English
Related papers

Related papers: Representational differences in how students compa…

200 papers

Various strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for…

Machine Learning · Computer Science 2019-09-04 Vu-Linh Nguyen , Sébastien Destercke , Eyke Hüllermeier

This paper introduces a qualitative measure of ambiguity and analyses its relationship with other measures of uncertainty. Probability measures relative likelihoods, while ambiguity measures vagueness surrounding those judgments. Ambiguity…

Artificial Intelligence · Computer Science 2013-03-08 Michael S. K. M. Wong , Z. W. Wang

Measurement uncertainty is key to assessing, stating and improving the reliability of measurements. An understanding of measurement uncertainty is the basis for confidence in measurements and is required by many communities; among others in…

Physics Education · Physics 2024-12-30 Katy Klauenberg , Peter Harris , Philipp Möhrke , Francesca Pennecchi

We describe the difficulties that advanced undergraduate and graduate students have with quantum measurement within the standard interpretation of quantum mechanics. We explore the possible origins of these difficulties by analyzing student…

Physics Education · Physics 2016-04-05 Guangtian Zhu , Chandralekha Singh

End-of-course assessments play important roles in the ongoing attempt to improve instruction in physics courses. Comparison of students' performance on assessments before and after instruction gives a measure of student learning. In…

Physics Education · Physics 2014-07-15 Leanne Doughty , Marcos D. Caballero

What kind of problem-solving instruction can help students apply what they have learned to solve the new and unfamiliar problems they will encounter in the future? We propose that mathematical sensemaking, the practice of seeking coherence…

Physics Education · Physics 2020-08-05 Eric Kuo , Michael M. Hull , Andrew Elby , Ayush Gupta

In the context of industrially mass-manufactured products, quality management is based on physically inspecting a small sample from a large batch and reasoning about the batch's quality conformance. When complementing physical inspections…

Applications · Statistics 2024-02-22 Simon Cramer , Tobias Müller , Robert H. Schmitt

The ability to flexibly transform between different representations (e.g., from mathematical to graphical representations) of the same concept is a hallmark of expertise. Prior research suggests that many introductory physics students show…

Physics Education · Physics 2020-06-30 Alexandru Maries , Shih-Yin Lin , Chandralekha Singh

Physics in schools is distinctly different from, and struggles to capture the excitement of, university research-level work. Initiatives where students engage in independent research linked to cutting-edge physics within their school over…

Physics Education · Physics 2021-04-12 Martin O. Archer , Jennifer DeWitt , Charlotte Thorley , Olivia Keenan

Judging the significance and reproducibility of quantitative research requires a good understanding of relevant uncertainties, but it is often unclear how well these have been evaluated and what they imply. Reported scientific uncertainties…

Applications · Statistics 2017-01-20 David C. Bailey

In quantum theory, it is known for a pair of noncommutative observables that there is no state on which they take simultaneously definite values, and that there is no joint measurement of them. They are called preparation uncertainty and…

Quantum Physics · Physics 2020-09-02 Ryo Takakura , Takayuki Miyadera

Investigations related to expertise in problem solving and ability to transfer learning from one context to another are important for developing strategies to help students perform more expert-like tasks. Here we analyze written responses…

Physics Education · Physics 2016-02-23 Chandralekha Singh

Developing expertise in physics requires appropriate integration and assimilation of physics and mathematics. Instructors and students often describe physics courses in terms of their emphasis on conceptual and quantitative problem-solving.…

Physics Education · Physics 2025-12-11 Apekshya Ghimire , Chandralekha Singh

Measuring uncertainties in the output of a deep learning method is useful in several ways, such as in assisting with interpretation of the outputs, helping build confidence with end users, and for improving the training and performance of…

Machine Learning · Computer Science 2022-09-20 Luke Whitbread , Mark Jenkinson

Over the last five years, the McGill University Office of Science Education (OSE) has partnered with faculty members from the Department of Physics to form an education research group with the aim of charting the progression of student…

Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes with more evidence. While existing work focuses on using the variance of the Bayesian posterior due to parameter uncertainty as a measure of epistemic…

Development of conceptual multiple-choice tests related to a particular physics topic is important for designing research-based learning tools to reduce the difficulties. We explore the difficulties that the advanced undergraduate and…

Physics Education · Physics 2016-02-22 Chandralekha Singh , Guangtian Zhu

Uncertainty relation is not only of fundamental importance to quantum mechanics, but also crucial to the quantum information technology. Recently, majorization formulation of uncertainty relations (MURs) have been widely studied, ranging…

Product shape is one of the factors that trigger preference decisions of customers. Congruity of shape elements and deformation of shape from the prototype are two factors that are found to influence aesthetic response, hence preference. We…

Computer Vision and Pattern Recognition · Computer Science 2017-09-12 Asli Genctav , Sibel Tari

We set up a model for reasoning about metric spaces with belief theoretic measures. The uncertainty in these spaces stems from both probability and metric. To represent both aspect of uncertainty, we choose an expected distance function as…

Artificial Intelligence · Computer Science 2012-07-02 Seunghwan Lee