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The learning sciences community is currently exploring new ways to enact productive and equitable co-design research-practice partnerships that are sensitive to all the concerns and needs of stakeholders. The paper contributes to that still-growing literature through an interaction analysis of a co-design discussion involving school district partners that unfolded about cultural relevance and sensitivity in relation to the use of a specific image in an elementary school coding lesson. The episode involved looking moment-by-moment at how district educators recognized and acknowledged that a specific design decision could be harmful for a minoritized population of students enrolled in the district. However, once a key change was made to be more culturally responsive and considerate, new and unexpected pedagogical challenges appeared. This case serves to illustrate some of the unexpected tensions that can appear in real-time when unanticipated questions about cultural relevance are foregrounded during lesson and materials co-design.

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

In combination with contextualized human interpretation, computational text analysis offers a quantitative approach to interrogating the nature of participation and social positioning in discourse. Using meeting transcript data from the development of a co-design research-practice partnership, we examine the roles and forms of participation that contribute to an effective collaboration between a multileveled school system and researcher partners. We apply computational methods to explore the language of co-design and multi-stakeholder perspectives in support of educational improvement science efforts and our theoretical understanding of partnership roles. Results indicate participation patterns align with documented roles in co- design partnerships and highlight the space dedicated to process reflection, context sharing, and logistical coordination.

Data-driven modeling is useful for reconstructing nonlinear dynamical systems when the true data generating mechanism is unknown or too expensive to compute. Having reliable uncertainty assessment of the forecast enables tools to be deployed to predict new scenarios that haven't been observed before. In this work, we derive internal uncertainty assessments from a few models for probabilistic forecasts. First, we extend the parallel partial Gaussian processes for predicting the one-step-ahead vector-valued transition function that links the observations between the current and next time points, and quantify the uncertainty of predictions by posterior sampling. Second, we show the equivalence between the dynamic mode decomposition and maximum likelihood estimator of a linear mapping matrix in a linear state space model. This connection provides data generating models of dynamic mode decomposition and thus, the uncertainty of the predictions can be obtained. Third, we draw close connections between data-driven models of nonlinear dynamical systems, such as proper orthogonal decomposition, dynamic mode decomposition and parallel partial Gaussian processes, through a unified view of data generating models. We study two numerical examples, where the inputs of the dynamics are assumed to be known in the first example and the inputs are unknown in the second example. The examples indicate that uncertainty of forecast can be properly quantified, whereas model or input misspecification can degrade the accuracy of uncertainty quantification.

In this exploratory case study, we investigate how a collegiate esports team makes sense of gameplay data visualizations. Through our intervention we introduced the team to new data collection practices, provided data analysis and visualization support, and organized sensemaking sessions with the team to discuss implications of the analytics. Through an exploratory analysis of video footage, we identified three different sensemaking activities.

This paper reports on a study of the dynamics of a Research-Practice Partnership (RPP) oriented around design, specifically the co-design model. The RPP is focused on supporting elementary school computer science (CS) instruction by involving paraprofessional educators and teachers in curricular co-design. A problem of practice addressed is that few elementary educators have backgrounds in teaching CS and have limited available instructional time and budget for CS. The co-design strategy entailed highlighting CS concepts in the mathematics curriculum during classroom instruction and designing computer lab lessons that explored related ideas through programming. Analyses focused on tensions within RPP interaction dynamics and how they were accommodated when RPP partners were designing for co-design activities, a critical component that leads to curricular co-design itself. We illustrate these tensions with examples of clusters of activity that appeared repeatedly among the research and practice team members when "designing for co-design".

Additional file 1. Supplementary figures.

Graph Neural Networks (GNNs) have greatly advanced the semi-supervised node classification task on graphs. The majority of existing GNNs are trained in an end-to-end manner that can be viewed as tackling a bi-level optimization problem. This process is often inefficient in computation and memory usage. In this work, we propose a new optimization framework for semi-supervised learning on graphs. The proposed framework can be conveniently solved by the alternating optimization algorithms, resulting in significantly improved efficiency. Extensive experiments demonstrate that the proposed method can achieve comparable or better performance with state-of-the-art baselines while it has significantly better computation and memory efficiency.

