Agency: NSF | Branch: Standard Grant | Program: | Phase: STEM + Computing (STEM+C) Part | Award Amount: 2.50M | Year: 2016
This project is funded by the STEM+Computing Partnership (STEM+C) program, which seeks to advance new approaches to, and evidence-based understanding of, the integration of computing in STEM teaching and learning. This project will contribute to that effort by developing new curriculum units for high school biology courses that integrate computational thinking within biology. The units will feature inquiry-oriented investigations that will require students to design procedures using a variety of computer-controlled sensors, actuators, video-capture devices, and other technologies to solve open-ended problems. Students will use a simplified visual programming language, called Dataflow, to automate experimental procedures, collect and visualize data, and wirelessly link to data sharing devices. To use these real-time resources effectively, students will need to link the causal (cause and effect) thinking of the natural sciences to the computational thinking skills of computer science. The project will develop 12 curriculum units comprising 6 background units, 3 highly structured set-piece experiments, and 3 open-ended projects. Sets of two background units, 1 set-piece experiment, and one open-ended project will allow students to progress through development of foundational knowledge and experimental skills before taking on the challenge of an open-ended project. The outcomes of this project have high potential to advance classroom practices in science by identifying ways of using computers and computational thinking to engage students more deeply in the processes and reasoning of scientific inquiry.
The project will conduct design-based research, using the first two project years to develop and validate a pedagogical model of integration between science and computational thinking practices and related activities. In year three, the project will undertake a pilot implementation to test the feasibility of the projects innovations in the biology classes taught by 26 teachers in diverse schools. The project will provide teachers with teaching resources, technology, and professional development designed to enable their students to undertake two science investigations requiring a total of four to six weeks of classes. A mixed-methods research strategy will be employed both to characterize and estimate student gains in applying computational thinking, carrying out science practices, and understanding core biology content knowledge related to ecosystems. Data will be gathered from classroom observations, pretests and posttests, embedded assessments in activities, student screencast reports, interviews with teachers and students, and log data automatically collected by learning software. This variety of data sources will be triangulated to validate research findings from multiple perspectives. Three questions guide the research: A) To what extent and under what conditions are students able to use the projects computational resources to undertake authentic scientific investigations? B) When students are engaged in science experimentation made possible by the resources, approach, and designed tools, what learning gains will be observed in student abilities to perform science practices, exercise computational thinking, and understand biology concepts? What factors are associated with differences in the gains, if any? and C) What kinds of background materials and assistance do teachers require for effective enactments of the intended curriculum? Specifically, how important is teacher background and experience with computers to support student use of technology-enabled experimentation? What was the impact of various kinds of teacher supports on the quality of the classroom enactments.
Agency: NSF | Branch: Standard Grant | Program: | Phase: STEM + Computing (STEM+C) Part | Award Amount: 541.43K | Year: 2016
The Cyberlearning and Future Learning Technologies Program funds efforts that will help envision the next generation of learning technologies and advance what we know about how people learn in technology-rich environments. Cyberlearning Exploration (EXP) Projects explore the viability of new kinds of learning technologies by designing and building new kinds of learning technologies and studying their possibilities for fostering learning and challenges to using them effectively. Citizens and workers in tomorrows world must be prepared to approach what have been called wicked problems involving multiple, interlinked complex systems, including issues such as climate change, crime, communicable diseases, transportation, and many more. Preparing learners with the background to face such problems is a primary challenge of our age. To gain a complete understanding of such systems, learners need to understand both the high-level aspects of a systems dynamics and the rules that govern its individual interacting elements. In this project, the Concord Consortium, the MIT Scheller Teacher Education Program and the Argonne National Laboratory Systems Science Center will combine two different proven educational technologies used for understanding complex systems to form a powerful hybrid technology. Using it with learners and researching its value for learning about systems dynamics, the project will shed light on how to foster deep understanding of multiple, interlinked complex systems. The project will conduct research with diverse school districts and project materials and technologies will be made available free of charge to both researchers and practitioners nationwide. Additionally, this project has potential to create tools and generate understanding of great utility to the large field of professionals who currently use technology to model and understand complex systems as part of their everyday work.
