Technology

Supports for Science and Mathematics Learning in Pre-Kindergarten Dual Language Learners: Designing and Expanding a Professional Development System

SciMath-DLL is an innovative preschool professional development (PD) model that integrates supports for dual language learners (DLLs) with high quality science and mathematics instructional offerings. It engages teachers with workshops, classroom-based coaching, and professional learning communities. Based on initial evidence of promise, the SciMath-DLL project will expand PD offerings to include web-based materials.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
1417040
Funding Period: 
Tue, 07/01/2014 to Sat, 06/30/2018
Full Description: 

The 4-year project, Supports for Science and Mathematics Learning in Pre-Kindergarten Dual Language Learners: Designing and Expanding a Professional Development System (SciMath-DLL), will address a number of educational challenges. Global society requires citizens and a workforce that are literate in science, technology, engineering, and mathematics (STEM), but many U.S. students remain ill prepared in these areas. At the same time, the children who fill U.S. classrooms increasingly speak a non-English home language, with the highest concentration in the early grades. Many young children are also at risk for lack of school readiness in language, literacy, mathematics, and science due to family background factors. Educational efforts to offset early risk factors can be successful, with clear links between high quality early learning experiences and later academic outcomes. SciMath-DLL will help teachers provide effective mathematics and science learning experiences for their students. Early educational support is critical to assure that all students, regardless of socioeconomic or linguistic background, learn the STEM content required to become science and mathematics literate. Converging lines of research suggest that participation in sustained mathematics and science learning activities could enhance the school readiness of preschool dual language learners. Positive effects of combining science inquiry with supports for English-language learning have been identified for older students. For preschoolers, sustained science and math learning opportunities enhance language and pre-literacy skills for children learning one language. Mathematics skills and science knowledge also predict later mathematics, science, and reading achievement. What has not been studied is the extent to which rich science and mathematics experiences in preschool lead to better mathematics and science readiness and improved language skills for preschool DLLs. Because the preschool teaching force is not prepared to support STEM learning or to provide effective supports for DLLs, professional development to improve knowledge and practice in these areas is required before children's learning outcomes can be improved.

SciMath-DLL is an innovative preschool professional development (PD) model that integrates supports for DLLs with high quality science and mathematics instructional offerings. It engages teachers with workshops, classroom-based coaching, and professional learning communities. Development and research activities incorporate cycles of design-expert review-enactment- analysis-redesign; collaboration between researcher-educator teams at all project stages; use of multiple kinds of data and data sources to establish claims; and more traditional, experimental methodologies. Based on initial evidence of promise, the SciMath-DLL project will expand PD offerings to include web-based materials, making the PD more flexible for use in a range of educational settings and training circumstances. An efficacy study will be completed to examine the potential of the SciMath-DLL resources, model, and tools to generate positive effects on teacher attitudes, knowledge, and practice for early mathematics and science and on children's readiness in these domains in settings that serve children learning two languages. By creating a suite of tools that can be used under differing educational circumstances to improve professional knowledge, skill, and practice around STEM, the project increases the number of teachers who are prepared to support children as STEM learners and, thus, the number of children who can be supported as STEM learners.

Supporting Secondary Students in Building External Models (Collaborative Research: Damelin)

This project will (1) develop and test a modeling tool and accompanying instructional materials, (2) explore how to support students in building and using models to explain and predict phenomena across a range of disciplines, and (3) document the sophistication of understanding of disciplinary core ideas that students develop when building and using models in grades 6-12. 

Lead Organization(s): 
Award Number: 
1417809
Funding Period: 
Fri, 08/01/2014 to Tue, 07/31/2018
Full Description: 

The Concord Consortium and Michigan State University will collaborate to: (1) develop and test a modeling tool and accompanying instructional materials, (2) explore how to support students in building and using models to explain and predict phenomena across a range of disciplines, and (3) document the sophistication of understanding of disciplinary core ideas that students develop when building and using models in grades 6-12. By iteratively designing, developing and testing a modeling tool and instructional materials that facilitate the building of dynamic models, the project will result in exemplary middle and high school materials that use a model-based approach as well as an understanding of the potential of this approach in supporting student development of explanatory frameworks and modeling capabilities. A key goal of the project is to increase students' learning of science through modeling and to study student engagement with modeling as a scientific practice. 

