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Rural students, schools, and communities have unique challenges that hinder academic achievement, growth, and opportunities, compared to other locales. While there is a need to study this community more, there is also a pressing need to bring the local community members together to support the future generation of learners in developing pathways that lead them to future career opportunities. This article focuses on how a Research Practice Partnership (RPP) can be developed in rural communities to support STEM pathways for local middle-school youth. RPPs are often described as long-term collaborations between both researchers and practitioners in which the participating partners leverage research to address specific persistent problems of practice. We present findings from a developing design-based RPP focused on bringing community members and organizations together to co-design opportunities for underserved youth in rural mountain communities.

LISA Pathfinder is a technology demonstrator space mission, aimed at testing key technologies for detecting gravitational waves in space. The mission is the precursor of LISA, the first space gravitational waves observatory, whose launch is scheduled for 2034. The LISA Pathfinder scientific payload includes two gravitational reference sensors (GRSs), each one containing a test mass (TM), which is the sensing body of the experiment. A mission critical task is to set each TM into a pure geodesic motion, i.e. guaranteeing an extremely low acceleration noise in the sub-Hertz frequency bandwidth. The grabbing positioning and release mechanism (GPRM), responsible for the injection of the TM into a geodesic trajectory, was widely tested on ground, with the limitations imposed by the 1-g environment. The experiments showed that the mechanism, working in its nominal conditions, is capable of releasing the TM into free-fall fulfilling the very strict constraint imposed on the TM residual velocity, in order to allow its capture on behalf of the electrostatic actuation. However, the first in-flight releases produced unexpected residual velocity components, for both the TMs. Moreover, all the residual velocity components were greater than maximum value set by the requirements. The main suspect is that unexpected contacts took place between the TM and the surroundings bodies. As a consequence, ad hoc manual release procedures had to be adopted for the few following injections performed during the nominal mission. These procedures still resulted in non compliant TM states which were captured only after impacts. However, such procedures seem not practicable for LISA, both for the limited repeatability of the system and for the unmanageable time lag of the telemetry/telecommand signals (about 4400 s). For this reason, at the end of the mission, the GPRM was deeply tested in-flight, performing a large number of releases, according to different strategies. The tests were carried out in order to understand the unexpected dynamics and limit its effects on the final injection. Some risk mitigation maneuvers have been tested aimed at minimizing the vibration of the system at the release and improving the alignment between the mechanism and the TM. However, no overall optimal release strategy to be implemented in LISA could be found, because the two GPRMs behaved differently., This work has been made possible by the LISA Pathfinder mission, which is part of the space-science programme of the European Space Agency.The French contribution has been supported by the CNES (Accord Specific de projet CNES 1316634/CNRS 103747), the CNRS, the Observatoire de Paris and the University Paris-Diderot.E. Plagnol and H. Inchauspé would also like to acknowledge the financial support of the UnivEarthS Labex program at Sorbonne Paris Cité (ANR-10-LABX-0023 and ANR-11-IDEX-0005-02).The Albert-Einstein-Institut acknowledges the support of the German Space Agency, DLR. The work is supported by the Federal Ministry for Economic Affairs and Energy based on a resolution of the German Bundestag (FKZ 50OQ0501 and FKZ 50OQ1601).The Italian contribution has been supported by Agenzia Spaziale Italiana and Istituto Nazionale di Fisica Nucleare.The Spanish contribution has been supported by contracts AYA2010-15709 (MICINN), ESP2013-47637-P, and ESP2015-67234-P (MINECO).M. Nofrarias acknowledges support from Fundacion General CSIC (Programa ComFuturo).F. Rivas acknowledges an FPI contract (MINECO). The Swiss contribution acknowledges the support of the Swiss Space Office (SSO) via the PRODEX Programme of ESA. L. Ferraioli is supported by the Swiss National Science Foundation.The UK groups wish to acknowledge support from the United Kingdom Space Agency (UKSA), the University of Glasgow, the University of Birmingham,Imperial College, and the Scottish Universities Physics Alliance (SUPA). J. I. Thorpe and J. Slutsky acknowledge the support of the US National Aeronautics and Space Administration (NASA).

