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Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include \"computational thinking\" as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new urgency has come to the challenge of defining computational thinking and providing a theoretical grounding for what form it should take in school science and mathematics classrooms. This paper presents a response to this challenge by proposing a definition of computational thinking for mathematics and science in the form of a taxonomy consisting of four main categories: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices. In formulating this taxonomy, we draw on the existing computational thinking literature, interviews with mathematicians and scientists, and exemplary computational thinking instructional materials. This work was undertaken as part of a larger effort to infuse computational thinking into high school science and mathematics auricular materials. In this paper, we argue for the approach of embedding computational thinking in mathematics and science contexts, present the taxonomy, and discuss how we envision the taxonomy being used to bring current educational efforts in line with the increasingly computational nature of modern science and mathematics.

This transformed DNA-extraction lab activity offers a framework that strategically draws upon the essential elements of both scientific and effective teaching practices to establish an alternative approach to the teaching and learning of science. The pedagogical methods utilized throughout this activity encourage students' motivation, engagement, and learning through inquirybased, teacher-facilitated scientific practices. Additionally, this activity emphasizes Dimension 1 of the Framework for K-12 Science Education (Scientific and Engineering Practices; National Research Council, 2012). In the activity, students worked in groups and were allowed to examine different traditional lab protocols and other resources. The students had the freedom of selecting an independent variable that could possibly have an effect on the DNA extraction. To demonstrate how this activity was implemented in the classroom, a running vignette of a DNA-extraction activity in a high school biology class, in which the teacher adhered to the elements of this framework, is included.

This paper provides a rationale for the changes advocated by the Framework for K-12 Science Education and the Next Generation Science Standards. It provides an argument for why the model embedded in the Next Generation Science Standards is seen as an improvement. The Case made here is that the underlying model that the new Framework presents of science better represents contemporary understanding of nature of science as a social and cultural practice. Second, it argues that the adopting a framework of practices will enable better communication of meaning amongst professional science educators. This, in turn, will enable practice in the classroom to improve. Finally, the implications for teacher education are explored.

Effective mentoring is a key component of academic and career success that contributes to overall measures of productivity. Mentoring relationships also play an important role in mental health and in recruiting and retaining students from groups underrepresented in STEM fields. Despite these clear and measurable benefits, faculty generally do not receive mentorship training, and feedback mechanisms and assessment to improve mentoring in academia are limited. Ineffective mentoring can negatively impact students, faculty, departments, and institutions via decreased productivity, increased stress, and the loss of valuable research products and talented personnel. Thus, there are clear incentives to invest in and implement formal training to improve mentorship in STEM fields. Here, we outline the unique challenges of mentoring in academia and present results from a survey of STEM scientists that support both the need and desire for more formal mentorship training. Using survey results and the primary literature, we identify common behaviors of effective mentors and outline a set of mentorship best practices. We argue that these best practices, as well as the key qualities of flexibility, communication, and trust, are skills that can be taught to prospective and current faculty. We present a model and resources for mentorship training based on our research, which we successfully implemented at the University of Colorado, Boulder, with graduate students and postdocs. We conclude that such training is an important and cost‐effective step toward improving mentorship in STEM fields. Effective mentoring is a key component to success in STEM fields and plays an important role in mental health and in retaining diverse students, yet scientists generally do not receive training in mentorship skills. We outline the unique challenges of mentoring in academia, present results from a survey of STEM scientists that supports the need for more mentorship training, identify common qualities of effective mentors, and outline a set of mentorship best practices. Finally, we provide a model and resources for mentorship training that we have successfully implemented.

A lack of interpretive power (i.e., the ability to understand individuals’ experiences and behaviors in relation to their cultural contexts) undermines psychology’s understanding of diverse psychological phenomena. Building interpretive power requires attending to cultural influences in research. We describe three characteristics of research that lacks interpretive power: normalizing and overgeneralizing from behaviors and processes of people in Western, educated, industrialized, rich, and democratic (WEIRD) contexts; making non-WEIRD people and processes invisible; and misapplying WEIRD findings in non-WEIRD contexts. We also describe research in which leveraging interpretive power prevented these negative consequences. Finally, using the culture-cycle framework, we outline a vision for creating culture change within psychology by implementing culture-conscious practices to guide the formation of research questions, empirical design, and data analysis and interpretation.

