Computer Science

Taking Games to School: Exploratory Study to Support Game-based Teaching and Learning In High-School Science Classes

This project is building a set of software tools, including a tool for annotating screen recordings of activities in games, a teacher data dashboard for information about students' in-game learning, and tools to help teachers customize activities in games to better align with curricular standards. The project will find out whether these new tools can enhance teaching and/or learning. 

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
1415284
Funding Period: 
Tue, 07/15/2014 to Sat, 06/30/2018
Full Description: 

Research shows that educational games can enhance students' science learning, but current work leaves teachers dependent on researchers and games companies to provide good games and game-based curricula. This project aims to study how teachers can be involved in making science learning games more effective, and how educational science games can better support good teaching. This project is building a set of software tools, including a tool for annotating screen recordings of activities in games, a teacher data dashboard for information about students' in-game learning, and tools to help teachers customize activities in games to better align with curricular standards. It will conduct studies with successful research-based educational games for learning science, and popularly available educational games from websites such as BrainPop, in a network of teachers who have experience using 'canned' games in their classrooms. The project will find out whether these new tools can enhance teaching and/or learning. It will also help develop a list of the types of customization options teachers need in order to be able to effectively use educational games in their classrooms. If successful, this research could point the way towards new tools that let teachers create activities that turn any game into an educational game, and to better use existing educational games in their classrooms. This could greatly speed up our ability to deliver high-quality learning experiences through educational games.

This project involves a participatory design process in which a small number of experienced teachers will feed into a principled, iterative refinement of prototypes of the tools (annotation, data dashboard, and level-builder) to be prototyped within the Brainplay suite. In the beta testing phase, a hierarchical linear model analysis will be conducted on both student and teacher outcomes in 25 classrooms. In addition to the quantitative analysis, qualitative studies involving classroom observations, focus groups, and teacher journaling will be conducted to examine impact on teaching practices and refine the functional specifications. Project dissemination will take place through the community around the previously-developed Leveling Up games (played around 10,000 times per week), and through existing professional networks such as Edmodo. The project will also work within the games community to help inform possible approaches to logging learning data and allowing teacher customization across all games.

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.

EarSketch: An Authentic, Studio-based STEAM Approach to High School Computing Education

This project will study the influence on positive student achievement and engagement (particularly among populations traditionally under-represented in computer science) of an intervention that integrates a computational music remixing tool -EarSketch- with the Computer Science Principles, a view of computing literacy that is emerging as a new standard for Advanced Placement and other high school computer science courses.

Award Number: 
1417835
Funding Period: 
Fri, 08/01/2014 to Tue, 07/31/2018
Project Evaluator: 
Mary Moriarity
Full Description: 

This project will study the influence on positive student achievement and engagement (particularly among populations traditionally under-represented in computer science) of an intervention that integrates a computational music remixing tool -EarSketch- with the Computer Science Principles, a view of computing literacy that is emerging as a new standard for Advanced Placement and other high school computer science courses. The project is grounded on the premise that EarSketch, a STEM + Art (STEAM) learning environment, embodies authenticity (i.e., its cultural and industry relevance in both arts and STEM domains), along with a context that facilitates communication and collaboration among students (i.e., through a studio-based learning approach). These elements are critical to achieving successful outcomes across diverse student populations. Using agent-based modeling, the research team will investigate what factors enhance or impede implementation of authentic STEAM tools in different school settings.

The researchers will be engaged in a multi-stage process to develop: a) an implementation-ready, web-based EarSketch learning environment that integrates programming, digital audio workstation, curriculum, audio loop library, and social sharing features, along with studio-based learning functionality to support student presentation, critique, discussion, and collaboration; and b) an online professional learning course for teachers adopting EarSketch in Computer Science Principles courses. Using these resources, the team will conduct a quasi-experimental study of EarSketch in Computer Science Principles high school courses across the state of Georgia; measure student learning and engagement across multiple demographic categories; and determine to what extent an EarSketch-based CS Principles course promotes student achievement and engagement across different student populations. The project will include measures of student performance, creativity, collaboration, and communication in student programming tasks to determine the extent to which studio-based learning in EarSketch promotes success in these important areas. An agent-based modeling framework in multiple school settings will be developed to determine what factors enhance or impede implementation of EarSketch under conditions of routine practice.