Our work builds on advances in deep learning for natural language processing to automatically analyze transcribed classroom discourse and reliably generate information about teachers’ uses of specific discursive strategies called ”talk moves.” Talk moves can be used by both teachers and learners to construct conversations in which students share their thinking, actively consider the ideas of others, and engage in sustained reasoning. Currently, providing teachers with detailed feedback about the talk moves in their lessons requires highly trained observers to hand code transcripts of classroom recordings and analyze talk moves and/or one-on-one expert coaching, a time-consuming and expensive process that is unlikely to scale. We created a bidirectional long short-term memory (bi-LSTM) network that can automate the annotation process. We have demonstrated the feasibility of this deep learning approach to reliably identify a set of teacher talk moves at the sentence level with an F1 measure of 65%.

With the proliferation of 3D printing technologies in schools and makerspaces, there is a need for teaching 3D modeling to students. Learning 3D modeling enhances spatial thinking skills, an essential skill for success in STEM. Creating 3D models requires students to have a deep understanding of 3D space, including rotating and scaling. In this study, we propose a framework developed through video-coding from analyzing screen recordings of middle-school students’ usage of a 3D modeling tool - Tinkercad. The proposed framework focuses on identifying challenges students encounter during 3D modeling. These challenges include spatial thinking skills, working with the Tinkercad interface, and mental model formation. We authenticated the framework by collecting and analyzing data from a 3D printing unit in three middle schools. Our results and subsequent analysis can guide educators and researchers on how to use this framework to support students in having productive learning experiences with Computer-Aided Design tools.

Engaging in physical computing activities involving both hardware and software provides a hands-on introduction to computer science. The move to remote learning for primary and secondary schools during the 2020-2021 school year due to COVID-19 made implementing physical computing activities especially challenging. However, it is important that these activities are not simply eliminated from the curriculum. This paper explores how a unit centered around students investigating how programmable sensors that can support data-driven scientific inquiry was collaboratively adapted for remote instruction. A case study of one teacher’s experience implementing the unit with a group of middle school students (ages 11 to 14) in her STEM elective class examines how her students could still engage in computational thinking practices around data and programming. The discussion includes both the challenges and unexpected affordances of engaging in physical computing activities remotely that emerged from her implementation.

Inclusion in mathematics education is strongly tied to discourse rich classrooms, where students ideas play a central role. Talk moves are specific discursive practices that promote inclusive and equitable participation in classroom discussions. This paper describes the development of the TalkMoves application, which provides teachers with detailed feedback on their usage of talk moves based on accountable talk theory. Building on our recent work to automate the classification of teacher talk moves, we have expanded the application to also include feedback on a set of student talk moves. We present results from several deep learning models trained to classify student sentences into student talk moves with performance up to 73% F1. The classroom data used for training these models were collected from multiple sources that were pre-processed and annotated by highly reliable experts. We validated the performance of the model on an out-of-sample dataset which included 166 classroom transcripts collected from teachers piloting the application.

This paper describes the design and classroom implementation of a week-long unit that aims to integrate computational thinking (CT) into middle school science classes using programmable sensor technology. The goals of this sensor immersion unit are to help students understand why and how to use sensor and visualization technology as a powerful data-driven tool for scientific inquiry in ways that align with modern scientific practice. The sensor immersion unit is anchored in the investigation of classroom data where students engage with the sensor technology to ask questions about and design displays of the collected data. Students first generate questions about how data data displays work and then proceed through a set of programming exercises to help them understand how to collect and display data collected from their classrooms by building their own mini data displays. Throughout the unit students draw and update their hand drawn models representing their current understanding of how the data displays work. The sensor immersion unit was implemented by ten middle school science teachers during the 2019/2020 school year. Student drawn models of the classroom data displays from four of these teachers were analyzed to examine students' understandings in four areas: function of sensor components, process models of data flow, design of data displays, and control of the display. Students showed the best understanding when describing sensor components. Students exhibited greater confusion when describing the process of how data streams moved through displays and how programming controlled the data displays.

