When integrated into online curriculum modules for students, educative curriculum materials (ECMs) can enhance teachers' enactment of these modules. This study investigated (1) the use of digitally enhanced ECMs built into an online plate tectonics curriculum module by teachers with different backgrounds and teaching experience, (2) the relationship between teachers' use of ECMs and student learning gains, and (3) teacher reflections on the value of the ECMs they used. We studied 26 teachers who taught middle and high school students (n = 1,098) by analyzing teacher log files automatically generated by the ECMs, teacher reflections collected with post-implementation surveys and interviews, student log files, and student learning gains from pretest to posttest. Results indicate that (1) there were large variations in the amounts and types of ECM features teachers accessed, (2) middle school teachers accessed significantly more ECM features than high school teachers p < .01, (3) students of teachers who used ECMs during class time made significantly higher learning gains than students of teachers who used them only before and/or between class time, p < .05, and (4) teachers most valued ECM features on student assessment. An overall non-significant, but positive, correlation between the total teacher interactions with ECMs and student learning gains was observed, r = 0.20, p = .32.

This case study examines how material resistance (limitations posed by the physical world) and graph interpretation intersected during a high school biology investigation using digital sensors. We use an extended episode from a small group to illustrate how, in an inquiry-based unit, measuring near the resolution limit of a sensor caused scaling issues in graphs. Qualitative videotape analysis focuses on both the students' attempts to make sense of a perceived lack of variation in the collected data and the teacher's and classroom researchers' misinterpretation of the students' difficulties with graph interpretation. We suggest that these educators, though experienced, could have benefited from additional strategies to help them recognise and respond to graph interpretation issues introduced by digital representations of real-world data, and that the students could have benefited from explicit prompts to discuss the limitations of their equipment. We describe several implications for researchers, teachers, and curriculum developers interested in implementing inquiry-based biology investigations using sensor data. We argue that students should be supported to recognise that encountering unexpected results from their investigations and working to understand what these results have to say about the real world is an important and valid part of the practice of real science.

This study addresses the measurement of high school students' epistemic knowledge associated with scientific experimentation (EKSE) which concerns how scientific experimentation generates knowledge and why that knowledge is justified. Based on philosophical, educational standards, and literature analyses, an EKSE construct is characterized as (1) underlying students' decisions and reasoning elicited during experiment design, data collection/measurement, and data analysis/interpretation and (2) progressing in five levels of sophistication: no information, nascent, dogmatic, contextualized, and reflective. This study psychometrically examined the structure of the EKSE construct using the construct modeling approach. An instrument was designed to ask students decisions and reasoning encountered while carrying out the activities embedded in experimental inquiry. High school students took the instrument as a pretest and a posttest to a physics curriculum where they learned to carry out independent scientific experiments. Results indicate that (1) all decisions and reasoning taking place during experiment design, data collection/measurement, and data analysis/interpretation contribute to the EKSE construct, (2) epistemic knowledge develops in the hypothesized order, (3) scientifically aligned decisions are likely to occur when students' epistemic knowledge is at or higher than the dogmatic level, and (4) students' EKSE can improve after engaging in a curriculum that encourages independent scientific experimentation with materials. Implications of these results and further research are discussed.

In order to characterize students’ risk assessment explanations based on the Geohazard Risk Framework, which describes four key elements of risk for high school science education, we investigate whether student explanations include the following risk elements: scientific factors, impacts, human influences, and likelihood. This study uses the Geohazard Risk Framework to analyze how students explain their risk assessments and risk mitigation strategies based on experimentation with an interactive computer simulation designed to model flooding risks and hazards. We analyzed students’ explanations using data from 375 students from three suburban, three urban, and three rural schools to learn (1) how secondary students experiment with the simulation and explain flooding risk based on evidence from the simulation and (2) how students carry out and explain model-based testing of a risk mitigation strategy with a simulation. We also analyzed snapshots created by students of the simulation that were used as evidence to support their explanations. Our findings reveal that while the majority of students could identify at least one risk element, those who engaged deeply with the simulation's features demonstrated a sophisticated understanding of the interconnected nature of risk factors. This study underscores the Geohazard Risk Framework’s utility in enhancing secondary students' comprehension of flood risks and offers insights into effective simulation-based learning strategies for broader geohazard education.