The project provides the nation with middle and high school resources that support students in developing and using models to explain and predict phenomena, a central scientific and engineering practice. Because the research and development work will be carried out in schools in which students typically do not succeed in science, the products will also help produce a population of citizens capable of continuing further STEM learning and who can participate knowledgeably in public decision making. The goals of the project are to (1) develop and test a modeling tool and accompanying instructional materials, (2) explore how to support students in building, using, and revising models to explain and predict phenomena across a range of disciplines, and (3) document the sophistication of understanding of disciplinary core ideas that students develop when building and using models in grades 6-12. Using a design-based research methodology, the research and development efforts will involve multiple cycles of designing, developing, testing, and refining the systems modeling tool and the instructional materials to help students meet important learning goals related to constructing dynamic models that align with the Next Generation Science Standards. The learning research will study the effect of working with external models on student construction of robust explanatory conceptual understanding. Additionally, it will develop a set of professional development resources and teacher scaffolds to help the expanding community of teachers not directly involved in the project take advantage of the materials and strategies for maximizing the impact of the curricular materials.

Researching the Impact of an Online MOOC Designed to Transform Student Engagement and Achievement in Mathematics

This study examines non-cognitive factors, mindsets, cognitive factors, and strategies for learning mathematics, in the context of a MOOC combined with classroom instruction for middle grades students in mathematics. No previous mindset study has researched the impact of mindset messages within mathematics, and the proposed study will add important knowledge to this field.

Lead Organization(s): 
Award Number: 
1443790
Funding Period: 
Tue, 07/01/2014 to Tue, 06/30/2015
Full Description: 

This project is designed to study a Massively Open Online Course (MOOC), expected to have approximately 2 million students, which will supplement middle grades mathematics classes to understand the impact on students' mathematical learning and engagement in mathematics. The MOOC learning environment, used with school aged children in concert with their regular mathematics course, focuses on helping students to develop positive and productive beliefs, or growth mindsets, about their own potential in mathematics and to teach the students a range of strategies that lead to mathematics success. A better understanding of growth mindsets and learning how to learn mathematics in the context of regular classroom instruction potentially makes important contributions in introducing a new intervention to tens of thousands of students. This contribution is made in concert with providing evidence of impact of using MOOCs coupled with classroom instruction with school aged children on student learning. If the study finds that the mathematics intervention MOOC significantly increases students' achievement and engagement with mathematics, it can be scaled nationally and potentially change the face of mathematics education in the United States.

This study examines non-cognitive factors, mindsets, cognitive factors, and strategies for learning mathematics, in the context of a MOOC combined with classroom instruction for middle grades students in mathematics. No previous mindset study has researched the impact of mindset messages within mathematics, and the proposed study will add important knowledge to this field. The study will also contribute to new knowledge of MOOCs, of their potential as learning opportunities and of the design of innovative pedagogies. Using a blocked randomized control trial of 10,000 students in two California districts, the statistical design employed will enable schools to implement the program across entire classes of students. The study employs measures of pre/post changes in mathematics engagement, mindset, use of mathematics strategies, and mathematics achievement, with close examination of the implications for girls, students of color, students of different socio-economic-status and low achieving students.

