Abstract
This theoretical article proposes using statewide weather-observing networks (Mesonets) to support data-intensive, issue-based teaching of atmospheric topics in middle and high school science. It is argued that the incorporation of this new technology and its affordances into the school curriculum can drastically change the ways that atmospheric topics are taught and learned in classroom settings, from dull lectures to engaging explorations of weather phenomena with potential not only to spark in-the-moment curiosity but also long-term interest in STEM. However, this educational revolution is contingent upon the availability of instructional materials that are pedagogically sound and developmentally appropriate. School-aged students require strategic instructional design and supportive pedagogic scaffolding to pursue their curiosity feelings and develop a motivational profile that is conducive to interest in STEM (self-efficacy, outcome expectation, etc.) as well as situational awareness. In addition to articulating the theoretical underpinnings of this proposition, an account is provided of ongoing efforts to turn this cutting-edge scientific technology into a curriculum space for students to explore weather phenomena, conduct map-based inquiries, and engage in data-based deliberation in the context of real-world issues. Centered on the provision of investigative cases that are locally situated and relevant to students’ lifeworld (place), the Backyard Weather Curriculum is presented to illustrate how this can be accomplished through the adoption of a place-based approach wherein relevance serves as an essential design principle for curricular development and enactment. Such curriculum, it is argued, can help promote student development from curious explorers to inquirers with a deep epistemic interest in STEM.
Similar content being viewed by others
Data Availability
Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
References
Aikenhead, G., Calabrese, A. B., & Chinn, P. W. U. (2006). Toward a politics of place-based science education. Cultural Studies of Science Education, 1, 403–416.
Ainley, J. (2000). Transparency in graphs and graphing tasks: An iterative design process. Journal of Mathematical Behavior, 19, 365–384.
Ainley, M. (2019). Curiosity and interest: Emergence and divergence. Educational Psychology Review, 31(4). https://doi.org/10.1007/s10648-019-09495-z
Alexander, J. M., Johnson, K. E., & Leibham, M. E. (2012). Emerging individual interests related to science in young children. In K. A. Renninger, M. Nieswandt, & S. Hidi (Eds.), Interest in mathematics and science learning (Vol. 96, pp. 261–279). AERA.
Anthamatten, P., Bryant, L. M. P., Ferrucci, B. J., Jennings, S., & Theobald, R. (2018). Giant maps as pedagogical tools for teaching geography and mathematics. Journal of Geography, 117, 183–192.
Arnone, M. P., Small, R. V., Chauncey, S. A., & McKenna, H. P. (2011). Curiosity, interest and engagement in technology-pervasive learning environments: A new research agenda. Educational Technology Research and Development, 59(2), 181–198.
Aydın-Güç, F., Özmen, Z. M., & Güven, B. (2022). Difficulties scatter plots pose for 11th grade students. The Journal of Educational Research. https://doi.org/10.1080/00220671.2022.2128018
Baker, D., & Leary, R. (2003). Letting girls speak out about science. Journal of Research in Science Teaching, 40(1), 176–200.
Bandura, A., Institute, N., & of Mental Health. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall Inc.
Banilower, E., Cohen, J., Pasley, J., & Weiss, I. (2010). Effective science instruction: What does the research tell us? (2nd ed.). RMC Research Corporation, Center on Instruction.
Berkeihiser, M., & Ray, D. (2013). Bringing STEM to life. Technology and Engineering Teacher, 72(5), 21–24.
Bernacki, M. L., & Walkington, C. (2018). The role of situational interest in personalized learning. Journal of Educational Psychology, 110(6), 864–881.
Boscolo, P., Ariasi, N., Del Favero, L., & Ballarin, C. (2011). Interest in an expository text: How does it flow from reading to writing? Learning and Instruction, 21(3), 467–480.
Bhattacharya, D., Steward, K. C., Chandler, M., & Forbes, C. (2020). Using climate models to learn about global climate change. The Science Teacher, 88(1), 58–66.