Computer Science in Secondary Schools (CS3): Studying Context, Enactment, and Impact

This project will examine the relationships among the factors that influence the implementation of the Exploring Computer Science (ECS), a pre-Advanced Placement curriculum that prepares students for further study in computer science. This study elucidates how variation in curricular implementation influences student learning and determines not only what works, but also for whom and under what circumstances.

Lead Organization(s): 
Award Number: 
1418149
Funding Period: 
Fri, 08/01/2014 to Tue, 07/31/2018
Full Description: 

Computational thinking is an important set of 21st century knowledge and skills that has implications for the heavily technological world in which we live. Multiple industries indicate the under supply of those trained to be effective in the computer science workforce. In addition, there are increasing demands for broadening the participation in the computer science workforce by women and members of minority populations. SRI International will examine the relationships among the factors that influence the implementation of the Exploring Computer Science (ECS), a pre-Advanced Placement curriculum that prepares students for further study in computer science. SRI will work in partnership with the ECS curriculum developers, teachers, and the nonprofit Code.org who are involved in the scaling of ECS. This study elucidates how variation in curricular implementation influences student learning and determines not only what works, but also for whom and under what circumstances.

SRI will conduct a pilot study in which they develop, pilot, and refine measures as they recruit school districts for the implementation study. The subsequent implementation study will be a 2 year examination of curriculum enactment, teacher practice, and evidence of student learning. Because no comparable curriculum currently exists, the study will examine the conditions needed to implement the ECS curriculum in ways that improve student computational thinking outcomes rather than determine whether the ECS curriculum is more effective than other CS-related curricula. The study will conduct two kinds of analyses: 1) an analysis of the influence of ECS on student learning gains, and 2) an analysis of the relationship between classroom-level implementation and student learning gains. Because of the clustered nature of the data (students nested within classrooms nested within schools), the project will use hierarchical linear modeling to examine the influence of the curriculum.

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.

Common Online Data Analysis Platform (CODAP)

This project aims to engage students in meaningful scientific data collection, analysis, visualization, modeling, and interpretation. It targets grades 9-12 science instruction. The proposed research poses the question "How do learners conceive of and interact with empirical data, particularly when it has a hierarchical structure in which parameters and results are at one level and raw data at another?"

Lead Organization(s): 
Award Number: 
1435470
Funding Period: 
Tue, 10/01/2013 to Fri, 09/30/2016
Full Description: 

This project aims to engage students in meaningful scientific data collection, analysis, visualization, modeling, and interpretation. It targets grades 9-12 science instruction. The proposed research poses the question "How do learners conceive of and interact with empirical data, particularly when it has a hierarchical structure in which parameters and results are at one level and raw data at another?" As working with data becomes an integral part of students' learning across STEM curricula, understanding how students conceive of data grows ever more important. This is particularly timely as science becomes more and more data driven.

The team will develop and test a Common Online Data Analysis Platform (CODAP). STEM curriculum development has moved online, but development of tools for students to engage in data analysis has yet to follow suit. As a result, online curriculum development projects are often forced to develop their own data analysis tools, settle for desktop tools, or do without. In a collaboration with NSF-funded projects at the Concord Consortium, Educational Development Center, and University of Minnesota, the project team is developing an online, open source data analysis platform that can be used not only by these three projects, but subsequently by others.