Two main approaches and technologies exist currently to aid learning to reason about complex systems. Systems dynamics approaches offer a broad, eagles eye view of a system that facilitates an almost-immediate sense of the structure and interactions within a system and its components, while agent-based approaches offer an ants eye view that lays bare the details and mechanisms behind the systems interactions. Interactions with these two approaches occur at similarly different grain sizes, with systems dynamics views offering the ability to instantiate and easily recast large-scale connections among components quickly and agent-based approaches offering a fine-control knob that enables subtle tweaking of the intricate rules underlying the systems individual actors--fine tweaks that, in complex systems, can often result in surprisingly large and anti-intuitive changes in the overall system itself. Without an explicit connection between these levels of interpretation, learners are left with fragmented experiences and understanding. Merging the agent-based modeling capabilities of MITs StarLogo with the systems modeling and diagramming capabilities of the Concord Consortiums SageModeler software, the project will develop an important new genre of educational technology termed linked-hybrid modeling and test it in K-12 science classrooms. This new technology genre, capable of permitting learners to move between detailed individual models and global views of stocks and flows for the first time, will enable whole new modes of experimentation and should ultimately foster levels of learner reasoning about complex systems and systems dynamics that are not currently possible. The project research will combine theoretical frameworks for both systems dynamics and systems emergence, applying a design-based research approach to study student reasoning of complex systems. By examining how use and design affordances of this new genre lead to productive complex systems reasoning and thus better understanding of systems, the project will lay the groundwork for understanding how to foster powerful learning in the context of wicked problems.
Agency: NSF | Branch: Continuing grant | Program: | Phase: DISCOVERY RESEARCH K-12 | Award Amount: 1.56M | Year: 2015
The Discovery Research K-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools (RMTs). Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. This project will develop and test a digital monitoring tool that will enable teachers to track student learning within a digital learning system and quickly adjust classroom instructional strategies to facilitate learning. The tool will be developed for use with an existing digital curriculum for high school genetics, and it will be tested in both introductory biology courses and advanced courses. The tool will be designed to analyze student progress within the digital curriculum and provide feedback to both students and teachers about challenges to learning as they are occurring. The system will simultaneously monitor and analyze the learning patterns of all students in a class, providing targeted feedback to individual students as needed, or enabling the teacher to make informed decisions about when some students need individual attention, or groups of students need help with a particular concept or learning challenge. As more digital learning experiences are incorporated into classroom practices, digital guidance systems such as this will be needed to help students and teachers effectively blend a variety of classroom learning experiences.
This design and development study focuses on improving student learning of domain-specific content and practices within traditional, technology-enriched classroom environments. The project will be guided by findings from prior research in two areas: the learning outcomes of game-like, simulation-based digital learning environments, and the effectiveness of intelligent tutoring systems. The system being developed in this project will construct analytic models of student knowledge and behavior from clickstream data and provide contextualized information at optimal junctures to students or teachers. This approach will enable a three-layered response system: a) direct, targeted support to individual students struggling with basic content; b) referral of a student to another student or group of students having encountered similar learning challenges; and c) feedback to teachers that would facilitate strategic guidance of student learning. Research questions guiding this project will focus on what information about student learning is most useful for guiding learning in digital environments, how can this information improve support for student learning within classrooms, and how does the availability and use of this information improve student knowledge and practices? The design and development work will be conducted over 4 years and will involve teachers and students in 22 classrooms in New England and North Carolina.
Agency: NSF | Branch: Standard Grant | Program: | Phase: DISCOVERY RESEARCH K-12 | Award Amount: 2.64M | Year: 2016
Despite recent research demonstrating the capacity of young children to engage deeply with science concepts and practices, challenging science curriculum is often lacking in the early grades. This project addresses this gap by developing a technology-supported, physical science curriculum that will facilitate kindergarten students conceptual understanding of matter and how matter changes. To accomplish these goals, the curriculum will include opportunities for students to participate in model-based inquiry in conjunction with the use of digital probeware and simulations that enable students to observe dynamic visualizations and make sense of the phenomena. To support the capacity of kindergarten teachers, a continuous model of teacher development will be implemented.
Throughout development, the project team will collaborate with kindergarten teachers and more than 300 demographically diverse students across eight classrooms in Massachusetts and Indiana. A design based research approach will be used to iteratively design and revise learning activities, technological tools, and assessments that meet the needs and abilities of kindergarten students and teachers. The project team will: 1) work with kindergarten teachers to modify an existing Grade 2 curricular unit for use with their students; 2) design a parallel curricular unit incorporating technology; 3) evaluate both units for feasibility and maturation effects; and 4) iteratively revise and pilot an integrated unit and assess kindergarten student conceptual understanding of matter and its changes. The results of this investigation will contribute important data on the evolving structure and content of childrens physical science models as well as demonstrate childrens understanding of matter and its changes.
