arXiv subjects

David Gibson

Publications and source records attributed to David Gibson.

3 recordsLinked to original sources

arXiv2026

Practitioner Beliefs and Behaviors in AI-Enhanced Education: DOT Framework Survey Evidence

This study reports findings from a cross-sectional survey (n = 72) of higher education practitioners examining beliefs, behaviors, and institutional conditions related to artificial intelligence (AI) integration in teaching and learning. Grounded in the DOT Framework, which integrates design thinking and open systems theory, the study investigates AI familiarity, usage patterns, design-oriented practices, and pedagogical beliefs. Exploratory factor analysis of 19 belief items identified a three-factor structure: AI Functional Capabilities, Oversight and Governance, and Instructor Collaboration and Planning ({\alpha} = .90). Results indicate that practitioners hold favorable views of AI as a pedagogical support while maintaining strong commitments to human oversight and critical evaluation. Reported practices emphasize iterative prompting and content generation, with less consistent use of needs assessment and feedback loops. Institutional barriers including limited policy, training, and infrastructure were widely reported. These findings provide preliminary empirical support for the DOT Framework as a descriptive model of practitioner beliefs and practices, while also highlighting gaps between design-oriented theory and current implementation. The study contributes an initial measurement structure and identifies directions for confirmatory validation and outcome-based research linking AI-supported design practices to instructional quality.

cs.AI↗

arXiv2025

Analyzing Students' Emerging Roles Based on Quantity and Heterogeneity of Individual Contributions in Small Group Online Collaborative Learning Using Bipartite Network Analysis

Understanding students' emerging roles in computer-supported collaborative learning (CSCL) is critical for promoting regulated learning processes and supporting learning at both individual and group levels. However, it has been challenging to disentangle individual performance from group-based deliverables. This study introduces new learning analytic methods based on student -- subtask bipartite networks to gauge two conceptual dimensions -- quantity and heterogeneity of individual contribution to subtasks -- for understanding students' emerging roles in online collaborative learning in small groups. We analyzed these two dimensions and explored the changes of individual emerging roles within seven groups of high school students ($N = 21$) in two consecutive collaborative learning projects. We found a significant association in the changes between assigned leadership roles and changes in the identified emerging roles between the two projects, echoing the importance of externally facilitated regulation scaffolding in CSCL. We also collected qualitative data through a semi-structured interview to further validate the quantitative analysis results, which revealed that student perceptions of their emerging roles were consistent with the quantitative analysis results. This study contributes new learning analytic methods for collaboration analytics as well as a two-dimensional theoretical framework for understanding students' emerging roles in small group CSCL.

cs.SI↗

arXiv2009

Molecular Line Observations of Infrared Dark Clouds II: Physical Conditions

Using a source selection biased towards high mass star forming regions, we used a Large Velocity Gradient (LVG) code to calculate the H2 densities and CS column densities for a sample of Midcourse Space Experiment (MSX) 8 micron infrared dark cores. Our average H2 density and CS column density were 1.14 x 10e6 cm-3 and 1.21 x 10e13 cm-2 respectively. In addition, we have calculated the Jeans mass and Virial mass for each core to get a better understanding of their gravitational stability. We found that core masses calculated from observations of N2H+ J = 1-0 and C18O J = 1-0 by Ragan et al. 2006 (Paper 1) were sufficient for collapse, though most regions are likely to form protoclusters. We have explored the star-forming properties of the molecular gas within our sample and find some diversity which extends the range of infrared dark clouds from very the massive clouds that will create large clusters, to clouds that are similar to some of our local counterparts (e.g. Serpens, Ophiuchus).

astro-ph.GA↗