M. Armano et al., The Laser Interferometer Space Antenna Pathfinder (LPF) main observable, labeled Δg, is the differential force per unit mass acting on the two test masses under free fall conditions after the contribution of all non-gravitational forces has been compensated. At low frequencies, the differential force is compensated by an applied electrostatic actuation force, which then must be subtracted from the measured acceleration to obtain Δg. Any inaccuracy in the actuation force contaminates the residual acceleration. This study investigates the accuracy of the electrostatic actuation system and its impact on the LPF main observable. It is shown that the inaccuracy is mainly caused by the rounding errors in the waveform processing and also by the random error caused by the analog to digital converter random noise in the control loop. Both errors are one order of magnitude smaller than the resolution of the commanded voltages. We developed a simulator based on the LPF design to compute the close-to-reality actuation voltages and, consequently, the resulting actuation forces. The simulator is applied during post-processing the LPF data., This work was supported by ETH Research Grant No. ETH-05 16-2, and it has been made possible by the LISA Pathfinder mission, which is part of the space-science program of the European Space Agency. The French contribution has been supported by the CNES (Accord Specific de Projet Grant No. CNES 1316634/CNRS 103747), the CNRS, the Observatoire de Paris, and University Paris-Diderot. E. Plagnol and H. Inchauspé would also like to acknowledge the financial support of the UnivEarthS Labex program at Sorbonne Paris Cité (Grant Nos. ANR-10-LABX-0023 and ANR-11-IDEX-0005-02). The Albert-Einstein-Institut acknowledges the support of the German Space Agency, DLR. The work is supported by the Federal Ministry for Economic Affairs and Energy based on a resolution of the German Bundestag (Grant Nos. FKZ 50OQ0501 and FKZ 50OQ1601). The Italian contribution has been supported by the Agenzia Spaziale Italiana and the Istituto Nazionale di Fisica Nucleare. The Spanish contribution has been supported by Contract Nos. AYA2010-15709 (MICINN), ESP2013-47637-P, ESP2015-67234-P, and ESP2017-90084-P (MINECO). Support from AGAUR (Generalitat de Catalunya) contract 2017-SGR-1469 is also acknowledged. M. Nofrarias acknowledges support from Fundacion General CSIC (Programa ComFuturo). F. Rivas acknowledges an FPI contract from MINECO. The Swiss contribution acknowledges the support of the Swiss Space Office (SSO) via the PRODEX Programme of the ESA. L. Ferraioli is supported by the Swiss National Science Foundation. The UK groups wish to acknowledge support from the United Kingdom Space Agency (UKSA), the University of Glasgow, the University of Birmingham, Imperial College, and the Scottish Universities Physics Alliance (SUPA). J. I. Thorpe and J. Slutsky acknowledge the support of the U.S. National Aeronautics and Space Administration (NASA). N. Korsakova would like to thank the support from the CNES Fellowship. The LISA Pathfinder collaboration would like to acknowledge Professor Pierre Binetruy (deceased 30 March 2017) and Professor José Alberto Lobo (deceased 30 September 2012) for their contribution to the LISA Pathfinder science.

LISA Pathfinder (LPF) has been a space-based mission designed to test new technologies that will be required for a gravitational wave observatory in space. Magnetically driven forces play a key role in the instrument sensitivity in the low-frequency regime (mHz and below), the measurement band of interest for a space-based observatory. The magnetic field can couple to the magnetic susceptibility and remanent magnetic moment from the test masses and disturb them from their geodesic movement. LISA Pathfinder carried on-board a dedicated magnetic measurement subsystem with noise levels of 10 $ \rm nT \ Hz^{-1/2}$ from 1 Hz down to 1 mHz. In this paper we report on the magnetic measurements throughout LISA Pathfinder operations. We characterise the magnetic environment within the spacecraft, study the time evolution of the magnetic field and its stability down to 20 $\mu$Hz, where we measure values around 200 $ \rm nT \ Hz^{-1/2}$ and identify two different frequency regimes, one related to the interplanetary magnetic field and the other to the magnetic field originating inside the spacecraft. Finally, we characterise the non-stationary component of the fluctuations of the magnetic field below the mHz and relate them to the dynamics of the solar wind., Comment: 16 pages, 17 figures. MNRAS LaTeX style file version 3.0

