Related papers: Online administration of a reasoning inventory in …
The COVID-19 pandemic highlighted the challenges of maintaining hands-on laboratory instruction in undergraduate physics education. In response, we developed and deployed an interactive online physics laboratory platform designed to closely…
Proficiency with calculating, reporting, and understanding measurement uncertainty is a nationally recognized learning outcome for undergraduate physics lab courses. The Physics Measurement Questionnaire (PMQ) is a research-based assessment…
We investigate users' perspectives on an online reflective question activity (RQA) that prompts people to externalize their underlying emotions on a troubling situation. Inspired by principles of cognitive behavioral therapy, our 15-minute…
This study investigates differences in student responses to in-class and online administrations of the Force Concept Inventory (FCI), Conceptual Survey of Electricity and Magnetism (CSEM), and the Colorado Learning Attitudes about Science…
The process of teaching has been greatly changed by the COVID-19 pandemic. It is possible that studying will not resemble anymore the process known by the previous generations of students. As the current generations learn by doing and use…
Laboratory courses are an important part of the undergraduate physics curriculum. During physics labs, students can engage in authentic, hands-on experimental practices, which can prepare them for graduate school, research laboratories, and…
This paper describes a test and case study of self-evaluation of online courses during the pandemic time. Due to the Covid-19, the whole world needs to sit on lockdown in different periods. Many things need to be done in all kinds of…
Process Reward Models (PRMs) have shown promise in enhancing the mathematical reasoning capabilities of Large Language Models (LLMs) through Test-Time Scaling (TTS). However, their integration into multimodal reasoning remains largely…
Reinforcement Learning (RL) has emerged as the key driver for post-training complex reasoning in Large Language Models (LLMs), yet online RL introduces significant instability and computational overhead. Offline RL offers a compelling…
The onset of the COVID-19 pandemic forced many universities to move to virtual instruction during the spring 2020 semester. The transition to remote learning was abrupt and overwhelming for teachers of all subjects, all across the US.…
Randomized control trials (RCTs) have been the gold standard to evaluate the effectiveness of a program, policy, or treatment on an outcome of interest. However, many RCTs assume that study participants are willing to share their…
Educational resources, such as web apps and self-directed tutorials, have become popular tools for teaching and active learning. Ideally, students - the intended users of these resources - should be involved in the resource development…
While much of the literature on student peer-review focusses on the success (or otherwise) of individual activities in specific classes (often implemented as part of scholarly research projects) there is little by way of published data…
Adaptive remote instruction has led to important lessons for the future, including rediscovery of known pedagogical principles in new contexts and new insights for supporting remote learning. Studying one computer science department that…
Research-based assessments represent a valuable tool for both instructors and researchers interested in improving undergraduate physics education. However, the historical model for disseminating and propagating conceptual and attitudinal…
Retrieval-augmented generation (RAG) systems often rely on static retrieval, limiting adaptation to evolving intent and content drift. We introduce Dynamic Memory Alignment (DMA), an online learning framework that systematically…
Reinforcement Learning (RL) has recently received significant attention from the process systems engineering and control communities. Recent works have investigated the application of RL to identify optimal scheduling decision in the…
Measuring a machine's understanding of human language often involves assessing its reasoning skills, i.e. logical process of deriving answers to questions. While recent language models have shown remarkable proficiency in text based tasks,…
One of the main challenges in reinforcement learning (RL) is that the agent has to make decisions that would influence the future performance without having complete knowledge of the environment. Dynamically adjusting the level of epistemic…
The COVID-19 pandemic has highlighted the importance of supply chains and the role of digital management to react to dynamic changes in the environment. In this work, we focus on developing dynamic inventory ordering policies for a…