Supplemental Material, sj-pdf-2-ccx-10.1177_10732748211029726 for Health-Related Quality of Life in Asian Differentiated Thyroid Cancer Survivors by Wing-Lok Chan, Horace Cheuk-Wai Choi, Brian Lang, Kai-Pun Wong, Kwok-Keung Yuen, Ka-On Lam, Victor Ho-Fun Lee and Dora Kwong in Cancer Control

Supplemental Material, sj-pdf-2-ccx-10.1177_10732748211029726 for Health-Related Quality of Life in Asian Differentiated Thyroid Cancer Survivors by Wing-Lok Chan, Horace Cheuk-Wai Choi, Brian Lang, Kai-Pun Wong, Kwok-Keung Yuen, Ka-On Lam, Victor Ho-Fun Lee and Dora Kwong in Cancer Control

Supplemental Material, sj-pdf-1-ccx-10.1177_10732748211029726 for Health-Related Quality of Life in Asian Differentiated Thyroid Cancer Survivors by Wing-Lok Chan, Horace Cheuk-Wai Choi, Brian Lang, Kai-Pun Wong, Kwok-Keung Yuen, Ka-On Lam, Victor Ho-Fun Lee and Dora Kwong in Cancer Control

This thesis is focused on the simulation of photoemission for the design of new photocathode materials for Free electron laser (FEL) applications. In order to understand and develop new photocathode materials, a photoemission model that simulates the quantum efficiency (QE) and the mean transverse energy (MTE) of the photoemitted electrons from first principles simulations of the electronic structure of a material is developed in this work. The model presented in this work extends the previous models of photoemission to include an explicit model of surfaces, finite temperature and the effects of an external electric field. The photoemission model presented in this work is applied to simulations on different metallic surfaces and the results are compared to experimental measurements of MTE done by the ASTeC group at STFC with reasonable agreement. The computed QE is within the range of previously reported experimental values. Discrepancies between the computed and experimental workfunction observed in this work are studied from the perspective that a change in surface chemistry and composition can produce the observed change in workfunction. Open Access

We study the online maximum coverage problem on a line, in which, given an online sequence of sub-intervals (which may intersect among each other) of a target large interval and an integer $k$, we aim to select at most $k$ of the sub-intervals such that the total covered length of the target interval is maximized. The decision to accept or reject each sub-interval is made immediately and irrevocably (no preemption) right at the release timestamp of the sub-interval. We comprehensively study different settings of this problem regarding both the length of a released sub-interval and the total number of released sub-intervals. We first present lower bounds on the competitive ratio for the settings concerned in this paper, respectively. For the offline problem where the sequence of all the released sub-intervals is known in advance to the decision-maker, we propose a dynamic-programming-based optimal approach as the benchmark. For the online problem, we first propose a single-threshold-based deterministic algorithm SOA by adding a sub-interval if the added length exceeds a certain threshold, achieving competitive ratios close to the lower bounds, respectively. Then, we extend to a double-thresholds-based algorithm DOA, by using the first threshold for exploration and the second threshold (larger than the first one) for exploitation. With the two thresholds solved by our proposed program, we show that DOA improves SOA in the worst-case performance. Moreover, we prove that a deterministic algorithm that accepts sub-intervals by multi non-increasing thresholds cannot outperform even SOA., Comment: An extended abstract of this full version is to appear in COCOA 2020

Additional file 1. Measurement invariance across gender and age groups.