As robots increasingly enter our daily lives, there is a need to understand how to design robots capable of emotional interaction with humans, especially children, due to their sensitivity and vulnerability. For example, robots that provide children with social and emotional support might be more effective at also helping children develop cognitive abilities, rather than designing robots that focus solely on helping children acquire cognitive skill. In this paper, we examine the design of robots that can provide human-like hugs as a particular form of social and emotional support. We first discuss the need to design robots that can interact emotionally with children. Then, we present the development of a shirt augmented with pressure sensors used to collect data on how humans hug each other. Finally, we detail the design of "Hugbot", a soft robot that could use this data to give human-like hugs, and discuss our planned future work on this system.

We report on the design and implementation of a 3-week long summer academy introducing high school students to 3D modeling and 3D printing experiences. Supporting youth in developing 3D modeling knowledge can enhance their capacity to effectively use an array of emerging technologies such as Virtual Reality, Augmented Reality, and digital fabrication. We used tools and practices from both formal and informal education, such as storylining, to inform the design of the curriculum. We collected data through surveys, artifacts, observations, screen recordings, and group videos. Our findings suggest that (1) emphasizing curricular coherence as a design goal and (2) providing youth with multiple avenues for engaging in 3D modeling can help to: spark youth interest in 3D printing/modeling, maintain youth engagement in learning activities over the course of several weeks, and provide youth with opportunities to develop their spatial thinking skills.

Cultural-historical activity theory (CHAT) approaches to intervention aim for transformative agency, that is, collective actions that expand and bring about new possibilities for activity. In this article, we draw on CHAT as a resource for organizing design research that promotes teachers’ agency in designing new science curriculum materials. We describe how CHAT informed our efforts to structure a collaborative design space in which teachers and other participants sought to develop new curriculum materials intended to help realize a new vision for science education. Specifically, we describe the tools and routines we deployed to support the design process, and we analyze the ways in which teachers took up elements of our design process as well as how they adapted, resisted, and suggested alternative tools and strategies to help develop new curriculum materials. In so doing, we illustrate ways in which CHAT can serve as a guide both for organizing collaborative design processes and for analyzing their eff...

This paper examines the impacts of technology (e.g., Chromebooks, Google Drive) on teacher learning and student activity in the development and implementation of a deeply digital high school biology unit. Using design-based implementation research, teachers co-designed with researchers and curriculum specialists a student-centered unit aligned to the Next Generation Science Standards (NGSS) that utilizes classroom technology. Qualitative and quantitative data were collected to understand the barriers that inhibit the implementation of a digital curriculum as well as the extent that teachers engage in the design process and begin to make shifts in their practice. We found that through the co-design process teachers began to shift their knowledge of NGSS, technology implementation, and adapted to tensions and barriers inherent in the process.

This experience report describes two iterations of a curriculum development process in which middle school teachers worked with our research team to collaboratively design and enact instructional units where students used sensors to investigate scientific phenomena. In this report, we examine the affordances of using a sensor platform to support the integration of disciplinary learning and computational thinking (CT) aligned with Next Generation Science Standards and the CT in STEM Taxonomy developed by Weintrop and colleagues. In the first unit, students investigated the conditions for mold growth within their school using a custom sensor system. After analyzing implementation experiences and student interest data, our team engaged in another round of co-design to develop a second instructional unit. This unit uses a different sensor system (the micro:bit) which supports additional CT in STEM practices due to its block-based programming interface and its real time data display. For the second unit we selected a different phenomenon: understanding and designing maglev trains.

