Human behavior is fundamentally generative: People create pictures, write stories, compose music, and engage in conversation. Traditional approaches in psychology and cognitive science have not focused on this open-endedness, instead favoring more constrained task settings that admit a limited set of outcomes. Although those approaches have been fruitful, new approaches might be needed to develop a unified understanding of the generative, open-ended behaviors that are so emblematic of human cognition. This article demonstrates the value of generative behaviors as targets for cognitive modeling by providing rich behavioral data that reveal how multiple cognitive processes coordinate. Drawing production serves as a case study illustrating this approach, showing how perception, memory, social inference, and motor control coordinate flexibly on the basis of communicative context. Recent advances in generative artificial intelligence offer both new tools for modeling open-ended human behavior and new comparative targets for understanding similarities and differences between human and machine intelligence. However, applying these tools effectively might require new experimental paradigms, larger data sets, and careful consideration of what mechanistic correspondence between models and human cognition is necessary for scientific progress. Embracing the open-ended nature of human thought and behavior poses methodological challenges but offers a promising path toward understanding the most distinctive aspects of human intelligence.

GenAI has rapidly entered instructional and learning settings as a teaching assistant or AI tutor. However, less is known about how pedagogical intent connects to the learning generated within these systems, especially when student-facing AI dialogues are fine-tuned through teacher orchestration in live classrooms. This study examines a classroom deployment of a "Classroom Teaching Aide" (TASD) system, which enables teachers to author both a teacher-to-AI setup prompt (instructional scaffold) and a student-facing conversation starter to launch AI-mediated classroom discussions. We analyze a multi-subject pilot conducted in Spring 2025, involving 20 participating teachers (16 of whom implemented the system), across 39 classrooms and 77 TASD settings, yielding 1,479 student-AI conversations with 878 unique students. Using platform logs, LLM coding with human validation, and post-study teacher interviews (N=10), we characterize teacher authoring choices and link them to enacted student-AI interaction outcomes. In deployment, student-AI conversations were largely aligned with instructional intent: 71% were fully on-track, and fewer than 1% were substantially off-track. However, a persistent design-enactment gap emerged for cognitive demand: 38% of conversations under-reached the teacher-targeted DOK level, approaching 50% when targeting DOK 3. The study also shows that explicit finish lines in the prompt reduced the DOK gap by 0.22 levels (p < .001), and "no direct answers" guardrails reduced AI final-answer rates by 8.5 percentage points. These findings position teacher-authored prompt layers as critical orchestration levers that translate pedagogical intent into structured student-AI dialogue, underscoring both their promise for scalable classroom integration and the need for additional supports to reliably sustain higher-order reasoning during enactment.

Spatial skills are critical for learning in STEM areas and are affected by spatial anxiety and working memory. Prior work also showed that there are interaction effects between spatial anxiety and verbal working memory (WM) on spatial skills, such that the negative relation between spatial anxiety and spatial skills is stronger among higher- than lower-verbal WM children. To date, this interaction effect has not been found for visuospatial WM. However, a recent meta-analysis showed that both verbal WM and visuospatial WM are impaired by anxiety to a similar extent. The current study hypothesized that visuospatial WM interacts with spatial anxiety on spatial skills and that this interaction effect gets stronger as age increases. We investigated spatial anxiety, visuospatial WM, and two spatial skills (mental transformation and mental rotation) in 402 U.S. children in first to fourth grades. We found a significant three-way interaction of spatial anxiety, visuospatial WM, and grade level on mental transformation skill: only fourth-graders with high visuospatial WM showed a significant relation between spatial anxiety and lower mental transformation skills, whereas low-visuospatial WM fourth-graders and children in Grades 1 to 3 did not show this relation. However, this effect was not significant for children's mental rotation skills. We discuss the results in terms of age-related differences in visuospatial WM and strategy use, as well as differences between the mental transformation and mental rotation tasks. Our findings indicate that the interaction effect between spatial anxiety and working memory on spatial skills extends beyond verbal WM to visuospatial WM and becomes more pronounced as children's age increases.

Engineering bridge and success programs have been used to support the transition and retention of students in engineering and computing majors at 4-year institutions in the U.S.Many bridge and success programs also aim to broaden participation in engineering and computing by recruiting underserved students such as underrepresented minorities (URMs), women, first-generation students, and low socio-economic status (SES) students.However, their program leaders frequently report difficulties recruiting students from these groups.There is little literature focusing on the recruitment of bridge and success programs.Understanding effective recruitment strategies to reach and convince underserved students to participate in those programs can increase the use of effective practices by program leaders and disseminate best recruitment practices.Our research aims to identify which recruitment strategies are in use, which are most effective, and barriers to communicating with prospective underserved students of the engineering bridge and success programs.The research project includes several stages, investigating perspectives from both program leaders and students.This stage of the study will explore the effectiveness of different communication channels and change agents to reach and persuade underserved students to participate in engineering bridge and success programs from program leaders' perspectives.This is an explanatory sequential mixed method study based on the Diffusion of Innovations (DOI) theory framework.This work-in-progress (WIP) paper will report on instrument development, sampling procedure, planned data analysis, and current progress.This research will also broaden knowledge on applying DOI theory to increase recruitment effectiveness.