Brotzge, J. A., Wang, J., Bain, N., Miller, S., & Perno, C. (2022). Camera network for use in weather operations, research and education. Bulletin of the American Meteorological Society, 103(9), E2000–E2016.
Brotzge, J. A., Wang, J., Thorncroft, C. D., Joseph, E., Bain, N., Bassill, N., Farruggio, N., Freedman, J. M., Jr., & K.H., Johnston, D., Kane, E. (2020). A technical overview of the New York State mesonet standard network. Journal of Atmospheric and Oceanic Technology, 37(10), 1827–1845.
Byars-Winston, A., Estrada, Y., Howard, C., Davis, D., & Zalapa, J. (2010). Influence of social cognitive and ethnic variables on academic goals of underrepresented students in science and engineering: A multiple groups analysis. Journal of Counseling Psychology, 57(2), 205–218.
Chak, A. (2010). Adult responses to children’s exploratory behaviors: An exploratory study. Early Child Development and Care, 180(5), 633–646.
Choi, I., Dalal, R., Kim-Prieto, C., & Park, H. (2003). Culture and judgment of causal relevance. Journal of Personality and Social Psychology, 84, 46–59.
Claesgens, J., Rubino-Hare, L., Bloom, N., Fredrickson, K., Henderson-Dahms, C., Menasco, J., & Sample, J. (2013). Professional development integrating technology: Does delivery format matter? Science Educator, 22(1), 10–18.
Coleman, J. S. M., Mitchell, & M. (2014). Active learning in the atmospheric science classroom and beyond through high-altitude ballooning. Journal of College Science Teaching, 44, 26–30.
Cravey, A. J., Arcury, T. A., & Quandt, S. A. (2000). Mapping as a means of farmworker education and empowerment. Journal of Geography, 99, 229–237.
Dewey, J. (1933). How we think: A restatement of the relation of reflective thinking to the educative process. Boston: D C Heath.
Doherty, C. (2015). The constraints of relevance on prevocational curriculum. Journal of Curriculum Studies, 47(5), 705–722.
Donovan, S. (2008). Big data: Teaching must evolve to keep up with advances. Nature, 455, 15260.
Eastwood, J. L., Sadler, T. D., Zeidler, D. L., Lewis, A., Amiri, L., & Applebaum, S. (2012). Contextualizing nature of science instruction in socioscientific issues. International Journal of Science Education, 34, 2289–2315.
Engel, S. (2011). Children’s need to know: Curiosity in schools. Harvard Educational Review, 81(4), 625–645.
Feltovich, P. J., Spiro, R. J., & Coulson, R. L. (1993). Learning, teaching, and testing for complex conceptual understanding. In N. Frederiksen & I. Bejar (Eds.), Test theory for a new generation of tests (pp. 181–217). LEA.
Finzer, W. (2013). The data science education dilemma. Technology Innovations in Statistics Education, 7(2), 1–9.
Gainor, K. A., & Lent, R. W. (1998). Social cognitive expectations and racial identity attitudes in predicting the math choice intentions of Black college students. Journal of Counseling Psychology, 45(4), 403–413.
Geier, R., Blumenfeld, P. C., Marx, R. W., Krajcik, J. S., Fishman, B., Soloway, E., et al. (2008). Standardized test outcomes for students engaged in inquiry-based science curricula in the context of urban reform. Journal of Research in Science Teaching, 45, 922–939.
Gibson, H. L., & Chase, C. (2002). Longitudinal impact of an inquiry-based science program on middle school students’ attitudes toward science. Science Education, 86, 693–705.
Gibson, J. P., & Mourad, T. (2018). The growing importance of data literacy in life science education. American Journal of Botany, 105(12), 1–4.
Goodwin, C. (1994). Professional vision. American Anthropologist, 96, 606–633.
Grotzer, T. A., & Baska, B. B. (2003). How does grasping the underlying causal structures of ecosystems impact students’ understanding? Journal of Biological Education, 38, 16–29.