Currently, providing teachers with detailed feedback about their classroom discourse strategies requires highly trained observers to hand code transcripts of classroom recordings to identify talk moves and/or one-on-one expert coaching. Both approaches are time-consuming and expensive, require considerable human expertise, and do not scale to large numbers of teachers. We are currently developing an innovative application, the TalkBack application, a new type of teacher learning environment based on the automated analysis of classroom recordings. The TalkBack application will utilize a big data infrastructure for managing and analyzing classroom recordings, including an embedded automated talk move classifier. The application will provide teachers with a detailed record of the discourse strategies used in their lessons. A central premise of our research is that this type of personalized, automated feedback can dramatically enhance teacher learning and support improvements in their instruction.The project will exemplify how next-generation repositories of classroom recordings can be architected to support large-scale research by enabling automated analyses based on machine learning models.

Several strands of Learning Sciences research have conceptualized teaching as a design activity and examined resulting instructional entailments. For example, research has shown that participating in teaching as a design process can help teachers learn new content and skills, enhance their students’ learning, while also serve to support and sustain curricular innovations. With the emergence of networked technologies to support learning, new questions about how to frame teaching as a design activity are again at the forefront. Within this realm, one important innovation is open educational resources (Oer) –freely available and customizable online teaching and learning resources. With their rapidly growing availability, teachers are increasingly turning to Oer to design instructional experiences for their students. This chapter explores the role technology can play in supporting teachers as they use, adapt, customize, and share Oer to design instructional experiences for their students. It first describes conceptual frameworks informing the teaching as design research agenda: pedagogical design capacity, peer production, and use diffusion. Then, two technology models are presented that support these processes and we describe their implementation in two software tools, the Instructional Architect and the Curriculum Customization Service. While the two tools occupy different parts of the design space, they aim to support teachers through leveraging peer production processes around teachers’ varying levels of pedagogical design capacity. The chapter summarizes findings from studies of teacher use of these two tools and concludes by discussing implications of this perspective for research and practice.

Scientific models represent ideas, processes, and phenomena by describing important components, characteristics, and interactions. Models are constructed across a variety of scientific disciplines, such as the food web in biology, the water cycle in Earth science, or the structure of the solar system in astronomy. Models are central for scientists to understand phenomena, construct explanations, and communicate theories. Constructing and using models to explain scientific phenomena is also an essential practice in contemporary science classrooms. Our research explores new techniques for understanding scientific modeling and engagement with modeling practices. We work with students in secondary biology classrooms as they use a web-based software tool - EcoSurvey - to characterize organisms and their interrelationships found in their local ecosystem. We use learning analytics and machine learning techniques to answer the following questions: 1) How can we automatically measure the extent to which students’ scientific models support complete explanations of phenomena? 2) How does the design of student modeling tools influence the complexity and completeness of students’ models? 3) How do clickstreams reflect and differentiate student engagement with modeling practices? We analyzed EcoSurvey usage data collected from two different deployments with over 1000 secondary students across a large urban school district. We observe large variations in the completeness and complexity of student models, and large variations in their iterative refinement processes. These differences reveal that certain key model features are highly predictive of other aspects of the model. We also observe large differences in student modeling practices across different classrooms and teachers. We can predict a student’s teacher based on the observed modeling practices with a high degree of accuracy without significant tuning of the predictive model. These results highlight the value of this approach for extending our understanding of student engagement with scientific modeling, an important contemporary science practice, as well as the potential value of analytics for identifying critical differences in classroom implementation.

This paper describes a methodology to analyze, in the frequency domain, the steady-state control performances of the LISA Pathfinder mission. In particular, it provides a technical framework to give a comprehensive understanding of the spectra of all the degrees of freedom by breaking them down into their various physical origins, hence bringing out the major contributions of the control residuals. A reconstruction of the measured in-loop output, extracted from a model of the closed-loop system, is shown as an instance to illustrate the potential of such a model breakdown of the data.