Additional file 1: Figure S1. IC50 values of palbociclib in different cancers as adapted from the Genomics of Drug Sensitivity in Cancer database [31] A. A mean Day-3 palbociclib IC50 of 35.7 μM was calculated from 770 cell lines corresponding to 32 types of cancer. The Day-3 IC50 of palbociclib was 49.2 μM in 26 head and neck cancer cell lines. B. Comparison of the Day-3 IC50 values of palbociclib for our NPC cell lines and those included in the databank. Figure S2. Quantification of protein expression in western blots of Fig. 1e. The grayscale value of each band in Fig. 1e was detected using MyImage software. The protein bands were normalized to the corresponding GAPDH level in each cell line and then compared with the normalized level in the control group. Figure S3. Cytostatic effect of palbociclib in three-dimensional cultures of NPC cell lines. Cells were allowed to form spheroids and treated with palbociclib at various doses. Three-dimensional spheroids of (A) C666–1 and (B) C17 NPC cells were grown in ultra-low attachment plates. The viability of spheroids was examined using the Cell Titer Glo assay. Dose–response curves were plotted for NPC spheroids after treatment with palbociclib for 3 and 5 days. Western blot analysis indicated the significant downregulation of RB (Ser780) phosphorylation and cyclin A expression in spheroids treated with palbociclib at concentrations greater than 0.2 μM. Figure S4. Expression of p16, RB, and cyclin D1 in NPC xenografts grown in mouse models. Although p16 was undetectable in all NPC models, RB, and cyclin D1 were detectable at various levels in all models. Figure S5. Comparison of the inhibitory effect of palbociclib on the growth of different xenografts in mice. The tumor growth curves (upper panel), final tumor volumes (lower left panel), and final tumor growth inhibition (lower right panel) of all xenografts in Fig. 2 are summarized to enable a comparison of drug efficacy. Figure S6. Enlarged IHC images of tissues as shown in Figure 2c. Figure S7. Analysis of Ki-67 expression cells in tumor sections. A. Whole-tumor sections were subjected to immunohistochemical analysis for Ki-67 and scanned using a Vectra Polaris imager. The images were imported into the Pheno-Chart 13 analysis program and divided into numerous 466 μm × 349 μm regions. B. Within each region, the software automatically compartmentalized each cell according to the nuclear stain. Blue spots represent the hematoxylin-stained nuclei, and brown spots represent the Ki-67–positive cells. C. After pooling all the data from each analyzed region, the percentage of Ki-67–positive cells was calculated as the number of positive DAB-stained cells / total number of tumor cells × 100%. Figure S8. Inhibition of NPC metastasis by palbociclib. Each NOD/SCID mouse was injected with 106 C666–1 cells via the tail vein, and treatment was initiated 10 days later. A. All control mice developed NPC metastases in the lungs; in contrast, only one of four mice in the palbociclib treatment group developed lung metastases. In addition, the metastatic nodules in the lungs of the control group were larger than those in the lungs of the affected treatment group mouse. B. Solid tumor nodules in the control group lung tissues are indicated by blue arrows. C. Hematoxylin-eosin (H&E) staining of representative tumors from each treatment group. The tumor nodules among the air sacs in the lungs are indicated by blue arrows. D. Higher magnification of the H&E-stained slides reveals the densely packed tumor cells next to the air sacs in the lung tissues of control group mice. Figure S9. Enlarged IHC images of tissues as shown in Fig. 4b. Figure S10. Suppression of metabolic activity in Xeno23 tumors after combined treatment with palbociclib and SAHA. Micro-PET/MRI scans of Xeno23-bearing mice reveal the suppressed metabolic activity in tumors treated with palbociclib + SAHA. The mice received vehicle or combined treatment for 17 days. Figure S11. Co-treatment with palbociclib and SAHA did not promote cell differentiation or apoptosis relative to palbociclib or SAHA monotherapy. NPC43 cells were treated with 15-μM palbociclib or 5-μM SAHA alone or in combination for 6 or 24 h. The levels of proteins related to cell cycle, differentiation, and apoptosis in the treated cells were examined by western blotting. Figure S12. Lack of prominent activation of the EBV lytic cycle in NPC cells subjected to palbociclib or SAHA monotherapy or combination treatment. RNAscope was used to detect the expression of BZLF1 (which encodes the early lytic protein, Zta) in C666–1 tumors isolated from mice in the control and treatment groups. Five random regions were selected from the slides corresponding to each treatment group. The numbers of cells that harbor positive signals for BZLF1 RNA were calculated using inForm analysis software. The numbers of positive BZLF1 hybridization signal were comparable between the tumors from all control and treatment groups. Fewer than 1% of all cells in the analyzed tumor areas were positive for BZLF1, indicating that EBV lytic reactivation was not a major factor in the cell death induced by the drug treatments. Figure S13. Assessment of autophagic flux and viability in NPC43 and C17 cells in response to palbociclib and SAHA monotherapy and combined treatment. A. Analysis of autophagic flux in NPC43 and C17 cells treated with palbociclib, SAHA, or palbociclib + SAHA and the autophagic flux inhibitor chloroquine (CQ, 25 μM) for 24 h. B. Bar chart of the fold changes in LC3-II in treated cells relative to the vehicle control as shown in Fig. A. C. Viability of NPC43 and C17 cells in response to the conditions described in A. Figure S14. Knockdown of the autophagy-related proteins BECN1 and ATG5 partially reversed the cytotoxic effects of palbociclib and SAHA in NPC cells. A. NPC43 cells were transfected with siRNAs specific for BECN1 or ATG5 and then treated with palbociclib and SAHA for 24 h. Cell viability was tested using a resazurin assay. B. Knockdown of ATG5 and beclin-1 protein expression after transfection with corresponding siRNAs was verified by western blotting. The normalized grayscale values corresponding to the protein band intensities are marked in red above the bands. Figure S15. Assessment of the autophagy levels in NPC43, C666–1 and C17 cell lines by visualizing the autophagic vacuoles with GFP-LC3II. The number of autophagic vacuole as indicated by the expression of GFP-LC3-II were counted and represented in the bar charts. Increased autophagic flux in cells treated by palbociclib (15 μM) and SAHA (5 μM) for 24 h was observed.

