Educational Technology

MothEd - Authentic Science for Elementary and Middle School Students

Widely-adopted science education standards have expanded expectations for students to learn science research processes. To address these needs, the project will research and develop curricular materials and classroom practices that teachers can use to bring authentic science into their classes and engage students as active science researchers. The project, called MothEd, will focus on the study of moths, which are well-suited to the project’s goal of having students conduct authentic scientific investigations.

Lead Organization(s): 
Award Number: 
2100990
Funding Period: 
Sun, 08/15/2021 to Thu, 07/31/2025
Full Description: 

There are few opportunities and curriculum materials that support teachers in engaging elementary and middle-school students in scientific research processes and in conducting their own investigations. Widely-adopted science education standards have expanded expectations for students to learn science research processes. To address these needs, the project will research and develop curricular materials and classroom practices that teachers can use to bring authentic science into their classes and engage students as active science researchers. The project, called MothEd, will focus on the study of moths, which are well-suited to the project’s goal of having students conduct authentic scientific investigations. Moths are ecologically important, easy to capture, and there is a lack of research on moths compared to many other insect species. In the project activities, students will construct moth traps and collect data through research processes that they design and carry out. The project is building on an approach called community science (sometimes called citizen science), where non-scientists in local communities voluntarily contribute to scientific research. Students and teachers will work in partnership with entomologists and science educators to develop and answer questions about local ecological conditions and will become genuine producers of knowledge within science learning communities. Students will work collaboratively within an online platform to design experiments using a complete suite of research tools for collection, expression, and analysis of data, including sensors, photographs, sketches, and graphs. The project will develop curricular materials that will provide teaching and learning materials that are focused on giving students place-based opportunities to conduct age-appropriate scientific investigations.

MothEd’s educational research will investigate several questions: (1) what students understand about scientific research processes and how they see themselves in that process; (2) how students can work as partners with scientists in discovery and what do they learn about research methods and moth ecology; and (3) What supports teachers need in order to support students as active science researchers. Using a mixed methods approach, the project will collect a variety of data for the research: in-class observations of student work; pre- and post- activity surveys about their knowledge of moth ecology and their view and understanding of science research processes; teacher interviews; and analysis of data collected by project software on student work and collaboration. The project will be designed to ensure that the MothEd education materials can be adopted and used independently by teachers across the country. Project research findings and materials will be shared via conferences, journal publications, and the project’s collaborative learning environment.

Using Natural Language Processing to Inform Science Instruction (Collaborative Research: Linn)

This project takes advantage of language to help students form their own ideas and pursue deeper understanding in the science classroom. The project will conduct a comprehensive research program to develop and test technology that will empower students to use their ideas as a starting point for deepening science understanding. Researchers will use a technology that detects student ideas that go beyond a student's general knowledge level to adapt to a student's cultural and linguistic understandings of a science topic.

Partner Organization(s): 
Award Number: 
2101669
Funding Period: 
Thu, 07/01/2021 to Mon, 06/30/2025
Full Description: 

Often, middle school science classes do not benefit from participation of underrepresented students because of language and cultural barriers. This project takes advantage of language to help students form their own ideas and pursue deeper understanding in the science classroom. This work continues a partnership among the University of California, Berkeley, Educational Testing Service, and science teachers and paraprofessionals from six middle schools enrolling students from diverse racial, ethnic, and language groups whose cultural experiences may be neglected in science instruction. The partnership will conduct a comprehensive research program to develop and test technology that will empower students to use their ideas as a starting point for deepening science understanding. Researchers will use a technology that detects student ideas that go beyond a student's general knowledge level to adapt to a student's cultural and linguistic understandings of a science topic. The partnership leverages a web-based platform to implement adaptive guidance designed by teachers that feature dialog and peer interaction. Further, the platform features teacher tools that can detect when a student needs additional help and alert the teacher. Teachers using the technology will be able to track and respond to individual student ideas, especially from students who would not often participate because of language and cultural barriers.