The Discovery Research K-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools (RMTs). Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.
Agency: NSF | Branch: Continuing grant | Program: | Phase: DISCOVERY RESEARCH K-12 | Award Amount: 886.09K | Year: 2016
The Discovery Research K-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools (RMTs). Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. This project will contribute to this mission by designing, developing, and examining the learning outcomes of a new curriculum unit for biology that embodies the conceptual framework of the Next Generation Science Standards (NGSS). The curriculum materials to be developed by this project will focus on two areas of study that are central to the life sciences: genetics and the processes of evolution by natural selection. These traditionally separate topics will be interlinked and will be designed to engage students in the disciplinary core ideas, crosscutting concepts, and the science and engineering practices defined by the NGSS. Once developed, the curriculum materials will be available online for use in high school biology courses nationwide.
This project will be guided by two main research questions: 1) How does learning progress when students experience a set of coherent biology learning materials that employ the principles of three-dimensional learning?; and 2) How do students abilities to transfer understanding about the relationships between molecules, cells, organisms, and evolution change over time and from one biological phenomenon to another? The project will follow an iterative development plan involving cycles of designing, developing, testing and refining elements of the new curricular model. The project team will work with master teachers to design learning sequences that use six case studies to provide examples of how genetic and evolutionary processes are interlinked. An online data exploration environment will extend learning by enabling students to simulate phenomena being studied and explore data from multiple experimental trials as they seek patterns and construct cause-and-effect explanations of phenomena. Student learning will be measured using a variety of assessment tools, including multiple-choice assessment of student understanding, surveys, classroom observations and interviews, and embedded assessments and log files from the online learning environment.
Agency: NSF | Branch: Standard Grant | Program: | Phase: STEM + Computing (STEM+C) Part | Award Amount: 2.49M | Year: 2016
Computing and computational thinking are an integral part of everyday practice within modern fields of science, technology, engineering, and math (STEM). As a result, the STEM+Computing Partnerships (STEM+C) program seeks to advance new multidisciplinary approaches to, and evidence-based understanding of, the integration of computing in STEM teaching and learning, and discipline-specific efforts in computing designed to build an evidence base for teaching and learning of computer science in K-12, including within diverse populations. This project will develop, implement, and study an innovative multi-week middle school curriculum unit in computational weather forecasting that integrates students learning and use of meteorology, mathematics, and computational thinking concepts and practices. Led by investigators at Concord Consortium, the project team includes weather scientists, computer scientists, education developers, and learning scientists from Argonne National Laboratory, Millersville University, and the University of Illinois at Chicago. The curriculum consists of instructional materials and technologies that transform classrooms into dynamic weather simulations and then scaffold students learning and use of science, mathematics, and computational thinking as they (a) collect and analyze data from the simulated weather events; (b) develop and refine computational models from these data; (c) and use computational models to make and evaluate weather predictions. Live webcasts with meteorologists enable students to learn about how they made predictions from same data sets students examined. Approximately 430 students will be involved with and benefit from the project. The diverse nature of the participating schools will both engage a demographically diverse student population in STEM and help the project achieve significant broader impacts, by assuring that the findings and curriculum developed reflect the needs of a broad diversity of people and places.
This project will address a daunting challenge to developing STEM literacy in students: integrating teaching and learning of key ideas and practices of science and mathematics with computational thinking in authentic, innovative and effective ways. The project will exploit young peoples universal interest in weather and the computationally intensive practice of modern meteorology to develop an inquiry-based curriculum in which students play the role of scientific experts that apply computational thinking as they explore, explain, and predict weather phenomena. The curriculum consists of a) four standards-aligned instructional sequences; b) a suite of technologies, software systems, and weather data sets; and c) professional development workshops and materials to support teachers curriculum implementation. The intervention will address specific needs of middle school students and teachers with regard to relevant disciplinary content, practices, and computation as specified in Next Generation Science Standards, Common Core State Standards for mathematics, and recent consensus frameworks for computational thinking in STEM. Over three years the project will engage eight teachers and their 430 students who will work with the project team members to test the curriculum in distinctive middle school settings in Illinois, Massachusetts, and Alaska. The mixed methods design-based research will use a rich set of student tests, reflections, narrated computer models (called screencasts), and video recordings of students during classroom activities as sources of evidence. The research seeks to understand how students use computational thinking practices to generate explanations and predictions of weather events, and how these explanations and predictions evolve with the sequence and complexity of computational thinking practices. The research further seeks to understand how a set of core design elements of the curriculum facilitate students computational thinking and reasoning about weather events. The research and external evaluation will explore replicability and scale by elucidating how findings and design elements generalize to unique populations, such as Alaskan Inuit students, and the contexts in which they learn. The project will produce an evidence-based trajectory of learning that describes how students become more sophisticated in their understanding of weather science and in their scientific explanations and predictions of weather events in conjunction with their use of key computational practices of collecting, interpreting, and representing data; evaluating and predicting with computational models. The project will also produce a set of evidence-based design principles for broader dissemination. The research findings will be shared with Concord Consortiums extensive network of more than 25,000 teachers, researchers, and policymakers, and by more traditional means, such as papers in peer-reviewed journals and conference presentations. The curriculum will be licensed via open source and open content licenses and freely distributed to other teachers, curriculum designers, and researchers through Concord Consortiums website.
