Data Science

Drones in Alaska Elementary Curriculum

Lynda McGilvary — Fri, 02 Aug 2024 23:10:47 +0000

Drones in Alaska Elementary Curriculum

Lynda McGilvary Fri, 08/02/2024 - 19:10

Drones in Alaska Elementary Curriculum

Description

This collection of three teacher's manuals, five videos, and one career exploration guide were created with and for Alaska Native communities as part of the Drone Research and Opportunities for Native Elementary Students project, funded by NSF I-TEST Grant Nos. 1850561 (Univ. of Alaska Fairbanks) and 1850556 (Univ. of Alaska Anchorage). The Drones in Alaska base curriculum has three versions, one with lexicon in Sugs'tun, one with lexicon in Lower Tanana Athabascan, and one with lexicon in Lower Koyukon Athabascan. The Teacher's manual for the Drones in Alaska base curriculum is applicable to all three student guides. The three place-based curricula introduce how drone-obtained data and imagery can be used to understand challenges faced in each of the three communities involved in the development of the resources. For Nenana, Alaska, the Luk'a and Drones unit focus on identifying salmon spawning grounds in the Nenana river. Associated lexicon is translated into Lower Tanana Athabascan (LTA). For Nulato, Alaska, the Studying Tokk'aano with Drones unit examines river erosion and related hazard mitigation. Associated lexicon is translated into Lower Koyukon Athabascan (LKA). For Valdez, Alaska, the Robe Lake unit addresses restoration of a culturally significant salmon harvesting site. Associated lexicon is translated into Sugs'tun. The base curriculum should be instructed prior to any of the place-based units. There is a Drones in Alaska student guide version that pairs to each dialect represented in the place-based units. Five videos supplement these print resources: the Drones in Alaska and Drone Safety videos are intended to pair with the Drones in Alaska base unit. Each of the place-based units have a corresponding video of the same name. The resources were pilot-tested with students in grades 4-6, however, due to the multi-grade classrooms in rural Alaska, some students in grades 2 and 3 also were able to interact with the curricula during pilot-testing.

Resources submitted by ITEST projects may be hosted on third-party sites or require a fee or membership for access. Permission to use these materials must be obtained from the publisher or the author listed on each resource.

Project Curricula Details

Topic(s)

Partnerships and Collaboration

Youth Motivation and Interests in STEM

Other

Website

Drones in Alaska Elementary Curriculum

Target Setting(s)

Elementary school (K-5)

Discipline(s)

Data Science

Environmental sciences

Geosciences

Life sciences

Investigating the relationships among making, wearables, curiosity, computational thinking and STEM careers

Gabriela Richard — Fri, 26 Apr 2024 00:26:32 +0000

Investigating the relationships among making, wearables, curiosity, computational thinking and STEM careers

Gabriela Richard Thu, 04/25/2024 - 20:26

Investigating the relationships among making, wearables, curiosity, computational thinking and STEM careers

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Our initial intervention includes: a) a curriculum that supports culturally-relevant and inclusive participation in e-textile making activities, and b) follow-up for participating high school students with interactions with engineering and scientists from wearable technology industries and academia. This two-year project will lay the groundwork for a revised summer camp curriculum, design principles, and empirical research findings on the relationships among curiosity, computational thinking, and STEM careers.

Pillar 2: Partnerships for Career and Workforce Preparation.

We will employ the multimodal framework to understand youths’ computational thinking concepts, practices, perspectives, and self-perceptions, and triangulate these results to youths’ measures on the science curiosity scale and STEM Career Interest Survey score. Recent CS and engineering graduates will serve as mentors. Near- peer mentoring) has been successfully applied in many informal STEM learning environments (Ross et al., 2018; Salzman & Strobel, 2011; Tenenbaum et al., 2014).

Pillar 3: Strategies for Equity in STEM Education

Our broader impacts and strategies for STEM educational equity include: (a) Addressing historic underrepresentation of low income and minoritized racial/ethnic youth in STEM through research-practice partnerships across Upward Bound sites; (b) Supporting iterative research on how science curiosity, STEM career interest, and computational thinking manifest in and can be enhanced through culturally-situated approaches; and (c) Augmenting research and practice through the development of design principles.