This project develops AI-based technology to help science teachers increase their impact on student science learning. The technology is aimed to provide accurate analysis of students' initial ideas and adaptive guidance that gets each student started on reconsidering their ideas and pursuing deeper understanding. Current methods in automated scoring primarily focus on detecting incorrect responses on test questions and estimating the overall knowledge level in a student explanation. This project leverages advances in natural language processing (NLP) to identify the specific ideas in student explanations for open-ended science questions. The investigators will conduct a comprehensive research program that pairs new NLP-based AI methods for analyzing student ideas with adaptive guidance that, in combination, will empower students to use their ideas as starting points for improving science understanding. To evaluate the idea detection process, the researchers will conduct studies that investigate the accuracy and impact of idea detection in classrooms. To evaluate the guidance, the researchers will conduct comparison studies that randomly assign students to conditions to identify the most promising adaptive guidance designs for detected ideas. All materials are customizable using open platform authoring tools.

Using Natural Language Processing to Inform Science Instruction (Collaborative Research: Riordan)

This project takes advantage of language to help students form their own ideas and pursue deeper understanding in the science classroom. The project will conduct a comprehensive research program to develop and test technology that will empower students to use their ideas as a starting point for deepening science understanding. Researchers will use a technology that detects student ideas that go beyond a student's general knowledge level to adapt to a student's cultural and linguistic understandings of a science topic.

Lead Organization(s): 
Award Number: 
2101670
Funding Period: 
Thu, 07/01/2021 to Mon, 06/30/2025
Full Description: 

Often, middle school science classes do not benefit from participation of underrepresented students because of language and cultural barriers. This project takes advantage of language to help students form their own ideas and pursue deeper understanding in the science classroom. This work continues a partnership among the University of California, Berkeley, Educational Testing Service, and science teachers and paraprofessionals from six middle schools enrolling students from diverse racial, ethnic, and language groups whose cultural experiences may be neglected in science instruction. The partnership will conduct a comprehensive research program to develop and test technology that will empower students to use their ideas as a starting point for deepening science understanding. Researchers will use a technology that detects student ideas that go beyond a student's general knowledge level to adapt to a student's cultural and linguistic understandings of a science topic. The partnership leverages a web-based platform to implement adaptive guidance designed by teachers that feature dialog and peer interaction. Further, the platform features teacher tools that can detect when a student needs additional help and alert the teacher. Teachers using the technology will be able to track and respond to individual student ideas, especially from students who would not often participate because of language and cultural barriers.

This project develops AI-based technology to help science teachers increase their impact on student science learning. The technology is aimed to provide accurate analysis of students' initial ideas and adaptive guidance that gets each student started on reconsidering their ideas and pursuing deeper understanding. Current methods in automated scoring primarily focus on detecting incorrect responses on test questions and estimating the overall knowledge level in a student explanation. This project leverages advances in natural language processing (NLP) to identify the specific ideas in student explanations for open-ended science questions. The investigators will conduct a comprehensive research program that pairs new NLP-based AI methods for analyzing student ideas with adaptive guidance that, in combination, will empower students to use their ideas as starting points for improving science understanding. To evaluate the idea detection process, the researchers will conduct studies that investigate the accuracy and impact of idea detection in classrooms. To evaluate the guidance, the researchers will conduct comparison studies that randomly assign students to conditions to identify the most promising adaptive guidance designs for detected ideas. All materials are customizable using open platform authoring tools.

DataX: Exploring Justice-Oriented Data Science with Secondary School Students

This project will develop an integrated, justice-oriented curriculum and a digital platform for teaching secondary students about data science in science and social studies classrooms. The platform will help students learn about data science using real-world data sets and problems. This interdisciplinary project will also help students meaningfully analyze real-world data sets, interpret social phenomena, and engage in social change.

Award Number: 
2101413
Funding Period: 
Thu, 07/01/2021 to Fri, 06/30/2023
Full Description: 

Understanding data is critical for informed citizens. Data science is a growing and emerging field that can incorporate statistics, mathematics, and computer science to develop disciplinary knowledge and address societal challenges. This project will develop an integrated, justice-oriented curriculum and a digital platform for teaching secondary students about data science in science and social studies classrooms. The platform will help students learn about data science using real-world data sets and problems. This project includes science and social studies teachers in the design of the resources and in testing them in secondary school classrooms. Research and development in data science education is needed to understand how students can learn more about the use of data in meaningful and authentic ways. This interdisciplinary project will also help students meaningfully analyze real-world data sets, interpret social phenomena, and engage in social change.