The proposed research breaks new ground both in questions to be investigated and in methodology. The investigations build on prior research on students' understanding of data representation, measures of center and spread, and data modeling to look more closely at students' understanding of data structures especially as they appear in real scientific situations. Collaborative design based on three disparate STEM projects will yield a flexible data analysis environment that can be adopted by additional projects in subsequent years. Such a design process increases the likelihood that CODAP will be more than a stand-alone tool, and can be meaningfully integrated into online curricula. CODAP's overarching goal is to improve the preparation of students to fully participate in an increasingly data-driven society. It proposes to do so by improving a critical piece of infrastructure: namely, access to classroom-friendly data analysis tools by curriculum developers who wish to integrate student engagement with data into content learning.

This project is asociated with award number 1316728 with the same title.

Activate Computational Thinking (ACT)

This project offers a two-year professional development model to support a cohort of 16 middle school science teachers of underrepresented students as the teachers gain computational thinking (CT) competencies and design and teach CT-integrated classroom science lessons that will provide students with CT learning experiences. The project will contribute to the understanding of what it takes to empower middle school science teachers as designers of CT learning opportunities for students from underrepresented groups.

Award Number: 
1223076
Funding Period: 
Sun, 07/15/2012 to Mon, 06/30/2014
Full Description: 

The Activate Computational Thinking (ACT) exploratory research project of California State University-San Marcos is a two-year professional development model to support a cohort of 16 middle school science teachers of underrepresented students as the teachers gain computational thinking (CT) competencies and design and teach CT-integrated classroom science lessons that will provide students with CT learning experiences.

The design of the ACT professional development (PD) supports an iterative process where teacher training is followed by the cycle of design, field-test, and refinement of CT lessons. The overall PD is then refined before the next training begins. Close monitoring of teacher and student responses to project activities will show how (1) teachers gain understanding and knowledge of CT concepts and capabilities; (2) teachers design science lesson plans that integrate CT learning experiences; (3) teachers implement science CT-integrated science lessons; and (4) students exhibit the use of CT skills in the course of completing their class assignments. Training will occur over two years in two summers and after school. The project combines participant learning and team teaching of a cohort of middle school students by trained participants during the first summer term. This will be followed by yearly training and mentoring. Teachers will produce science lesson plans that incorporate computational thinking.

The project will contribute to the understanding of what it takes to empower middle school science teachers as designers of CT learning opportunities for students from underrepresented groups. With an estimate of 90 students per middle school teacher per year, the project is expected to impact more than 2,800 students as the 16 project teachers implement CT-integrated science lessons into classroom activities over two years.

CAREER: Supporting Computational Algorithmic Thinking (SCAT)—Exploring the Development of Computational Algorithmic Thinking Capabilities in African-American Middle School Girls

The project at Spelman College includes activities that develop computational thinking and encourage middle school, African-American girls to consider careers in computer science. Over a three-year period, the girls attend summer camp sessions of two weeks where they learn to design interactive games. Experts in Computational Algorithmic Thinking as well as undergraduate, computer science majors at Spelman College guide the middle-school students in their design of games and exploration of related STEM careers.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
1737442
Funding Period: 
Sun, 07/15/2012 to Sun, 06/30/2019
Full Description: 

The Supporting Computational Algorithmic Thinking (SCAT) project at Spelman College includes activities that develop computational thinking and encourage middle school, African-American girls to consider careers in computer science. Over a three-year period, the girls attend summer camp sessions of two weeks where they learn to design interactive games. They participate in workshops, field trips, and game-design competitions. Experts in Computational Algorithmic Thinking as well as undergraduate, computer science majors at Spelman College guide the middle-school students in their design of games and exploration of related STEM careers.

Research on the development of Computational Algorithmic Thinking is an integral part of the project. The researcher is investigating how middle-school girls develop computational thinking and problem solving skills. Game design has been shown to be an area that is attractive to adolescents and it requires extensive problem solving and computational algorithmic thinking. Within the context of designing games individually and within groups, the researcher is assessing how the girls develop computational algorithmic thinking, and what difficulties they experience. Researchers are also assessing how the project experiences influence the students' self-perceptions of themselves as problem solvers. At the same time, the girls engaged in educational experiences where they are expected to gain knowledge in mathematics, programming, and reasoning, as well as game design. Research data consists of artifacts that the students have created, observations, participant journals, and interviews.