Grotzer, T. A., & Lincoln, R. (2007). Educating for ‘“intelligent environmental action”’ in an age of global warming. In S. C. Moser & L. Dilling (Eds.), Creating a climate for change: Communicating climate change and facilitating global change (pp. 266–280). Cambridge University Press.
Grotzer, T. A., & Perkins, D. N. (2000). A taxonomy of causal models: The conceptual leaps between models and students’ reflections on them. Paper presented at the Annual Meeting of the National Association for Research in Science Teaching, New Orleans, LA.
Hayden, K., Ouyang, Y., Scinski, L., Olszewski, B., & Bielefeldt, T. (2011). Increasing student interest and attitudes in STEM: Professional development and activities to engage and inspire learners. Contemporary Issues in Technology and Teacher Education, 11(1), 47–69.
Herreid, C. F. (2005). Too much, too little, or just right? How much information should we put into a case study? Journal of College Science Teaching, 35(1), 12–14.
Hey, T., Tansley, S., & Tolle, K. (2009). The fourth paradigm: Data-intensive scientific discovery. Microsoft Corporation.
Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127.
Hidi, S., Renninger, K. A., & Krapp, A. (2004). Interest, a motivational variable that combines affective and cognitive functioning. In D. Y. Dai & R. J. Sternberg (Eds.), Motivation, emotion, and cognition: Integrative perspectives on intellectual functioning and development (pp. 89–115). Lawrence Erlbaum Associates Inc.
Hogan, K. (2002). Small group’s ecological reasoning while making an environmental management decision. Journal of Research in Science Teaching, 39, 341–368.
Høgheim, S., & Reber, R. (2015). Supporting interest in middle school students in mathematics through context personalization and example choice. Contemporary Educational Psychology, 42, 17–25.
Jant, E. A., Uttal, D. H., Kolvoord, R., James, K., & Msall, C. (2020). Defining and measuring the influences of GIS-based instruction on students’ STEM-relevant reasoning. Journal of Geography, 119(1), 22–31.
Johnson, K. E., Alexander, J. M., Spencer, S., Leibham, M. E., & Neitzel, C. (2004). Factors associated with the early emergence of intense interests within conceptual domains. Cognitive Development, 19(3), 325–343.
Kidd, C., & Hayden, B. Y. (2015). The psychology and neuroscience of curiosity. Neuron, 88, 449–460.
Kidd, C., Piantadosi, S. T., & Aslin, R. N. (2014). The goldilocks effect in infant auditory perception. Child Development, 85, 1795–1804.
Konold, C., Finzer, W., & Kreetong, K. (2017). Modeling as a core component of structuring data. Statistics Education Research Journal, 16(2), 191–212.
Kozhevnikov, M., Motes, M. A., & Hegarty, M. (2007). Spatial visualization in physics problem solving. Cognitive Science, 31, 549–579.
Leppink, J., Paas, F., Van der Vleuten, C. P. M., Van Gog, T., & Van Merrienboer, J. J. G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072.
Lynch, S., Kuipers, J., Pyke, C., & Szesze, M. (2005). Examining the effects of a highly rated science curriculum unit on diverse students: Results from a planning grant. Journal of Research in Science Teaching, 42, 912–946.
Mackin, K. J., Cook-Smith, N., Illari, L., Marshall, J., & Sadler, P. (2012). The effectiveness of rotating tank experiments in teaching undergraduate courses in atmospheres, oceans, and climate sciences. Journal of Geoscience Education, 60, 67–82.
Maddux, W. W., & Yuki, M. (2006). The “ripple effect”: Cultural differences in perceptions of the consequences of events. Personality and Social Psychology Bulletin, 32, 669–683.
Mahmood, R., et al. (2017). Mesonets: Mesoscale weather and climate observations for the United States. Bulletin of the American Meteorological Society, 98(7), 1349–1361.