Discipline(s)

Computer and informational technology science

Data Science

Engineering

Target Gradespan(s)

Middle school (6-8)

High school (9-12)

Target Participant(s)

Youth / students

Educators

Girls (or women)

American Indian/Alaska Native participants

Black/African American participants

Hispanic/Latino participants

Students eligible for free lunch or reduced-price lunch

Other

Project Setting(s)

Informal Education

Category

Exploring Theory and Design Principles (ETD)

Data Science, AI & You (DSAIY) in Healthcare

Kathryn Eller — Thu, 25 Apr 2024 20:41:34 +0000

Data Science, AI & You (DSAIY) in Healthcare

Kathryn Eller Thu, 04/25/2024 - 16:41

Data Science, AI & You (DSAIY) in Healthcare

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Our program educates students in Rhode Island about the impact of bias on machine learning predictions in healthcare. We prepare students for tech careers and interdisciplinary collaboration. Our differentiated curriculum and PD makes concepts accessible to diverse learners. We ensure equitable access through tailored support tools, preparing students for engagement in community learning events. By merging cutting-edge tools with culturally sensitive methods, we engage high schoolers with expertise from diverse fields.

Pillar 2: Partnerships for Career and Workforce Preparation.

Our hive learning ecosystem includes a diverse group from across the country of healthcare professionals, data scientists, teachers, college students, and researchers from EBEC, Scoutlier, TERC, MIT, and other institutions. We developed a semester-long curriculum that engages students in Rhode Island. It is concluded with a datathon about different biases in medical research.

Pillar 3: Strategies for Equity in STEM Education

DSAIY is a culturally responsive program that includes many research-based strategies proven to engage student populations currently underrepresented in stem including students of color and girls.

Discipline(s)

Data Science

Target Gradespan(s)

High school (9-12)

Target Participant(s)

Youth / students

Project Setting(s)

Formal Education

Category

Exploratory

Connecting Traditional Knowledge and Emerging STEM Through Smart AgTech

Dylan Bulseco — Thu, 25 Apr 2024 18:28:27 +0000

Connecting Traditional Knowledge and Emerging STEM Through Smart AgTech

Dylan Bulseco Thu, 04/25/2024 - 14:28

Connecting Traditional Knowledge and Emerging STEM Through Smart AgTech

Pillar 1: Innovative Use of Technologies in Learning and Teaching

We will use integrated STEM to bring innovative technologies into the classroom. Integrated STEM require that the curriculum find ways to embed all STEM disciplines. We will develop an AgTech curriculum that uses smart farm approaches, combined with problem-based learning that will allow students some independence and creative input within the constraints of the curriculum. The Jukebox is a closed smart farm with environmental control systems and sensor technologies for data acquisition and processing.

Pillar 2: Partnerships for Career and Workforce Preparation.

The curriculum will immerse students in the growth of a historically important crop in Hawaii, limu, while preparing them for a career in AgTech. The approach we will take follows the paradigm of: Observe, Interpret, Decide and Act, connecting traditional knowledge with modern AgTech practices. Students will work with community mentors to learn both modern AgTech practices (Oceanic Institute, Windward Community College) as well as traditional Hawaiian aquaculture approaches (Waikalua Loko Iʻa Fishpond).

Pillar 3: Strategies for Equity in STEM Education

This project will be launched in the Castle-Kahuku district on Oahu, Hawaii, where 43% of the student population is Native Hawaiian or Pacific Islander and 44% of the students are considered economically disadvantaged. The curriculum will focus on connecting STEM and AgTech with traditional Hawaiian practices, connecting these students with community members that may serve as mentors and future employers. Students will learn valuable STEM concepts while learning meaningful skills that contribute to food security.

Discipline(s)

Chemistry

Data Science

Emerging Tech (Artificial Intelligence, Quantum Computing, and Blockchain)

Target Gradespan(s)

High school (9-12)

Target Participant(s)

Youth / students

Educators

Project Setting(s)

Formal Education

Category

Developing and Testing Innovations (DTI)

Stimulating GIS Careers through Geospatial Explorers

Steven McGee — Thu, 25 Apr 2024 11:06:51 +0000

Stimulating GIS Careers through Geospatial Explorers

Steven McGee Thu, 04/25/2024 - 07:06

Stimulating GIS Careers through Geospatial Explorers

Pillar 1: Innovative Use of Technologies in Learning and Teaching

At the core of the GSS project is support for student spatial analyses using ArcGIS online, which is the most widely-used professional GIS tool. ESRI provides ArcGIS licenses for free to K12 students. The GSS project has co-designed lessons with teachers to provide structured scaffolding that helps students develop basic GIS skills and through those skills, students are able to investigate a variety of personally-relevant topics. We are also exploring the use of an AI chatbot that can guide students’ use of ArcGIS.

