This year we published a dozen articles in researcher and teacher practitioner journals that showcase the state of the field in STEM educational technology in 2020.
Learn what students with low and high spatial skills notice in computer visualizations of plate tectonics (#2), how students can experience authentic messy data exploration of meaningful questions (#10), the effects of automated feedback on students’ written scientific explanations (#1), accommodations for special education students in high school physics classes (#7), best practices to explore chemistry concepts with infrared thermography (#12), a Bayesian network model to dynamically and automatically assess students’ engagement with engineering design tasks (#4), and much more!
1. Using cluster analysis to explore students’ interactions with automated feedback in an online Earth science task
Digitally delivered tasks can provide students opportunities to interact with disciplinary content while their interactions with the tasks can be recorded as time-stamped log events. Through post-hoc analysis of log data, we can re-enact and discover patterns in students’ activities. This study addresses an online Earth science module where students engaged in writing and revising scientific arguments in a structured format. We adopted natural language processing techniques to analyze students’ responses, which enabled us to provide immediate feedback to students on their responses and revisions.
Zhu, M., Liu, O. L., & Lee, H.-S. (2020). Using cluster analysis to explore students’ interactions with automated feedback in an online Earth science task. The International Journal of Quantitative Research in Education, 5(2).
2. Focus on the notice: Evidence of spatial skills’ effect on middle school learning from a computer simulation
We present the findings from the qualitative portion of a mixed methods study that investigated the impact of middle school students’ spatial skills on their plate tectonics learning while using a computer visualization. Higher spatial skills have been linked to higher STEM achievement, while use of computer visualizations has mixed results for helping various students with different spatial levels. This study endeavors to better understand the difference between what high and low spatial-skilled middle school students notice and interpret while using a plate tectonic computer visualization. We also examine the differences in the quantity and quality of students’ spatial language.
Epler-Ruths, C. M., McDonald, S., Pallant, A., & Lee, H.-S. (2020). Focus on the notice: Evidence of spatial skills’ effect on middle school learning from a computer simulation. Cognitive Research: Principles and Implications, 5(61).
3. Ten simple rules for partnering with K–12 teachers to support broader impact goals
Contributing to broader impacts is an important aspect of scientific research. Engaging practicing K–12 teachers as part of a research project can be an effective approach for addressing broader impacts requirements of grants, while also advancing researcher and teacher professional growth. Our focus is on leveraging teachers’ professional expertise to develop science education materials grounded in emerging scientific research. We describe ten simple rules for planning, implementing, and evaluating teacher engagement to support the broader impact goals of your research project.
Warwick, A. R., Kolonich, A., Bass, K. M., Mead, L. M., & Reichsman, F. (2020). Ten simple rules for partnering with K–12 teachers to support broader impact goals. PLoS Computational Biology, 16(10): e1008225.
4. Automatic assessment of students’ engineering design performance using a Bayesian network model
Integrating engineering design into K-12 curricula is increasingly important as engineering has been incorporated into many STEM education standards. However, the ill-structured and open-ended nature of engineering design makes it difficult for an instructor to keep track of the design processes of all students simultaneously and provide personalized feedback on a timely basis. This study proposes a Bayesian network model to dynamically and automatically assess students’ engagement with engineering design tasks and to support formative feedback.
Xing, W., Li, C., Chen, G., Huang, X., Chao, J., Massicotte, J., & Xie, C. (2020). Automatic assessment of students’ engineering design performance using a Bayesian network model. Journal of Educational Computing Research.
5. How does augmented observation facilitate multimodal representational thinking? Applying deep learning to decode complex student construct
We demonstrate how machine learning could be used to quickly assess a student’s multimodal representational thinking, the complex construct that encodes how students form conceptual, perceptual, graphical, or mathematical symbols in their mind. The augmented reality (AR) technology is adopted to diversify student’s representations. The AR technology utilized a low-cost, high-resolution thermal camera attached to a smartphone to allow students to explore the unseen world of thermodynamics. Ninth-grade students engaged in a prediction–observation–explanation inquiry cycle scaffolded to leverage the augmented observation provided by the device.
Sung, S., Li, C., Chen, G., Huang, X., Xie, C., Massicotte, J. & Shen, J. (2020). How does augmented observation facilitate multimodal representational thinking? Applying deep learning to decode complex student construct. Journal of Science Education and Technology.
6. Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A network approach
From a network perspective, self-regulated learning (SRL) can be conceptualized as networks of mutually interacting self-regulatory learning behaviors. Nevertheless, the research on how SRL behaviors dynamically interact over time in a network architecture is still in its infancy, especially in the context of STEM learning. We used a multilevel vector autoregression model to examine the temporal dynamics of SRL behaviors as 101 students designed green buildings in Energy3D, a simulation-based computer-aided design environment. We examined how different performance groups differed in SRL competency, actual SRL behaviors, and SRL networks.
