
STEM Cyberlearning
Our team designs and evaluates the innovative use of cyberlearning technologies in STEM instructional settings, with the goal of developing and identifying tools and strategies that lead to highly resourced students and teachers. We are a highly collaborative team that works across disciplines and institutions to partner with research institutions and places of learning. A particular focus is helping North Carolina K-12 schools develop the next generation of STEM workers and learners.
Updates
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Professor Eric Wiebe Joins New NSF AI Institute for Engaged Learning to Advance Learning and Education
Eric Wiebe, a professor of STEM education in the NC State College of Education and senior faculty fellow at the Friday Institute for Educational Innovation, is part of a new National Science Foundation (NSF) research initiative that will create and utilize artificial intelligence (AI) tools to advance learning and education.
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EdNC: Perspective | Friday Institute Champions STEM
EdNC highlights the Friday Institute for their monthly feature, the NC STEM ScoreCard, which builds broader awareness of how P-20 STEM education is being nurtured across North Carolina.
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Ed Week: Two Ways to Add ‘Computational Thinking’ to Middle School Science
During AERA's annual conference, researchers from NC State University and the University of Colorado, Boulder, highlighted two pilot programs to use computational thinking to enhance standard science units.
Selected Resources
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Cultivating Effective Teacher-Game Partnerships in Science ClassroomsFI Education BriefTeachers play a critical role in student success with digital game-based learning in the classroom. That is a key finding from a study the Friday Institute for Educational Innovation’s STEM Cyberlearning team recently conducted to investigate how teachers utilized a digital genetics game to meet students’ learning needs.
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PRIME Technical Report 1: The Development and Validation of the Computer Science Concepts Assessment for Undergraduate Students (UG-CSCA): Preliminary ResultsReportThe Undergraduate Computer Science Concept Assessment (UG-CSCA) is intended to assess STEM undergraduate students’ understanding of basic computer science and programming concepts – variables, conditionals, loops, and algorithms. The validation process of this assessment was guided and informed by a Focal Knowledge, Skills, and Abilities (FKSAs) Framework proposed by Grover and Basu (2017) and the K-12 CS Framework (K–12 Computer Science Framework, 2016). Block-based programming is used as the context for each item in the UG-CSCA. Several studies suggest that block-based programming is effective and appropriate computer programming for novices (Grover, Pea, & Cooper, 2015; Weintrop & Wilensky, 2015), and thus aligns with the intention of this assessment. The current version of the UG-CSCA was written for undergraduate students who are novices in computer science and programming. We believe that this assessment will be useful to instructors who teach introductory computer science and programming courses, as well as computer science education researchers. The instrument was designed for use in pre-intervention-post or longitudinal contexts, as well as for a diagnostic tool. We suggest providing 30-35 minutes for students to complete the assessment which consists of 26 multiple-choice questions.
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The Development of Computer Science Concepts Inventory for Middle School Students: Preliminary ResultsReportThe CS Concepts Inventory is intended to measure students’ understanding of the four core concepts of CS—variables, conditionals, loops and algorithms— taught at the middle school level. Additionally, we incorporated the concepts of debugging, comprehension and development into the assessment. The assessment was guided by a conceptual framework informed by a Focal Knowledge, Skills and Abilities—FKSAs framework developed by Grover and Basu (2017), the K-12 CS Framework (K–12 Computer Science Framework, 2016) and the Computer Science Teachers Association (CSTA) Standards (CSTA, 2017). The assessment utilizes elements from a block-based programming environment as the context for every question, based on findings that suggest learners, especially novice ones, experience less conceptual and cognitive difficulties using these tools (e.g., Grover, Pea & Cooper, 2015; Robins, Rountree, & Rountree, 2003).
Selected Projects
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AAiM: Adapting to Affect in Multimodal Dialogue-Rich InteractionThis project will design, develop and iteratively refine an integrated affect and dialogue management model that adaptively responds to students’ affective states in the course of their learning interactions.
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ENGAGE: Immersive Game-Based Learning for Middle Grade Computational FluencyBuilding upon science standards, emerging computer science curricula and leveraging significant advances in game-based learning, the ENGAGE project will deeply infuse computational thinking into middle school science education.
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FLECKS: Fostering Collaborative Computer Science Learning with Intelligent Virtual Companions for Upper Elementary StudentsA design-based research project that is guided by the input of teachers and students with the ultimate objective of developing a digital learning environment that fosters student collaboration in programming activities with virtual learning companions.
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GUIDE: Guiding Understanding via Information from Digital EnvironmentsThe overall goal of the GUIDE project is to improve student learning of genetic concepts and scientific practices at the high school level.
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Game-Changing Research Incentive Program (GRIP): Computer Science for All K-12 StudentsThis project will establish NC State as a national center addressing the critical need to provide pre-college students with foundational knowledge and skills in computational thinking.
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Multimodal AI Literacy: Supporting the Learning of Artificial Intelligence (AI) Through Multimodal Narrative CreationThis project takes a first step to investigate how digital literacies practices and the ways youth engage with them may provide meaningful opportunities for them to create and understand AI technologies.
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PRIME: Engaging STEM Undergraduate Students in Computer Science with Intelligent Tutoring SystemsThe PRIME project has the overarching objective of transforming introductory computing for STEM majors by creating an intelligent tutoring system that provides individualized problem-solving and motivational support.