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In recent years, much of the emphasis for transformation of introductory STEM courses has focused on "active learning", and while this approach has been shown to produce more equitable outcomes for students, the construct of "active learning" is somewhat ill-defined and is often used as a "catch-all" that can encompass a wide range of pedagogical techniques. Here we present an alternative approach for how to think about the transformation of STEM courses that focuses instead on what students should know and what they can do with that knowledge. This approach, known as three-dimensional learning (3DL), emerged from the National Academy's "A Framework for K-12 Science Education", which describes a vision for science education that centers the role of constructing productive causal accounts for phenomena. Over the past 10 years, we have collected data from introductory biology, chemistry, and physics courses to assess the impact of such a transformation on higher education courses. Here we report on an analysis of video data of class sessions that allows us to characterize these sessions as active, 3D, neither, or both 3D and active. We find that 3D classes are likely to also involve student engagement (i.e. be active), but the reverse is not necessarily true. That is, focusing on transformations involving 3DL also tends to increase student engagement, whereas focusing solely on student engagement might result in courses where students are engaged in activities that do not involve meaningful engagement with core ideas of the discipline. [ABSTRACT FROM AUTHOR]

We argue that students should take on roles as epistemic agents—those who shape knowledge production and practices of a community. In this study, the research team—a science educator and two scientists—worked with a sixth‐grade teacher to provide 90 students with opportunities to take up epistemic agency over a 22‐day unit about moth ecology. We used cultural historical activity theory (CHAT) to analyze the co‐configuration of activity. During planning, tensions arose among the research team around how and why to position students as epistemic agents, while still attending to meaningful science questions. During instruction, students prompted the constant negotiation of epistemic roles and practices as they acted on shifting agentic participation structures. [ABSTRACT FROM AUTHOR]

The need to integrate computation into the physics curriculum has long been established: using simulations and computational modeling can enhance students' conceptual understanding, and the computational skills students acquire are both useful and necessary in their careers. However, making changes to an established physics course is a challenge on many fronts: instructors need to be comfortable with their own computational skills, they need time to find and adapt appropriate materials, and they may have questions about how to integrate computation into their course(s). The Partnership for Integration of Computation into Undergraduate Physics (PICUP) was organized to identify the barriers underlying these challenges, to document the current state of computation use in physics courses, and to explore avenues to reach and engage physics teachers. [ABSTRACT FROM AUTHOR]

Preliminary results from the revamping of Michigan State University's physics graduate admissions process suggest that the changes have made the procedure fairer for all. [ABSTRACT FROM AUTHOR]

Although much of the research done by modern physicists would be impossible without the use of computers, today's physics curricula do not generally reflect their importance and utility. To more tightly connect undergraduate preparation with professional practice, we integrated computational instruction into middle-division classical mechanics at the University of Colorado Boulder. Our model for integration works with the constraints of faculty who do not specialize in computation by placing a strong emphasis on an adaptable curriculum. It also includes the construction of learning goals and the design of activities consistent with those goals, including assessment of student computational fluency. We present critiques of our model as we work to develop an effective and sustainable model for computational instruction in the undergraduate curriculum. [ABSTRACT FROM AUTHOR]

The article discusses aspects related to modeling instruction curriculum used to introduce high school physics students to computational problem-solving and numerical computation. Topics include the U.S. National Research Council's (NRC) framework for K-12 science standards, benefits of using simulation model programs by students and importance of numerical computation in physics modeling. The VPython modeling software and a modeling activity used in ninth-grade physics course are discussed.

The performance of over 5000 students in introductory calculus-based mechanics courses at the Georgia Institute of Technology was assessed using the Force Concept Inventory (FCI). Results from two different curricula were compared: a traditional mechanics curriculum and the Matter & Interactions (M&I) curriculum. Both were taught with similar interactive pedagogy. Post-instruction FCI averages were significantly higher for the traditional curriculum than for the M&I curriculum; the differences between curricula persist after accounting for factors such as preinstruction FCI scores, grade point averages, and SAT scores. FCI performance on categories of items organized by concepts was also compared; traditional averages were significantly higher in each concept. We examined differences in student preparation between the curricula and found that the relative fraction of homework and lecture topics devoted to FCI force and motion concepts correlated with the observed performance differences. Concept inventories, as instruments for evaluating curricular reforms, are generally limited to the particular choice of content and goals of the instrument. Moreover, concept inventories fail to measure what are perhaps the most interesting aspects of reform: the non-overlapping content and goals that are not present in courses without reform. [ABSTRACT FROM AUTHOR]

The authors argue that science faculty should transform science, technology, engineering, and mathematics (STEM) education to enable student learning. Topics include the notions of core ideas and crosscutting concepts, evidence-based approaches to learning about scientific practices, and the document "A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas" by the U.S. National Research Council.