Maltese, A. V., Harsh, J. A., & Svetina, D. (2015). Data visualization literacy: Investigating data interpretation along the novice–expert continuum. Journal of College Science Teaching, 45, 84–90.
Marcum-Dietrich, N., Bruozas, M., & Staudt, S. (2019). Precipitating change: Integrating meteorology, mathematics, and computational thinking: Research on students’ learning and use of data, modeling, and prediction practices for weather forecasting. Paper presented at the International Society for Technology in Education (ISTE) Conference, Philadelphia, PA.
Marx, V. (2013). Biology: The big challenges of big data. Nature, 498, 255–260.
Mayer, R. E. (2005). A cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 41–61). Cambridge University Press.
McGee S., & Pea, R. D. (1994). Cyclone in the classroom: Bringing the atmospheric sciences community into the high school. In Proceedings of the Third American Meteorological Society Symposium on Education, 74th Annual Meeting of the AMS (pp. 23–26), Nashville TN: American Meteorological Society.
McPherson, R. A., et al. (2007). Statewide monitoring of the mesoscale environment: A technical update on the Oklahoma mesonet. Journal of Atmospheric and Oceanic Technology, 24, 301–321.
Mulvany, J. A., Bentley, M., & Pyle, E. (2008). Meteorology and climatology: Online weather studies. Journal of Mathematics and Science: Collaborative Explorations, 10(1), 55–65.
New York State Education Department. (2016). New York State P-12 science learning standards. Retrieved August 10, 2022, from http://www.nysed.gov/curriculum-instruction/science-learning-standards
NGSS Lead States. (2013). Next Generation Science Standards: For states, by states. The National Academies Press.
Nolan, E., Rubino-Hare, L., & Whitworth, B. A. (2019). A lesson in geospatial inquiry. The Science Teacher, 87(4), 26–33.
Oliveira, A. W. (2010). Engaging students in guided science inquiry discussions; elementary teachers’ oral strategies. Journal of Science Teacher Education, 21(7), 747–765.
Oliveira, A. W., Akerson, V. L., & Oldfield, M. (2012). Environmental argumentation as sociocultural activity. Journal of Research in Science Teaching, 49, 869–897.
Pertzborn, R. A., & Limaye, S. W. (2000). Using Earth and weather satellite data in the classroom. IGARSS 2000, IEEE 2000 International geoscience and remote sensing symposium. Taking the Pulse of the Planet: THe Role of Remote Sensing in Managing the Environment, Proceedings, 2, 573–575.
Plant, E. A., Baylor, A. L., Doerr, C. E., & Rosenberg-Kima, R. B. (2009). Changing middle-school students’ attitudes and performance regarding engineering with computer-based social models. Computers & Education, 53(2), 209–215.
Renninger, K. A. (2000). Individual interest and its implications for understanding intrinsic motivation. In C. Sansone & J. M. Harackiewicz (Eds.), Intrinsic motivation: Controversies and new directions (pp. 373–404). Academic Press.
Renninger, K. A. (2010). Working with and cultivating interest, self-efficacy, and self regulation. In D. Preiss & R. Sternberg (Eds.), Innovations in educational psychology: Perspectives on learning, teaching and human development (pp. 107–138). Springer.
Rosenberg, J., Edwards, A., & Chen, B. (2020). Getting messy with data: Tools and strategies to help students analyze and interpret complex data sources. The Science Teacher, 87(5), 30–34.
Sadler, T. D., Barab, S. A., & Scott, B. (2007). What do students gain by engaging in socioscientific inquiry? Research in Science Education, 37, 371–391.
Sawyer, C. F., Butler, D. R., & Curtis, M. (2010). Using webcams to show change and movement in the physical environment. Journal of Geography, 109, 251–263.
Schiefele, U. (2009). Situational and individual interest. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 197–222). Routledge.
Semken, S. (2005). Sense of place and place-based introductory geoscience teaching for American Indian and Alaska native undergraduates. Journal of Geoscience Education, 53(2), 149–157.