Using computational methods to produce and interpret multiple scientific representations is now a common practice in many science disciplines. Research has shown students have difficulty in moving across, connecting, and sensemaking from multiple representations. There is a need to develop task-specific representational competencies for students to reason and conduct scientific investigations using multiple representations. In this study, we focus on three representational competencies: 1) linking between representations, 2) disciplinary sensemaking from multiple representations, and 3) conceptualizing domain-relevant content derived from multiple representations. We developed a block code-based computational modeling environment with three different representations and embedded it within an online activity for students to carry out investigations around the earthquake cycle. The three representations include a procedural representation of block codes, a geometric representation of land deformation build-up, and a graphical representation of deformation build-up over time. We examined the extent of students' representational competencies and which competencies are most correlated with students' future performance in a computationally supported geoscience investigation. Results indicate that a majority of the 431 students showed at least some form of representational competence. However, a relatively small number of students showed sophisticated levels of linking, sensemaking, and conceptualizing from the representations. Five of seven representational competencies, the most prominent being code sensemaking (eta(2) = 0.053, p < 0.001), were significantly correlated to student performance on a summative geoscience investigation.

From the experiential learning perspective, this study investigates middle and high school students (n = 1009) who used an online module to learn about wildfire hazards, risks, and impacts through computational simulations of wildfire phenomena. These students were taught by 18 teachers in urban, rural, and suburban schools across the United States. We analyzed students’ simulation behaviors captured in log files, responses to an assessment administered before and after the module, and demographic surveys, as well as teachers’ responses to a post-module implementation survey. Using mixed effects generalized linear modeling, we investigated whether students’ simulation experiences, their prior real-world wildfire experience, and the strategies used by their teachers predicted their understanding of wildfire concepts. In estimating the effect sizes of these variables, we controlled for student variables such as gender, race, English language status, prior wildfire knowledge (pre-test), and module completion rate. Results indicate that students’ simulation experience and teacher variation were the two most significant effects, followed by students’ real-life wildfire experience. Teacher variations were further explained by differences in teachers’ pedagogical strategies while implementing the module. Implications of these findings are discussed for the design and further research of simulations used as proxies for experiential learning of natural hazards.

Computation has become an essential part of today's personal, educational, civic, and career living, which necessitates preparation of a generation of future citizens who are knowledgeable of computational thinking (CT) concepts and are able to apply CT skills in daily life and work.Because of CT's use across fields, it is important that we take an interdisciplinary approach and ground the teaching and learning of CT in authentic and meaningful contexts with learners.This symposium explores different approaches of integrating CT into topics in formal settings.The six empirical studies of this symposium present designs of authentic environments that aim to infuse CT into plant science, literacy, ecosystems, general biology, and chemistry.These studies also report the findings of teacher and student learning from the implementations.Collectively these studies provide unique insights towards design recommendations, challenges, pedagogies, and opportunities in the rapidly developing STEM+C field.

This white paper describes outcomes from a conference about using non-science art in science education. Art and science are often combined for entertainment, education (STEAM) and inspirational value. However, in almost all cases the art is science-themed (scientific visualizations, science-inspired art, art about scientific topics, etc.). This conference asks the question “what about art for arts-sake?”. That is, what is the impact of non-scientific art in science education? Can non-science themed art be used to broaden perspectives about science? Can it cue people to think about science more and differently? The conference, held at the Museum of Science and Industry in Chicago with support from the National Science Foundation, assembled artists, art educators, education researchers and scientists to discuss the potential of these topics and ways to study them.

