We investigate the multilingual and multimodal performance of a large language model-based artificial intelligence (AI) system, GPT-4o, using a diverse set of physics concept inventories spanning multiple languages and subject categories. The inventories, sourced from the PhysPort website, cover classical physics topics such as mechanics,…

Given the widespread presence of competition in educational settings and its complex impact on science learning, how students complete tasks in competitive environments has attracted a wide range of attention. The purpose of this study is to compare students' performance on physics conceptual questions in noncompetitive and competitive…

The Force Concept Inventory (FCI) is a popular multiple-choice instrument used to measure a student's conceptual understanding of Newtonian mechanics. Recently, a network analytic technique called module analysis has been used to identify responses to the FCI and other conceptual instruments that are preferentially selected together by students;…

Exploring students' physics conceptions, science engagement and misconceptions in physics together with their association to certain variables (gender, educational background, and program of study) are essential in improving physics teaching and learning. However, few studies have been conducted to explore these three measures and almost none…

In this study, we investigated female and male students' sense of belonging and their academic performance measured by the Force Concept Inventory (FCI) and course grades in a calculus-based introductory physics course at a large public university in the United States. We find that compared to male students, female students' sense of belonging was…

Research-based assessments (RBAs) allow researchers and practitioners to compare student performance across different contexts and institutions. In recent years, research attention has focused on the student populations these RBAs were initially developed with because much of that research was done with "samples of convenience" that were…

Generative AI technologies such as large language models show novel potential to enhance educational research. For example, generative large language models were shown to be capable of solving quantitative reasoning tasks in physics and concept tests such as the Force Concept Inventory (FCI). Given the importance of such concept inventories for…

In science education, it is vital for teachers to consider students' academic and emotional needs. Teachers' prediction of students' learning states has been commonly regarded as an indicator to measure that competence to understand students. This study aimed to explore the outcome and the process of prediction to reflect teachers' pedagogical…

The typical introductory physics lecture requires students to consolidate and assimilate a large quantity of complex information that is often novel to them. This can leave students overwhelmed, slow the pace of their learning, and lower their motivation. We find that carefully designed multimedia summaries in the form of one-minute videos and…

We studied the impact of metacognitive reflections on recently-completed work as a way to improve the retention of newly learned problem-solving techniques. Students video recorded themselves talking through problems immediately after finishing them, completed ongoing problem-solving strategy maps or problem-sorting exercises, and filled out…