Zheng Fang

We investigated naturally arisen leadership within collaborative learning environments via Bayesian hierarchical modeling and network analysis using data from engineering students engaged in the RescuShell virtual internship. We used Monte Carlo simulations to craft synthetic networks of individual and group interactions and validated the model via Jaccard Similarity and additional visual methods. Our study identifies emergent leadership roles in educational contexts where explicit designations of leadership are ordinarily absent and highlights the impact of these naturally arisen leaders on group cohesion.

Coding discourse data is critical to many learning analytics studies. To code their data, researchers may use manual techniques, automated techniques, or a combination thereof. Manual coding can be time-consuming and error prone; automated coding can be difficult to implement for non-technical users. Generative artificial intelligence (GAI) offers a user friendly alternative to automated discourse coding via prompting and APIs. We assessed the ability of GAI, specifically the GPT class of models, at automatically coding discourse in the context of a learning analytics study using a variety of prompting and training strategies. We found that fine-tuning approaches produced the best results; however, no results achieved standard thresholds for reliability in our field.

This paper derives a Minimum Message Length (MML) criterion for the model selection of the Autoregressive Moving Average (ARMA) time series model. The MML87 performances on the ARMA model compared with other well known model selection criteria, Akaike Information Criterion (AIC), Corrected AIC (AICc), Bayesian Information Criterion (BIC), and Hannan Quinn (HQ). The experimental results show that the MML87 is outperformed the other model selection criteria as it select most of the models with lower prediction errors and the models selected by MML87 to have a lower mean squared error in different in-sample and out-sample sizes.