Global perspectives of the impact of the COVID-19 pandemic on learning science in higher education.

The COVID-19 pandemic required higher education institutions to rapidly transition to Emergency Remote Instruction (ERI) with little preparation. Discussions are now underway globally to learn the lessons of COVID-19 and to use this knowledge to shape the future of learning science in higher education. In this study, we examined the experiences of instructors and students to ERI in three universities across three continents-America, Europe, and Australia. We measured the instructional strategies used by instructors including assessment types, and interaction opportunities during and outside class schedules. We also measured the learning challenges experienced by students including planning, distractions, technology, learning resources, their views on educational quality and what characterized quality interactions during ERI. Our findings suggest that most instructional strategies used by instructors changed little during ERI, although the nature of instructor and student interactions during class relied more heavily on technology. Students reported significant learning challenges which included distractions from their physical and social media environments and access to technology. Both instructors and students reported that interactions with each other and their peers were concerningly low, albeit similar to pre COVID-19 pandemic levels. There were differences in the perceptions of instructors and students on whether instructor-student interactions were better or worse online. Common among all universities, there was a large proportion of students reporting mental health and work-related stress. Lessons to be learned from the COVID-19 pandemic include ensuring more support for instructors to implement effective and equitable pedagogies and an increased recognition of the importance of practicals, and the social, interactive and hands-on aspects of learning science in higher education. We predict that the incorporation of active learning pedagogies and strategies which increase student engagement and foster a sense of belonging will be ongoing global challenges for learning science in a post COVID-19 campus.

A Detailed Characterization of the Expert Problem-Solving Process in Science and Engineering: Guidance for Teaching and Assessment.

A primary goal of science and engineering (S&E) education is to produce good problem solvers, but how to best teach and measure the quality of problem solving remains unclear. The process is complex, multifaceted, and not fully characterized. Here, we present a detailed characterization of the S&E problem-solving process as a set of specific interlinked decisions. This framework of decisions is empirically grounded and describes the entire process. To develop this, we interviewed 52 successful scientists and engineers ("experts") spanning different disciplines, including biology and medicine. They described how they solved a typical but important problem in their work, and we analyzed the interviews in terms of decisions made. Surprisingly, we found that across all experts and fields, the solution process was framed around making a set of just 29 specific decisions. We also found that the process of making those discipline-general decisions (selecting between alternative actions) relied heavily on domain-specific predictive models that embodied the relevant disciplinary knowledge. This set of decisions provides a guide for the detailed measurement and teaching of S&E problem solving. This decision framework also provides a more specific, complete, and empirically based description of the "practices" of science.

Mixed results from a multiple regression analysis of supplemental instruction courses in introductory physics.

Providing less prepared students with supplemental instruction (SI) in introductory STEM courses has long been used as a model in math, chemistry, and biology education to improve student performance, but this model has received little attention in physics education research. We analyzed the course performance of students enrolled in SI courses for introductory mechanics and electricity and magnetism (E&M) at Stanford University compared with those not enrolled in the SI courses over a two-year period. We calculated the benefit of the SI course using multiple linear regression to control for students' level of high school physics and math preparation. We found that the SI course had a significant positive effect on student performance in E&M, but that an SI course with a nearly identical format had no effect on student performance in mechanics. We explored several different potential explanations for why this might be the case and were unable to find any that could explain this difference. This suggests that there are complexities in the design of SI courses that are not fully understood or captured by existing theories as to how they work.

What factors impact student performance in introductory physics?

In a previous study, we found that students' incoming preparation in physics-crudely measured by concept inventory prescores and math SAT or ACT scores-explains 34% of the variation in Physics 1 final exam scores at Stanford University. In this study, we sought to understand the large variation in exam scores not explained by these measures of incoming preparation. Why are some students' successful in physics 1 independent of their preparation? To answer this question, we interviewed 34 students with particularly low concept inventory prescores and math SAT/ACT scores about their experiences in the course. We unexpectedly found a set of common practices and attitudes. We found that students' use of instructional resources had relatively little impact on course performance, while student characteristics, student attitudes, and students' interactions outside the classroom all had a more substantial impact on course performance. These results offer some guidance as to how instructors might help all students succeed in introductory physics courses.