In 2015 the Utah State Legislature passed H.B. 213, “Safe Technology Utilization and Digital Citizenship in Public Schools,” mandating that K–12 schools provide digital citizenship instruction. This study presents an exploratory endeavor to understand how school librarians in a state that adopted digital citizenship legislation engage with digital citizenship instruction and their perceptions of a school librarian’s role in providing this instruction. We conducted a statewide survey of Utah school librarians, including questions focusing on digital citizenship resources used, current instruction within the school, and inquiries about improvements to current instruction. School librarians expressed a desire to be more involved in the instruction process, the need for more time, and the desire for consistent collaboration with teachers and administration.

Extreme weather is one of the main mechanisms through which climate change will directly impact human society. Coping with such change as a global community requires markedly improved understanding of how global warming drives extreme weather events. While alternative climate scenarios can be simulated using sophisticated models, identifying extreme weather events in these simulations requires automation due to the vast amounts of complex high-dimensional data produced. Atmospheric dynamics, and hydrodynamic flows more generally, are highly structured and largely organize around a lower dimensional skeleton of coherent structures. Indeed, extreme weather events are a special case of more general hydrodynamic coherent structures. We present a scalable physics-based representation learning method that decomposes spatiotemporal systems into their structurally relevant components, which are captured by latent variables known as local causal states. For complex fluid flows we show our method is capable of capturing known coherent structures, and with promising segmentation results on CAM5.1 water vapor data we outline the path to extreme weather identification from unlabeled climate model simulation data.

We present TigerGraph, a graph database system built from the ground up to support massively parallel computation of queries and analytics. TigerGraph's high-level query language, GSQL, is designed for compatibility with SQL, while simultaneously allowing NoSQL programmers to continue thinking in Bulk-Synchronous Processing (BSP) terms and reap the benefits of high-level specification. GSQL is sufficiently high-level to allow declarative SQL-style programming, yet sufficiently expressive to concisely specify the sophisticated iterative algorithms required by modern graph analytics and traditionally coded in general-purpose programming languages like C++ and Java. We report very strong scale-up and scale-out performance over a benchmark we published on GitHub for full reproducibility.

We regret that PRL did not accept our Reply for publication. We believe that both our Reply and the original paper (Phys. Rev. Lett. 118, 135901 (2017)) is sound and correct. We post our reply here and let readers to judge., Comment: 4 pages, 1 figure

A recent and important innovation in design-based research (DBR) is the use of conjecture maps, where researchers explicitly articulate the conjectured mediational relations between their designed goals and the learning designs and contexts. In this paper, we present a design case as an iterative sequence of evolving conjecture maps. As each conjecture map was tested, we revised it to highlight and refine our articulation of the tools and processes that embodied our design approach. Out design case involves small-town and rural community and school libraries in the United States as partners and DBR sites, with the goal of supporting librarians as they learn to develop and enact new STEM-oriented maker programs for youth. We show how conjecture mapping informed and supported our DBR work and how it helped push for specificity in hypothesized relations between the design, the learning setting, the outcomes. while also forcing a refection on design constraints.

What it means to work with data has changed significantly since the preparation and publication of America’s Lab Report(Singer, Hilton, & Schweingruber, 2006)in ways that are impacting students, educators, and the very practice of science. This change is expressing itself most obviously in the abundance of data that can be collected and accessed by students and teachers. There are also notable changes in the types of data (e.g., GPS data, network data, qualitative/verbal data) that are now readily available, and the purposes for which data are collected and analyzed. These shifts have both generated enthusiasm and raised a number of questions for K-12 science educators as new science standards are being adopted across the United States. This commissioned paper reviews the current state of data use in middle and secondary educational settings given the push toward adoption of Next Generation Science Standards and the changes that have taken place in technology infrastructure and research since the publication of America's Lab Report (2006). We identify emerging types of data and discuss what current literature suggests about their effective use in science investigations. Additionally, we consider how the emergence of 'data science' is reflected in the current research and suggest future research directions for the field.

