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Revealing Students’ Career Interests From Textual Responses: An Application of Structural Topic Model
No full textThis study utilizes the Structural Topic Model (STM; Roberts et al., 2014) to uncover key career interests among secondary school students from their open-ended survey responses. Textual data from students were analyzed to identify five core career interest themes, and covariate analysis revealed gender differences and associations between giftedness scores and career interests. These findings demonstrate the potential of STM as a robust tool for analyzing large-scale textual data, enabling educators to tailor career guidance programs
Enhancing AI Literacy for ELL Instruction: An Intervention With Pre-Service Teachers
Get PDFThis study examines pre-service teachers’(PSTs) familiarity with and use of AI tools, specifically ChatGPT, for teaching and assessing English Language Learners (ELLs). 90 PSTs from a Midwest U.S. university completed pre- and post-intervention surveys. The intervention included a lecture on AI and five 10-minute ChatGPT-3 sessions focused on ELL lesson planning. Results showed significant increases in AI familiarity and usage. Effect sizes, however, were small and medium, respectively, suggesting the need for more targeted interventions to enhance PSTs’ skills
Design and Development of an Inert Controlled Environmental Chamber for Evaluation of Contaminant Mass Transfer
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Expanding Landscapes: Intersections between Writing Center Work and Other Academic Fields
Get PDFThis presentation explores the intersections between writing center work and math education, counseling psychology, and speech/hearing sciences. The four fields share more similarities than first expected, such as prioritizing relational approaches and maintaining client agency. Implications for writing centers include the need to adjust consultant education to overtly explore such overlaps in order to increase new consultants’ confidence and circumvent potential problems caused by differing assumptions
Access Control at Major/Minor Road Intersection through CAV in Mixed Traffc
Get PDFRoadway intersections are among the major causes of traffic congestion besides lane reduction bottlenecks. When a major road intersects a minor road at an unsignalized intersection without the control of a traffic signal, the mainline vehicles are given priority over the minor road vehicles to go through the intersection, and the latter can only enter or cross the intersection when there is a sufficient gap between successive vehicles on the major road. Connected and automated vehicles (CAV) are packed with tracking and lane-keeping assistance and adaptive speed control to ensure that vehicles do not collide while reducing traffic congestion. This research aims to utilize CAVs to help generate usable gaps for minor road vehicles to enter the intersection without interrupting the mainline traffic flow. A probability function is developed to study the probability in which CAVs can create additional usable gaps for the minor road vehicles based on the headway distribution of the mainline vehicles. The control logic algorithm is utilized when vehicle arrivals on the main road permit implementing the control strategy to improve intersection efficiency in mixed traffic conditions. The proposed control logic is simulated under two traffic scenarios, unsignalized and semi-actuated signal control, to study the effectiveness of the proposed method. Additionally, a field investigation is conducted at two intersections to verify the feasibility of the control logic implementation in real traffic conditions. Simulation results of the unsignalized intersection scenario show that the delay and queue length of the minor road approach is minimized without causing a significant delay to the mainline. The minor road delay is reduced by as much as 90% when the percentage of CAVs on the major road is 70% compared with the benchmark of no CAVs on the major road. The modified algorithm of semi-actuated signal control reduces the major road interruptions with the increase of CAVs penetration. Additionally, the intersection capacity increases with the increase of the number of gaps created on the major road. The minor road drivers are mostly willing to accept gaps created when CAVs reduce speed, where more than 60% of gaps created were accepted. This research has shown that deploying CAVs in the road network with the proposed method can positively impact the traffic efficiency while maintaining safety of the intersection