Exploring bias in mechanical engineering students' perceptions of classmates.

Gender disparity in science, technology, engineering, and math (STEM) fields is an on-going challenge. Gender bias is one of the possible mechanisms leading to such disparities and has been extensively studied. Previous work showed that there was a gender bias in how students perceived the competence of their peers in undergraduate biology courses. We examined whether there was a similar gender bias in a mechanical engineering course. We conducted the study in two offerings of the course, which used different instructional practices. We found no gender bias in peer perceptions of competence in either of the offerings. However, we did see that the offerings' different instructional practices affected aspects of classroom climate, including: the number of peers who were perceived to be particularly knowledgeable, the richness of the associated network of connections between students, students' familiarity with each other, and their perceptions about the course environment. These results suggest that negative bias against female students in peer perception is not universal, either across institutions or across STEM fields, and that instructional methods may have an impact on classroom climate.

Enhancing Diversity in Undergraduate Science: Self-Efficacy Drives Performance Gains with Active Learning.

Efforts to retain underrepresented minority (URM) students in science, technology, engineering, and mathematics (STEM) have shown only limited success in higher education, due in part to a persistent achievement gap between students from historically underrepresented and well-represented backgrounds. To test the hypothesis that active learning disproportionately benefits URM students, we quantified the effects of traditional versus active learning on student academic performance, science self-efficacy, and sense of social belonging in a large (more than 250 students) introductory STEM course. A transition to active learning closed the gap in learning gains between non-URM and URM students and led to an increase in science self-efficacy for all students. Sense of social belonging also increased significantly with active learning, but only for non-URM students. Through structural equation modeling, we demonstrate that, for URM students, the increase in self-efficacy mediated the positive effect of active-learning pedagogy on two metrics of student performance. Our results add to a growing body of research that supports varied and inclusive teaching as one pathway to a diversified STEM workforce.

Concepts first, jargon second improves student articulation of understanding.

In this experiment, students in a large undergraduate biology course were first exposed to the concepts without new technical vocabulary ("jargon") in a pre-class reading assignment. Their learning of the concepts and jargon was compared with that of an equivalent group of students in another section of the same course, whose pre-class reading presented both the jargon and concepts together in the traditional manner. Both groups had the same active-learning classes with the same instructor, and then completed the same post-test. Although the two groups performed the same on the multiple choice questions of the post-test, the group exposed to concepts first and jargon second included 1.5 times and 2.5 times more correct arguments on two free-response questions about the concepts. The correct use of jargon between the two groups was similar, with the exception of one jargon term that the control group used more often. These results suggest that modest instructional changes whereby new concepts are introduced in a concepts-first, jargon-second manner can increase student learning, as demonstrated by their ability to articulate their understanding of new concepts.

Teaching critical thinking.

The ability to make decisions based on data, with its inherent uncertainties and variability, is a complex and vital skill in the modern world. The need for such quantitative critical thinking occurs in many different contexts, and although it is an important goal of education, that goal is seldom being achieved. We argue that the key element for developing this ability is repeated practice in making decisions based on data, with feedback on those decisions. We demonstrate a structure for providing suitable practice that can be applied in any instructional setting that involves the acquisition of data and relating that data to scientific models. This study reports the results of applying that structure in an introductory physics laboratory course. Students in an experimental condition were repeatedly instructed to make and act on quantitative comparisons between datasets, and between data and models, an approach that is common to all science disciplines. These instructions were slowly faded across the course. After the instructions had been removed, students in the experimental condition were 12 times more likely to spontaneously propose or make changes to improve their experimental methods than a control group, who performed traditional experimental activities. The students in the experimental condition were also four times more likely to identify and explain a limitation of a physical model using their data. Students in the experimental condition also showed much more sophisticated reasoning about their data. These differences between the groups were seen to persist into a subsequent course taken the following year.

The teaching practices inventory: a new tool for characterizing college and university teaching in mathematics and science.

We have created an inventory to characterize the teaching practices used in science and mathematics courses. This inventory can aid instructors and departments in reflecting on their teaching. It has been tested with several hundred university instructors and courses from mathematics and four science disciplines. Most instructors complete the inventory in 10 min or less, and the results allow meaningful comparisons of the teaching used for the different courses and instructors within a department and across different departments. We also show how the inventory results can be used to gauge the extent of use of research-based teaching practices, and we illustrate this with the inventory results for five departments. These results show the high degree of discrimination provided by the inventory, as well as its effectiveness in tracking the increase in the use of research-based teaching practices.

