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: Standard Grant | Program: | Phase: ITEST | Award Amount: 1.20M | Year: 2015
This project will advance efforts of the Innovative Technology Experiences for Students and Teachers (ITEST) program to better understand and promote practices that increase students motivations and capacities to pursue careers in fields of science, technology, engineering, or mathematics (STEM). In particular, it touches on three of the ITEST Programs focal areas: (1) investigating coherent sets of experiences that support student competency; (2) modeling the roles business and industry workforce members play in motivating students; and (3) develop examples of productive ways to support broadening participation through industry/afterschool partnerships.
Biotechnology, and genetics in particular, are rapidly advancing areas that will offer new jobs across the spectrum from technicians to scientists. Each year, U.S. companies spend nearly a billion dollars on STEM education programs. Yet we lack research-based models for engaging students with industry professionals to further their awareness of STEM careers and help them learn the content these careers demand. This three-year ITEST Strategies project, GeniConnect, focuses on middle school student engagement in genetics and biotechnology using game-based learning (Geniverse). With guidance and mentoring from industry professionals, research scientists, and community volunteers, 150 diverse students from Cambridge, MA will participate in an eight-week afterschool program. Students will also conduct hands-on experiments at the Biogen Idec Community Lab while becoming familiar with STEM careers.
The project framework will be guided by an evidence-centered design, aligned with project activities intended to develop better models to sustain successful industry and afterschool partnerships. Two key research questions guide the investigation: (1) How can a suite of experiences involving game-based learning, laboratory activities and mentoring by industry professionals foster student understanding and motivation in genetics and biotechnology and further student awareness of careers and real-world connections? This question forms the iterative design and pilot study components of the research. Analysis will help the team understand both foundational concepts needed for understanding modern genetics and the dimensions of motivation concerning students feelings of relevance, personal interest and self-efficacy toward genetics and biotechnology; and (2) What methods and processes can aid industry groups and afterschool programs in forming meaningful and productive partnerships? This question forms the feasibility research. It explores the essential aspects of STEM professionals engagement, preparation and relationship building with afterschool groups as well as how participation and program design aspects are valued. Data collection strategies will include survey, log data from the Geniverse platform, focus groups, and in-depth interviews; outcome measures will center on content knowledge, motivation, value, interest and self-efficacy. The contexts for research and design will be the home and afterschool program.
Project partners include the Concord Consortium, a nonprofit with extensive experience researching and developing STEM educational technology; Purdue University; and East End House, a nonprofit community-based organization serving under-resourced youth in Cambridge, MA. Project results will inform not only the research community of the design and delivery of effective afterschool STEM programs for career awareness and learning, but also supply afterschool programs and industry partners with a pathway for success. Using this framework, the project will create a STEM Partnership Toolkit to be distributed to approximately 500 community-based organizations and afterschool programs nationally that are member organizations of the Alliance for Strong Families and Communities.
Agency: NSF | Branch: Standard Grant | Program: | Phase: DISCOVERY RESEARCH K-12 | Award Amount: 1.35M | Year: 2015
The Cyberlearning and Future Learning Technologies Program funds efforts that support envisioning the future of learning technologies and advance what we know about how people learn in technology-rich environments. Development and Implementation (DIP) Projects build on proof-of-concept work that shows the possibilities of the proposed new type of learning technology, and teams build and refine a minimally-viable example of their proposed innovation that allows them to understand how such technology should be designed and used in the future and that allows them to answer questions about how people learn, how to foster or assess learning, and/or how to design for learning. One important challenge is helping teach people how to use technology and statistics to understand numerical data through analysis and modeling. This project refines and studies technology for data science games: essentially, a game is embedded in a data analysis environment, in which the game can only be won by doing data modeling. Research will examine how students learn to analyze and model data in high school biology, chemistry, and physics; how the game can support this learning; and how such games can fit into high school science classrooms (as tested in a large urban school district).
This project uses design-based research methodology to understand how students engage with and learn about data science (specifically, concepts of center, spread, distribution, and inference) and to identify social, technological, and pedagogical supports to allow classroom use, including various types of data representations in the data games (flat, hierarchical, tree, digraph, and relational). Semi-clinical interviews and direct observation of students using the games in controlled settings will lead to broader workshop- and classroom-based observations of individuals and dyads using think-aloud protocols. This data will be analyzed both using grounded theory and using diSessas knowledge analysis method and analysis of discourse using Hmelo-Silver et al.s CORTDRA diagrams. In addition, teacher focus groups and classroom video will be used to help identify affordances related to classroom adoption. The design and development work will be driven by the empirical research, and will utilize Squires game-based learning design principles, starting with the existing CODAP (Common Online Data Analysis Platform) software. Four design iterations will take place, each culminating in user testing, initially with a summer workshop of students, then with two rounds of classroom testing in six high school classrooms, and finally in an open adoption phase in which 50 teachers will be recruited for data collection (but more teachers may adopt the software and curricula).