Computational Algorithmic Thinking is an essential skill for most STEM careers. African-American women are underrepresented in many STEM fields and especially in computer science. The goals of the project are to prepare girls with these essential skills and to increase their confidence in participating in STEM education. The project is also exposing participating girls to a wide variety of STEM careers. In addition, the materials, lesson plans, and activities generated in the project are available to be used, without charge, by other groups interested in designing similar programs.

This project was previously funded under award #1150098.

Morehouse College DR K-12 Pre-service STEM Teacher Initiative

This project recruited high school African American males to begin preparation for science, technology, engineering and mathematics teaching careers. The goal of the program was to recruit and prepare students for careers in secondary mathematics and science teaching thus increasing the number of African Americans students in STEM. The research will explore possible reasons why the program is or is not successful for recruiting and retaining students in STEM Teacher Education programs  

Lead Organization(s): 
Award Number: 
1119512
Funding Period: 
Fri, 07/15/2011 to Sat, 06/30/2018
Project Evaluator: 
Melissa K. Demetrikopoulos
Full Description: 

Morehouse College proposed a research and development project to recruit high school African American males to begin preparation for secondary school science, technology, engineering and mathematics(STEM) teaching as a career. The major goal of the program is to recruit and prepare students for careers in secondary mathematics and science teaching thus increasing the number of African Americans students in STEM. The research will explore possible reasons why the program is or is not successful for recruiting and retaining students in STEM Teacher Education programs including: (a) How do students who remain in STEM education differ from those who leave and how do these individual factors (e.g. student preparation, self-efficacy, course work outcomes, attitudes toward STEM/STEM education, connectivity to STEM/STEM education communities, learning styles, etc) enhance or inhibit interest in STEM teaching among African American males? (b) What organizational and programmatic factors (e.g. high school summer program, Saturday Academy, pre-freshman program, summer research experience, courses, enhanced mentoring, cyber-infrastructure, college admissions guidance, leadership training, instructional laboratory, program management, faculty/staff engagement and availability, Atlanta Public Schools and Morehouse College articulation and partnership) affect (enhance or inhibit) interest in STEM teaching among African American males?

This pre-service program for future secondary STEM teachers recruits promising African American male students in eleventh grade and prepares them for entry into college.  The program provides academic guidance and curriculum-specific activities for college readiness, and creates preparation for secondary science and math teaching careers.   This project is housed within the Division of Science and Mathematics at Morehouse College and engages in ongoing collaboration with the Atlanta Public School (APS) system and Fulton County School District (FCS). The APS-FCS-MC collaboration fosters access and success of underrepresented students through (a) early educational intervention practices; (b) enhanced academic preparation; and (c) explicit student recruitment. 

The program consists of six major program components: High School Summer Program; Saturday Academy I, II, and III; Pre-Freshman Summer Program; and Summer Research Experience, which begins in the summer between the student’s junior and senior years of high school and supports the student through his sophomore year of college.  To date, collaborations between education and STEM faculty as well as between Morehouse, APS, and FCS faculty have resulted in development and implementation of all six program components.   Students spent six weeks in an intensive summer program with a follow-up Saturday Academy during their senior year before formally beginning their academic careers at Morehouse College. The program integrates STEM education with teacher preparation and mentoring in order to develop secondary teachers who have mastery in both a STEM discipline as well as educational theory. 

This pre-service program for future teachers recruited promising eleventh grade African American male students from the Atlanta Public School District to participate in a four-year program that will track them into the Teacher Preparation program at Morehouse College. The research focuses on the utility and efficacy of early recruitment of African American male students to STEM teaching careers as a mechanism to increase the number of African American males in STEM teaching careers.

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