Research in Educational Technology in 2019

We’re making an impact with 12 publications in researcher and teacher practitioner journals that showcase the state of the field in STEM educational technology in 2019.

Learn about a theoretical framework that positions students as data producers rather than merely data collectors (#10), automated text scoring and feedback in Earth science curriculum modules (#3, #12), our new Geniventure dragon genetics game highlighting the role of proteins in genetics (#4), why educators should pay explicit attention to data moves to support student learning about data (#6), a CAD-based research platform for data-driven design thinking studies (#7), the development of students’ system modeling competence (#9), and more.

Read all Concord Consortium articles and papers.

Science Teacher cover

1. Using scientific argumentation to understand human impact on the Earth

The Next Generation Science Standards include Engaging in Argument from Evidence as a key science and engineering practice. Students need experiences that help them understand, use, and interpret scientific explanations, evaluate evidence, and think about the development of scientific knowledge. An 11th grade Earth science teacher shares her experience using High-Adventure Science modules as a valuable tool for students to consider scientific evidence as they develop scientific argumentation skills.

Harmon, S., Pallant, A., & Pryputniewicz, S. (2019). Using scientific argumentation to understand human impact on the Earth. The Science Teacher, 86(6), 28-36.

2. Using automatic image processing to analyze visual artifacts created by students in scientific argumentation

We used automated image processing techniques to extract relevant information from student‐generated visual artifacts. We automatically extracted and quantified features of images created by students to serve as evidence in support of their scientific arguments, and identified the relationships between the features and students’ performance levels in constructing scientific arguments. We describe how automatic image processing can successfully identify image features that affect students’ performance, and discuss implications for incorporating automated image processing into further research into scientific argumentation and the development of automated feedback.

Pei, B., Xing, W., & Lee, H.–S. (2019). Using automatic image processing to analyze visual artifacts created by students in scientific argumentation. British Journal of Educational Technology, 50(6), 3391-3404.

3. Automated text scoring and real-time adjustable feedback: Supporting revision of scientific arguments involving uncertainty

HASBot is an automated text scoring and real‐time feedback system designed to support student revision of scientific arguments. Students submit open‐ended text responses to explain how their data support claims and how the limitations of their data affect the uncertainty of their explanations. HASBot automatically scores these text responses and returns the scores with feedback to students. Data were collected from 343 middle and high school students taught by 9 teachers in 7 states. Linear regression analysis results indicate that students’ HASBot use significantly contributed to their post-test performance on uncertainty‐infused scientific argumentation. We identify several affordances and limitations of HASBot.

Lee, H.-S., Pallant, A., Pryputniewicz, S., Lord, T., Mulholland, M., & Liu, O. L. (2019). Automated text scoring and real-time adjustable feedback: Supporting revision of scientific arguments involving uncertainty. Science Education, 103(3), 590-622.

4. Genetics with dragons: Using an online learning environment to help students achieve a multilevel understanding of genetics

With our collaborators, we developed a free digital genetics game, Geniventure, with an engaging narrative featuring dragons that need to be saved from extinction. We redesigned the original Geniverse for a middle school audience and highlighted the role of proteins in genetics, including the multilevel components responsible for physical traits. Through scaffolded virtual investigations, students explore the physical traits that result from allele combinations, then zoom into cells and manipulate the proteins that ultimately give rise to those traits.

McElroy-Brown, K., & Reichsman, F. (2019). Genetics with dragons: Using an online learning environment to help students achieve a multilevel understanding of genetics. Science Scope, 42(8), 56-63.

5. How to support secondary school students’ consideration of uncertainty in scientific argument writing: A case study of a High-Adventure Science curriculum module

Geoscientists routinely make inferences about the Earth based on observations of the present, and test those observations against hypotheses about Earth’s history and processes that are not readily observable. In an online Earth science curriculum module called “Will there be enough fresh water?” students think about uncertainty when writing scientific arguments. The impact of the module on student learning of uncertainty attribution was based on 546 middle and high school students taught by 9 teachers in 6 states. Students achieved significant gains from pre-test to post-test on scientific argumentation.

Pallant, A., Lee, H.-S., & Pryputniewicz, S. (2019). How to support secondary school students’ consideration of uncertainty in scientific argument writing: A case study of a High-Adventure Science curriculum module. Journal of Geoscience Education.

6. Data moves

Experienced analysts often transform datasets by grouping or filtering data, calculating new variables and summary measures, or reorganizing a dataset by changing its structure or merging it with other information. These actions are called “data moves,” and they background, highlight, or even fundamentally change particular features of the data, allowing different types of questions to be explored. We argue that paying explicit attention to data moves, as well as their purposes and consequences, is necessary for educators to support student learning about data.