Learning Linkages: Integrating Data Streams of Multiple Modalities and Timescales (Collaborative Research: Sherin)

In this project, researchers will collaborate to enhance understanding of influences on learning, and improve teaching and learning in high school and middle school STEM classes. They will leverage the latest tools for data processing and many different streams of data that can be collected in technology-rich classrooms to (1) identify classroom factors that affect learning and (2) explore how to use that data to automatically track development of students' understanding and capabilities over time.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
1418020
Funding Period: 
Mon, 09/01/2014 to Thu, 08/31/2017
Full Description: 

This Research on Education and Learning (REAL) project arises from an October 2014 Ideas Lab on Data-intensive Research to Improve Teaching and Learning. The intentions of that effort were to (1) bring together researchers from across disciplines to foster novel, transformative, multidisciplinary approaches to using the data in large education-related data sets to create actionable knowledge for improving STEM teaching and learning environments in the medium term; and (2) revolutionize learning in the longer term. In this project, researchers from Carnegie-Mellon University, Wested, Arizona State University, and Northwestern University will collaborate to enhance understanding of influences on learning, and improve teaching and learning in high school and middle school STEM classes. To accomplish this, they will leverage the latest tools for data processing and many different streams of data that can be collected in technology-rich classrooms to (1) identify classroom factors that affect learning and (2) explore how to use that data to automatically track development of students' understanding and capabilities over time.

Two forces are poised to transform research on learning. First, more and more student work is conducted on computers and online, producing vast amounts of learning-related data. At the same time, advances in computing, data mining, and learning analytics are providing new tools for the collection, analysis, and representation of these data. Together, the available data and analytical tools enable smart and responsive systems that personalize learning experiences for individual learners. The PIs aim to collect highly enriched data that go far beyond typical computer data capture, leveraging the latest tools for data processing to generate new insights about STEM teaching and learning. Working to maximize the potential while mitigating the risks of automated data collection and analysis, they will: (1) collect and integrate diverse sources of data including log files, videos, and written artifacts from across eight different two-week enactments of two different computer supported learning environments (one used in middle school math and one in high school science); and (2) compare analyses of log-file data with analyses of integrated datasets to understand the possibilities and limitations in using log-file data for assessment of student learning and proficiency. The collaborators expect their findings will inform both theories and practical recommendations applicable across a wide range of disciplines and settings.

Investigating How to Enhance Scientific Argumentation through Automated Feedback in the Context of Two High School Earth Science Curriculum Units

This project responds to the need for technology-enhanced assessments that promote the critical practice of scientific argumentation--making and explaining a claim from evidence about a scientific question and critically evaluating sources of uncertainty in the claim. It will investigate how to enhance this practice through automated scoring and immediate feedback in the context of two high school curriculum units--climate change and fresh-water availability--in schools with diverse student populations. 

Lead Organization(s): 
Award Number: 
1418019
Funding Period: 
Mon, 09/01/2014 to Fri, 08/31/2018
Full Description: 

With the current emphasis on learning science by actively engaging in the practices of science, and the call for integration of instruction and assessment; new resources, models, and technologies are being developed to improve K-12 science learning. Student assessment has become a nationwide educational priority due, in part, to the need for relevant and timely data that inform teachers, administrators, researchers, and the public about how all students perform and think while learning science. This project responds to the need for technology-enhanced assessments that promote the critical practice of scientific argumentation--making and explaining a claim from evidence about a scientific question and critically evaluating sources of uncertainty in the claim. It will investigate how to enhance this practice through automated scoring and immediate feedback in the context of two high school curriculum units--climate change and fresh-water availability--in schools with diverse student populations. The project will apply advanced automated scoring tools to students' written scientific arguments, provide individual students with customized feedback, and teachers with class-level information to assist them with improving scientific argumentation. The key outcome of this effort will be a technology-supported assessment model of how to advance the understanding of argumentation, and the use of multi-level feedback as a component of effective teaching and learning. The project will strengthen the program's current set of funded activities on assessment, focusing these efforts on students' argumentation as a complex science practice.