During a two-year project period, we aim to iteratively advance three design components of the DataX program: (a) a justice-oriented data science curriculum integrated in secondary science and social studies; (b) a web-based learning platform that extends the Common Online Data Analysis Platform (CODAP) to support collaboration and sophisticated data practices; and (c) pedagogical practices that involve learners to work collectively as community. The guiding research question is: What scaffolds and resources are necessary to support the co-development of data, disciplinary, and critical literacies in secondary classrooms? To address this, the project will use participatory design research with science and social studies teachers to develop and test the curriculum, the learning platform, and the pedagogical practices. The data collected will include qualitative sources gathered from participatory design workshops and classrooms, as well as quantitative data from questionnaires and system logs. Using the data, we examine students' data science skills, data dispositions, and social participation in collaborative data investigations.

Developing and Evaluating Assessments of Problem-Solving in Computer Adaptive Testing Environments (Collaborative Research: Bostic)

The Common Core State Standards for Mathematics (CCSSM) problem-solving measures assess students’ problem-solving performance within the context of CCSSM math content and practices. This project expands the scope of the problem-solving measures use and score interpretation. The project work advances mathematical problem-solving assessments into computer adaptive testing. Computer adaptive testing allows for more precise and efficient targeting of student ability compared to static tests.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
2100988
Funding Period: 
Sun, 08/01/2021 to Fri, 07/31/2026
Full Description: 

Problem solving has been a priority within K-12 mathematics education for over four decades and is reflected throughout the Common Core State Standards for Mathematics (CCSSM) initiative, which have been adopted in some form by 41 states. Broadly defined, problem solving involves the mathematical practices in which students engage as they solve intellectually-challenging mathematical tasks. In prior research, problem-solving measures aligned to CCSSM for grades 3-5 were developed and validated to supplement previously established problem-solving measures in grades 6-8. The problem-solving measures assess students’ problem-solving performance within the context of CCSSM math content and practices. This project expands the scope of the problem-solving measures use and score interpretation. The project work advances mathematical problem-solving assessments into computer adaptive testing. Computer adaptive testing allows for more precise and efficient targeting of student ability compared to static tests. Few measures designed to assess students’ mathematical problem-solving ability use this technology. Shorter tests require less in-class time for assessment than current paper-pencil problem-solving measures and increase classroom instruction time. The computer-adaptive problem-solving measures have sufficient reliability and strong validity evidence, and may limit test-taker fatigue. Finally, the project will benchmark current grades 6-8 instruments using an objective standard-setting method, which allows for improved score interpretations with content-related feedback. Immediate results of student- and class-level reports will be produced through the computer adaptive testing system allowing for teachers to modify instruction to improve students’ learning.

This five-year project aims to advance the use of computer adaptive testing and assessment development for use in mathematics instruction. The project applies an iterative and stakeholder-informed design science-based methodology as well as employs the use of Rasch modeling for the psychometric analysis during item development and validation. The project aims to: (a) benchmark the previously established grades 6-8 problem-solving measures; (b) develop, calibrate, and validate criterion-referenced computer adaptive testing for each measure; (c) construct student- and class-level score reports for integration into the computer adaptive testing system; and (d) investigate teachers’ capacity for implementing, interpreting, and using the assessments and results in STEM learning settings. The project addresses the following set of research questions: (RQ1) What benchmark performance standards define different proficiency levels on problem-solving measures for each grade level? (RQ2) What are the psychometric properties of new problem-solving measures items developed for the computer adaptive testing item bank? (RQ3) Is there significant item drift across student populations on the new problem-solving measure items? (RQ4) To what extent are problem-solving measures item calibrations stable within the computer adaptive testing system? (RQ5) What recommendations for improvements do teachers and students have for the new problem-solving measures items, computer adaptive testing platform and reporting system, if any? (RQ6) To what extent do teachers interact with, perceive, and make sense of the assessment information generated for use in practice? and (RQ7) Does an online learning module build teacher capacity for problem solving measures, computer adaptive testing implementation, interpretation, and use of student assessment outcomes in STEM learning settings? An experimental design will be utilized to investigate teachers’ capacity for implementing, interpreting, and using problem solving measures in a computer adaptive testing system. The project has the potential to impact the field by providing school districts and researchers a means to assess students’ mathematical problem-solving performance at one time or growth over time efficiently and effectively; address future online learning needs; and improve classroom teaching through more precise information about students’ strengths with less class time focused on assessment.