Erickson, T., Wilkerson, M., Finzer, W., & Reichsman, F. (2019). Data moves. Technology Innovations in Statistics Education, 12(1).

7. A computer-aided design based research platform for data-driven design thinking studies

We introduce Energy3D, our computer-aided design (CAD) software, and its design process logger, embedded design experiment and tutorials, and interactive CAD interfaces and dashboard — features that make Energy3D a capable testbed for research related to engineering design thinking and design theory, such as search strategies, design decision-making, artificial intelligence in design, and design cognition. Using a case study on an energy-plus home design challenge, we demonstrate how such a platform enables a complete research cycle of studying designers’ sequential decision-making behaviors based on fine-grained design action data and unsupervised clustering methods.

Rahman, M., Schimpf, C., Xie, C., & Sha, Z. (2019). A computer-aided design based research platform for data-driven design thinking studies. Journal of Mechanical Design, 141(12).

8. The exploration of automated image processing techniques in the study of scientific argumentation

We report on automated image processing used to quantify image features relevant to secondary students’ scientific arguments when they worked in an interactive model and made claims about whether rain water was trapped underground. Chi-square tests and independent samples t-tests were used to determine the relationships between the extracted features and the argumentation. The results revealed that the presence of a line on a student’s snapshot had a significant effect on that student’s claim and explanation scores, and the starting and endpoints of the students’ lines significantly influenced their explanation scores, but not their claim scores.

Xing, W., Pei, B., & Lee, H.-S. (2019). The exploration of automated image processing techniques in the study of scientific argumentation. In M. D. Lytras, N. Aljohani, L. Daniela, & A. Visvizi (Eds.), Cognitive computing in technology-enhanced learning (pp. 175-190). Hershey, PA: IGI Global.

9. Designing technology environments to support system modeling competence

We focus on the development of students’ system modeling competence as they engaged in the modeling practice using an online modeling tool in a high school chemistry unit. We describe four aspects of system modeling competence: (1) defining the boundaries of the system by including components in the model that are relevant to the phenomena under investigation, (2) determining appropriate relationships between components in the model, (3) using evidence and reasoning to build, evaluate, and revise models, and (4) interpreting the behavior of a model to determine its usefulness in explaining and making predictions about phenomena.

Bielik, T., Stephens, L., Damelin, D., & Krajcik, J. (2019). Designing technology environments to support system modeling competence. In A. Upmeier zu Belzen, D. Krüger, & J. van Driel, Jan (Eds.), Towards a competence-based view on models and modeling in science education. Springer.

10. From data collectors to data producers: Shifting students’ relationship to data

We propose a theoretical framework informed by historical, philosophical, and ethnographic studies of science practice to argue that data should be considered to be actively produced, rather than passively collected. We further argue that traditional school science laboratory investigations misconstrue the nature of data and overly constrain student agency in their production. We use our “Data Production” framework to analyze the activity of and interviews with students who created data using sensors and software in a ninth grade integrated science class, and present the case of one student as she produced data for her own personally relevant purposes.

Hardy, L., Dixon, C., & Hsi, S., (20190. From data collectors to data producers: Shifting students’ relationship to data. Journal of the Learning Sciences.

11. For science and self: Youth interactions with data in community and citizen science

In youth-focused community and citizen science, youth produce data that scientists, resource managers, and community members use. This “nested” data situates learners’ scientific activity within larger datasets, projects, and communities, with consequences for youth agency. We report on how youth interact with data across eight school and community-based project sites and how youth talk about their data and work. From analysis of 54 participant interviews, we found that youth perceived the data they produced as being used for: (1) broader scientific work, (2) their own learning, and (3) community endeavors.

Harris, E. M., Dixon, C. G. H., Bird, E. B., & Ballard, H. L. (2019). For science and self: Youth interactions with data in community and citizen science. Journal of the Learning Sciences.

12. The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing

This study investigates a formative feedback system that incorporates automated scoring technologies integrated into an online curriculum module on climate change and focuses on students’ constructed responses. By analyzing log files, we explore how student revisions enabled by the formative feedback system correlate with student performance and learning gains. Our results showed that (1) students with higher initial scores on average were more likely to revise after the automated feedback, (2) revisions were positively related to score increases, and (3) contextualized feedback was more effective in assisting learning.

Zhu, M., Liu, O. L., & Lee, H.-S. (2020). The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing. Computers & Education, 143.

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