This design and development research targets high school students (n=1,940) and teachers (n=22) in up to 10 states over four years. The research questions are: (1) To what extent can automated scoring tools, such as c-rater and c-rater-ML, diagnose students' explanations and uncertainty articulations as compared to human diagnosis?; (2) How should feedback be designed and delivered to help students improve scientific argumentation?; (3) How do teachers use and interact with class-level automated scores and feedback to support students' scientific argumentation with real-data and models?; and (4) How do students perceive their overall experience with the automated scores and immediate feedback when learning core ideas in climate change and fresh-water availability topics through scientific argumentation enhanced with modeling? In Years 1 and 2, plans are to conduct feasibility studies to build automated scoring models and design feedback for previously tested assessments for the two curriculum units. In Year 3, the project will implement design studies in order to identify effective feedback through random assignment. In Year 4, a pilot study will investigate if effective feedback should be offered with or without scores. The project will employ a mixed-methods approach. Data-gathering strategies will include classroom observations; screencast and log data of teachers' and students' interaction with automated feedback; teachers' and students' surveys with selected- and open-ended questions; and in-depth interviews with teachers and students. All constructed-response explanations and uncertainty items will be scored using automated scoring engines with fine-grained rubrics. Data analysis strategies will include multiple criteria to evaluate the quality of automated scores; descriptive statistical abalyses; analysis of variance to investigate differences in outcomes from the designed studies' pre/posttests and embedded assessments; analysis of covariance to investigate student learning trajectories; two-level hierarchical linear modeling to study the clustering of students within a class; and analysis of screencasts and log data.

GRIDS: Graphing Research on Inquiry with Data in Science

The Graphing Research on Inquiry with Data in Science (GRIDS) project will investigate strategies to improve middle school students' science learning by focusing on student ability to interpret and use graphs. GRIDS will undertake a comprehensive program to address the need for improved graph comprehension. The project will create, study, and disseminate technology-based assessments, technologies that aid graph interpretation, instructional designs, professional development, and learning materials.

Award Number: 
1418423
Funding Period: 
Mon, 09/01/2014 to Sat, 08/31/2019
Full Description: 

The Graphing Research on Inquiry with Data in Science (GRIDS) project is a four-year full design and development proposal, addressing the learning strand, submitted to the DR K-12 program at the NSF. GRIDS will investigate strategies to improve middle school students' science learning by focusing on student ability to interpret and use graphs. In middle school math, students typically graph only linear functions and rarely encounter features used in science, such as units, scientific notation, non-integer values, noise, cycles, and exponentials. Science teachers rarely teach about the graph features needed in science, so students are left to learn science without recourse to what is inarguably a key tool in learning and doing science. GRIDS will undertake a comprehensive program to address the need for improved graph comprehension. The project will create, study, and disseminate technology-based assessments, technologies that aid graph interpretation, instructional designs, professional development, and learning materials.

GRIDS will start by developing the GRIDS Graphing Inventory (GGI), an online, research-based measure of graphing skills that are relevant to middle school science. The project will address gaps revealed by the GGI by designing instructional activities that feature powerful digital technologies including automated guidance based on analysis of student generated graphs and student writing about graphs. These materials will be tested in classroom comparison studies using the GGI to assess both annual and longitudinal progress. Approximately 30 teachers selected from 10 public middle schools will participate in the project, along with approximately 4,000 students in their classrooms. A series of design studies will be conducted to create and test ten units of study and associated assessments, and a minimum of 30 comparison studies will be conducted to optimize instructional strategies. The comparison studies will include a minimum of 5 experiments per term, each with 6 teachers and their 600-800 students. The project will develop supports for teachers to guide students to use graphs and science knowledge to deepen understanding, and to develop agency and identity as science learners.

Engineering Teacher Pedagogy: Using INSPIRES to Support Integration of Engineering Design in Science and Technology Classrooms

This Engineering Teacher Pedagogy project implements and assesses the promise of an extended professional development model coupled with curriculum enactment to develop teacher pedagogical skills for integrating engineering design into high school biology and technology education classrooms. 

Award Number: 
1418183
Funding Period: 
Mon, 09/01/2014 to Sat, 08/31/2019
Full Description: 

National college and career readiness standards call for integrating engineering practices into science and mathematics instruction. Very few models for doing this have been implemented and studied. This Engineering Teacher Pedagogy project implements and assesses the promise of an extended professional development model coupled with curriculum enactment to develop teacher pedagogical skills for integrating engineering design into high school biology and technology education classrooms. Professional development is provided to twenty high school biology teachers and twenty technology education teachers in the Baltimore County Public Schools.