Pillar 2: Partnerships for Career and Workforce Preparation.

GIS is one of the fastest growing segments within the IT field. Students have the opportunity to develop their spatial reasoning abilities, a key skill for STEM career success. The GSS project has developed career profiles of 8 GIS professionals in Chicago, published through Futurum. Teachers have incorporated this guide into their GIS lessons to give students a vision of careers in GIS. Our research has shown that students' who were exposed to GIS through the career guides had higher expectancies of success in GIS.

Pillar 3: Strategies for Equity in STEM Education

Through iterative co-design between teachers and researchers, 92% of the GIS-infused lessons were judged to be culturally responsive. Our work serves diverse CPS students—in our most recent GIS-infused unit, 33% of students identified as black and 23% identified as Latinx. Students’ perceptions of equity in their GIS-infused classes predicted expectancy of success in computer science (CS) and interest in taking more CS courses. In addition, 100% of the students in Puerto Rico identify as Hispanic.

Discipline(s)

Computer and informational technology science

Data Science

Other

Target Gradespan(s)

High school (9-12)

Target Participant(s)

Youth / students

Educators

Project Setting(s)

Formal Education

Category

SPrEaD

Data Jam: Engaging Students with Local Ecological Data in Puerto Rico

Steven McGee — Wed, 24 Apr 2024 20:35:39 +0000

Data Jam: Engaging Students with Local Ecological Data in Puerto Rico

Steven McGee Wed, 04/24/2024 - 16:35

Data Jam: Engaging Students with Local Ecological Data in Puerto Rico

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Students use CODAP to conduct their analyses of data. CODAP’s drag-and-drop interface creates an accessible environment for creating a variety of high-quality visualizations. The Data Jam project has curated Luquillo LTER datasets about hydrology, shrimp populations and dragonfly populations to scaffold students in using CODAP to investigate their own research questions. Data Jam provides students with structured challenge activities that build data science capacity and empower them to develop their own projects.

Pillar 2: Partnerships for Career and Workforce Preparation.

Luquillo LTER scientists serve as Data Jam mentors for both teachers & students. They meet regularly with a team of teachers to provide feedback on Data Jam progress. They also present to each teacher’s class, describing their professional journey and providing an example of Luquillo research. At the end of Data Jam, the mentors attend the symposium and provide students with feedback on their posters. Both teachers and students have commented on the importance of the mentors for building students’ scientific identity.

Pillar 3: Strategies for Equity in STEM Education

An important pillar of Ladson-Billings’ culturally relevant pedagogy model is student empowerment. She stresses that schools should empower students to apply what they are learning to their daily lives. Data Jam exemplifies this empowerment by engaging students in conducting research about their environment. For many teachers, Data Jam is the 1st time they have been able to introduce students to authentic data from a local iconic forest. Data Jam has provided students with a new perspective on their local environment.

Discipline(s)

Data Science

Environmental sciences

Life sciences

Target Gradespan(s)

Middle school (6-8)

High school (9-12)

Target Participant(s)

Youth / students

Educators

Project Setting(s)

Formal Education

Category

Developing and Testing Innovations (DTI)

Visualizing Funds of Identity while Exploring Data Literacy Concepts

Merijke Coenraad — Wed, 24 Apr 2024 19:27:31 +0000

Visualizing Funds of Identity while Exploring Data Literacy Concepts

Merijke Coenraad Wed, 04/24/2024 - 15:27

Visualizing Funds of Identity while Exploring Data Literacy Concepts

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Our project utilizes the network visualization tool Net.Create (https://netcreate.org/) to provide opportunities for students to create data visualizations of subject-area content. These visualizations provide unique data representations through which students and teachers have the opportunity to engage with data literacy concepts and content knowledge while also exploring connections between themselves, the content, and their peers.

Pillar 2: Partnerships for Career and Workforce Preparation.

Through the novel opportunity to engage with Net.Create and data visualizations, our project introduces students to the ways in which network visualizations and networking are central to common technologies and integral to many career paths. Building on our partner district’s existing computational thinking and STEAM pathways, this work provides students with deeper exploration of skills needed for careers and workforce opportunities related to data literacy and network visualizations.