Shapiro, A., Klein, P. M., Arms, S. C., Bodine, D., & Carney, M. (2009). The Lake Thunderbird Micronet project. Bulletin of the American Meteorological Society, 90, 811–824.
Shapiro, C. A., & Sax, L. J. (2011). Major selection and persistence for women in STEM. New Directions for Institutional Research, 152, 5–18.
Shellito, C. (2020). Student-constructed weather instruments facilitate scientific inquiry. Journal of College Science Teaching, 49, 10–15.
Silvia, P. J. (2006). Exploring the psychology of interest. Oxford University Press.
Stedman, A. B. L. (2002). Toward a social psychology of place: Predicting behavior from place-based cognitions, attitude, and identity. Environment and Behavior, 34(5), 561–581.
Subotnik, R. F., Tai, R. H., Rickoff, R., & Almarode, J. (2010). Specialized public high schools of science, mathematics, and technology and the STEM pipeline: What do we know now and what will we know in 5 years? Roeper Review, 32(1), 7–16.
Tanamachi, R., Dawson, D., & Parker, L. C. (2020). Students of Purdue Observing Tornadic Thunderstorms for Research (SPOTTR): A severe storms field work course at Purdue University. Bulletin of the American Meteorology Society, 101, E847–E868.
U.S. Global Change Research Program (USGCRP). (2009). Climate literacy: The essential principles of climate science (ver. 2). Washington, DC: Retrieved from https://downloads.globalchange.gov/Literacy/climate_literacy_highres_english.pdf
Waller, B. (2006). Math interest and choice intentions of non-traditional African American college students. Journal of Vocational Behavior, 68, 538–547.
Wang, X. (2013). Why students choose STEM majors: Motivation, high school learning, and postssecondary context of support. American Educational Research Journal, 50(5), 1081–1121.
Weaver, (2019). Epigenetics in psychology. In J.A. Cummings & L. Sanders (eds), Introduction to Psychology. Canada: University of Saskatchewan Open Press.
Wilson, C. D., Taylor, J. A., Kowalski, S. M., & Carlson, J. (2010). The relative effects and equity of inquiry-based and commonplace science teaching on students’ knowledge, reasoning and argumentation. Journal of Research in Science Teaching, 47, 276–301.
Wilson, T. (2020). Using a convolutional neural network to assist in situational awareness. National Weather Association annual meeting, virtual.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical Approval
Not applicable.
Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Conflict of Interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix
BWS Sample Case: 1–2-3 Strikes You’re out! On our way to Cooperstown
Grade Level
8–10
Driving Question
What causes the different weather and the movement of storms in the area?
Learning Goal
Students will use weather data to predict if the weather will affect their area.
Crosscutting Concepts/Science and Engineering Practices Addressed
Developing and using models
Analyzing and interpreting data
Obtaining, evaluating, and communicating information
Patterns
Cause and effect
Stability and change
NYS Learning Standards
HS-ESS-2–8: Evaluate data and communicate information to explain how the movement and interactions of air masses result in changes in weather conditions.
Scenario
Cooperstown is home to the baseball hall of fame museum and little league baseball. Baseball players from all over the state come to play summer tournaments in Cooperstown. This is the pinnacle of a little leaguers’ career. Many teams fundraise for many months to be able to participate in such an event. Teams are given a specific week in the summer to compete in Cooperstown, ending the week with a championship game. Cooperstown is located approximately 60 miles southwest of Albany, 67 miles southeast of Syracuse, and 145 miles northwest of NYC. See the maps below. The closest Mesonet Station is Springfield NY.
A little league team has been staying in Cooperstown the week of June 27, 2022, and is scheduled to play their championship game on July 1 at 6:30 pm. There is a storm that is forecasted to pass over during the championship game. Hotels are booked months before the event, and there is no time to reschedule the game. It is 5 pm on July 1, and the organizers of Cooperstown have asked you to see if the championship game could be played safely. You are asked to use the NYS Mesonet database to determine if the game will be playable. The decision must be made at 6:10 pm. The clock is ticking (Fig. 6)!