Developing and Evaluating Assessments of Problem-Solving in Computer Adaptive Testing Environments (Collaborative Research: Sondergeld)

The Common Core State Standards for Mathematics (CCSSM) problem-solving measures assess students’ problem-solving performance within the context of CCSSM math content and practices. This project expands the scope of the problem-solving measures use and score interpretation. The project work advances mathematical problem-solving assessments into computer adaptive testing. Computer adaptive testing allows for more precise and efficient targeting of student ability compared to static tests.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
2101026
Funding Period: 
Sun, 08/01/2021 to Fri, 07/31/2026
Full Description: 

Problem solving has been a priority within K-12 mathematics education for over four decades and is reflected throughout the Common Core State Standards for Mathematics (CCSSM) initiative, which have been adopted in some form by 41 states. Broadly defined, problem solving involves the mathematical practices in which students engage as they solve intellectually-challenging mathematical tasks. In prior research, problem-solving measures aligned to CCSSM for grades 3-5 were developed and validated to supplement previously established problem-solving measures in grades 6-8. The problem-solving measures assess students’ problem-solving performance within the context of CCSSM math content and practices. This project expands the scope of the problem-solving measures use and score interpretation. The project work advances mathematical problem-solving assessments into computer adaptive testing. Computer adaptive testing allows for more precise and efficient targeting of student ability compared to static tests. Few measures designed to assess students’ mathematical problem-solving ability use this technology. Shorter tests require less in-class time for assessment than current paper-pencil problem-solving measures and increase classroom instruction time. The computer-adaptive problem-solving measures have sufficient reliability and strong validity evidence, and may limit test-taker fatigue. Finally, the project will benchmark current grades 6-8 instruments using an objective standard-setting method, which allows for improved score interpretations with content-related feedback. Immediate results of student- and class-level reports will be produced through the computer adaptive testing system allowing for teachers to modify instruction to improve students’ learning.

This five-year project aims to advance the use of computer adaptive testing and assessment development for use in mathematics instruction. The project applies an iterative and stakeholder-informed design science-based methodology as well as employs the use of Rasch modeling for the psychometric analysis during item development and validation. The project aims to: (a) benchmark the previously established grades 6-8 problem-solving measures; (b) develop, calibrate, and validate criterion-referenced computer adaptive testing for each measure; (c) construct student- and class-level score reports for integration into the computer adaptive testing system; and (d) investigate teachers’ capacity for implementing, interpreting, and using the assessments and results in STEM learning settings. The project addresses the following set of research questions: (RQ1) What benchmark performance standards define different proficiency levels on problem-solving measures for each grade level? (RQ2) What are the psychometric properties of new problem-solving measures items developed for the computer adaptive testing item bank? (RQ3) Is there significant item drift across student populations on the new problem-solving measure items? (RQ4) To what extent are problem-solving measures item calibrations stable within the computer adaptive testing system? (RQ5) What recommendations for improvements do teachers and students have for the new problem-solving measures items, computer adaptive testing platform and reporting system, if any? (RQ6) To what extent do teachers interact with, perceive, and make sense of the assessment information generated for use in practice? and (RQ7) Does an online learning module build teacher capacity for problem solving measures, computer adaptive testing implementation, interpretation, and use of student assessment outcomes in STEM learning settings? An experimental design will be utilized to investigate teachers’ capacity for implementing, interpreting, and using problem solving measures in a computer adaptive testing system. The project has the potential to impact the field by providing school districts and researchers a means to assess students’ mathematical problem-solving performance at one time or growth over time efficiently and effectively; address future online learning needs; and improve classroom teaching through more precise information about students’ strengths with less class time focused on assessment.

Supporting High School Students and Teachers with a Digital, Localizable, Climate Education Experience

This partnership of BSCS Science Learning, Oregon Public Broadcasting, and the National Oceanic and Atmospheric Administration advances curriculum materials development for high quality units that are intentionally designed for adaptation by teachers for their local context. The project will create a base unit on carbon cycling as a foundation for understanding how and why the Earth's climate is changing, and it will study the process of localizing the unit for teachers to implement across varied contexts to incorporate local phenomena, problems, and solutions.

