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 field of learning analytics has made notable strides in automating the detection of complex learning processes in multimodal data. However, most advancements have focused on individualized problem-solving instead of collaborative, open-ended problem-solving, which may offer both affordances (richer data) and challenges (low cohesion) to behavioral prediction. Here, we extend predictive models to automatically detect socially shared regulation of learning (SSRL) behaviors in collaborative computational modeling environments using embedding-based approaches. We leverage large language models (LLMs) as summarization tools to generate task-aware representations of student dialogue aligned with system logs. These summaries, combined with text-only embeddings, context-enriched embeddings, and log-derived features, were used to train predictive models. Results show that text-only embeddings often achieve stronger performance in detecting SSRL behaviors related to enactment or group dynamics (e.g., off-task behavior or requesting assistance). In contrast, contextual and multimodal features provide complementary benefits for constructs such as planning and reflection. Overall, our findings highlight the promise of embedding-based models for extending learning analytics by enabling scalable detection of SSRL behaviors, ultimately supporting real-time feedback and adaptive scaffolding in collaborative learning environments that teachers value.

In digital learning systems, gaming the system refers to occasions when students attempt to succeed in an educational task by systematically taking advantage of system features rather than engaging meaningfully with the content. Often viewed as a form of behavioral disengagement, gaming the system is negatively associated with short- and long-term learning outcomes. However, little research has explored this phenomenon beyond its behavioral representation, leaving questions such as whether students are cognitively disengaged or whether they engage in different self-regulated learning (SRL) strategies when gaming largely unanswered. This study employs a mixed-methods approach to examine students’ cognitive engagement and SRL processes during gaming versus non-gaming periods, using utterance length and SRL codes inferred from think-aloud protocols collected while students interacted with an intelligent tutoring system for chemistry. We found that gaming does not simply reflect a lack of cognitive effort; during gaming, students often produced longer utterances, were more likely to engage in processing information and realizing errors, but less likely to engage in planning, and exhibited reactive rather than proactive self-regulatory strategies. These findings provide empirical evidence supporting the interpretation that gaming may represent a maladaptive form of SRL. With this understanding, future work can address gaming and its negative impacts by designing systems that target maladaptive self-regulation to promote better learning.

Programming courses can be challenging for first year university students, especially for those without prior coding experience.Students initially struggle with code syntax, but as more advanced topics are introduced across a semester, the difficulty in learning to program shifts to learning computational thinking (e.g., debugging strategies).This study examined the relationships between students' rate of programming errors and their grades on two exams.Using an online integrated development environment, data were collected from 280 students in a Java programming course.The course had two parts.The first focused on introductory procedural programming and culminated with exam 1, while the second part covered more complex topics and object-oriented programming and ended with exam 2. To measure students' programming abilities, 51095 code snapshots were collected from students while they completed assignments that were autograded based on unit tests.Compiler and runtime errors were extracted from the snapshots, and three measures -Error Count, Error Quotient and Repeated Error Density -were explored to identify the best measure explaining variability in exam grades.Models utilizing Error Quotient outperformed the models using the other two measures, in terms of the explained variability in grades and Bayesian Information Criterion.Compiler errors were significant predictors of exam 1 grades but not exam 2 grades; only runtime errors significantly predicted exam 2 grades.The findings indicate that leveraging Error Quotient with multiple error types (compiler and runtime) may be a better measure of students' introductory programming abilities, though still not explaining most of the observed variability.

Numerous studies demonstrate the importance of self-regulation during learning by problem-solving. Recent work in learning analytics has largely examined students’ use of SRL concerning overall learning gains. Limited research has related SRL to in-the-moment performance differences among learners. The present study investigates SRL behaviors in relationship to learners’ moment-by-moment performance while working with intelligent tutoring systems for stoichiometry chemistry. We demonstrate the feasibility of labeling SRL behaviors based on AI-generated think-aloud transcripts, identifying the presence or absence of four SRL categories (processing information, planning, enacting, and realizing errors) in each utterance. Using the SRL codes, we conducted regression analyses to examine how the use of SRL in terms of presence, frequency, cyclical characteristics, and recency relate to student performance on subsequent steps in multi-step problems. A model considering students’ SRL cycle characteristics outperformed a model only using in-the-moment SRL assessment. In line with theoretical predictions, students’ actions during earlier, process-heavy stages of SRL cycles exhibited lower moment-by-moment correctness during problem-solving than later SRL cycle stages. We discuss system re-design opportunities to add SRL support during stages of processing and paths forward for using machine learning to speed research depending on the assessment of SRL based on transcription of think-aloud data.

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.

When students procrastinate on programming assignments, it can hinder the quality of their code and negatively impact their grades. In contrast, when students actively delay working on assignments to prepare to code (e.g., reading or seeking help), it can be an effective self-regulated learning (SRL) strategy beneficial to programming performance. However, distinguishing active delay from procrastination is methodologically challenging. To address this, we tracked what students did when they behaviorally delayed starting an assignment. Most students prepared to code by using multiple course resources across programming assignments. We found that many students delayed starting to code by seeking help in the Q&A platform, and this was beneficial to the quality of their code. Also, some pre-coding activities were related to behavioral delay in starting to code, but benefitted students' grades, and thus may indicate active delay, but not all pre-coding activities were beneficial. By considering pre-coding activities, we gain a comprehensive view of students' approach to coding in CS education.

In past work, time management interventions involving prompts, alerts, and planning tools have successfully nudged students in online courses, leading to higher engagement and improved performance. However, few studies have investigated the effectiveness of these interventions over time, understanding if the effectiveness maintains or changes based on dosage (i.e., how often an intervention is provided). In the current study, we conducted a randomized controlled trial to test if the effect of a time management intervention changes over repeated use. Students at an online computer science course were randomly assigned to receive interventions based on two schedules (i.e., high-dosage vs. low-dosage). We ran a two-way mixed ANOVA, comparing students' assignment start time and performance across several weeks. Unexpectedly, we did not find a significant main effect from the use of the intervention, nor was there an interaction effect between the use of the intervention and week of the course.

The effort to learn and the regulation of learning are key to successful learning. Voluntary practice has been shown to improve learning and is associated with having generally good self-regulated learning. At the same time, procrastination often slows the learning process and is associated with less than ideal regulation of learning. In this paper, we present the results of a study exploring the impact of voluntary practice and procrastination on the learning outcomes of novice programmers. We used data from an introductory programming course (CS1) at a large university and found that most students engaged in voluntary practice. However, students with higher prior performance and non-procrastinators were more likely to participate in the voluntary practice. We also found that participating in the voluntary practice did not have a significant impact on course performance. Furthermore, the study showed a weak negative correlation between procrastination and time spent on the homework and a weak negative correlation between procrastination and distributed practice. Finally, we found that non-procrastinators performed significantly better than procrastinators on the majority of homeworks.