The Classroom Observation Protocol for Undergraduate STEM (COPUS): a new instrument to characterize university STEM classroom practices.

Instructors and the teaching practices they employ play a critical role in improving student learning in college science, technology, engineering, and mathematics (STEM) courses. Consequently, there is increasing interest in collecting information on the range and frequency of teaching practices at department-wide and institution-wide scales. To help facilitate this process, we present a new classroom observation protocol known as the Classroom Observation Protocol for Undergraduate STEM or COPUS. This protocol allows STEM faculty, after a short 1.5-hour training period, to reliably characterize how faculty and students are spending their time in the classroom. We present the protocol, discuss how it differs from existing classroom observation protocols, and describe the process by which it was developed and validated. We also discuss how the observation data can be used to guide individual and institutional change.

Improved learning in a large-enrollment physics class.

We compared the amounts of learning achieved using two different instructional approaches under controlled conditions. We measured the learning of a specific set of topics and objectives when taught by 3 hours of traditional lecture given by an experienced highly rated instructor and 3 hours of instruction given by a trained but inexperienced instructor using instruction based on research in cognitive psychology and physics education. The comparison was made between two large sections (N = 267 and N = 271) of an introductory undergraduate physics course. We found increased student attendance, higher engagement, and more than twice the learning in the section taught using research-based instruction.

Formation of bright matter-wave solitons during the collapse of attractive Bose-Einstein condensates.

We observe bright matter-wave solitons form during the collapse of (85)Rb condensates in a three-dimensional (3D) magnetic trap. The collapse is induced by using a Feshbach resonance to suddenly switch the atomic interactions from repulsive to attractive. Remnant condensates containing several times the critical number of atoms for the onset of instability are observed to survive the collapse. Under these conditions a highly robust configuration of 3D solitons forms such that each soliton satisfies the condition for stability and neighboring solitons exhibit repulsive interactions.

Bose-Einstein condensation in a dilute gas: the first 70 years and some recent experiments (Nobel Lecture).

Bose-Einstein condensates of dilute gases offer a rich field to study fundamental quantum-mechanical processes, manipulation of the speed at which light propogates, observation of atomic pair-formation and superfluidity, or even simulating white dwarf stars. Still more radical applications are on the horizon. However, their initial creation was a masterpiece of experimental physics. After an initial process of laser cooling (which itself won its developers the 1997 Nobel Prize), atoms in a magnetic-optical trap must be safely transferred into a purely magnetic trap, where the condensation process begins at 170 nK and 20 nK a pure condensate of 2000 atoms could be created. More astonishingly, Wieman and Cornell showed these low temperatures could be achieved in "bench scale" equipment rather than the massive pieces normally demanded by cryoscience. For their 1995 discovery of this new state of matter, they were awarded the 2001 Nobel Prize in Physics.

Atom-molecule coherence in a Bose-Einstein condensate.

Recent advances in the precise control of ultracold atomic systems have led to the realisation of Bose Einstein condensates (BECs) and degenerate Fermi gases. An important challenge is to extend this level of control to more complicated molecular systems. One route for producing ultracold molecules is to form them from the atoms in a BEC. For example, a two-photon stimulated Raman transition in a (87)Rb BEC has been used to produce (87)Rb(2) molecules in a single rotational-vibrational state, and ultracold molecules have also been formed through photoassociation of a sodium BEC. Although the coherence properties of such systems have not hitherto been probed, the prospect of creating a superposition of atomic and molecular condensates has initiated much theoretical work. Here we make use of a time-varying magnetic field near a Feshbach resonance to produce coherent coupling between atoms and molecules in a (85)Rb BEC. A mixture of atomic and molecular states is created and probed by sudden changes in the magnetic field, which lead to oscillations in the number of atoms that remain in the condensate. The oscillation frequency, measured over a large range of magnetic fields, is in excellent agreement with the theoretical molecular binding energy, indicating that we have created a quantum superposition of atoms and diatomic molecules two chemically different species.