University of Alabama

This meta-analysis examined special education professional development (PD) designed to improve outcomes for students with disabilities or to train teachers on evidence-based instruction and interventions in special education. The primary focus was on the impact of special education PD on teachers’ skills, beliefs, and knowledge. A secondary aim was to examine the impact on student outcomes. Results indicated that PD had a greater effect on teacher-related measures ( g = 0.53) compared with student measures ( g = 0.17). All studies included active participation opportunities and PD topics covered literacy, behavioral support, explicit instruction, and data-based decision-making. The findings emphasize the importance for PD to apply content knowledge to evidence-based practices in special education.

Our research in this paper lies at the intersection of Generative AI (GenAI) and search-as-learning (SAL). GenAI technologies (e.g., ChatGPT) have revolutionized how people search for and interact with information. However, we do not yet fully understand how people use GenAI systems to learn about complex topics. SAL research has studied how different tools can support learning with traditional document retrieval systems. Our research closely relates to SAL work that has investigated the effects of goal-setting on learning during search. We explore the influence of goal-setting on learning during information-seeking sessions with a GenAI system. We report on a between-subjects crowdsourced study (N = 120) in which participants were asked to learn about a complex topic using a GenAI system. The study had four conditions that varied along two factors (a 2 × 2 design). The first factor involved displaying related web results in addition to the GenAI output. The second factor involved giving participants access to the Subgoal Manager (SM), a tool designed to help people develop subgoals and take notes. We investigated the effects of both factors on: (RQ1) perceptions; (RQ2) behaviors; (RQ3) learning and retention; (RQ4) the types of requests issued to the system; and (RQ5) participants’ motivations for engaging (or not engaging) with the related web results. Results found that participants with access to the SM had higher post-task learning outcomes, did less copy/pasting into their notes, perceived the task as more difficult, and requested more examples and support for differentiating concepts from the GenAI system.

NanoparticleAtomCounter converts transmission electron microscopy (TEM) derived geometry into atom counts for supported nanoparticles. Using the radius and contact angle of a nanoparticle, NanoparticleAtomCounter models nanoparticles as spherical caps and analytically estimates the total, surface, interfacial, and perimeter atoms without any atomistic modelling, allowing the conversion of hundreds of thousands of geometries into atom counts in seconds.