Tag Archives: educational technology

Concord Consortium Publishes Important Research in Educational Technology

Nine publications illuminate our research in educational technology in 2017. Learn about engineering design tools that may help bridge the design-science gap (#5), a systems modeling tool that supports students in the NGSS practice of developing and using models and the crosscutting concept of systems (#1), an Earth science curriculum that increases student scientific argumentation abilities (#6), the relative ease of creating hierarchical data structures (#9), automated analysis of collaborative problem solving in electronics (#8), and more.

1. New systems modeling tool supports students

The NGSS identify systems and system models as one of the crosscutting concepts, and developing and using models as one of the science and engineering practices. However, students do not naturally engage in systems thinking or in building models to make sense of phenomena. The Concord Consortium and Michigan State University developed a free, web-based, open-source systems modeling tool called SageModeler and a curricular approach designed to support students and teachers in engaging in systems modeling.

Damelin, D., Krajcik, J., McIntyre, C., & Bielik, T. (2017). Students making system models: An accessible approach. Science Scope, 40(5), 78-82.

2. Students should face the unknown and engage in frontier science questions

Students should see science as an ongoing process rather than as a collection of facts. Six High-Adventure Science curriculum modules provide an opportunity to bring contemporary science and the process of doing science into the classroom. Interactive, dynamic models help students make sense of complex Earth systems. Embedded assessments prompt students to interpret data to make scientific arguments and evaluate claims while considering the uncertainty inherent in frontier science.

Pallant, A. (2017). High-Adventure Science: Exploring evidence, models, and uncertainty related to questions facing scientists today. The Earth Scientist, 33, 23-28.

3. Automated feedback helps students write scientific arguments

Automated scoring and feedback support students’ construction of written scientific arguments while learning about factors that affect climate change. Results showed that 77% of students made revisions to their open-ended argumentation responses after receiving feedback. Students who revised had significantly higher final scores than those who did not, and each revision was associated with an increase on the final scores.

Zhu, M., Lee, H.-S., Wang, T., Liu, O. L., Belur, V., & Pallant, A. (2017). Investigating the impact of automated feedback on students’ scientific argumentation. International Journal of Science Education, 1–21.

4. Review of research on women’s underrepresentation in computing fields

This literature review synthesizes research on women’s underrepresentation in computing fields across four life stages: 1) pre-high school; 2) high school; 3) college major choice and persistence; and 4) postbaccalaureate employment. Access to and use of computing resources at the pre-high school and high school levels are associated with gender differences in interest and attitudes toward computing. In college, environmental context contributes to whether students will major in computing, while a sense of belonging and self-efficacy as well as departmental culture play a role in persistence in computing fields. Work-life conflict, occupational culture, and mentoring/networking opportunities play a role in women’s participation in the computing workforce.

Main, J. B., & Schimpf, C. (2017). The underrepresentation of women in computing fields: A synthesis of literature using a life course perspective. IEEE Transactions on Education, 60(4), 296-304.

5. Students improve knowledge by designing with robust engineering tools

Eighty-three 9th grade students completed an energy-efficient home design challenge using our Energy3D software. Students substantially improved their knowledge. Their learning gains were positively associated with three types of design actions—representation, analysis, and reflection—measured by the cumulative counts of computer logs. These findings suggest that tools are not passive components in a learning environment, but shape design processes and learning paths, and offer possibilities to help bridge the design-science gap.

Chao, J., Xie, C., Nourian, S., Chen, G., Bailey, S., Goldstein, M. H., Purzer, S., Adams, R. S., & Tutwiler, M. S. (2017). Bridging the design-science gap with tools: Science learning and design behaviors in a simulated environment for engineering design. Journal of Research in Science Teaching, 54(8), 1049-1096.

6. Students improve their scientific argumentation skills

Making energy choices means considering multiple factors, exploring competing ideas, and reaching conclusions based on the best available evidence. Our High-Adventure Science project created a free online energy module in which students compare the effects of energy sources on land use, air quality, and water quality using interactive models, real-world data on energy production and consumption, and scaffolded argumentation tasks. We analyzed pre- and post-test responses to argumentation items for 1,573 students from three middle schools and seven high schools. Students significantly improved their scientific argumentation abilities after using the energy module.

Pallant, A., Pryputniewicz, S. & Lee, H-S. (2017). The future of energy. The Science Teacher, 84(3), 61-68.

7. Students learn about sustainability

Educators must figure out how to prepare students to think about complex systems and sustainability. We elucidate a set of design principles used to create online curriculum modules related to Earth’s systems and sustainability and give examples from the High-Adventure Science module “Can we feed the growing population?” The module includes interactive, computer-based, dynamic Earth systems models that enable students to track changes over time. Embedded prompts help students focus on stocks and flows within the system, and identify important resources in the models, explain the processes that change the availability of the stock, and explore real-world examples.

Pallant, A., & Lee, H. S. (2017). Teaching sustainability through systems dynamics: Exploring stocks and flows embedded in dynamic computer models of an agricultural system. Journal of Geoscience Education, 65(2), 146-157.

8. Automated analysis sheds light on collaborative problem solving

The Teaching Teamwork project created an online simulated electronic circuit, running on multiple computers, to assess students’ abilities to work together as a team. Modifications to the circuit made by any team member, insofar as they alter the behavior of the circuit, can affect measurements made by the others. We log all relevant student actions, including calculations, measurements, online student communications, and alterations made by the students to the circuit itself. Automated analysis of the resulting data sheds light on the problem-solving strategy of each team.

Horwitz, P., von Davier, A., Chamberlain, J., Koon, A., Andrews, J., & McIntyre, C. (2017). Teaching Teamwork: Electronics instruction in a collaborative environment. Community College Journal of Research and Practice, 41(6), 341-343.

9. Students understand how to structure data

In this study participants were presented with diagrams of traffic on two roads with information about eight attributes (e.g., type of vehicle, its speed and direction) and asked to record and organize the data to assist city planners in its analysis. Overall, 79% of their data sheets successfully encoded the data. Even 62% of the middle school students created a structure that could hold the critical information from the diagrams. Students were more likely to create nested data structures than they were to produce one flat table, suggesting that hierarchical structures might be more intuitive and easier to interpret than flat tables.

Konold, C., Finzer, W., & Kreetong, K. (2017). Modeling as a core component of structuring data. Statistics Education Research Journal, 16(2), 191-212.