Scientific literacy has many meanings: it can be thought of as foundational knowledge, foundational critical thinking skills, or the application of these two foundations to everyday decision making. Here, we examine the far transfer scenario: do increases in science education lead to everyday decision-making becoming more consistent with consensus scientific knowledge? We report on a large sample of employees of a mixed urban/rural county representing a diverse range of careers, who completed an anonymous survey about their environmental conservation actions at home, as well as their general education level and their science coursework. Across broad and narrow measures of science education, we find little impact on action. Possible causes of this failure of transfer and the implications for changes in science instruction are discussed.

Overall interest in science has been argued to drive learner participation and engagement. However, there are other important aspects of interest such as breadth of interest within a science domain (e.g., biology, earth science). We demonstrate that intensity of science interest is separable from topic breadth using surveys from a sample of 600 middle school students. We also show that these two dimensions contribute differently to learning-relevant behavioral tendencies. Specially, regression analyses show: (1) that intensity of interest predicts both self-reported science classroom engagement and preferences to participate in optional science learning; and (2) that breadth of interest predicts science choice preference, but not science classroom engagement. These findings have implications for the conceptualization of interest, the measurement of interest, and practical applications for educators.

Due to substantial scientific and practical progress, learning technologies can effectively adapt to the characteristics and needs of students. This article considers how learning technologies can adapt over time by crowdsourcing contributions from teachers and students - explanations, feedback, and other pedagogical interactions. Considering the context of ASSISTments, an online learning platform, we explain how interactive mathematics exercises can provide the workflow necessary for eliciting feedback contributions and evaluating those contributions, by simply tapping into the everyday system usage of teachers and students. We discuss a series of randomized controlled experiments that are currently running within ASSISTments, with the goal of establishing proof of concept that students and teachers can serve as valuable resources for the perpetual improvement of adaptive learning technologies. We also consider how teachers and students can be motivated to provide such contributions, and discuss the plans surrounding PeerASSIST, an infrastructure that will help ASSISTments to harness the power of the crowd. Algorithms from machine learning (i.e.; multi-armed bandits) will ideally provide a mechanism for managerial control, allowing for the automatic evaluation of contributions and the personalized provision of the highest quality content. In many ways, the next 25 years of adaptive learning technologies will be driven by the crowd, and this article serves as the road map that ASSISTments has chosen to follow. © 2016 International Artificial Intelligence in Education Society.

This study assessed the impact of flipped instruction on students' out-of-class study time, exam performance, preference, motivation, and perceptions in two sections of a large undergraduate chemistry course. Flipped instruction caused a shift in student workload without appreciably changing the overall study time. The treatment impact on student performance gradually diminished over time, showing a small but statistically significant effect with the final exam. No marked interaction was identified, indicating that flipped instruction benefited students of diverse backgrounds uniformly. Students in the flipped section showed mixed feelings with about one fifth of them displaying polarized attitudes. Open-ended student survey responses revealed non-compliance with pre-class studying as a serious implementation issue: By slowing down the overall pace of the class, it negatively affected students with different study behaviors and characteristics in ways that partly explained the small, diminishing treatment effect and absence of marked interaction. (C) 2016 Elsevier Ltd. All rights reserved.

Learners encounter science in a wide variety of contexts beyond the science classroom which collectively could be quite influential on student attitudes and abilities. But relatively little is known about the relative influence of different forms of informal science experiences, especially for the kinds of experiences that students typically access. We conduct factor and regression analyses on data collected from a large number of diverse public-school attending 6th and 8th graders drawn from two regions in the USA. Students completed a science reasoning measure and surveys of attitudes, previously completed informal science learning experiences, and demographic factors. Factor analyses identify four dimensions of informal science learning participation (in home, semiformal, nature, and museums). Regression analyses find a relative specificity of effects, with particular outcomes associated with a subset of the forms of informal science participation, highlighting the importance of controlling for correlated factors. There were also a few differences by grade level, with different experiences influencing the development of competency beliefs in science in early vs. late middle schoolers.

Over the past two decades, cultural institutions such as museums are beginning to develop their capacity for engaging in long-term research on teaching and learning (Rennie et al. 2003; see also Crowley 2014). In this article, we describe one museum’s efforts to develop an educational research agenda in relationship to these broader efforts. We explain how we got started; share steps taken; describe the agenda itself; and give examples of some of our current research studies. We end with insights into some of the challenges we’ve faced in developing this work and how we’ve addressed them and our next steps. © 2016 Wiley Periodicals, Inc.

Problem-solving strategies that physics undergraduates learn should prepare them for real-world contexts as they transition from novices to experts. Yet, graduate students in physics-intensive research face problems that go beyond problem sets they experienced as undergraduates and are solved by different strategies than are typically emphasized in undergraduate coursework. We conducted semi-structured interviews with ten graduate students to determine problem-solving strategies they found useful in their research. We coded these interviews using emergent and grounded theory approaches. Our findings explore problem-solving strategies (e.g., planning ahead, breaking down problems, evaluating options), contexts (e.g., designing software and troubleshooting equipment), and characteristics of successful problem-solvers (e.g., initiative, persistence, and motivation). Graduate students also relied on problem representations such as test cases, approximations, and simulations in their problem-solving process. Understanding problem-solving strategies, contexts, and characteristics has implications for how we approach problem-solving in undergraduate physics and physics education research.

Problem-solving in the undergraduate curriculum typically occurs in content-focused courses that emphasize applying a conceptual and mathematical understanding of key physics principles to given situations. This project expands the notion of problem-solving by characterizing the breadth of problem-solving activities carried out by graduate students in physics-intensive research. In 10 in-depth interviews, PhD students were asked to describe routine, difficult, and important problems they engage in. A grounded theory analysis resulted in a framework with three dimensions: problem context (e.g., experiments, software, or math), activity (e.g., design or troubleshooting), and feature that made the problem hard (e.g., complexity or insufficient resources). Problem contexts usually extended beyond theory and mathematics (e.g., experiments, data analysis, and computation). Important problem contexts blended soft and technical skills (e.g., communication and collaboration). Routine problem activities tended to be well-defined (e.g., troubleshooting) while important ones were more open-ended and had multiple solution paths (e.g., evaluating options). The results can inform curriculum development and PER with an expanded view of problem-solving.

An interactive demonstration on how to design and implement randomized controlled experiments at scale within the ASSISTments TestBed, a new collaborative for educational research funded by the National Science Foundation (NSF). The Assessment of Learning infrastructure (ALI), a unique data retrieval and analysis tool, is also demonstrated.

In this paper, we present a dataset consisting of data generated from 22 previously and currently running randomized controlled experiments inside the ASSIStments online learning platform. This dataset provides data mining opportunities for researchers to analyze ASSISTments data in a convenient format across multiple experiments at the same time. The data preprocessing steps are explained in detail to inform researchers about how this dataset was generated. A list of column descriptions is provided to define the columns in the dataset and a set of summary statistics are presented to briefly describe the dataset.