Cooperstown Baseball Regulations
-
The fields are made of clay and can only take on a certain amount of precipitation. This clay field can take 0.3 inches per hour of rain if the soil is dry. If there was precipitation in the past day, the field can take 0.2 inches per hour of rain.
-
Games during this tournament have a maximum time limit of 2 h. There are also no lights on the fields. For safety reasons, games will be called 30 min after sunset.
-
If lightning is seen, the game is delayed 30 min from the last strike of lightning.
Your Task
Your mission is to help the Cooperstown little league directors and make an educated decision to see if the forecasted storm will impact the championship game. You will be using radar imagery and the NYS Mesonet data.
Phase 1: Explore
This initial phase of the lesson is aimed at setting the stage for learning by peaking students’ interest and inspiring a “need to know.” To this end, students watch a video of an approaching storm, are given the background of the Cooperstown tournament, and are introduced to the task (the scenario above). Working in small groups, students then brainstorm questions, possible questions that they need to research to help the Little League Director. These may include:
-
1.
What time is the storm coming?
-
2.
Will the storm hit Cooperstown?
-
3.
Which direction is the storm coming from?
-
4.
How fast is the storm moving?
-
5.
How much precipitation will occur?
-
6.
Will there be thunderstorms?
-
7.
What is the weather like west of Cooperstown?
-
8.
When does the sun rise and set on Jul 1st, 2022?
Teacher Notes
Students need to decide on the specific data (numerical/images) they will need to examine in order to complete their task. The teacher facilitates a discussion about important parameters (day, time, geographical location, types of measurements), helping them recognize the need to explore a location west of Cooperstown during the day/time leading up to the game.
Phase 2: Explore
After obtaining the decided weather data from NYS Mesonet, students now explore radar images and analyze measurements (examples are provided below). With the teacher’s guidance, students construct tentative ideas or explanations (Figs. 7, 8, 9, 10, 11, 12, 13 and 14).
Phase 3: Explain
In this phase of the lesson, the teacher provides “direct instruction” (or “review”) of skills/concepts/terms (fronts, air masses, atmospheric variables). Students are taught specific skills/concepts, misconceptions are addressed, and an essential vocabulary is taught, such as radar, satellite, dewpoint, relative humidity, planetary winds, air mass, fronts, and atmosphere.
Phase 4: Elaborate
This phase is designed to allow students to practice/apply the “new” or “reviewed” skills/concepts. To this end, students are prompted to go back to the previously explored forecast, radar imagery, Mesonet camera images, and data charts and determine what weather can be expected for Cooperstown during the evening of July 1 2022 (6–9 pm) by applying the concepts learned in the Explain phase. Students are also prompted to answer questions such as:
-
1.
How fast is the storm moving and which direction? Explain how you derived your answer?
-
2.
When does the sun rise and set on July 1, 2022?
-
3.
Should the championship game start at 6:30 pm?
-
4.
What are multiple pieces of evidence that can support your claim?
-
5.
What is your scientific reasoning?
Phase 5: Evaluate
In this final phase, student understanding and mastery are assessed. To this end, students are provided with the following prompt:
-
“The next day, you are asked to meet with the little league coordinators to debrief on your decision. You are asked to look at the weather data from the storm (below) that passed over Cooperstown on July 1, 2022 and to indicate whether your decision about the baseball gale was correct or not, and to explain why. The purpose of this meeting is to educate the directors and help predict the weather for future games.”
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Oliveira, A.W., Wang, J., Perno, C. et al. The Backyard Weather Science Curriculum: Using a Weather-Observing Network to Support Data-Intensive Issue-Based Atmospheric Inquiry in Middle and High School. J Sci Educ Technol 32, 181–210 (2023). https://doi.org/10.1007/s10956-022-10021-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10956-022-10021-0