Agency: NSF | Branch: Continuing grant | Program: | Phase: DISCOVERY RESEARCH K-12 | Award Amount: 1.50M | Year: 2016
The Discovery Research K-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools (RMTs). Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. This project will contribute to the Earth science education communitys understanding of how engaging students with dynamic computer-based systems models supports their learning of complex Earth science concepts regarding Earths surface phenomena and sub-surface processes. It will also extend the fields understandings of how students develop modeling practices and how models are used to support scientific endeavors. This research will shed light on the role uncertainty plays when students use models to develop scientific arguments with model-based evidence. The GEODE project will directly involve over 4,000 students and 22 teachers from diverse school systems serving students from families with a variety of socioeconomic, cultural, and racial backgrounds. These students will engage with important geoscience concepts that underlie some of the most critical socio-scientific challenges facing humanity at this time. The GEODE project research will also seek to understand how teachers practices need to change in order to take advantage of these sophisticated geodynamic modeling tools. The materials generated through design and development will be made available for free to all future learners, teachers, and researchers beyond the participants outlined in the project.
The GEODE project will develop and research the transformational potential of geodynamic models embedded in learning progression-informed online curricula modules for middle school teaching and learning of Earth science. The primary goal of the project is to conduct design-based research to study the development of model-based curriculum modules, assessment instruments, and professional development materials for supporting student learning of (1) plate tectonics and related Earth processes, (2) modeling practices, and (3) uncertainty-infused argumentation practices. The GEODE software will permit students to program a series of geologic events into the model, gather evidence from the emergent phenomena that result from the model, revise the model, and use their models to explain the dynamic mechanisms related to plate motion and associated geologic phenomena such as sedimentation, volcanic eruptions, earthquakes, and deformation of strata. The project will also study the types of teacher practices necessary for supporting the use of dynamic computer models of complex phenomena and the use of curriculum that include an explicit focus on uncertainty-infused argumentation.
Agency: NSF | Branch: Continuing grant | Program: | Phase: DISCOVERY RESEARCH K-12 | Award Amount: 1.89M | Year: 2016
This project will create technology-enhanced classroom activities and resources that increase student learning of science practices in high school biology, chemistry, and physics courses. The project addresses the urgent national priority to improve science education as envisioned in the Next Generation Science Standards (NGSS) by focusing less on learning facts and equations and instead providing students with the time, skills, and resources to experience the conduct of science and what it means to be a scientist. This project builds on prior work that created a sequence of physics activities that significantly improved students abilities to undertake data-based experiments and led to productive independent investigations. The goal of the InquirySpace project is to improve this physics sequence, extend the approach to biology and chemistry, and adapt the materials to the needs of diverse students by integrating tailored formative feedback in real time. The result will be student and teacher materials that any school can use to allow students to experience the excitement and essence of scientific investigations as an integral part of science instruction. The project plans to create and iteratively revise learning materials and technologies, and will be tested in 48 diverse classroom settings. The educational impact of the projects approach will be compared with that of business-as-usual approaches used by teachers to investigate to what extent it empowers students to undertake self-directed experiments. To facilitate the widest possible use of the project, a complete set of materials, software, teacher professional development resources, and curriculum design documents will be available online at the project website, an online teacher professional development course, and teacher community sites. The Discovery Research K-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools (RMTs). Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.