LISA Pathfinder, the European Space Agency's technology demonstrator mission for future spaceborne gravitational wave observatories, was launched on 3 December 2015, from the European space port of Kourou, French Guiana. After a short duration transfer to the final science orbit, the mission has been gathering science data since. This data has allowed the science community to validate the critical technologies and measurement principle for low frequency gravitational wave detection and thereby confirming the readiness to start the next generation gravitational wave observatories, such as LISA. This paper will briefly describe the mission, followed by a description of the science operations highlighting the performance achieved. Details of the various experiments performed during the nominal science operations phase can be found in accompanying papers in this volume.

Modeling has a strong focus in current science learning frameworks as a critical skill for students to learn. However, understanding students' scientific models and their modeling practices at scale is a difficult task that has not been taken up by the research literature. The complex variables involved in classroom learning, such as teacher differences, increase the difficulty of understanding this problem. This work begins with an exploration of the methods used to explore students' scientific modeling in the learning sciences space and the frameworks developed to characterize student modeling practices. Learning analytics can be used to leverage these frameworks of scientific modeling practices to explore questions around students' scientific models and their modeling practices. These analyses are focused around the use of EcoSurvey, a collaborative, digital tool used in high-school biology classrooms to model the local ecosystem. This tool was deployed in ten biology classrooms and used with varying degrees of success. There are significant teacher-level differences found in the activity sequences of students using the EcoSurvey tool. The theoretical metrics around scientific modeling practices and automatically extracted feature sequences were also used in a classification task to automatically determine a particular student's teacher. These results underline the power of learning analytics methods to give insight into how modeling practices are realized in the classroom. This work also informs changes to modeling tools, associated curricula, and supporting professional development around scientific modeling.

Iterative design is a powerful method for developing digital classroom tools and curricula. We explore how infusing learning analytics into this process has influenced our development of EcoSurvey, a digital modeling tool for mapping the organisms and interactions in the local ecosystem. We have found that analytic techniques can help us discover areas in which students struggle to engage with scientific modeling, and we can iteratively use learning analytics to demonstrate the impact of design changes.

We present a simple, easy-to-replicate monolingual aligner that demonstrates state-of-the-art performance while relying on almost no supervision and a very small number of external resources. Based on the hypothesis that words with similar meanings represent potential pairs for alignment if located in similar contexts, we propose a system that operates by finding such pairs. In two intrinsic evaluations on alignment test data, our system achieves F1 scores of 88–92%, demonstrating 1–3% absolute improvement over the previous best system. Moreover, in two extrinsic evaluations our aligner outperforms existing aligners, and even a naive application of the aligner approaches state-of-the-art performance in each extrinsic task.

Efficient learning from Web resources can depend on accurately assessing the quality of each resource. We present a methodology for developing computational models of quality that can assist users in assessing Web resources. The methodology consists of four steps: 1) a meta-analysis of previous studies to decompose quality into high-level dimensions and low-level indicators, 2) an expert study to identify the key low-level indicators of quality in the target domain, 3) human annotation to provide a collection of example resources where the presence or absence of quality indicators has been tagged, and 4) training of a machine learning model to predict quality indicators based on content and link features of Web resources. We find that quality is a multifaceted construct, with different aspects that may be important to different users at different times. We show that machine learning models can predict this multifaceted nature of quality, both in the context of aiding curators as they evaluate resources submitted to digital libraries, and in the context of aiding teachers as they develop online educational resources. Finally, we demonstrate how computational models of quality can be provided as a service, and embedded into applications such as Web search.

This research demonstrates the utility of the theory of weak ties for understanding the patterns of resource usage and sharing in an online professional learning community. Our context of study is a community of educators using and sharing teaching resources such as lesson plans, presentation slides and animations. We consider whether the deduced relationships between members of the community of educators constitute weak ties. A deduced relationship exists when two educators access the same resource. If these deduced relationships do constitute weak ties, then other theorized network properties should also be manifest, namely homophily and triadic closures. Our findings support these theoretical conjectures. Firstly, results indicate that the strength of a tie is directly proportional to the level of similarity between users in the network in terms of their propensity to use and share resources and their level of comfort with and use of technology (homophily property). Secondly, we found strong support for the triadic closure property (formation of a weak tie between unconnected nodes that share a common neighbor). Thus, we developed a computational model to predict the formation of weak ties via triadic closures with an accuracy of 97.8 %. Finally, we show that augmenting collaborative and hybrid recommender systems with our triadic closure prediction model can improve the performance of these systems.