Learning to teach is a culturally situated activity. As teachers learn, it is important to understand not only what teachers learn, but how they learn. This article describes a qualitative case study of a subset of four teachers' learning during a professional development surrounding a plate tectonics curriculum. Using qualitative methods, this study tells the story of how the four teachers negotiated professional vision for science teaching around dilemmas that emerged throughout the professional development. By taking a sociocultural perspective on professional vision, researchers can gain insight into how and what teachers learn in professional development settings because it renders teacher learning complex and nuanced. Additionally, we argue negotiating professional vision parallels sensemaking. Sensemaking around science teaching includes grappling with epistemic issues of science in addition to pedagogy and curriculum. Implications for science teacher education are discussed. Specifically, we argue learning to teach requires teachers to engage in conversations that create opportunities to "get somewhere" in relation to dilemmas they have about teaching. In this way, professional vision is an ongoing process of learning that has no endpoint or ideal articulation of teaching or science. Therefore, by framing professional vision as a process of learning we are able to push back on simplistic descriptions of teaching and science.

Explaining phenomena associated with a system involves describing a system's structure and articulating the process through which the system's structure changes over time. This paper defines geo-sequential reasoning in the context of plate tectonics and uses it to analyse how students explain the geological processes that occur along convergent boundaries as part of the plate tectonics system. This study was part of design-based research on an online Plate tectonics module that included simulation-based modelling developed for secondary school students. We analysed students' explanations (n = 950) about phenomena found along a convergent boundary (1) as an oceanic plate and a continental plate move towards each other and (2) between two oceanic plates located on the opposite side of a tectonic plate from a divergent boundary. We also analysed images created by students of the simulation as evidence to support their explanations. We found that a majority of students used simulation-based evidence when describing the sequence of events along the convergent boundary and that the synced planet surface and cross-section views in the simulation supported students' inclusion of processes responsible for the events. These findings have implications for how teaching and research with dynamic simulations can support reasoning built with temporal evidence.

As computational methods are widely used in science disciplines, integrating computational thinking (CT) into classroom materials can create authentic science learning experiences for students. In this study, we classroom-tested a CT-integrated geoscience curriculum module designed for secondary students. The module consisted of three inquiry investigations that were age-appropriately translated from the computational practices of volcanologists who study tephra hazards and risks. We developed a domain-specific, block-code-based computational modeling environment where students carried out 1 of the 3 science practices in each inquiry investigation: experimentation, data visualization and interpretation, and modeling. We examined (1) student learning outcomes using pre- and post-tests and (2) student reflections on the computationally supported science practices surveyed at the end of each inquiry investigation during the module. Results indicate that students made statistically significant gains in science content as well as in computationally supported experimentation, data visualization and interpretation, and modeling practices. Over 80% of students identified different ways in which block coding supported their practices during inquiry investigations. Based on these findings, we discuss implications for the future development of computationally supported inquiry investigations in science.

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsKraig A. WrayKraig A. Wray, PhD, (kraig.wray@gmail.com) is director of instruction and a science teacher in Pittsburgh, PA. Scott McDonald, PhD, (smcdonald@psu.edu) is professor of education in the Department of Curriculum and Instruction at Pennsylvania State University, State College, PA. Amy Pallant is a senior research scientist at Concord Consortium, Concord, MA. Hee-Sun Lee, PhD is a senior research scientist at Concord Consortium, Emeryville, CA.Scott McDonaldKraig A. Wray, PhD, (kraig.wray@gmail.com) is director of instruction and a science teacher in Pittsburgh, PA. Scott McDonald, PhD, (smcdonald@psu.edu) is professor of education in the Department of Curriculum and Instruction at Pennsylvania State University, State College, PA. Amy Pallant is a senior research scientist at Concord Consortium, Concord, MA. Hee-Sun Lee, PhD is a senior research scientist at Concord Consortium, Emeryville, CA.Hee-Sun LeeKraig A. Wray, PhD, (kraig.wray@gmail.com) is director of instruction and a science teacher in Pittsburgh, PA. Scott McDonald, PhD, (smcdonald@psu.edu) is professor of education in the Department of Curriculum and Instruction at Pennsylvania State University, State College, PA. Amy Pallant is a senior research scientist at Concord Consortium, Concord, MA. Hee-Sun Lee, PhD is a senior research scientist at Concord Consortium, Emeryville, CA.Amy PallantKraig A. Wray, PhD, (kraig.wray@gmail.com) is director of instruction and a science teacher in Pittsburgh, PA. Scott McDonald, PhD, (smcdonald@psu.edu) is professor of education in the Department of Curriculum and Instruction at Pennsylvania State University, State College, PA. Amy Pallant is a senior research scientist at Concord Consortium, Concord, MA. Hee-Sun Lee, PhD is a senior research scientist at Concord Consortium, Emeryville, CA.