Figure S1. (A) The temperature, (B) density, (C) energy and (D) backbone RMSD of the delE746_A750-gefitinib complex as functions of time. The system finally reaches a stable state after a series of equilibration operations. Figure S2. Class discrimination power of projected tensor features. (A) Class discrimination power of all projected tensor features of EGFR mutant-gefitinib complexes. (B) Class discrimination power of the first 30 most discriminative projected tensor features of EGFR mutant-gefitinib complexes. (C) Class discrimination power of all projected tensor features of EGFR mutant-erlotinib complexes. (D) Class discrimination power of the first 30 most discriminative projected tensor features of EGFR mutant-erlotinib complexes. (DOCX 115Â kb)

Deep learning is a promising tool to determine the physical model that describes our universe. To handle the considerable computational cost of this problem, we present CosmoFlow: a highly scalable deep learning application built on top of the TensorFlow framework. CosmoFlow uses efficient implementations of 3D convolution and pooling primitives, together with improvements in threading for many element-wise operations, to improve training performance on Intel(C) Xeon Phi(TM) processors. We also utilize the Cray PE Machine Learning Plugin for efficient scaling to multiple nodes. We demonstrate fully synchronous data-parallel training on 8192 nodes of Cori with 77% parallel efficiency, achieving 3.5 Pflop/s sustained performance. To our knowledge, this is the first large-scale science application of the TensorFlow framework at supercomputer scale with fully-synchronous training. These enhancements enable us to process large 3D dark matter distribution and predict the cosmological parameters $\Omega_M$, $\sigma_8$ and n$_s$ with unprecedented accuracy.

Wearable activity tracking devices associated with the Quantified Self movement have potential benefit for educational settings because they produce authentic and granular data about activities and experiences already familiar to youth. This article explores how that potential could be realized through explicit acknowledgment of and response to tacit design assumptions about how such technologies will be used in practice and strategic design for use in a classroom. We argue that particular practical adaptations that we have identified serve to ensure that the classroom and educational use cases are appropriately considered. As an example of how those adaptations are realized in actual elementary classrooms, we describe an effort to provide fifth-grade students each with their own Fitbit activity trackers in the context of a multi-week unit exploring core ideas in elementary statistics. Observational descriptions and transcript excerpts of students and teachers discussing their own Fitbit data are presented to illustrate what opportunities exist to leverage youth familiarity with daily activities in a way that targets development of statistical thinking. Quantitative written test results showing learning gains and differences between traditional and wearable device-enhanced instruction are also presented. Improvement on several statistical thinking constructs is identified, including in the areas of data display, conceptions of statistics, modeling variability, and informal inference.

The Knowledge in Pieces theoretical perspective posits p-prims as an important knowledge element in intuitive reasoning. Because p-prims are a class of knowledge elements developed and abstracted from everyday physical experiences, it seems plausible that immediate physical experiences, both in terms of sensations and actual observations of motion, would cue knowledge in different ways than when those experiences are just discussed as hypotheticals. This paper presents two cases to show that immediate embodied experiences with everyday objects does change which p-prims are cued and how they are deployed by students to explain situations involving motion. These cases come from a corpus of videorecorded interviews with high school students who were asked to explain predicted and enacted motions that involved deliberate sensory engagement with their bodies. Findings suggest that a connection is indeed present, and learners' embodied experiences should be leveraged in future work to support conceptual change in science.

In large part due to the highly prescribed nature of the typical school day for children, efforts to design new interactions with technology have often focused on less-structured after-school clubs and other out-of-school environments. We argue that while the school day imposes serious restrictions, school routines can and should be opportunistically leveraged by designers and by youth. Specifically, wearable activity tracking devices open some new avenues for opportunistic collection of and reflection on data from the school day. To demonstrate this, we present two cases from an elementary statistics classroom unit we designed that intentionally integrated wearable activity trackers and child-created data visualizations. The first case involves a group of students comparing favored recess activities to determine which was more physically demanding. The second case is of a student who took advantage of her knowledge of teachers' school day routines to test the reliability of a Fitbit activity tracker against a commercial mobile app.

