The role of mathematics and self-efficacy in learning quantum mechanics

Peer reviewe

Futurising science education: students' experiences from a course on futures thinking and quantum computing

To promote students' value-based agency, responsible science and sustainability, science education must address how students think about their personal and collective futures. However, research has shown that young people find it difficult to fully relate to the future and its possibilities, and few studies have focused on the potential of science education to foster futures thinking and agency. We report on a project that further explored this potential by developing future-oriented science courses drawing on the field of futures studies. Phenomenographic analysis was used on interview data to see what changes upper-secondary school students saw in their futures perceptions and agentic orientations after attending a course which adapted futures thinking skills in the context of quantum computing and technological approaches to global problems. The results show students perceiving the future and technological development as more positive but also more unpredictable, seeing their possibilities for agency as clearer and more promising (especially by identifying with their peers or aspired career paths), and feeling a deeper connection to the otherwise vague idea of futures. Students also felt they had learned to question deterministic thinking and to think more creatively about their own lives as well as technological and non-technological solutions to global problems. Both quantum physics and futures thinking opened new perspectives on uncertainty and probabilistic thinking. Our results provide further validation for a future-oriented approach to science education, and highlight essential synergies between futures thinking skills, agency, and authentic socio-scientific issues in developing science education for the current age.Peer reviewe

Self-efficacy and conceptual knowledge in quantum mechanics during teaching reforms and the COVID-19 pandemic

Physics instruction is often unable to support students' self-efficacy. The remote teaching brought on by the COVID-19 pandemic has also affected learning. We surveyed an introductory quantum mechanics course for three years during a transition into the spin first approach, adapting the student-centered prime-time learning model and using it through the remote teaching during the pandemic. Prime-time learning includes weekly meetings where students and instructors discuss in a small group, and the assessment is based on exercises, group work, and self-assessment. We show that this teaching method improved students' self-efficacy. Students' conceptual knowledge post teaching remained high throughout the teaching reform, as measured by an abbreviated Quantum Mechanics Concept Assessment test. We also find that the prime-time model is remarkably stable during remote teaching: in contrast to many other studies, we did not see a decline in conceptual learning outcomes or self-efficacy in remote teaching during the COVID-19 pandemic.Peer reviewe

Characterising heavy-tailed networks using q-generalised entropy and q-adjacency kernels

Heavy-tailed networks, which have degree distributions characterised by slower than exponentially bounded tails, are common in many different situations. Some interesting cases, where heavy tails are characterised by inverse powers A in the range 1 <lambda <2, arise for associative knowledge networks, and semantic and linguistic networks. In these cases, the differences between the networks are often delicate, calling for robust methods to characterise the differences. Here, we introduce a method for comparing networks using a density matrix based on q-generalised adjacency matrix kernels. It is shown that comparison of networks can then be performed using the q-generalised Kullback-Leibler divergence. In addition, the q-generalised divergence can be interpreted as a q-generalised free energy, which enables the thermodynamic-like macroscopic description of the heavy-tailed networks. The viability of the q-generalised adjacency kernels and the thermodynamic-like description in characterisation of complex networks is demonstrated using a simulated set of networks, which are modular and heavy-tailed with a degree distribution of inverse power law in the range 1 <lambda <2. (C) 2020 Elsevier B.V. All rights reserved.Peer reviewe

How Do Physics Teacher Candidates Substantiate Their Knowledge? An Analytical Framework for Examining the Epistemic Dimensions of Content Knowledge in Higher Education

Supporting teacher candidates’ learning of coherent and well-ordered content knowledge is one of the most important educational aims in subject teacher education. To reach this aim, teacher educators need suitable tools to enhance the formation of such knowledge. In this article, we present an analytical framework to examine conceptual knowledge, meaning the ability to define the relevant concepts pertaining to a task; relational knowledge, i.e., the ability to consider interrelations between the concepts; and strategic knowledge, i.e., the ability to use the knowledge by providing (experimental or modeling) procedures, which build new knowledge. A sample analysis of 16 teacher candidates’ written reports is presented to illustrate how this framework can be used. The aim of the study was to reveal what kind of variation in teacher candidates’ content knowledge can be found. This study suggests that teacher candidates’ written reports can reveal remarkable differences in the epistemic dimensions of content knowledge. The framework shows the differences among the teacher candidates as well as produces information for teacher educators of the critical aspects, when and where to intervene, and where to focus using different teaching practices

How Do Physics Teacher Candidates Substantiate Their Knowledge? An Analytical Framework for Examining the Epistemic Dimensions of Content Knowledge in Higher Education

Supporting teacher candidates’ learning of coherent and well-ordered content knowledge is one of the most important educational aims in subject teacher education. To reach this aim, teacher educators need suitable tools to enhance the formation of such knowledge. In this article, we present an analytical framework to examine conceptual knowledge, meaning the ability to define the relevant concepts pertaining to a task; relational knowledge, i.e., the ability to consider interrelations between the concepts; and strategic knowledge, i.e., the ability to use the knowledge by providing (experimental or modeling) procedures, which build new knowledge. A sample analysis of 16 teacher candidates’ written reports is presented to illustrate how this framework can be used. The aim of the study was to reveal what kind of variation in teacher candidates’ content knowledge can be found. This study suggests that teacher candidates’ written reports can reveal remarkable differences in the epistemic dimensions of content knowledge. The framework shows the differences among the teacher candidates as well as produces information for teacher educators of the critical aspects, when and where to intervene, and where to focus using different teaching practices