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Title: Supporting Middle School Students’ Understanding of Time-Series Data With Graph Comparisons
After participating in an afterschool program where they used the Common Online Data Analysis Platform (CODAP) to study time-series data about infectious diseases, four middle school students were interviewed to determine how they understood features of and trends within these graphs. Our focus was on how students compared graphs. Students were readily able to compare cumulative/total infection rates among two countries with differently sized populations. It was more challenging for them to link a graph of yearly cases to the corresponding graph of cumulative cases. Students offered reasonable interpretations for spikes or steady periods in the graphs. Time-series graphs are accessible for 11- to 14-year-old students, who were able to make comparisons within and between graphs. Students used proportional reasoning for one comparison task, and on the other task, while it was challenging, they were beginning to understand how yearly and cumulative graphs were related. Time-series graphs are ubiquitous and socially relevant: Students should study time-series data more regularly in school, and more research is needed on the progression of sense-making with these graphs. more »« less
Lassance, Carlos; Gripon, Vincent; Mateos, Gonzalo
(, 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP))
null
(Ed.)
Graphs are nowadays ubiquitous in the fields of signal processing and machine learning. As a tool used to express relationships between objects, graphs can be deployed to various ends: (i) clustering of vertices, (ii) semi-supervised classification of vertices, (iii) supervised classification of graph signals, and (iv) denoising of graph signals. However, in many practical cases graphs are not explicitly available and must therefore be inferred from data. Validation is a challenging endeavor that naturally depends on the downstream task for which the graph is learnt. Accordingly, it has often been difficult to compare the efficacy of different algorithms. In this work, we introduce several ease-to-use and publicly released benchmarks specifically designed to reveal the relative merits and limitations of graph inference methods. We also contrast some of the most prominent techniques in the literature.
McNutt, David W.; Underwood, Nora; Inouye, Brian D.
(, Teaching Issues and Experiments in Ecology)
THE ECOLOGICAL QUESTION How have long term changes in climate affected the phenology of wildflowers growing in subalpine habitats? FOUR DIMENSIONAL ECOLOGY EDUCATION (4DEE) FRAMEWORK This is a three-part project. In part I, students research the natural history of one subalpine plant species (e.g., Delphinium nuttallianum, Erigeron speciosus, Helianthella quinquenervis, Lupinus bakeri). In part II, they are given a data set consisting of > 45 years of climate data (1976-2022) from a location where flowering of these plants has been surveyed yearly over that same time period (Rocky Mountain Biological Laboratory in Gothic, CO). The students use the data to graph and analyze trends in snow and temperature and develop hypotheses about how the phenology and fitness (e.g., interactions with pollinators) of their assigned plant species will respond to these changes. In part III, the students receive > 45 years of data on the flowering phenology of their plant species at the same site (1974-2020) and make graphs to test their hypotheses. The students communicate their findings with a written scientific report, conference-style poster, or oral presentation.
Gardner, Stephanie M.; Suazo-Flores, Elizabeth; Maruca, Susan; Abraham, Joel K.; Karippadath, Anupriya; Meir, Eli
(, Journal of Science Education and Technology)
null
(Ed.)
Abstract Graphing is an important practice for scientists and in K-16 science curricula. Graphs can be constructed using an array of software packages as well as by hand, with pen-and-paper. However, we have an incomplete understanding of how students’ graphing practice vary by graphing environment; differences could affect how best to teach and assess graphing. Here we explore the role of two graphing environments in students’ graphing practice. We studied 43 undergraduate biology students’ graphing practice using either pen-and-paper (PP) ( n = 21 students) or a digital graphing tool GraphSmarts (GS) ( n = 22 students). Participants’ graphs and verbal justifications were analyzed to identify features such as the variables plotted, number of graphs created, raw data versus summarized data plotted, and graph types (e.g., scatter plot, line graph, or bar graph) as well as participants’ reasoning for their graphing choices. Several aspects of participant graphs were similar regardless of graphing environment, including plotting raw vs. summarized data, graph type, and overall graph quality, while GS participants were more likely to plot the most relevant variables. In GS, participants could easily make more graphs than in PP and this may have helped some participants show latent features of their graphing practice. Those students using PP tended to focus more on ease of constructing the graph than GS. This study illuminates how the different characteristics of the graphing environment have implications for instruction and interpretation of assessments of student graphing practices.
Altindis, Nigar; Bowe, Kathleen_A; Couch, Brock; Bauer, Christopher_F; Aikens, Melissa_L
(, International Journal of STEM Education)
Abstract BackgroundThis study investigates undergraduate STEM students’ interpretation of quantities and quantitative relationships on graphical representations in biology (population growth) and chemistry (titration) contexts. Interviews (n = 15) were conducted to explore the interplay between students’ covariational reasoning skills and their use of disciplinary knowledge to form mental images during graphical interpretation. ResultsOur findings suggest that disciplinary knowledge plays an important role in students’ ability to interpret scientific graphs. Interviews revealed that using disciplinary knowledge to form mental images of represented quantities may enhance students’ covariational reasoning abilities, while lacking it may hinder more sophisticated covariational reasoning. Detailed descriptions of four students representing contrasting cases are analyzed, showing how mental imagery supports richer graphic sense-making. ConclusionsIn the cases examined here, students who have a deep understanding of the disciplinary concepts behind the graphs are better able to make accurate interpretations and predictions. These findings have implications for science education, as they suggest instructors should focus on helping students to develop a deep understanding of disciplinary knowledge in order to improve their ability to interpret scientific graphs.
