferred to demographics that were not observed during the training process. Our experimental results show that the performance of these models is close to equal across all the demographic and intersectional groups. These findings establish the feasibility of validating educational algorithms for intersectional groups and indicate that these algorithms can be fairly used for diverse students at scale.

Informal financial cooperation strategies have emerged as a solution to improve the resilience of low-income communities, being one of the most popular ones the rotating savings and credit associations (ROSCAs). In this article, using computational tools and dynamical systems modeling, we study the performance of two variations of ROSCAs that can potentially increase the resilience of communities. First, we propose a strategy that saves a percentage of the contributions of each member of the ROSCA to reduce the impact of individuals who stop contributing to the association and study how this strategy impacts the financial life of the individuals and trust among community members. Second, we study a decentralized version of the ROSCA in which individuals contribute to more than one association and analyze the impact of the size of the cooperation scheme and the number of associations where each individual participates. Through mathematical and simulation analyses, we show how the cooperation strategies impact the resilience of low-income communities.

Individuals who have very low income and belong to an underprivileged community have a challenging task when managing their financial life, typically due to negative unpredictable events and lack of health and financial services. Cooperation strategies based on savings and credits are becoming a very important option for these communities to address such challenges and to have some level of financial stability. Since designing new cooperation strategies takes a significant amount of time and effort, computational tools have been recently introduced to simulate and evaluate communities that implement these financial cooperation schemes. In this article, based on the theory that underlies these computational tools and theoretical concepts of cooperation, we propose a new cooperation strategy based on donations that are distributed between the members of the community according to the given network topology. Through mathematical and simulation analyses, we show the scenarios where the proposed cooperation strategy, based on altruistic behavior, can potentially improve the resiliency of the community to negative unpredictable events.