In this paper, we describe the individuals and factors contributing to the emergence of a community makerspace in a small city in the United States. As research into how makerspaces have come into existence is still in a nascent stage, this single case study is intended to describe and highlight some of the complexities involved in creating such a facility. Based on analysis of onsite observations, interviews of adults connected with the space, and electronic communications, we present a story of how two co-founders of a youth-focused makerspace went from having initial interest in extracurricular activities for their own children to creating a makerspace serving all interested youth in the community. Following the resultant narrative, we also observe that while the co-founders were important agents in establishing this makerspace, awareness of the broad set of social, material, and institutional resources to which the co-founders had unique access helps to explain how this still new makerspace was ultimately established. This observation has important implications for other groups and organizations that are being encouraged to establish their own makerspaces.

For datasets in Collaborative Filtering (CF) recommendations, even if the identifier is deleted and some trivial perturbation operations are applied to ratings before they are released, there are research results claiming that the adversary could discriminate the individual's identity with a little bit of information. In this paper, we propose $k$-coRating, a novel privacy-preserving model, to retain data privacy by replacing some null ratings with "well-predicted" scores. They do not only mask the original ratings such that a $k$-anonymity-like data privacy is preserved, but also enhance the data utility (measured by prediction accuracy in this paper), which shows that the traditional assumption that accuracy and privacy are two goals in conflict is not necessarily correct. We show that the optimal $k$-coRated mapping is an NP-hard problem and design a naive but efficient algorithm to achieve $k$-coRating. All claims are verified by experimental results.

Learners’ physical performances can serve as focal objects for reflection and insight across a variety of contexts and content areas. This session brings together a set of projects that leverage the physical performances of learners, construct concrete and abstract representations of those performances, and investigate how learners reflect on and understand the relationships between their performances and target content—physics, health and fitness, data literacy and navigation, animal foraging, and climate change. The session will share findings and design principles from each of the studies around constructing technological scaffolds for physical performance reflections. The symposium highlights the various ways performance can be used to engage learners, and how different settings and learning goals affect the designs of performance representations.

While learning involves changes in one’s participation within a community of practice, changes in participants can also change access to resources key to newcomer participation. This poster presents a case study of a recreational cycling community illustrating how community changes diminished newcomers’ access to resources for drafting.

Rapid adoption of wearable tracking devices and motion sensitive apps has led to the development of the “Quantified Self” movement (QS). Some in the learning sciences community have begun to take notice and incorporate ideas from QS into the research and design of new learning environments. Yet the QS movement is still new enough that very little is known about it, and there are many open questions about how QS might be of value to the learning sciences. This paper provides some history of the movement and through a qualitative analysis of a public video corpus of QS presentations, identifies the variety of participants, the reported motivations driving individuals to self-quantify, and the data analysis activities of some individuals active in the movement. Opportunities for future research and design efforts in the learning sciences are also discussed.

This paper describes the design and development of a clinical interview protocol, the “Interactive Toy Interview” (ITI), that was used to assess prior knowledge and resultant learning among undergraduate students enrolled in a new, university-level “Craft Technologies” course. This new course involved several weeks of project work with electronic textiles and soft circuits. The ITI draws on prior work assessing learning with e-textiles, such as circuit diagram drawing tasks and ‘debuggems’, but it is based on use of existing commercial toys and objects. We present excerpts from interviews with a student who reported no prior background with sewing, circuitry, or programing and discuss what kinds of progress we see in her thinking about interactive toys as they relate to experiences she had in the Craft Technology course.

Recess is often a time for children in school to engage recreationally in physically demanding and highly interactive activities with their peers. This paper describes a design effort to encourage fifth-grade students to examine sensitivities associated with different measures of center by having them analyze activities during recess using over the course of a week using Fitbit activity trackers and TinkerPlots data visualization software. We describe the activity structure some observed student behaviors during the activity. We also provide a descriptive account, based on video records and transcripts, of two students who engaged thoughtfully with their recess data and developed a more sophisticated understanding of when and how outliers affect means and medians.

The introduction of sensor technologies to sports has allowed athletes to quantify and track their performance, adding an information-based layer to athletic practices. This information layer is particularly prevalent in practices involving formal competition and high levels of physical endurance, such as biking and running. We interviewed 20 athletes who participated in distance cycling or endurance running and also had experience using these technologies. This paper presents two cases and a number of shorter descriptive examples from these interviews that illustrate the factors salient to the introduction of these athletes to their respective sports, their continued participation in running or cycling, and their use of physical activity data. The effects of these data and logging practices among these individuals are examined, including some of the tensions that these athletes have with respect to quantifications of their performance and how they see themselves as athletic individuals in light of the increased presence of digital data. Educational implications are also discussed.

