Exploring Generative AI assisted feedback writing for students' written responses to a physics conceptual question with prompt engineering and few-shot learning

Instructor's feedback plays a critical role in students' development of conceptual understanding and reasoning skills. However, grading student written responses and providing personalized feedback can take a substantial amount of time. In this study, we explore using GPT-3.5 to write feedback to student written responses to conceptual questions with prompt engineering and few-shot learning techniques. In stage one, we used a small portion (n=20) of the student responses on one conceptual question to iteratively train GPT. Four of the responses paired with human-written feedback were included in the prompt as examples for GPT. We tasked GPT to generate feedback to the other 16 responses, and we refined the prompt after several iterations. In stage two, we gave four student researchers the 16 responses as well as two versions of feedback, one written by the authors and the other by GPT. Students were asked to rate the correctness and usefulness of each feedback, and to indicate which one was generated by GPT. The results showed that students tended to rate the feedback by human and GPT equally on correctness, but they all rated the feedback by GPT as more useful. Additionally, the successful rates of identifying GPT's feedback were low, ranging from 0.1 to 0.6. In stage three, we tasked GPT to generate feedback to the rest of the student responses (n=65). The feedback was rated by four instructors based on the extent of modification needed if they were to give the feedback to students. All the instructors rated approximately 70% of the feedback statements needing only minor or no modification. This study demonstrated the feasibility of using Generative AI as an assistant to generating feedback for student written responses with only a relatively small number of examples. An AI assistance can be one of the solutions to substantially reduce time spent on grading student written responses

Picturing the physics behind equations and graphs: a grounded cognition based model for multimedia learning and its application in physics education

This thesis tries to answer a fundamental question in physics education: How does the design of instructional representations affect the process of constructing physics knowledge? This question is important for the creation of instructional materials of any form, ranging from printed textbooks to blackboard writings in the classroom. It is especially critical for the creation of computerized multimedia lectures, as the visualization power of the computer opens up almost limitless possibilities to represent physics concepts in novel ways. To answer this question, I bring together knowledge from three different areas: physics education research (PER), multimedia learning (MML) theory, and most importantly, the perceptual symbols system (PSS) framework of grounded cognition. I argue that neither the existing PER theories nor the existing MML models are able to provide a satisfactory answer to this question alone. The reason of which, I believe, is that these theories are based on an amodal symbol view of cognition. The PSS framework, however, “grounds” human cognition in “modal symbols”: neural activation of sensory/motor modals of the brain. By adopting this framework, I have constructed a new cognitive model for physics learning from multimedia representations that has much greater predictive power compared to the existing models, especially with respect to the effectiveness of visual representations. This new model predicts that the perceptual features of instructional representations (graphs, equations and text), can have a significant impact on students’ learning outcome. If correctly designed, perceptual features can greatly improve the effectiveness of instructional materials. We examined the major predictions of the model in two clinical experiments. The results of experiment 1 shows that perceptually enhanced design based on the new model has a positive impact on students’ conceptual understanding, as well as on their ability to transfer the knowledge learned to a different context. The results of experiment 2 suggest that perceptually enhanced design may also improve knowledge activation and facilitate the creation of multi-step solutions. However, several other factors not included in this model may also have a significant impact on the learning outcomes. None of the existing models of MML are able to account for these results. In the last chapter, we discuss several factors of the learning process that are not covered in the current model, and point out several possible directions for future improvements

A Practical Approach to Disturbance Decoupling Control

In this paper, a unique dynamic disturbance decoupling control (DDC) strategy, based on the active disturbance rejection control (ADRC) framework, is proposed for square multivariable systems. With the proposed method, it is shown that a largely unknown square multivariable system is readily decoupled by actively estimating and rejecting the effects of both the internal plant dynamics and external disturbances. By requiring as little information on plant model as possible, the intention is to make the new method practical. The stability analysis shows that both the estimation error and the closed-loop tracking error are bounded and the error upper bounds monotonously decrease with the bandwidths. Simulation results obtained on two chemical process problems show good performance in the presence of significant unknown disturbances and unmodeled dynamics

A Practical Approach to Disturbance Decoupling Control

In this paper, a unique dynamic disturbance decoupling control (DDC) strategy, based on the active disturbance rejection control (ADRC) framework, is proposed for square multivariable systems. With the proposed method, it is shown that a largely unknown square multivariable system is readily decoupled by actively estimating and rejecting the effects of both the internal plant dynamics and external disturbances. By requiring as little information on plant model as possible, the intention is to make the new method practical. The stability analysis shows that both the estimation error and the closed-loop tracking error are bounded and the error upper bounds monotonously decrease with the bandwidths. Simulation results obtained on two chemical process problems show good performance in the presence of significant unknown disturbances and unmodeled dynamics