Lead Organization(s): 
Award Number: 
2100808
Funding Period: 
Thu, 07/01/2021 to Mon, 06/30/2025
Full Description: 

Teachers regularly adapt curriculum materials to localize for their school or community context, yet curriculum materials are not always created to support this localization. Developing materials that are intentionally designed for localization has potential to support rich science learning across different contexts, especially for a topic like climate change where global change can have varied local effects. This partnership of BSCS Science Learning, Oregon Public Broadcasting, and the National Oceanic and Atmospheric Administration advances curriculum materials development for high quality units that are intentionally designed for adaptation by teachers for their local context. It will develop and test a design process bringing together national designers and teachers across the country. Teachers will be supported through professional learning to adapt from the base unit to create a local learning experience for their students. The project will create a base unit on carbon cycling as a foundation for understanding how and why the Earth's climate is changing, and it will study the process of localizing the unit for teachers to implement across varied contexts to incorporate local phenomena, problems, and solutions. The unit will be fully digital with rich visual experiences, simulations, and computer models that incorporate real-time data and the addition of localized data sets. These data-based learning experiences will support students in reasoning with data to ask and answer questions about phenomena. Research will study the unit development and localization process, the supports appropriate for teachers and students, and the impact on classroom practice.

The project will adopt an iterative design process to create a Storyline base unit, aligned to Next Generation Science Standards, for localization, piloting, and an implementation study with 40 teachers. To support teacher learning, the project adopts the STeLLA teacher professional learning model. To support student learning, the project addresses climate change content knowledge with a focus on socioscientific issues and students’ sense of agency with environmental science. The project will research how the educative features in the unit and the professional development impact teachers’ practice, including their content knowledge, comfort for teaching a socioscientific issue, and their ability to productively localize materials from a base unit. The study uses a cohort-control quasi-experimental design to examine the impact of the unit and professional learning experience on dimensions of students' sense of agency with environmental science. The study will also include exploratory analyses to examine whether all students benefit from the unit. It uses a pre-post design to examine impacts on teacher knowledge and practice.

Fostering Computational Thinking through Neural Engineering Activities in High School Biology Classes

This project will develop and study a curriculum and app that support computational thinking (CT) in a high school biology unit. The project will engage students in rich data practices by gathering, manipulating, analyzing, simulating, and visualizing data of bioelectrical signals from neural sensors, and in so doing give the students opportunities to apply CT principles.

Lead Organization(s): 
Award Number: 
2101615
Funding Period: 
Wed, 09/01/2021 to Sun, 08/31/2025
Full Description: 

Computational thinking (CT) is a set of processes to identify and solve problems using algorithms or steps, and can be applied not only in computer science but in other disciplines. This project will develop and study a curriculum and app that support CT in a high school biology unit. Through a month-long neural engineering unit, approximately 500 students in 18 classes will measure their own muscle and brain activity with a low-cost, portable, wearable technology. Students will then analyze the data and design a brain-computer interface to turn neural signals into real-world output (e.g., a mechanical claw controlled by brain activity). The curriculum will be supported by: (1) a web-based instructional application that will guide students through the neural engineering design process; (2) neuroscience and engineering PhD students and postdocs acting as STEM mentors; and (3) a professional development program for teachers and mentors. The goal is to increase the students’ knowledge and interest regarding neurobiology, engineering, and computational thinking. This can contribute to their long-term capacity to pursue STEM careers. By integrating CT education into high school science, this expands the accessibility of the engineering and computing experiences beyond other efforts that focus primarily on programming and computer science courses.

The project will engage students in rich data practices by gathering, manipulating, analyzing, simulating, and visualizing data of bioelectrical signals from neural sensors, and in so doing give the students opportunities to apply computational thinking principles. The project will produce curriculum materials for the neural sensors and associated data practices. It will develop an app to help students design and construct a brain-computer interface, including computational elements like coding blocks, sensor and data simulation, and connecting to external devices. The five proposed research questions of the study are: How does students’ CT change throughout their participation in the neural engineering design process? What is the cross-cultural validity of two CT scales in a sample of high school students in the US? How does the process of collecting and analyzing real-world data relate to students’ experience of he engineering design process? How do students’ attitudes toward STEM change over the course of their participation in a neural engineering design process? How does teachers’ self-efficacy for fostering CT in their students via engineering design change through their participation in professional development and in implementation of the proposed curriculum?