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsKraig A. WrayKraig A. Wray (kraig.wray@gmail.com) is director of instruction and a science teacher in Pittsburgh, PA. Jonathan D. McCausland (jdmccausland@nmhu.edu) is an assistant professor of STEM Education at New Mexico Highlands University in Las Vegas, New Mexico. Scott McDonald (smcdonald@psu.edu) is a professor of education in the Department of Curriculum and Instruction at Pennsylvania State University in State College, Pennsylvania. Amy Pallant is a senior research scientist at Concord Consortium in Concord, Massachusetts Hee-Sun Lee is a senior research scientist at Concord Consortium in Emeryville, California.Jonathan D. MccauslandKraig A. Wray (kraig.wray@gmail.com) is director of instruction and a science teacher in Pittsburgh, PA. Jonathan D. McCausland (jdmccausland@nmhu.edu) is an assistant professor of STEM Education at New Mexico Highlands University in Las Vegas, New Mexico. Scott McDonald (smcdonald@psu.edu) is a professor of education in the Department of Curriculum and Instruction at Pennsylvania State University in State College, Pennsylvania. Amy Pallant is a senior research scientist at Concord Consortium in Concord, Massachusetts Hee-Sun Lee is a senior research scientist at Concord Consortium in Emeryville, California.Scott McdonaldKraig A. Wray (kraig.wray@gmail.com) is director of instruction and a science teacher in Pittsburgh, PA. Jonathan D. McCausland (jdmccausland@nmhu.edu) is an assistant professor of STEM Education at New Mexico Highlands University in Las Vegas, New Mexico. Scott McDonald (smcdonald@psu.edu) is a professor of education in the Department of Curriculum and Instruction at Pennsylvania State University in State College, Pennsylvania. Amy Pallant is a senior research scientist at Concord Consortium in Concord, Massachusetts Hee-Sun Lee is a senior research scientist at Concord Consortium in Emeryville, California.Amy PallantKraig A. Wray (kraig.wray@gmail.com) is director of instruction and a science teacher in Pittsburgh, PA. Jonathan D. McCausland (jdmccausland@nmhu.edu) is an assistant professor of STEM Education at New Mexico Highlands University in Las Vegas, New Mexico. Scott McDonald (smcdonald@psu.edu) is a professor of education in the Department of Curriculum and Instruction at Pennsylvania State University in State College, Pennsylvania. Amy Pallant is a senior research scientist at Concord Consortium in Concord, Massachusetts Hee-Sun Lee is a senior research scientist at Concord Consortium in Emeryville, California.Hee-Sun LeeKraig A. Wray (kraig.wray@gmail.com) is director of instruction and a science teacher in Pittsburgh, PA. Jonathan D. McCausland (jdmccausland@nmhu.edu) is an assistant professor of STEM Education at New Mexico Highlands University in Las Vegas, New Mexico. Scott McDonald (smcdonald@psu.edu) is a professor of education in the Department of Curriculum and Instruction at Pennsylvania State University in State College, Pennsylvania. Amy Pallant is a senior research scientist at Concord Consortium in Concord, Massachusetts Hee-Sun Lee is a senior research scientist at Concord Consortium in Emeryville, California.

This study uses a computerized formative assessment system that provides automated scoring and feedback to help students write scientific arguments in a climate change curriculum. We compared the effect of contextualized versus generic automated feedback on students' explanations of scientific claims and attributions of uncertainty to those claims. Classes were randomly assigned to the contextualized feedback condition (227 students from 11 classes) or to the generic feedback condition (138 students from 9 classes). The results indicate that the formative assessment helped students improve their scores in both explanation and uncertainty scores, but larger score gains were found in the uncertainty attribution scores. Although the contextualized feedback was associated with higher final scores, this effect was moderated by the number of revisions made, the initial score, and gender. We discuss how the results might be related to students' familiarity with writing scientific explanations versus uncertainty attributions at school.

