Linguistic synchrony, or alignment, has been shown to be critical for student learning, particularly for L2 students (second language learners), whose patterns of synchrony often differ from fluent speakers due to proficiency constraints.While many studies have explored various dimensions of synchrony in global language tutoring contexts, there is a gap in understanding how linguistic synchrony evolves dynamically over the course of a tutoring session and how tutors' pedagogical strategies influence this process.This study incorporates three dimensions of synchrony-lexical, syntactic, and semantic-along with tutors' dialogue acts to evaluate their association with student performance using multivariate time-series analysis.Results indicate that lower-performing L2 students tend to lexically align with their tutor more consistently in the long term and with higher intensity in the short term.In contrast, higher-performing students demonstrate greater alignment with the tutor in syntactic and semantic dimensions.Furthermore, the dialogue acts of eliciting, scaffolding, and enquiry were found to play the strongest roles in influencing synchrony and impacting learning outcomes.

This full research paper aims to investigate methods for systematically identifying core tasks within undergraduate engineering work-integrated learning (WIL) opportunities, such as internships and co-ops. It achieves this by automatically analyzing WIL opportunities using transformer models. A dataset of 4,833 engineering internship postings from the last ten years was obtained through a partnership with the University's Career Connections Center. From this, a subset of 374 aerospace engineering internships, yielding 1,913 unique job tasks, was extracted for human labeling. We applied the Llama 2 architecture, a sophisticated pre-trained LLM, to extract a list of specific responsibilities and tasks from the internship postings. The job tasks were used to train an automated classification system to map each task to the established seven ABET student outcomes. Each job task was human-labeled by three subject matter experts, achieving a high level of inter-rater reliability of 0.998, according to Krippendorff's alpha. RoBERTa resulted in the optimal model indicating a label ranking average precision of 0.892 on the validation set and 0.857 on the testing set. Our findings provide novel insights into understanding the evolving skill expectations of undergraduate interns, offering a basis for tailoring engineering education to address these demands. Furthermore, the automated analysis of internship tasks demonstrates the potential for a scalable way to address the gap in understanding the core responsibilities within WIL experiences.

This innovative practice full paper analyzed over 900 responses related to awareness of the hidden curriculum to explore the relationships between expressed emotions among students and faculty members. The hidden curriculum refers to the implicit values, behaviors, and norms that are not formally included in educational programs but are learned through the social and cultural environment of an institution. Using both emotional and demographic data collected from a larger national survey of engineering educators and students across the U.S., we examined correlations between participants' responses and the sentiment classifications observed. Our analysis employed two distinct tools: VADER for sentiment analysis and a pre-trained Recurrent Neural Network (RNN) model capable of classifying six specific emotions. Through detailed analysis and comparison, we uncovered significant insights into the emotional dynamics within engineering education. Overall, we found a predominance of positive sentiment, with “joy” emerging as the most frequently expressed emotion among both students and faculty. However, we also identified nuanced variations in emotional expression, influenced by factors such as gender, engineering disciplines, and the sentiment analysis methods employed. These findings contribute to the ongoing discourse on the hidden curriculum's impact on emotional experiences, emphasizing the importance of considering both sentiment and distinct emotional states in educational research and practice. By providing a deeper understanding of emotional patterns, this research offers valuable perspectives on how emotions shape the educational experience in engineering.

This study presents the results of a computational discourse analysis of discussion threads within an online Math tutoring platform. This work is theoretically motivated by prior work that established the importance of linguistic and semantic features in the discourse in mathematics education. The end goal of this study is to understand the characteristics of language that is produced and used within a discussion board for math. The discussion board corpus comprises of posts from 4,720 students, teachers, and study experts who interacted within an online teaching and learning tutoring platform for math. Linguistic profiles of the discussion board discourse were estimated using Principal Component Analysis (PCA) based on Coh-Metrix linguistic features related to cohesion, language sophistication, and lexical characteristics. The PCA analysis yielded seven Math Discourse Linguistic Components, which collectively explained 49% of the variance in the dataset. Theoretical and conceptual validation of components revealed that the linguistic features align with the communication goal and the nature of mathematics. The linguistic profiles that characterized the discussion board discourse included referential cohesion, information density, instructional language, lexical variation, compare and contrast devices, explicit relations devices, and syntactic complexity. The dominance of cohesive cues within the linguistic profiles demonstrate the communication goals within the Math discourse such as elaboration, providing instruction, compare and contrast, establishing explicit relations, and presenting information. As such, these components characterize the Math Discussion Board discourse in terms of variations in cohesive and task-oriented cues within communication among students.