Pillar 3: Strategies for Equity in STEM Education

Our project explores how network visualizations can heighten student engagement in learning by integrating student interests, content knowledge, and data literacy. Through the integration of student interests and identities, the lessons and visualizations are more responsive to students and they are able to see themselves reflected within the data literacy and subject-area content. Further, teachers learn about students’ individual and collective identities and interests from what they share within the visualizations.

Discipline(s)

Data Science

Other

Target Gradespan(s)

Middle school (6-8)

Target Participant(s)

Youth / students

Project Setting(s)

Formal Education

Category

Developing and Testing Innovations (DTI)

SRMPmachine: Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research

Mark Weckel — Wed, 24 Apr 2024 18:58:10 +0000

SRMPmachine: Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research

Mark Weckel Wed, 04/24/2024 - 14:58

SRMPmachine: Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research

Pillar 1: Innovative Use of Technologies in Learning and Teaching

During the Summer Institute, students conduct several mini scientific investigations while navigating the ML pipeline. Through plugged and unplugged techniques, students use four different ML methods: linear regression, principal component analysis, decision trees, and artificial neural networks. The culminating project involves using “Wallace,” a code-light professional platform used by biodiversity scientists, to run ML models to predict species distribution using locality data and climate variables.

Pillar 2: Partnerships for Career and Workforce Preparation.

SRMP is a STEM workforce development program that provides mentored-research experiences and scaffolded supports. SRMPmachine was co-developed by scientists, STEM educators, MIT AI experts, and SRMP alumni. Our alumni, all emerging computer science and IT professionals in academia and industry, were critical to the development of SRMPmachine. These young professionals captured both the spirit of what students need in their informal education experience and the skill sets that have propelled them in their early careers

Pillar 3: Strategies for Equity in STEM Education

AI requires broad participation to fulfill its potential and minimize the risk of harm, which means creating learning spaces that support a diversity of learners as valued members of the AI community. We employed the YESTEM Core Equitable Practices (CEP) of “Authority Sharing” & “Shifting Narratives” to guide the development of the Summer Institute. These CEPs position educators and youth as "co-learners, co-disruptors, and co-creators of a more just world" within and through STEM (Greenberg & Calabrese Barton, 2021; YE

Discipline(s)

Data Science

Emerging Tech (Artificial Intelligence, Quantum Computing, and Blockchain)

Interdisciplinary

Life sciences

Physics and astronomy

Target Gradespan(s)

High school (9-12)

Target Participant(s)

Youth / students

Project Setting(s)

Informal Education

Category

Developing and Testing Innovations (DTI)

Integrating language-based AI across the curriculum to create diverse pathways to AI-rich careers

Rebecca Ellis — Wed, 24 Apr 2024 18:47:23 +0000

Integrating language-based AI across the curriculum to create diverse pathways to AI-rich careers

Rebecca Ellis Wed, 04/24/2024 - 14:47

Integrating language-based AI across the curriculum to create diverse pathways to AI-rich careers

Pillar 1: Innovative Use of Technologies in Learning and Teaching

The project is refining the StoryQ app and developing a Markov Chain model app to help students visualize text classification and text generation AI models. The use of these models is integrated into secondary disciplinary courses for maximum access and relatability.

Pillar 2: Partnerships for Career and Workforce Preparation.

By leveraging the intrinsic connections between AI and disciplines already taught in schools, the project envisions a series of learning opportunities, presenting discipline-specific scenarios for students to dive deep into aspects of AI and to develop awareness and interest in various AI applications and careers.

Pillar 3: Strategies for Equity in STEM Education

This project will create an effective, scalable, and sustainable exemplary program for high school students, especially those underrepresented and underserved in the field of AI, to develop foundational knowledge of AI and interest in AI-rich careers.

Discipline(s)

Computer and informational technology science

Data Science

Emerging Tech (Artificial Intelligence, Quantum Computing, and Blockchain)

Interdisciplinary

Mathematical sciences

Other

Target Gradespan(s)

High school (9-12)

Target Participant(s)

Youth / students

Educators

Black/African American participants

Hispanic/Latino participants

Students eligible for free lunch or reduced-price lunch

Project Setting(s)

Formal Education

Category

Developing and Testing Innovations (DTI)

SRMPmachine: Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research

Mark Weckel — Wed, 24 Apr 2024 18:40:53 +0000

SRMPmachine: Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research

Mark Weckel Wed, 04/24/2024 - 14:40

SRMPmachine: Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Pillar 2: Partnerships for Career and Workforce Preparation.