The need for automatic methods capable of characterizing adoption and use has grown in operational digital libraries. This paper describes a computational method for producing two, inter-related, user typologies based on use diffusion. Furthermore, a case study is described that demonstrates the utility and applicability of the method: it is used to understand how middle and high school science teachers participating in an academic year-long field trial adopted and integrated digital library resources into their instructional planning and teaching. Use diffusion theory views technology adoption as a process that can lead to widely different patterns of use across a given population of potential users; these models use measures of frequency and variety to characterize and describe such usage patterns. By using computational techniques such as clickstream entropy and clustering, the method produces both coarse- and fine-grained user typologies. As a part of improving the initial coarse-grain typology, clickstream entropy improvements are described that aim at better separation of users. In addition, a fine-grained user typology is described that identifies five different types of teacher-users, including "interactive resource specialists" and "community seeker specialists." This typology was validated through comparison with qualitative and quantitative data collected using traditional educational field research methods. Results indicate that qualitative analyses correlate with the computational results, suggesting automatic methods may prove an important tool in discovering valid usage characteristics and user types.

Educative sensemaking focuses on the needs of self-directed learners, a nonexpert population of thinkers who must locate relevant information sources, evaluate the applicability and accuracy of digital resources for learning, and determine how and when to use these resources to complete educational tasks. Self-directed learners face a sensemaking paradox: They must employ deep-level thinking skills to process information sources meaningfully, but they often lack the requisite domain knowledge needed to deeply analyze information sources and to successfully integrate incoming information with their own existing knowledge. In this article, we focus on the needs of college-aged students engaged in learning about natural sciences using web-based learning resources. We explored the impact of cognitive personalization technologies on students' sensemaking processes using a controlled study in which students' cognitive and metacognitive processes were analyzed as they completed a common educational task: writing...

This paper describes our progress towards automating adaptive personalized instruction based on student conceptual understandings using digital libraries. The reported approach merges conversational learning theory with advances in natural language processing to enable personalized pedagogical interactions. Multi-document summarization techniques serve as the computational basis to process digital library resources and automatically construct a rich domain model on earthquakes and plate tectonics for high school age learners. Shallow semantic analysis and graph-based techniques are used to computationally diagnose student understandings that enable conceptual personalizations integrating digital library resources. The evaluation of the implemented algorithms indicates that digital libraries may serve as knowledge platforms to support the automated construction of rich domain models and the diagnosis of student conceptual understandings. Furthermore, this approach introduces a novel and effective alternative to prior work in adaptive learning environments in terms of scalability and portability, thus tackling important challenges associated with supporting personalized instruction using digital libraries.

We show that weak ties offer a useful theoretical lens for understanding the sharing and usage of community-contributed resources amongst educators in a large urban school district. Community-contributed resources include a rich variety of teaching and learning resources such as lesson plans, presentation slides, animations and simulations. In this research, we consider whether the deduced relationships between members of the community constitute weak ties. A deduced relationship exists when two community members view or access the same resource. If these deduced relationships do constitute weak ties then other theorized network properties should also be manifest, namely homophily and triadic closures. Our findings support these theoretical conjectures. Firstly, results indicate that the strength of a tie is directly proportional to the level of similarity between users in the network (homophily property). Secondly, we found strong support for the triadic closure property as well; we developed a computational model to predict the formation of weak ties via triadic closures with an accuracy of 97.8%. Insights from our model can be used to improve a collaborative filtering approach for resource recommendation by predicting future similarity between users in the network.