Oftentimes engineering design tasks are thought of as acultural and devoid of community inclusion and values. However, engineering design is inherently a cultural endeavor. Problems needing engineering solutions or design thinking are situated in a specific community and need community solutions. This work in progress paper describes initial efforts from a project to help elementary and middle school teachers create culturally relevant engineering design tasks for implementation in their classrooms. To integrate best practices for culturally relevant pedagogy, the engineering design framework developed by UTeach Engineering was adapted to specifically address community needs and cultural values. Changes to the framework also include culturally relevant instructional strategies for classroom implementation. To situate the engineering design steps within a culturally relevant framework questions involving communities and students’ cultural needs, values, and expectations were posed in each stage of the design process. A water filtration engineering design task was situated in the cultural concept of “Mni Wiconi” (Water is life in the Dakota language). This was taught in a summer professional development workshop for a cohort of elementary and middle school teachers, in rural North Dakota, with school districts comprised of large Native American student populations. Teachers adapted this design task for their individual classrooms and content areas (science, math, social studies, ELA) and implemented it in their classrooms in the fall of 2021. Additional support for teachers was provided with fall workshop days aimed at helping them with the facilitation of a culturally relevant engineering task. To integrate culturally relevant teaching and good engineering design tasks, the North Dakota Department of Public Instruction’s Native American Essential Understandings Teachings of our Elder’s website was used. This allowed teachers and students to have firsthand knowledge of how various science and engineering concepts are framed within the indigenous community. Professional development focused on how to situate culturally responsive teaching in engineering design. For example, in one of the school districts the water filtration task was related to increased pollution of a nearby lake which holds significant importance for the local Tribal Nation. In addition to being able to visibly witness the demand for cleaner water, the book “We are Water Protectors” written by Carole Lindstrom, was used to provide cultural grounding for the Identify and Describe stages of the engineering design framework. Case studies of how teachers incorporated the water filtration design task into their lesson plans are presented along with their suggestions on how to improve classroom implementation. Future work in the program includes teachers and their students developing engineering design tasks situated in their own communities and cultures.
Mokros, Jan, Sagrans, Jacob, and Noyce, Pendred. Supporting Middle School Students’ Understanding of Time-Series Data With Graph Comparisons. Retrieved from https://par.nsf.gov/biblio/10638699. Journal of Statistics and Data Science Education . Web. doi:10.1080/26939169.2025.2560338.
Mokros, Jan, Sagrans, Jacob, & Noyce, Pendred. Supporting Middle School Students’ Understanding of Time-Series Data With Graph Comparisons. Journal of Statistics and Data Science Education, (). Retrieved from https://par.nsf.gov/biblio/10638699. https://doi.org/10.1080/26939169.2025.2560338
Mokros, Jan, Sagrans, Jacob, and Noyce, Pendred.
"Supporting Middle School Students’ Understanding of Time-Series Data With Graph Comparisons". Journal of Statistics and Data Science Education (). Country unknown/Code not available: Taylor & Francis. https://doi.org/10.1080/26939169.2025.2560338.https://par.nsf.gov/biblio/10638699.
@article{osti_10638699,
place = {Country unknown/Code not available},
title = {Supporting Middle School Students’ Understanding of Time-Series Data With Graph Comparisons},
url = {https://par.nsf.gov/biblio/10638699},
DOI = {10.1080/26939169.2025.2560338},
abstractNote = {After participating in an afterschool program where they used the Common Online Data Analysis Platform (CODAP) to study time-series data about infectious diseases, four middle school students were interviewed to determine how they understood features of and trends within these graphs. Our focus was on how students compared graphs. Students were readily able to compare cumulative/total infection rates among two countries with differently sized populations. It was more challenging for them to link a graph of yearly cases to the corresponding graph of cumulative cases. Students offered reasonable interpretations for spikes or steady periods in the graphs. Time-series graphs are accessible for 11- to 14-year-old students, who were able to make comparisons within and between graphs. Students used proportional reasoning for one comparison task, and on the other task, while it was challenging, they were beginning to understand how yearly and cumulative graphs were related. Time-series graphs are ubiquitous and socially relevant: Students should study time-series data more regularly in school, and more research is needed on the progression of sense-making with these graphs.},
journal = {Journal of Statistics and Data Science Education},
publisher = {Taylor & Francis},
author = {Mokros, Jan and Sagrans, Jacob and Noyce, Pendred},
}
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