Li, S., Du, H., Xing, W., Zheng, J., Chen, C., & Xie, C. (2020). Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A network approach. Computers & Education.
7. Accessible physics for all: Providing equity of access for high school physics with extended experimentation and data analysis
We describe accommodations to support student learning with technology-based activities and independent experimentation investigating force and motion in a ninth grade physics class. The class was led by two co-teachers, a certified physics teacher and a special education teacher, and included 11 students with learning disabilities and/or Individualized Education Plans (IEPs). The special education teacher designed individualized supports for students with remarkable success. When compared with another class using the same research-based, inquiry-oriented curriculum, their culminating independent experiments were notably similar.
Haavind, S., & Murtha, M. (2020). Accessible physics for all: Providing equity of access for high school physics with extended experimentation and data analysis. The Science Teacher, 87(9), 54–58.
8. The role of self-regulated learning on science and design knowledge gains in engineering projects
Research on self-regulated learning (SRL) in engineering design is growing. While SRL is an effective way of learning, not all learners can regulate themselves successfully. There is a lack of research regarding how student characteristics, such as science knowledge and design knowledge, interact with SRL. Adapting the SRL theory in the field of engineering design, this study proposes a research model to examine the mediation and causal relationships among science knowledge, design knowledge, and SRL activities.
Zheng, J., Xing, W., Huang, X., Li, S., Chen, G., & Xie, C. (2020). The role of self-regulated learning on science and design knowledge gains in engineering projects. Interactive Learning Environments.
9. Longitudinal clustering of students’ self-regulated learning behaviors in engineering design
It is vital to develop an understanding of students’ self-regulatory processes in STEM domains for the quality delivery of STEM education. However, most studies have followed a variable-centered approach, leaving open the question of how specific self-regulated learning (SRL) behaviors group within individual learners. Furthermore, little is known about how students’ SRL profiles unfold over time in STEM education, specifically in the context of engineering design. We examined the change of students’ SRL profiles over time as 108 middle school students designed green buildings in a simulation-based computer-aided design environment.
Li, S., Chen, G., Xing, W., Zheng, J., & Xie, C. (2020). Longitudinal clustering of students’ self-regulated learning behaviors in engineering design. Computers & Education.
10. Messy data, real science: Exploring harmful algal blooms with real-world data
To learn with data, students need data to explore. We describe ways to foster project-based learning and deep inquiry experiences with data by focusing on a question of interest to students and connection to the world at large. Exploring the frequency of harmful algal blooms in waters within and around the United States as an example, we offer tips that can serve as guideposts to creating experiences that equip students to thrive in a world of messy data.
Hammett, A., & Dorsey, C. (2020). Messy data, real science: Exploring harmful algal blooms with real-world data. The Science Teacher, 87(8), 40–48.
11. Mathematical modeling with R: Embedding computational thinking into high school math classes
Mathematical modeling is routinely used to represent, analyze, and simulate natural and human-made systems. Many students graduating from high school have no experience in computer science, believing that because they are not experienced programmers, they are not cut out for jobs in machine learning or big data. A new initiative to eliminate the misconception that to think computationally is a synonym to programming proposes embedding computational thinking into curricular subjects. We discuss one such effort and its encouraging results from a curriculum implementation in a U.S. high school.
Wiedemann, K., Chao, J., Galluzzo, B., & Simoneau, E. (2020). Mathematical modeling with R: Embedding computational thinking into high school math classes. ACM Inroads, 11(1), 33–42.
12. Invisibility cloaks and hot reactions: Applying infrared thermography in the chemistry education laboratory
Infrared (IR) thermography renders invisible infrared radiation with intuitive coloration in images and videos of objects, reactions, and processes. Through IR thermography, students can visualize otherwise invisible evidence of what is occurring on the molecular level in a variety of chemical processes such as evaporative cooling, phase change, dissolution, titration, and enzymatic reactions. We report on several laboratory activities and best practices that will facilitate the exploration of specific chemistry concepts through the use of IR thermography, as well as integration of this technique into existing general chemistry laboratory courses.
Green, T., Gresh, R., Cochran, D., Crobar, K., Blass, P., Ostrowski, A., Campbell, D., Xie, C., & Torelli, A., (2020). Invisibility cloaks and hot reactions: Applying infrared thermography in the chemistry education laboratory. Journal of Chemical Education, 97(3), 710–718.