InquirySpace will incorporate several innovative technological and pedagogical features that will enable students to undertake scientific experimentation that closely mirrors current science research. These features will include (1) educational games to teach data analysis and interpretation skills needed in the approach, (2) reduced dependence on reading and writing through the use of screencast instructions and reports, (3) increased reliance on graphical analysis that can make equations unnecessary, and (4) extensive use of formative feedback generated from student logs. The project uses an overarching framework called Parameter Space Reasoning (PSR) to scaffold students through a type of experimentation applicable to a very large class of experiments. PSR involves an integrated set of science practices related to a question that can be answered with a series of data collection runs for different values of independent variables. Data can be collected from sensors attached to the computer, analysis of videos, scientific databases, or computational models. A variety of visual analytic tools will be provided to reveal patterns in the graphs. Research will be conducted in three phases: design and development of technology-enhanced learning materials through design-based research, estimation of educational impact using a quasi-experimental design, and feasibility testing across diverse classroom settings. The project will use two analytical algorithms to diagnose students learning of data analysis and interpretation practices so that teachers and students can modify their actions based on formative feedback in real time. These algorithms use computationally optimized calculations to model the growth of student thinking and investigation patterns and provide actionable information to teachers and students almost instantly. Because formative feedback can improve instruction in any field, this is a major development that has wide potential.
Agency: National Science Foundation | Branch: | Program: STTR | Phase: Phase I | Award Amount: 225.00K | Year: 2016
This STTR Phase I project will carry out research and development on a cloud-based pluggable data analytics engine to address the educational game market?s need of real-time assessment for learning. Educational games will become much more successful if learning from games can be well quantified so that buyers will be assured that the time spent using games is productive. However, currently game makers are not qualified or funded to provide the statistics and cognitive assessment required for such analysis. This project will thus build a prototype of commercial pluggable third-party engine that traces the growth of the learner's knowledge in real time without interference and provides customized assessment summary and feedback to educational stakeholders. The prototype will be developed and tested with games that teach data literacy in three high schools representing diverse demographic groups. The testing in a commercial environment will begin in collaboration with two successful educational game companies. The innovative use of data-intensive assessment technology will aid in currently struggling STEM education in the United States by providing streamlined and accurate information while learning occurs. This project will also help launch a new business that has potential to boost the market value of educational games and digital learning. This STTR Phase I project utilizes the Monte-Carlo Bayesian Knowledge Tracing (MC-BKT) algorithm. This algorithm was recently developed in-house based on techniques distilled through years of research in physics, education, and computation, and makes it possible to perform individualized knowledge tracing in real-time for the first time. In prior research, post hoc MC-BKT analysis led to identification of up to seven distinct patterns associated with knowledge growth during game segments, with 84% accuracy as compared with human judgments based on video analysis of game screens and players' discourse. This project will conduct research to test whether this assessment potential of the MC-BKT algorithm can be extended beyond initial research to players with games involving different content domains, in a greater number of classrooms with diverse demographics (involving around 600 high school students), and in real time. Based on research results, this project will build a prototype commercial product around the MC-BKT algorithm in the form of a cloud-based pluggable engine. Two popular commercial educational games as well as various games internally sourced within this project will be test-connected to the engine for real-time testing of knowledge tracing, learning problem detection, and feedback delivery to teachers, parents, game designers, and learners.
Agency: NSF | Branch: Continuing grant | Program: | Phase: DISCOVERY RESEARCH K-12 | Award Amount: 1.11M | Year: 2016
The primary goal of this project is to help middle school students deepen and communicate their understanding of mathematics. The project will develop and test a digital platform for middle school mathematics classrooms. The digital platform will allow students to collaboratively create representations of their mathematics thinking, incorporate ideas from other students, and share their work with the class. The digital learning environment makes use of a problem-centered mathematics curriculum that evolved from extensive development, field-testing and evaluation, and is widely used in middle schools. The research will also contribute to understanding about the design and innovative use of digital resources and collaboration in classrooms as an increasing number of schools are drawing on these kinds of tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.
The project will support students to collaboratively construct, manipulate, and interpret shared representations of mathematics using digital inscriptional resources. The research activities will significantly enhance our understanding of student learning in mathematics in three important ways. The project will report on how (1) evidence of student thinking is made visible through the use of digital inscriptional resources, (2) student inscriptions are documented, discussed, and manipulated in collaborative settings, and (3) students conceptual growth of big mathematical ideas grows over time. An iterative design research process will incorporate four phases of development, testing and revision, and will be conducted to study student use of the digital learning space and related inscriptional resources. Data sources will include: classroom observations and artifacts, student and teacher interviews and surveys, student assessment data, and analytics from the digital platform. The process will include close collaboration with teachers to understand the implementation and create revisions to the resources.