skip to main content

Title: Impact of a Blended Immersive and Computational Modeling Tool on Elementary Ecosystems Science Learning
Integrating computational thinking and scientific modeling in elementary school is challenging, but provides opportunities to meet important 21st century learning goals. Furthermore, computational modeling deepens the level of understanding of the modeling process and the phenomena being modeled, making science content more accessible. This paper presents findings from EcoMOD, a research project that blends an immersive virtual ecosystem and a 2D modeling environment to support computational modeling and ecosystem science learning in 3rd grade. As part of the study, students filled out pre- and post- surveys about science content understanding, affective measures, and scientific modeling. Findings for the overall student gains across the three dimensions of the surveys suggest that the EcoMOD curriculum was effective in learning ecosystem science content and practice, as well as developing an understanding of the value of computational modeling.
Authors:
; ; ; ; ; ;
Award ID(s):
1639545
Publication Date:
NSF-PAR ID:
10212676
Journal Name:
Annual meeting program American Educational Research Association
ISSN:
0163-9676
Sponsoring Org:
National Science Foundation
More Like this
  1. Remote access technology in STEM education fills dual roles as an educational tool to deliver science education (Educational Technology) and as a means to teach about technology itself (Technology Education). A five-lesson sequence was introduced to 11 and 12-year-old students at an urban school. The lesson sequences were inquiry-based, hands-on, and utilized active learning pedagogies, which have been implemented in STEM classrooms worldwide. Each lesson employed a scanning electron microscope (SEM) and energy dispersive spectroscopy (EDS) accessed remotely. Students were assessed using multiple-choice questions to ascertain (1) technology education learning gains: did students gain an understanding of how electron microscopes work? and (2) educational technology learning gains: did students gain a better understanding of lesson content through use of the electron microscope? Likert-item surveys were developed, distributed, and analyzed to established how remote access technology affected student attitudes toward science, college, and technology. Participating students had a positive increase in attitudes toward scientific technology by engaging in the lesson sequences, reported positive attitudes toward remote access experiences, and exhibited learning gains in the science behind the SEM technology they accessed remotely. These findings suggest that remote experiences are a strong form of technology education, but also that future research couldmore »explore ways to strengthen remote access as an educational technology (a tool to deliver lesson content), such as one-on-one engagement. This study promotes future research into inquiry-based, hands-on, integrated lessons approach that utilize educational technology learning through remote instruments as a pedagogy to increase students’ engagement with and learning of the T in STEM.« less
  2. We will present emerging findings from an ongoing study of instruction at the intersection of science and computer science for middle school science classrooms. This paper focuses on student knowledge and dispositional outcomes in relation to a 2 week/10-lesson learning sequence. Instruction aims to broaden participation in STEM pathways through a virtual simulated internship in which students inhabit the role of interns working to develop a restoration plan to improve the health of coral reef populations. Through this collaborative work, students construct understanding of biotic and abiotic interactions within the reef and develop a computational model of the ecosystem. Analysis of pre/post surveys for n=381 students revealed that students who participated in the 2 week/10 lesson integrated computational thinking in science learning sequence demonstrated significant learning gains on an external measure of CT (0.522***; effect size=0.32). Drawing on scales from the Activation Lab suite of measures, pre/post surveys revealed increased competency beliefs about computer programming (mean difference =1.13***; effect size=1.01), and increased value assigned to STEM (0.78***; effect size=0.945). We also discuss the design of the instructional sequence and the theoretical framework for its development.
  3. Abstract

    This paper shares findings from a teacher designed physics and computing unit that engaged students in learning physics and computing concurrently thru inquiry. Using scientific inquiry skills and practices, students were tasked with assessing the validity of local rollercoaster g-force ratings as posted to the public. Students used computational electronic textile circuits (e-textiles) to engage in “myth busting” amusement park g-force ratings. In doing so, students engaged computing and computational thinking skills in service to answering their scientific inquiry. Findings from this study indicate that physics classes are ideal spaces for engaging in computing’s Big Ideas as laid out by Grover and Pea (Educational Researcher 42, 38–43, 2013) as well as the pillars of computational thinking (Wing, Communications of the ACM 49, 33–35, 2006). However, essential to this dual engagement is a need for computing content to act in service to the better acquisition of physics content within the physics classroom space. Findings indicate that the teachers’ use of e-textiles to integrate physics and computing broadened and deepened student learning by providing affordances for computational thinking within the structure of physical science inquiry.

  4. As technology advances, data driven work is becoming increasingly important across all disciplines. Data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to “benefit from a fundamental awareness of and competence in data science”[9]. This project has formed a research practice partnership that brings together STEM+C instructors and researchers from three universities and an education research and consulting group. We aim to use high frequency monitoring data collected from real-world systems to develop and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of datamore »science curricula that are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students’ year of study, students’ reasons for taking class, and students’ background expertise and knowledge. These findings indicate the challenges in developing data modules for multidisciplinary use. We hope that the analysis and reflections on our initial offerings has improved our understanding of these challenges, and how we may address them when designing future data science teaching modules. These are the first steps in a design-based approach to developing data science modules that may be offered across multiple courses.« less
  5. As technology advances, data-driven work is becoming increasingly important across all disciplines. Data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to “benefit from a fundamental awareness of and competence in data science”[9]. This project has formed a research-practice partnership that brings together STEM+C instructors and researchers from three universities and an education research and consulting group. We aim to use high-frequency monitoring data collected from real-world systems to develop and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of data science curricula thatmore »are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students’ year of study, students’ reasons for taking class, and students’ background expertise and knowledge. These findings indicate the challenges in developing data modules for multidisciplinary use. We hope that the analysis and reflections on our initial offerings have improved our understanding of these challenges, and how we may address them when designing future data science teaching modules. These are the first steps in a design-based approach to developing data science modules that may be offered across multiple courses.« less