Online Practice Suite: Practice Spaces, Simulations and Virtual Reality Environments for Preservice Teachers to Learn to Facilitate Argumentation Discussions in Math and Science

This project will develop, pilot, and refine a set of coordinated and complementary activities that teacher education programs can use in both online and face-to-face settings to provide practice-based opportunities for preservice teachers to develop their ability to facilitate argumentation-focused discussions in mathematics and science.

Lead Organization(s): 
Award Number: 
2037983
Funding Period: 
Sat, 08/15/2020 to Mon, 07/31/2023
Full Description: 

In teacher education it is widely acknowledged that learning to teach requires that preservice teachers have robust, authentic, and consistent opportunities to engage in the work of teaching—ideally across different contexts, with diverse student populations, and for varied purposes—as they hone their instructional practice. Practice teaching experiences in K-12 classrooms, such as field placements and student teaching, are the most widely used approaches to provide these opportunities. In an ideal world these experiences are opportunities for preservice teachers to observe and work closely with mentor teachers and try out new instructional strategies with individual, small groups, and whole classes of K-12 students. While these experiences are critical to supporting preservice teachers' learning, it can be difficult to help preservice teachers transition from university classrooms to field placements in ways that provide them with opportunities to enact ambitious instructional strategies. This need is particularly acute in mathematics and science education, where classrooms that model strong disciplinary discourse and argumentation are not always prevalent. This challenge is amplified by the COVID-19 pandemic environment; with schools and universities across the nation operating online, many preservice teachers will miss out on opportunities to practice teaching both within their courses and in K-12 classrooms. To address this urgent challenge in STEM education, project researchers will develop, pilot, and refine a set of coordinated and complementary activities that teacher education programs can use in both online and face-to-face settings to provide practice-based opportunities for preservice teachers to develop their ability to facilitate argumentation-focused discussions in mathematics and science, a critical teaching practice in these content areas. The practice-based activities include: (1) interactive, online digital games that create targeted practice spaces to engage preservice teachers to respond to students' content-focused ideas and interactions; (2) facilitating group discussions with upper elementary or middle school student avatars in a simulated classroom using performance-based tasks; and (3) an immersive virtual reality whole-classroom environment that allows for verbal, textual and non-verbal interactions between a teacher avatar and 24 student avatars. The online practice suite, made up of these activities along with supports to help teacher educators use them effectively, represents not just an immediate remedy to the challenge of COVID-19, but a rich and flexible set of resources with the potential to support and improve teacher preparation well beyond the COVID-19 challenge.

This study will use design-based research to create this integrated system of practice teaching opportunities. This approach will involve developing and refining the individual practice activities, the integrated online practice suite, and the teacher educator support materials by working with a teacher educator community of practice and engaging up to 20 teacher educators and 400 preservice teachers in multiple rounds of tryouts and piloting during the three-year project. The project will proceed in three phases: a first phase of small-scale testing, a second phase trying the materials with teacher educators affiliated with the project team, and a third phase piloting materials with a broader group of mathematics and science teacher educators. Data sources include surveys of preservice teachers' background characteristics, perceptions of the practice activities, beliefs about content instruction, perceptions about preparedness to teach, and understanding of argumentation and discussion, videos and/or log files of their performances for each practice teaching activity, and scores on their practice teaching performances. The project team will also observe the in-class instructional activities prior to and after the use of each practice teaching activity, conduct interviews with teacher educators, and collect instructional logs from the teacher educators and instructional artifacts used to support preservice teachers' learning. Data analysis will include pre and post comparisons to examine evidence of growth in preservice math and science teachers' beliefs, perceptions, understanding, and teaching performance. The project team will also build a series of analytic memos to describe how each teacher educator used the online practice suite within the mathematics or science methods course and the factors and decisions that went into that each use case. Then, they will describe and understand how the various uses and adaptations may be linked to contextual factors within these diverse settings. Findings will be used to produce empirically and theoretically grounded design principles and heuristics for these types of practice-based activities to support teacher learning.


