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Traditional tests are not effective tools for diagnosing the content and structure of students' knowledge of physics. As a possible alternative, a set of term-association tasks (the "ConMap" tasks) was developed to probe the…
The effectiveness of prompt learning has been demonstrated in different pre-trained language models. By formulating suitable template and choosing representative label mapping, prompt learning can be used as an efficient knowledge probe.…
Assessment tasks provide opportunities for students to make sense of novel contexts in light of their existing ideas. Consequently, investigations in physics education research have extensively developed and analyzed assessments that…
Understanding how an instructional approach shapes student's cognitive resources and engagement is central to improving undergraduate physics education especially for novice learners. This study examines how three instructional modalities…
Physics education researchers have argued that authentic physics education includes computation as part of a physics student's training, and many parties have made efforts towards this goal. However, most research on this teaching modality…
This research investigate that science process skills significantly improve after doing some practicum activities. The research population are fifth semester physics education students and the research sample are fifth semester physics…
Reinforcement Learning (RL) has made significant strides in enabling artificial agents to learn diverse behaviors. However, learning an effective policy often requires a large number of environment interactions. To mitigate sample…
Large language models trained on a mixture of NLP tasks that are converted into a text-to-text format using prompts, can generalize into novel forms of language and handle novel tasks. A large body of work within prompt engineering attempts…
Physics-based reinforcement learning tasks can benefit from simplified physics simulators as they potentially allow near-optimal policies to be learned in simulation. However, such simulators require the latent factors (e.g. mass, friction…
We discuss a case study of the influence of epistemology on learning for a student in an introductory college physics course. An analysis of videotaped class work, written work, and interviews indicates that many of the student's…
In this paper, we investigate the brain activity elicited during perception of animated shapes as stimuli, which have been found to evoke mental state attributions. Contrary to a previous study, we incorporated the participants' responses…
Blended mathematical sensemaking in science (MSS) involves deep conceptual understanding of quantitative relationships describing scientific phenomena. Previously we developed the cognitive framework describing proficiency in MSS across…
This work in the field of developmental cognitive robotics aims to devise a new domain bridging between reinforcement learning and imitation learning, with a model of the intrinsic motivation for learning agents to learn with guidance from…
The effectiveness of Multimodal Chain-of-Thought (MCoT) prompting is often limited by the use of randomly or manually selected examples. These examples fail to account for both model-specific knowledge distributions and the intrinsic…
People often strive for deep engagement in activities which is usually associated with feelings of flow: a state of full task absorption accompanied by a sense of control and fulfillment. The intrinsic factors driving such engagement and…
Student persistence in science, technology, engineering, and mathematics (STEM) has long been a focus of educational research, with both quantitative and qualitative methods being used to investigate patterns and mechanisms of attrition.…
In introductory physics laboratory instruction, students often expect to confirm or demonstrate textbook physics concepts (Wilcox & Lewandowski, 2017; Hu & Zwickl, 2017; Hu & Zwickl, 2018). This expectation is largely undesirable: labs that…
We propose a new deep learning model for goal-driven tasks that require intuitive physical reasoning and intervention in the scene to achieve a desired end goal. Its modular structure is motivated by hypothesizing a sequence of intuitive…
Systems factorial technology (SFT; Townsend & Nozawa, 1995) is regarded as a useful tool to diagnose if features (or dimensions) of the investigated stimulus are processed in a parallel or serial fashion. In order to use SFT, one has to…
Immersion in a task is a prerequisite for creativity. However, excessive arousal in a single task has drawbacks, such as overlooking events outside of the task. To examine such a negative aspect, this study constructs a computational model…