The professional development consists of two five day sessions in two consecutive summers and follow up in two academic years as the teachers learn content, pedagogical content knowledge and classroom management skills. The project investigates the teachers' learning trajectories using validated instruments. A longitudinal study investigates teachers' change in practice and its role on student learning through classroom observations and examination of student artifacts. The study also investigates whether the change in practice persists over time and the extent to which the change in practice transfers to other learning environments. This study should elucidate the issues of teaching science concepts through the use of science and engineering practices.

Teaching STEM with Robotics: Design, Development, and Testing of a Research-based Professional Development Program for Teachers

Using design-based research, with teachers as design partners, the project will create and refine project-based, hands-on robotics curricula such that science and math content inherent in robotics and related engineering design practices are learned. To provide teachers with effective models to capitalize on robotics for elucidating science and math concepts, a design-based Professional Development program will be built using principles of technological, pedagogical, and content knowledge (TPACK).

Lead Organization(s): 
Award Number: 
1417769
Funding Period: 
Mon, 09/01/2014 to Fri, 08/31/2018
Full Description: 

Offering meaningful and motivating engineering contexts, such as robotics, within science and math courses constitutes a compelling strategy to address the Next Generation Science Standards and the Common Core State Standards for Math while enhancing science and math learning for all students. Using design-based research, with teachers as design partners, the project will create and refine project-based, hands-on curricula such that science and math content inherent in robotics and related engineering design practices are learned. To provide teachers with effective models to capitalize on robotics for elucidating science and math concepts, a design-based Professional Development program will be built using principles of technological, pedagogical, and content knowledge (TPACK). To ensure that teachers are well prepared, research-based practices and features of effective Professional Development will be adopted. Experts in robotics, engineering, education, curriculum design, and assessment--with experience in K-12 education, training, and outreach--have formed an interdisciplinary team to make robotics central to and sustainable in middle school science and math classrooms.

The research questions addressed in this project are qualitative in nature as appropriate for design research questions. The methodologies include teacher needs assessment, teachers' perceptions of robotics, pre and post testing, classroom observations, and surveys. Examples of the research questions are:

What characteristics of robotics promote effective learning of middle school science and math?

What elements of Professional Development engender teachers' TPACK of robotics and link it with classroom science and math?

What are student prerequisites to effectively use robotics in science and math learning?

What are the gains in students' STEM engagement, interest, persistence, and career awareness?

The robotics curriculum will include physical science used in robot performance expectations and motion stability. Additionally the curriculum will include the engineering design process consisting of problem definition, solution development, and design improvement. Robotics provides opportunities to support science and engineering practices of the Next Generation Science Standards such as developing and using models, planning and conducting investigations, designing solutions, and analyzing and interpreting data. The project will be aimed at middle school students and will provide substantial teacher professional development to implement the new curriculum modules. The partner schools have student bodies drawn from a diverse student population in New York City.

CodeR4STATS - Code R for AP Statistics

This project builds on prior efforts to create teaching resources for high-school Advanced Placement Statistics teachers to use an open source statistics programming language called "R" in their classrooms. The project brings together datasets from a variety of STEM domains, and will develop exercises and assessments to teach students how to program in R and learn the underlying statistics concepts.

Lead Organization(s): 
Award Number: 
1418163
Funding Period: 
Mon, 09/01/2014 to Sat, 08/31/2019
Full Description: 

Increasingly, all STEM fields rely on being able to understand data and use statistics. This project builds on prior efforts to create teaching resources for high-school Advanced Placement Statistics teachers to use an open source statistics programming language called "R" in their classrooms. The project brings together datasets from a variety of STEM domains, and will develop exercises and assessments to teach students how to program in R and learn the underlying statistics concepts. Thus, this project attempts to help students learn coding, statistics, and STEM simultaneously in the context of AP Stats. In addition, researchers will examine the extent to which students learn statistical concepts, computational fluency, and critical reasoning skills better with the online tools.

