Samples of work
NARST 2020 Proposal
NARST 2020 Proposal
I co-authored a proposal "Understanding the Perceived Usefulness of Mobile Technology in Physics Learning: A Pedagogical Perspective", which was accepted to the 2020 annual conference of the National Association of Research in Science Teaching. This study was conducted by collecting 831 high-school students perception and use frequency of mobile technology, their physics learning interest and performance. This study investigated the differences in students' perceived usefulness across three pedagogical categories of mobile uses in physics learning as well as the actual use frequency. Further, the study investigated how the perceived usefulness and use frequency were associated with students’ physics achievement by gender.
EDIT 9990 Doctoral Topical Project
Understanding the Pedagogical Roles Students and Teachers Play in Processes of Using Mobile Technology in Science Education is the final project for the Doctoral Topical Seminar course I took with Dr. Kopcha. In this project, I articulated a published three-dimensional pedagogy framework of Mobile learning from the Instructional design perspective. The major remarks were the four recommendations of the instructional design and implementation in mobile-technology enhanced learning environment. Based on this project, I started to work my current meta-analysis project.
EDIT 8190 Design Product
Machine learning is rapidly used in education due to its automaticity. In Dr. Rieber's design studio class, I explored the conjecture model in the design and implementation of machine learning automatic scoring platform. The purpose of this design is to make scoring easy and fast without burdening teachers in terms of time and cost. My design triggered my research interest in the machine learning automatic scoring and feedback area.
DEIT 8100 Foundation of LDT Proposal
Consisted with my research interest, I tried to propose a theoretical mobile feedback model by integrating mobile technology and machine learning, which will be the final project of Dr. Hill's Foundation of LDT course in Spring semester 2021. Here, I showed the initial proposal of this project. Mobile technology, as the locative media, can be used to assist students learning on the field learning. However, such locative media has many irrelevant feature for learning. Machine learning can be used to effectively classify these features of the learning environment that are pedagogically relevant for learning. The machine learning classification can support teachers' feedback and instructional decision making.
The Colorado Learning Attitude of Science Survey (CLASS) is a new instrument to measure students attitudes about physics and how they learn physics. In this proposal, I use the Rasch analysis to validate the CLASS scale in terms of item difficulty, item statistics fit, and differential item functionality. The purpose of the proposal is to examine whether the Rasch analysis will be aligned with the CLASS conclusion. What validity issues emerge when using the Rasch model to validate the CLASS attitude scale. I will continually work on this project for the final project in Dr. Engelhard course. Eventually, this project will be a journal manuscript.