The Quantified Self (QS) movement is a growing global effort to use new mobile and wearable technologies to automatically obtain personal data about everyday activities. The social and material infrastructure associated with the Quantified Self (QS) movement provides a number of ideas that educational technologists should consider incorporating and using. This article discusses some recent efforts to bring the movement to the practices of the educational technology field and presents some issues to consider in the future.

Even when following best practices for participatory design, the appropriation of tools in formal education settings can be hampered by a number of factors. Drawing from a case of a web tool built to help teachers in five school districts find and share free resources in an educational digital library, we describe patterns of tool use and provide some explanations for variability in tool appropriation. We also suggest that future research consider school districts as complex systems of professionals whose interactions and inter-relationships may yield unexpected technology adoption behaviors.

In recent years, an emphasis on scientific argumentation in classrooms has brought into focus collaborative consensus-building as an instructional strategy. In these situations, students with differing and competing arguments are asked to work with one another in order to establish a shared perspective. However, the literature suggests that consensus-building can be challenging for students because their interpretations of the argumentative task and context may not enable their productive engagement with counter-arguments and evidence. In this paper, our goal is to explore the ways in which interactions of students support or inhibit their consensus-building. To that end, we examine and describe three cases that represent different ways in which initially dissenting students try to work towards a consensus with their peers. Through these cases, we demonstrate that legitimization of disparate or incorrect ideas can enable students whose arguments rely on incorrect ideas to feel that their ideas were heard and valued by the rest of their group. As such, we suggest that this legitimization is important because it can help students ‘save face’. This enables students to move away from the competitive and persuasive aspects of argumentation towards interactions that align more closely with sensemaking and consensus-building.

The proliferation of physical activity data monitoring devices had led to an increase in technoathletes—individuals who combine athletic training and performance with the collection and evaluation of personally-relevant data in an effort to better understand their own abilities. We interviewed 20 technoathletes who were actively involved within either cycling or running communities. Qualitative vignettes of technoathletic engagement with data and the practice of data logging, in specific, are discussed and illustrated. Individual relationships that technoathletes have with their data are also examined. Through the examples, we highlight some commonalities in the data that were obtained and how various athletes represented that information. We also consider some of the tensions that technoathletes have with respect to the data they can obtain and how they saw themselves in light of their data and consider some implications for instruction.

We examine differences in authorship, word usage, and references in full papers from the 1991 and 2010 ICLS proceedings. Through a series of analyses, we observe that, while authors largely hail from the US, national and regional participation in the LS community has broadened. Word usage suggests a shift in emphasis from cognitive issues to ones that are both cognitive and cultural. Reference analysis indicates a shift in core literatures and influential authors.

The cause of the seasons is often associated with a very particular alternative conception: that the Earth’s orbit around the sun is highly elongated and the differences in distance result in variations in temperature. It has been suggested that the standard diagrams used to depict the Earth’s orbit may be in some way responsible for the initial appearance and overall maintenance of this incorrect conceptualization; the elongated shape of the orbit is thought of as a conceptualization cue that invites a fairly predictable way of reasoning. To test if that is indeed the case, six variants of diagrams depicting differently shaped Earth orbits around the sun were presented to 652 ninth-grade students in the United States. From responses to a written assessment, students’ ideas about what caused the seasons were identified and analyzed. Elongation of orbit did not appear to have an effect, and there was no reinforcement effect for students who initially believed in an elongated orbit. Additional analyses show instead that other features in the diagrams can instead be more influential as conceptualization cues, such as shading or overlapping shapes, but those cues’ influence on student reasoning depend on which other cues accompany them.

This paper examines a “misconstrual hypothesis” regarding diagrams of the Earth’s orbit around the sun and how middle school students explain the cause of the seasons. Drawing from 24 semi-structured interviews, I present qualitative analyses of students’ explanations of why temperatures vary in summer and winter and how those are influenced by the elliptical shape of perspective drawings of the Earth’s orbit, common to many science textbooks. The results of the analysis suggest that diagram interpretation does not necessarily follow what has been often predicted in the literature and that conceptualizations can shift quite rapidly as different diagram features are noticed. A knowledge-in-pieces approach for understanding diagram interpretation is ultimately recommended through specific examples.