A design study was conducted to test a machine learning (ML)-enabled automated feedback system developed to support students’ revision of scientific arguments using data from published sources and simulations. This paper focuses on three simulation-based scientific argumentation tasks called Trap, Aquifer, and Supply. These tasks were part of an online science curriculum module addressing groundwater systems for secondary school students. ML was used to develop automated scoring models for students’ argumentation texts as well as to explore emerging patterns between students’ simulation interactions and argumentation scores. The study occurred as we were developing the first version of simulation feedback to augment the existing argument feedback. We studied two cohorts of students who used argument only (AO) feedback (n = 164) versus argument and simulation (AS) feedback (n = 179). We investigated how AO and AS students interacted with simulations and wrote and revised their scientific arguments before and after receiving their respective feedback. Overall, the same percentages of students (49% each) revised their arguments after feedback, and their revised arguments received significantly higher scores for both feedback conditions, p < 0.001. Significantly greater numbers of AS students (36% across three tasks) reran the simulations after feedback as compared with the AO students (5%), p < 0.001. For AS students who reran the simulation, their simulation scores increased for the Trap task, p < .001, and for the Aquifer task, p < 0.01. AO students who did not receive simulation feedback but reran the simulations increased simulation scores only for the Trap task, p < .05. For the Trap and Aquifer tasks, students who increased simulation scores were more likely to increase argument scores in their revisions than those who did not increase simulation scores or did not revisit simulations at all after simulation feedback was provided. This pattern was not found for the Supply task. Based on these findings, we discuss strengths and weaknesses of the current automated feedback design, in particular the use of ML.

Constructing scientific arguments is an important practice for students because it helps them to make sense of data using scientific knowledge and within the conceptual and experimental boundaries of an investigation. In this study, we used a text mining method called Latent Dirichlet Allocation (LDA) to identify underlying patterns in students written scientific arguments about a complex scientific phenomenon called Albedo Effect. We further examined how identified patterns compare to existing frameworks related to explaining evidence to support claims and attributing sources of uncertainty. LDA was applied to electronically stored arguments written by 2472 students and concerning how decreases in sea ice affect global temperatures. The results indicated that each content topic identified in the explanations by the LDA— “data only,” “reasoning only,” “data and reasoning combined,” “wrong reasoning types,” and “restatement of the claim”—could be interpreted using the claim–evidence–reasoning framework. Similarly, each topic identified in the students’ uncertainty attributions— “self-evaluations,” “personal sources related to knowledge and experience,” and “scientific sources related to reasoning and data”—could be interpreted using the taxonomy of uncertainty attribution. These results indicate that LDA can serve as a tool for content analysis that can discover semantic patterns in students’ scientific argumentation in particular science domains and facilitate teachers’ providing help to students.

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 in the system server. Through post-hoc analysis of log data, we can re-enact and discover patterns in students' activities. This study addresses the online Earth science module where students engaged in writing and revising scientific arguments in a structured format. We adopted natural language processing (NLP) techniques to analyse students' responses, which enabled us to provide immediate feedback to students on their responses and revisions. Cluster analyses were conducted on the action sequences in four argumentation tasks embedded in the module. For each task, the cluster analyses identified two clusters of students who showed different revision patterns with allocation of time on different items. In addition, students in those two clusters also differed in their initial item scores and item score changes after revision.

Application of new automated scoring technologies, such as natural language processing and machine learning, makes it possible to provide automated feedback on students' short written responses. Even though many studies investigated the automated feedback in the computer-mediated learning environments, most of them focused on the multiple-choice items instead of the constructed response items. This study focuses on the latter and investigates a formative feedback system integrated into an online science curriculum module teaching climate change. The feedback system incorporates automated scoring technologies to support students' revision of scientific arguments. By analyzing the log files from the climate module, we explore how student revisions enabled by the formative feedback system correlate with student performance and learning gains. We also compare the impact of generic feedback (context-independent) vs. contextualized feedback (context-dependent). 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. The findings of this study provide insights into the use of automated feedback to improve scientific argumentation writing as part of classroom instruction.