Identifying and annotating student use of debugging strategies when solving computer programming problems can be a meaningful tool for studying and better understanding the development of debugging skills, which may lead to the design of effective pedagogical interventions. However, this process can be challenging when dealing with large datasets, especially when the strategies of interest are rare but important. This difficulty lies not only in the scale of the dataset but also in operationalizing these rare phenomena within the data. Operationalization requires annotators to first define how these rare phenomena manifest in the data and then obtain a sufficient number of positive examples to validate that this definition is reliable by accurately measuring Inter-Rater Reliability (IRR). This paper presents a method that leverages Large Language Models (LLMs) to efficiently exclude computer programming episodes that are unlikely to exhibit a specific debugging strategy. By using LLMs to filter out irrelevant programming episodes, this method focuses human annotation efforts on the most pertinent parts of the dataset, enabling experts to operationalize the coding scheme and reach IRR more efficiently. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Background: Learning analytics (LA) research often aggregates learning process data to extract measurements indicating constructs of interest. However, the warranty that such aggregation will produce reliable measurements has not been explicitly examined. The reliability evidence of aggregate measurements has rarely been reported, leaving an implicit assumption that such measurements are free of errors. Objectives: This study addresses these gaps by investigating the psychometric pros and cons of aggregate measurements. Methods: This study proposes a framework for aggregating process data, which includes the conditions where aggregation is appropriate, and a guideline for selecting the proper reliability evidence and the computing procedure. We support and demonstrate the framework by analysing undergraduates' academic procrastination and programming proficiency in an introductory computer science course. Results and Conclusion: Aggregation over a period is acceptable and may improve measurement reliability only if the construct of interest is stable during the period. Otherwise, aggregation may mask meaningful changes in behaviours and should be avoided. While selecting the type of reliability evidence, a critical question is whether process data can be regarded as repeated measurements. Another question is whether the lengths of processes are unequal and individual events are unreliable. If the answer to the second question is no, segmenting each process into a fixed number of bins assists in computing the reliability coefficient. Major Takeaways: The proposed framework can be a general guideline for aggregating process data in LA research. Researchers should check and report the reliability evidence for aggregate measurements before the ensuing interpretation. © 2024 John Wiley & Sons Ltd.

This mixed-method study focuses on young children's engagement in engineering design activities through the lens of goal orientation. The purpose is to build evidence-based knowledge for educators and researchers to better support children's motivation in engineering. The preliminary results indicate that young children (7-14 years old) have multifaceted goal orientations while engaging in engineering activities, and their goal orientations may be related to the context and setting of the activities (e.g., physical and social environments). The data also indicate differences between families of historically minoritized communities and general museum visitors and caregivers' goal orientations for their children's learning.

Studies have found that most novice programmers have low proficiency in writing code. However, it is unclear what subskills compose code writing and which subskills novice programmers struggle with. This study utilizes programming traces to identify latent subskills that constitute code writing so that teachers can offer specific instruction on the weak subskills. Data were collected from an undergraduate course teaching introductory computer science in Java. Six hundred and fourteen students made submissions to homework programming questions in a web-based learning system. Based on the submission traces, we computed 11 features related to correctness and time students spent on their submissions. We conducted an exploratory factor analysis on two-thirds of students selected randomly and identified four factors. The first factor, code style proficiency, was mainly related to code style errors. The second, syntactic proficiency, concerned compiler errors. The third is semantic proficiency, which concerns runtime and logic errors. The fourth, syntactic debugging proficiency, concerned the success rate and time required for fixing compiler and code style errors. A confirmatory factor analysis conducted on the remaining one-third of the data supported the four-factor structure. The factor model showed measurement invariance between the data set where the model was developed and two new datasets, one from the same sample but collected at a different time point and another from a different sample and context (onsite course vs. online course). The factors were related to prior programming abilities, programming language familiarity, and future exam performance. These associations provided validity evidence for the factor model.

It is widely recognized that debugging is challenging for novice programmers and, as such, computing educators and researchers have called for explicit debugging instruction. Debugging requires various knowledge and skills, and different students may show different strengths and weaknesses. An understanding of such individual differences is important as it may guide personalized instruction. The current study investigated individual differences in debugging in an undergraduate introductory computer science course. We extracted variables related to debugging from students' submission traces to programming problems in the first month of the course. We applied latent profile analysis to these variables and identified three distinctive profiles. Profile A showed higher debugging accuracy and speed. Profile B showed lower debugging performance in runtime and logic errors, while profile C had lower performance in syntactic errors and tended to make large code edit every submission. Students' gender and self-rated programming ability predicted profile membership. Moreover, profile A got higher scores than the others in the first exam, and this difference persisted in the second and third exam, even controlling for background variables and score on the first exam. We investigated how students transitioned across debugging profiles over the duration of the course. From the beginning to the end of the course, a large part of students stayed in lower performance profiles. Overall, these findings support the call that debugging should be taught at an early stage and suggest that different groups may need different debugging instructions or support.

Debugging is a distinct subject in programming that is both comprehensive and challenging for novice programmers. However, instructors have limited opportunities to gain insights into the difficulties students encountered in isolated debugging processes. While qualitative studies have identified debugging strategies that novice programmers use and how they relate to theoretical debugging frameworks, limited larger scale quantitative analyses have been conducted to investigate how students' debugging behaviors observed in log data align with the identified strategies and how they relate to successful debugging. In this study, we used submission log data to understand how the existing debugging strategies are employed by students in an introductory CS course when solving homework problems. We identified strategies from existing debugging literature that can be observed with trace data and extracted features to reveal how efficient debugging is associated with debugging strategy usage. Our findings both align with and contradict past assumptions from previous studies by suggesting that minor code edition can be a beneficial strategy and that width and depth aggregations of the same debugging behavior can reveal opposite effects on debugging efficiency. © 2023 ACM.