The resources developed by the project aim to enhance statistics learning through an integrated application of strategies previously documented to be effective: a focus on data visualization and representation, engaging students in meaningful investigations with complex real-world data sets, utilizing computational tools and techniques to analyze data, and better preparing educators for the needs of a more complex and technologically-rich mathematical landscape. This project will unite these lines of work into one streamlined pedagogical environment called CodeR4STATS with three kinds of resources: computing resources, datasets, and assessment resources. Computing resources will include freely available access to an instance of the cloud-based R-studio with custom help pages. Data resources will include over 800 scientific datasets from Woods Hole Oceanographic Institute, Harvard University's Institute for Quantitative Social Science, Hubbard Brook Experimental Forest, Boston University, and Tufts University with several highlighted in case studies for students; these will be searchable within the online environment. Assessment and tutoring resources will be provided using the tutoring platform ASSISTments which uses example tracing to provide assessment, feedback, and tailored instruction. Teacher training and a teacher online discussion board will also be provided. Bringing these resources together will be programming lab activities, five real-world case studies, and sixteen statistics assignments linked to common core math standards. Researchers will use classroom observational case studies from three classrooms over two years, including cross-case comparison of lessons in the computational environment versus offline lessons; student and teacher interviews; and an analysis of learner data from the online system, especially the ASSISTments-based assessment data. This research will examine learning outcomes and help refine design principles for statistics learning environments.

Changing Culture in Robotics Classroom (Collaborative Research: Shoop)

Computational and algorithmic thinking are new basic skills for the 21st century. Unfortunately few K-12 schools in the United States offer significant courses that address learning these skills. However many schools do offer robotics courses. These courses can incorporate computational thinking instruction but frequently do not. This research project aims to address this problem by developing a comprehensive set of resources designed to address teacher preparation, course content, and access to resources.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
1418199
Funding Period: 
Mon, 09/01/2014 to Thu, 08/31/2017
Full Description: 

Computational and algorithmic thinking are new basic skills for the 21st century. Unfortunately few K-12 schools in the United States offer significant courses that address learning these skills. However many schools do offer robotics courses. These courses can incorporate computational thinking instruction but frequently do not. This research project aims to address this problem by developing a comprehensive set of resources designed to address teacher preparation, course content, and access to resources. This project builds upon a ten year collaboration between Carnegie Mellon's Robotics Academy and the University of Pittsburgh's Learning Research and Development Center that studied how teachers implement robotics education in their classrooms and developed curricula that led to significant learning gains. This project will address the following three questions:

1.What kinds of resources are useful for motivating and preparing teachers to teach computational thinking and for students to learn computational thinking?

2.Where do teachers struggle most in teaching computational thinking principles and what kinds of supports are needed to address these weaknesses?

3.Can virtual environments be used to significantly increase access to computational thinking principles?

The project will augment traditional robotics classrooms and competitions with Robot Virtual World (RVW) that will scaffold student access to higher-order problems. These virtual robots look just like real-world robots and will be programmed using identical tools but have zero mechanical error. Because dealing with sensor, mechanical, and actuator error adds significant noise to the feedback students' receive when programming traditional robots (thus decreasing the learning of computational principles), the use of virtual robots will increase the learning of robot planning tasks which increases learning of computational thinking principles. The use of RVW will allow the development of new Model-Eliciting Activities using new virtual robotics challenges that reward creativity, abstraction, algorithms, and higher level programming concepts to solve them. New curriculum will be developed for the advanced concepts to be incorporated into existing curriculum materials. The curriculum and learning strategies will be implemented in the classroom following teacher professional development focusing on computational thinking principles. The opportunities for incorporating computationally thinking principles in the RVW challenges will be assessed using detailed task analyses. Additionally regression analyses of log-files will be done to determine where students have difficulties. Observations of classrooms, surveys of students and teachers, and think-alouds will be used to assess the effectiveness of the curricula in addition to pre-and post- tests to determine student learning outcomes.

Pages

Subscribe to Technology