Pillar 3: Strategies for Equity in STEM Education

Discipline(s)

Data Science

Emerging Tech (Artificial Intelligence, Quantum Computing, and Blockchain)

Interdisciplinary

Life sciences

Physics and astronomy

Target Gradespan(s)

High school (9-12)

Target Participant(s)

Youth / students

Project Setting(s)

Informal Education

Category

Developing and Testing Innovations (DTI)

Data Science

Drones in Alaska Elementary Curriculum

Drones in Alaska Elementary Curriculum

Drones in Alaska Elementary Curriculum

Description

Project Curricula Details

Investigating the relationships among making, wearables, curiosity, computational thinking and STEM careers

Investigating the relationships among making, wearables, curiosity, computational thinking and STEM careers

Investigating the relationships among making, wearables, curiosity, computational thinking and STEM careers

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Pillar 2: Partnerships for Career and Workforce Preparation.

Pillar 3: Strategies for Equity in STEM Education

Collaborative Research: Exploring Theory and Design Principles (ETD): Investigating the relationships among making, wearables, curiosity, computational thinking and STEM careers

Data Science, AI & You (DSAIY) in Healthcare

Data Science, AI & You (DSAIY) in Healthcare

Data Science, AI & You (DSAIY) in Healthcare

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Pillar 2: Partnerships for Career and Workforce Preparation.

Pillar 3: Strategies for Equity in STEM Education

A Learning Ecosystem for Teaching High School Students Machine Learning Concepts and Data Science Skills in Healthcare and Medicine

Connecting Traditional Knowledge and Emerging STEM Through Smart AgTech

Connecting Traditional Knowledge and Emerging STEM Through Smart AgTech

Connecting Traditional Knowledge and Emerging STEM Through Smart AgTech

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Pillar 2: Partnerships for Career and Workforce Preparation.

Pillar 3: Strategies for Equity in STEM Education

Connecting Indigenous Culture and Integrated STEM in Hawaii Through Smart AgTech

Stimulating GIS Careers through Geospatial Explorers

Stimulating GIS Careers through Geospatial Explorers

Stimulating GIS Careers through Geospatial Explorers

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Pillar 2: Partnerships for Career and Workforce Preparation.

Pillar 3: Strategies for Equity in STEM Education

Adapting and Implementing a Geospatial High School Course in Career and Technical Education Clusters in Urban Settings

Data Jam: Engaging Students with Local Ecological Data in Puerto Rico

Data Jam: Engaging Students with Local Ecological Data in Puerto Rico

Data Jam: Engaging Students with Local Ecological Data in Puerto Rico

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Pillar 2: Partnerships for Career and Workforce Preparation.

Pillar 3: Strategies for Equity in STEM Education

Supporting the Scientific Practice of Data Analysis through Creative Investigations of Long-Term Ecological Datasets

Visualizing Funds of Identity while Exploring Data Literacy Concepts

Visualizing Funds of Identity while Exploring Data Literacy Concepts

Visualizing Funds of Identity while Exploring Data Literacy Concepts

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Pillar 2: Partnerships for Career and Workforce Preparation.

Pillar 3: Strategies for Equity in STEM Education

Integrating Students' Interests, Identities and Ways of Knowing with Network Visualization Tools to Explore Data Literacy Concepts

SRMPmachine: Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research

SRMPmachine: Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research

SRMPmachine: Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Pillar 2: Partnerships for Career and Workforce Preparation.

Pillar 3: Strategies for Equity in STEM Education

Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research

Integrating language-based AI across the curriculum to create diverse pathways to AI-rich careers

Integrating language-based AI across the curriculum to create diverse pathways to AI-rich careers

Integrating language-based AI across the curriculum to create diverse pathways to AI-rich careers

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Pillar 2: Partnerships for Career and Workforce Preparation.

Pillar 3: Strategies for Equity in STEM Education

Integrating Language-Based AI Across the High School Curriculum to Create Diverse Pathways to AI-Rich Careers

SRMPmachine: Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research

SRMPmachine: Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research

SRMPmachine: Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research

Pillar 1: Innovative Use of Technologies in Learning and Teaching

Pillar 2: Partnerships for Career and Workforce Preparation.

Pillar 3: Strategies for Equity in STEM Education

Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research