Agency: NSF | Branch: Continuing grant | Program: | Phase: DISCOVERY RESEARCH K-12 | Award Amount: 1.08M | 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.
In this project, SmartCAD: Guiding Engineering Design with Science Simulations, the Concord Consortium (lead), Purdue University, and the University of Virginia investigate how real time formative feedback can be automatically composed from the results of computational analysis of student design artifacts and processes with the envisioned SmartCAD software. Through automatic feedback based on visual analytic science simulations, SmartCAD is able to guide every student at a fine-grained level, allowing teachers to focus on high-level instruction. Considering the ubiquity of computer-aided design (CAD) software in the workplace and their diffusion into precollege classrooms, this research provides timely results that could motivate the development of an entire genre of CAD-based learning environments and materials to accelerate and scale up K-12 engineering education. The project conducts design-based research on SmartCAD, which supports secondary science and engineering with three embedded computational engines capable of simulating the mechanical, thermal, and solar performance of the built environment. These engines allow SmartCAD to analyze student design artifacts on a scientific basis and provide automatic formative feedback in forms such as numbers, graphs, and visualizations to guide student design processes on an ongoing basis.
The research hypothesis is that appropriate applications of SmartCAD in the classroom results in three learning outcomes: 1) Science knowledge gains as indicated by a deeper understanding of the involved science concepts and their integration at the completion of a design project; 2) Design competency gains as indicated by the increase of iterations, informed design decisions, and systems thinking over time; and 3) Design performance improvements as indicated by a greater chance to succeed in designing a product that meets all the specifications within a given period of time. While measuring these learning outcomes, this project also probes two research questions: 1) What types of feedback from simulations to students are effective in helping them attain the outcomes? and 2) Under what conditions do these types of feedback help students attain the outcomes? To test the research hypothesis and answer the research questions, this project develops three curriculum modules based on the Learning by Design (LBD) Framework to support three selected design challenges: Solar Farms, Green Homes, and Quake-Proof Bridges. This integration of SmartCAD and LBD situate the research in the LBD context and shed light on how SmartCAD can be used to enhance established pedagogical models such as LBD. Research instruments include knowledge integration assessments, data mining, embedded assessments, classroom observations, participant interviews, and student questionnaires. This research is carried out in Indiana, Massachusetts, and Virginia simultaneously, involving more than 2,000 secondary students at a number of socioeconomically diverse schools. Professional development workshops are provided to familiarize teachers with SmartCAD materials and implementation strategies prior to the field tests. An external Critical Review Committee consisting of five engineering education researchers and practitioners oversee and evaluate this project formatively and summative. Project materials and results are disseminated through publications, presentations, partnerships, and the Internet.
Agency: NSF | Branch: Standard Grant | Program: | Phase: DISCOVERY RESEARCH K-12 | Award Amount: 50.00K | Year: 2015
A new age of technology is dawning on the field of speech recognition and analysis. This has begun to become publicly visible through the increasing availability of impressive tools such as Siri and Google Translate, but these consumer-ready tools only scratch the surface of the potential that research-quality speech engineering applications unlock. However, these capabilities remain practically unrecognized by the education research community. This one-year Capacity-Building Project (CAP) will build partnerships among top researchers and develop expertise from both education and speech engineering backgrounds through a series of workshops to bring together four leading spoken language technology and education research groups.
This project will unite the extensive education research and educational technology backgrounds at the Concord Consortium and SRI Internationals Center for Technology in Learning and bring them together with two of the strongest groups in spoken language technology research, the Speech Technology and Research Laboratory at SRI International and the Center for Robust Speech Systems at the University of Texas at Dallas. This will provide the foundations for educational research areas as diverse as collaboration, argumentation, discourse analysis, teacher questioning, emotion, and engagement. Spoken language technologies will provide efficiencies, insight, and entire new methodologies for approaching these research areas, and they will do so while students and teachers assume more natural modes - those of the naturalistic language-based interactions that have formed the basis of educational interchange for millennia. This project will gather and summarize applicable knowledge about the current state of these fields and generate central papers and momentum suitable for bringing together and helping launch a new interdisciplinary field of study, spoken language technology for education. The work of this CAP proposal will generate the initial necessary connections and create the first definitions required to move toward proposals, actions, and research.