New frontiers in simulation-based teacher training have been unveiled with the advancement of artificial intelligence (AI). Integrating AI into virtual student agents increases the accessibility and affordability of teacher training simulations, but little is known about how preservice teachers interact with AI-powered student agents. This study analyzed the discourse behavior of 15 preservice teachers who undertook simulation-based training with AI-powered student agents. Using a framework of ambitious science teaching, we conducted a pattern analysis of teacher and student talk moves, looking for evidence of academically productive discourse. Comparisons are made with patterns found in real classrooms with professionally trained science teachers. Results indicated that preservice teachers generated academically productive discourse with AI-powered students by using ambitious talk moves. The pattern analysis also revealed coachable moments where preservice teachers succumbed to cycles of unproductive discourse. This study highlights the utility of analyzing classroom discourse to understand human-AI communication in simulation-based teacher training.

This work-in-progress paper reports on the establishment of preliminary reliability for a domain-agnostic evidence-centered assessment model to measure computational thinking (CT) in an online virtual world. Preliminary reliability was established between two researchers through manually coding 800 minutes of recorded learning sessions and over 350 minutes of consultation. Participants were three adolescents diagnosed with autism spectrum disorder. Findings indicate an acceptable level of reliability between the two coders, opening the way to more extensive application of the model in future studies.

In this work-in-progress research we exploited and investigated a virtual reality (VR) based, flexibility learning environment (FLE) in which adolescents with autism use, customize, and design an assortment of simulation games that represent and exemplify the application of forces and Newton's laws of motion. The simulation/game modeling and making tasks acted as the primers of practicing and assessing representational flexibility in solving the engineering design problems with computational thinking. The participants' participation behaviors and verbal utterances during the intervention sessions have been archived via screen and webcam recordings. The current study findings indicated that two approaches of speech or text data mining, multi-label classification and similarity index, can act as the in-situ performance assessment methods to evaluate the representational flexibility development for engineering design and computational thinking of a heterogeneous learner group.

The purpose of this work-in-progress paper is to explore the learners' interactions with task narratives in a Game-Based Learning (GBL) environment for math problem-solving. We present a qualitative case study to demonstrate how the learners interact with task narratives in a GBL environment for math problem-solving. Three preliminary patterns were identified: (1) narrative immersion mediated math problem-solving; (2) task narratives and the game world afforded cognitive connection for math problem-solving; (3) narrative structure influenced math problem-solving. Implications and future directions are discussed.

The purpose of this paper is to identify central issues regarding user interactions in a virtual environment for immersive learning. This preliminary argument explores the importance of categorization to match with cognitive domains and natural user interfaces in the immersive virtual environment. An example of applying immersive virtual environment (IVE) in cognitive domains is provided. The research idea will help practitioners in advanced learning technologies to design a virtual platform aligning with instructional goals.

Recently, higher education has seen an increasing emphasis on the prominent role of computational thinking in all disciplines. Computational thinking is advocated as not only a fundamental skill or concept in computer science but also a core competency for all disciplines. Teaching students in non-computer science majors computing thinking is challenging because students do not have experts' mental models. This study investigates the knowledge gap that non-computing major college students (n=126) possess about computational thinking in an introductory MS Excel course by measuring their performance using spreadsheet functions in three categories: recall, application, and problem solving. The empirical result, analyzed using ANOVA, shows that students can recall the meaning of those functions but seem to have trouble using them correctly and precisely (cued or uncued). Students' test results suggest the following issues: (1) problems with understanding the data type, (2) failure in translating problems to productive representations using spreadsheet functions, and (3) inadequate stipulation of the computational representations in precise forms. Addressing these problems early and explicitly in future classes could improve the education of computational thinking and alleviate difficulties students may experience in using computational thinking in learning and problem solving.