 Project Videos

2021 STEM for All Video Showcase

Title: Simulation and Virtual Reality Tools in Teacher Education

Presenter(s): Jamie Mikeska, Heather Howell, Pamela Lottero-Perdue, & Calli Shekell


SimSnap: Orchestrating Collaborative Learning in Biology through Reconfigurable Simulations (Collective Research: Puntambekar)

This project will develop and research collaborative learning in biology using tablet-style computers that support simulations of biological systems and that can be used individually or linked together. The project will be implemented over 4 years in middle school life science classes, in which students will solve important socio-scientific problems, such as growing healthy plants in community gardens to address the need to grow sufficient produce to fulfill ever increasing and varying demands.

Award Number: 
2010357
Funding Period: 
Sat, 08/01/2020 to Wed, 07/31/2024
Full Description: 

The project will develop and research collaborative learning in biology using tablet-style computers that support simulations of biological systems and that can be used individually or linked together. The project will be implemented over 4 years in middle school life science classes, in which students will solve important socio-scientific problems, such as growing healthy plants in community gardens to address the need to grow sufficient produce to fulfill ever increasing and varying demands. Working within a digital plant habitat simulation, students will investigate how different environmental and genetic factors affect the health of a variety of plants and vegetables. As students engage in design tasks, they will be able to seamlessly move between individual and collaborative work with peers by "snapping" their tablets together (by placing them next to each other) to create a single shared simulation that spans all their devices. Students will be able to drop elements of their individual inquiry activities (e.g., plant types, soil compositions) into their shared simulation, providing opportunities for collaborative discussion and knowledge integration. When students "unsnap" their tablets, their collaborative work will stay with them in a digital journal, for individual reflection or as a resource for future collaborative activities (with potentially new group members). Project curriculum units will help students see the connections between the science concepts and principles they are learning, as they iteratively work on their designs through a combination of individual, collaborative and whole class learning. This work will also develop new approaches that help teachers understand the state of the class when students are taking part open-ended biology investigations, and support the teacher classroom orchestration and facilitation. Project research findings, materials and software will be made available to interested teachers, administrators, policymakers, and researchers nationwide on the project website.

The project will research collaborative learning along three planesindividual, small group and whole classwith technologies and classroom teachers supporting learning in innovative ways. Research has shown that technology can support collaborative learning, but there is limited research on how it can support transitions between individual and collaborative learning. While research has also shown that collaborative or individual learning may be more beneficial depending on the task or learning goal, there are relatively few studies that examine the potential for learning when students move between these social planes. Further, as these configurations become increasingly complex, there is also the challenge of how to support teachers' orchestration and facilitation. Studies will focus around four main research questions: 1) How does engaging in personally relevant biology curriculum through user-driven investigations help students understand the underlying science content? 2) How are students using and sharing the work of others to develop their own understanding about the underlying science concepts? 3) How do designs that allow for the movement between individual, small group, and whole class configurations allow students to work as a learning community? 4) How does the technology platform support teachers in orchestrating and facilitating classroom activities? Project studies will follow a design-based research methodology, guided by the premise that learning in naturalistic settings is the product of multiple interacting variables that cannot be reduced to a small set of controlled factors. The research will be broken down across four main developmental arcs: Technology design and iteration; Facilitation, user testing, and co-design; Classroom implementation; and Research and analysis. Each of the designed technologies will be user tested in the lab prior to being deployed in the classroom. Part of the analysis will focus on how the different technologies (i.e., individual and connected tablets, the teacher orchestration tablet) support learning and collaboration in naturalistic settings. The project research framework provides a way to examine the usability, usefulness and impact of interactions in a multi-user collaborative context using a mixed-method approach with various quantitative measures and qualitative indicators. Teachers will be prepared to use the system through 2-week summer institutes, during which they will also participate in co-design of the curriculum and the technology. Project research findings, materials and software will be made available to interested teachers, administrators, policymakers, and researchers nationwide on the project website, as well as being disseminated to appropriate audiences via conference presentations and publications.

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