This study uses actor-oriented transfer perspective to investigate different ways in which students make connections across the domains of mathematics and computing. We interview first-year students at the University of Oslo as they work with a set of tutorials that we designed to integrate knowledge from both domains. The cases we present here demonstrate four different types of cross-domain connections: (a) mathematically reproducing the work of a computer program, (b) cyclically improving a program to produce better output, (c) coupling math to output to justify program improvements and (d) coupling math to code to justify program design. We provide rich examples of the ways in which students make these connections and discuss affordances for mathematical learning in this context. • Examples of students connecting mathematics and computing in four distinct ways. • Students can make mathematical models that represent computer programs. • Students can use math to turn interpretation of program output into revised code. • Students can couple math to output to justify program improvements. • Students can couple math to code to justify program design. [ABSTRACT FROM AUTHOR]

The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto's Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA). [ABSTRACT FROM AUTHOR]

The importance of improving STEM education is of perennial interest, and to this end, the education community needs ways to characterize transformation efforts. Three-dimensional learning (3DL) is one such approach to transformation, in which core ideas of the discipline, scientific practices, and crosscutting concepts are combined to support student development of disciplinary expertise. We have previously reported on an approach to the characterization of assessments, the Three-Dimensional Learning Assessment Protocol (3D-LAP), that can be used to identify whether assessments have the potential to engage students in 3DL. Here we present the development of a companion, the Three-Dimensional Learning Observation Protocol (3D-LOP), an observation protocol that can reliably distinguish between instruction that has potential for engagement with 3DL and instruction that does not. The 3D-LOP goes beyond other observation protocols, because it is intended not only to characterize the pedagogical approaches being used in the instructional environment, but also to identify whether students are being asked to engage with scientific practices, core ideas, and crosscutting concepts. We demonstrate herein that the 3D-LOP can be used reliably to code for the presence of 3DL; further, we present data that show the utility of the 3D-LOP in differentiating between instruction that has the potential to promote 3DL from instruction that does not. Our team plans to continue using this protocol to evaluate outcomes of instructional transformation projects. We also propose that the 3D-LOP can be used to support practitioners in developing curricular materials and selecting instructional strategies to promote engagement in three-dimensional instruction. [ABSTRACT FROM AUTHOR]

Many calls to improve science education in college and university settings have focused on improving instructor pedagogy. Meanwhile, science education at the K-12 level is undergoing significant changes as a result of the emphasis on scientific and engineering practices, crosscutting concepts, and disciplinary core ideas. This framework of “three-dimensional learning” is based on the literature about how people learn science and how we can help students put their knowledge to use. Recently, similar changes are underway in higher education by incorporating three-dimensional learning into college science courses. As these transformations move forward, it will become important to assess three-dimensional learning both to align assessments with the learning environment, and to assess the extent of the transformations. In this paper we introduce the Three-Dimensional Learning Assessment Protocol (3D-LAP), which is designed to characterize and support the development of assessment tasks in biology, chemistry, and physics that align with transformation efforts. We describe the development process used by our interdisciplinary team, discuss the validity and reliability of the protocol, and provide evidence that the protocol can distinguish between assessments that have the potential to elicit evidence of three-dimensional learning and those that do not. [ABSTRACT FROM AUTHOR]

As the frontiers of biology become increasingly interdisciplinary, the physics education community has engaged in ongoing efforts to make physics classes more relevant to life science majors. These efforts are complicated by the many apparent differences between these fields, including the types of systems that each studies, the behavior of those systems, the kinds of measurements that each makes, and the role of mathematics in each field. Nonetheless, physics and biology are both sciences that rely on observations and measurements to construct models of the natural world. In this article, we propose that efforts to bridge the teaching of these two disciplines must emphasize shared scientific practices, particularly scientific modeling. We define modeling using language common to both disciplines and highlight how an understanding of the modeling process can help reconcile apparent differences between the teaching of physics and biology. We elaborate on how models can be used for explanatory, predictive, and functional purposes and present common models from each discipline demonstrating key modeling principles. By framing interdisciplinary teaching in the context of modeling, we aim to bridge physics and biology teaching and to equip students with modeling competencies applicable in any scientific discipline. [ABSTRACT FROM AUTHOR]

This letter is a response to a recent article in this Journal (Reed, J. J.; Brandriet, A. R.; Holme, T. A. J. Chem. Educ. 2017, 94, 3-10) about characterization of the presence of scientific practices in assessment items. [ABSTRACT FROM AUTHOR]