The intervention study presented in this paper explored pre-service science teachers' (PSSTs) understanding of scientific inquiry (SI) and scientific practices (SPs) during a laboratory application in science education course. Thirty-nine secondary school PSSTs, who study in the Science Education Department in a public university in Turkey, enrolled in a 14-week-long course and volunteered to participate in the study. The participants were exposed to a method is called \"the 4-phase implementation\" that includes laboratory-based inquiry activities addressing SI and SPs and they completed microteaching presentations. Their understanding of SI and SPs was examined through the course period. The main data sources included \"Views about Scientific Inquiry (VASI) Instrument\" and concept maps were used to track the changes in these understandings. The findings indicated that PSSTs had inadequate understanding of inquiry on some aspects even after the treatment. Yet, the method had positive impact in PSSTs' understanding inquiry especially in terms of facilitating the comprehension that scientific investigations begin with questions, there is no single method in investigations, and explanations are derived from collected data. The concept maps created by some of the participants also supported these results and revealed a more coherent and holistic understanding of SPs by integrating both epistemic and social components into their maps. However, PSSTs did not seem to have totally understood other aspects of inquiry including the inquiry procedures, the research conclusions, and the difference between data and evidence. Further implications are critically discussed in terms of designing future laboratory applications for science education courses.

Assessing students’ participation in science practices presents several challenges, especially when aiming to differentiate meaningful (vs. rote) forms of participation. In this study, we sought to use machine learning (ML) for a novel purpose in science assessment: developing a construct map for students’ consideration of generality, a key epistemic understanding that undergirds meaningful participation in knowledge-building practices. We report on our efforts to assess the nature of 845 students’ ideas about the generality of their model-based explanations through the combination of an embedded written assessment and a novel data analytic approach that combines unsupervised and supervised machine learning methods and human-driven, interpretive coding. We demonstrate how unsupervised machine learning methods, when coupled with qualitative, interpretive coding, were used to revise our construct map for generality in a way that allowed for a more nuanced evaluation that was closely tied to empirical patterns in the data. We also explored the application of the construct map as a framework for coding used as a part of supervised machine learning methods, finding that it demonstrates some viability for use in future analyses. We discuss implications for the assessment of students’ meaningful participation in science practices in terms of their considerations of generality, the role of unsupervised methods in science assessment, and combining machine learning and human-driven approach for understanding students’complex involvement in science practices.

•Good scientific practice (GSP) describes recommended methods and procedures.•GSP minimizes errors and biases, and facilitates collaboration and reproducibility.•We outline current and developing MEEG GSP insights reflecting the state of the art.•This overview includes resources, tools, and thoughts to support GSP in MEEG research.•In all parts of the research cycle, we identify an increased tendency to collaborate. Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.

In the past decade, a growing awareness of citizen science, and the potential of school participation in citizen science (SPICES) has developed. At the heart of any SPICES endeavor lies a partnership representing an unconventional blend of ideas, practices, and agendas, grounded in the realms of both educational practice and scientific research. This paper serves as an introduction to the special issue, substantiating SPICES as a field of research, with conceptualization and exploration of opportunities and tensions as main components. We provide an initial roadmap for future research in the field, with four research trajectories: (a) the notion of mutualism, (b) cognitive challenges that students often face, (c) scientific practices that are uniquely afforded in this field, and how students may be supported in developing them, and (d) emerging design guidelines that can help bridge cultural, epistemic, and organizational gaps within SPICES partnerships. By showing how the six empirical papers that make up this special issue exemplify these trajectories, we claim that SPICES fulfills its literal meaning of a small-in-portion, but significant ingredient within educational systems. SPICES holds the promise of making the grand difference in what schooling can offer students, teachers, communities and society.

One of the critical issues in the philosophy of science is to understand scientific knowledge. This paper proposes a novel approach to the study of reflection on science, called “cognitive metascience”. In particular, it offers a new understanding of scientific knowledge as constituted by various kinds of scientific representations, framed as cognitive artifacts. It introduces a novel functional taxonomy of cognitive artifacts prevalent in scientific practice, covering a huge diversity of their formats, vehicles, and functions. As a consequence, toolboxes, conceptual frameworks, theories, models, and individual hypotheses can be understood as artifacts supporting our cognitive performance. It is also shown that by empirically studying how artifacts function, we may discover hitherto undiscussed virtues and vices of these scientific representations. This paper relies on the use of language technology to analyze scientific discourse empirically, which allows us to uncover the metascientific views of researchers. This, in turn, can become part of normative considerations concerning virtues and vices of cognitive artifacts.