This article explores the use of a multimedia search interface for digital libraries based on strand maps developed by the American Association for the Advancement of Science. As semantic-spatial displays, strand maps provide a visual organization of relevant conceptual information that may promote the use of science content during digital library use. A study was conducted to compare users' cognitive processes during information seeking tasks when using a multimedia strand maps interface, versus the textual search interface currently implemented in the Digital Library for Earth System Education. Quantitative and qualitative data from think-aloud protocols revealed that students were more likely to engage with science content (e.g., analyzing the relevance of science concepts with regard to task needs) during search when using the strand maps interface compared to those using textual searching. In contrast, students using a textual search interface engaged more frequently with surface-level information (e.g., the type of a resource regardless of its science content) during search and retrieval. As a multimedia search interface for digital libraries, strand maps appear to be promising tools to promote conceptual discovery and learning through content-based processes that promote learner engagement with relevant science knowledge.

We report on the development of the LISA Technology Package (LTP) experiment that will fly onboard the LISA Pathfinder mission of the European Space Agency in 2008. We first summarize the science rationale of the experiment aimed at showing the operational feasibility of the so-called transverse–traceless coordinate frame within the accuracy needed for LISA. We then show briefly the basic features of the instrument and we finally discuss its projected sensitivity and the extrapolation of its results to LISA.

Keyphrase extraction is a fundamental technique in natural language processing. It enables documents to be mapped to a concise set of phrases that can be used for indexing, clustering, ontology building, auto-tagging and other information organization schemes. Two major families of unsupervised keyphrase extraction algorithms may be characterized as statistical and graph-based. We present a hybrid statistical-graphical algorithm that capitalizes on the heuristics of both families of algorithms and is able to outperform the state of the art in unsupervised keyphrase extraction on several datasets.

We describe a set of top-performing systems at the SemEval 2015 English Semantic Textual Similarity (STS) task. Given two English sentences, each system outputs the degree of their semantic similarity. Our unsupervised system, which is based on word alignments across the two input sentences, ranked 5th among 73 submitted system runs with a mean correlation of 79.19% with human annotations. We also submitted two runs of a supervised system which uses word alignments and similarities between compositional sentence vectors as its features. Our best supervised run ranked 1st with a mean correlation of 80.15%.

Thermal Diagnostics experiments to be carried out on board LISA Pathfinder (LPF) will yield a detailed characterisation of how temperature fluctuations affect the LTP (LISA Technology Package) instrument performance, a crucial information for future space based gravitational wave detectors as the proposed eLISA. Amongst them, the study of temperature gradient fluctuations around the test masses of the Inertial Sensors will provide as well information regarding the contribution of the Brownian noise, which is expected to limit the LTP sensitivity at frequencies close to 1mHz during some LTP experiments. In this paper we report on how these kind of Thermal Diagnostics experiments were simulated in the last LPF Simulation Campaign (November, 2013) involving all the LPF Data Analysis team and using an end-to-end simulator of the whole spacecraft. Such simulation campaign was conducted under the framework of the preparation for LPF operations. Spanish Ministry of Science and Innovation (MICINN)/AYA2010-15709

Monolingual alignment is the task of pairing semantically similar units from two pieces of text. We report a top-performing supervised aligner that operates on short text snippets. We employ a large feature set to (1) encode similarities among semantic units (words and named entities) in context, and (2) address cooperation and competition for alignment among units in the same snippet. These features are deployed in a two-stage logistic regression framework for alignment. On two benchmark data sets, our aligner achieves F1 scores of 92.1% and 88.5%, with statistically significant error reductions of 4.8% and 7.3% over the previous best aligner. It produces top results in extrinsic evaluation as well.

Reuse of educational materials is integral to many educator tasks, from designing a course to preparing for a lab or class. We are studying the reuse of educational materials in the context of the Digital Library for Earth System Education (DLESE). DLESE is a community-owned and governed facility offering high-quality teaching and learning resources for Earth system education. Our studies build on a cognitive framework that posits that people engage in three cyclical processes—location, comprehension, and modification—when reusing resources from large digital repositories. Our formative evaluations and cataloging experiences in DLESE suggest that the ‘findability’ and reusability of community-created digital educational resources is highly dependent on the presentational and structural design of the resources themselves. Educational resource designers often do not develop components with reuse in mind, making it more difficult or impossible for other educators to find and use their material. We share the ...