skip to main content


Title: Anchoring Computational Thinking in Upper Elementary Physical Science Through Problem-Centered Storytelling and Play
In an effort to infuse computational thinking practices in upper elementary science, and to promote positive student dispositions toward STEM, this project investigates a new narrative-centered maker environment involving: 1) problem-based learning research and modeling of physical science concepts, 2) application of learned concepts to original digital stories created using block-based programming, and 3) further communication of science understanding through play with fabricated story sets and characters reflective of narratives.  more » « less
Award ID(s):
1921503
NSF-PAR ID:
10247745
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
14th International Conference of the Learning Sciences (ICLS) 2020
Volume:
3
Page Range / eLocation ID:
1743-1744
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Background

    Around the world, efforts are underway to include engineering design as part of elementary science instruction. A common rationale for those efforts is that Engineering Design-based Science Teaching (EDST) is a productive pedagogical approach for developing students’ understanding of core science concepts. Effectively utilizing EDST requires that teachers develop design activities that are highly connected to science content so that students can apply and expand their understanding of relevant concepts. In this study, we examine how a group of elementary (grades 3–5) pre-service and in-service teachers incorporated EDST into their planned science instruction. Those teachers were participants in a professional development project aimed at supporting EDST. We examine the ways that participants used EDST, the extent to which engineering design activities were connected to science concepts, and factors associated with those connections.

    Results

    Most of the participants in the study developed science units in which an engineering design activity was placed at the end of the unit. Approximately half of those design activities lacked connections to the science concepts in the unit; they were typically related to the topic of the science unit, but did not require the use or development of key science ideas. Eleven percent of participants developed engineering activities with deep connections to science concepts, and 35% developed activities with shallow connections. No differences were found between life science, physical science, and earth/space science units in terms of the extent of conceptual connections. However, we did find that participants who utilized and adapted published engineering curriculum materials rather than make them from scratch were more likely to have unit plans with higher levels of conceptual connections.

    Conclusions

    Our findings suggest that elementary teachers need additional support in order to effectively utilize EDST in their classrooms. Even within the context of a supportive professional development project, most of the engineering activities developed by our participants lacked substantial connections to the science concepts in their unit plans. Our findings highlight the value of high-quality curriculum materials to support EDST as well as the need to further expand the curriculum resources that are available to elementary teachers.

     
    more » « less
  2. With increasingly technology-driven workplaces and high data volumes, instructors across STEM+C disciplines are integrating more data science topics into their course learning objectives. However, instructors face significant challenges in integrating additional data science concepts into their already full course schedules. Streamlined instructional modules that are integrated with course content, and cover relevant data science topics, such as data collection, uncertainty in data, visualization, and analysis using statistical and machine learning methods can benefit instructors across multiple disciplines. As part of a cross-university research program, we designed a systematic structural approach–based on shared instructional and assessment principles–to construct modules that are tailored to meet the needs of multiple instructional disciplines, academic levels, and pedagogies. Adopting a research-practice partnership approach, we have collectively developed twelve modules working closely with instructors and their teaching assistants for six undergraduate courses. We identified and coded primary data science concepts in the modules into five common themes: 1) data acquisition, 2) data quality issues, 3) data use and visualization, 4) advanced machine learning techniques, and 5) miscellaneous topics that may be unique to a particular discipline (e.g., how to analyze data streams collected by a special sensor). These themes were further subdivided to make it easier for instructors to contextualize the data science concepts in discipline-specific work. In this paper, we present as a case study the design and analysis of four of the modules, primarily so we can compare and contrast pairs of similar courses that were taught at different levels or at different universities. Preliminary analyses show the wide distribution of data science topics that are common among a number of environmental science and engineering courses. We identified commonalities and differences in the integration of data science instruction (through modules) into these courses. This analysis informs the development of a set of key considerations for integrating data science concepts into a variety of STEM + C courses. 
    more » « less
  3. With increasingly technology-driven workplaces and high data volumes, instructors across STEM+C disciplines are integrating more data science topics into their course learning objectives. However, instructors face significant challenges in integrating additional data science concepts into their already full course schedules. Streamlined instructional modules that are integrated with course content, and cover relevant data science topics, such as data collection, uncertainty in data, visualization, and analysis using statistical and machine learning methods can benefit instructors across multiple disciplines. As part of a cross-university research program, we designed a systematic structural approach–based on shared instructional and assessment principles–to construct modules that are tailored to meet the needs of multiple instructional disciplines, academic levels, and pedagogies. Adopting a research-practice partnership approach, we have collectively developed twelve modules working closely with instructors and their teaching assistants for six undergraduate courses. We identified and coded primary data science concepts in the modules into five common themes: 1) data acquisition, 2) data quality issues, 3) data use and visualization, 4) advanced machine learning techniques, and 5) miscellaneous topics that may be unique to a particular discipline (e.g., how to analyze data streams collected by a special sensor). These themes were further subdivided to make it easier for instructors to contextualize the data science concepts in discipline-specific work. In this paper, we present as a case study the design and analysis of four of the modules, primarily so we can compare and contrast pairs of similar courses that were taught at different levels or at different universities. Preliminary analyses show the wide distribution of data science topics that are common among a number of environmental science and engineering courses. We identified commonalities and differences in the integration of data science instruction (through modules) into these courses. This analysis informs the development of a set of key considerations for integrating data science concepts into a variety of STEM + C courses. 
    more » « less
  4. Performance assessment (PA) has been increasingly advocated as a method for measuring students’ conceptual understanding of scientific phenomena. In this study, we describe preliminary findings of a simulation- based PA utilized to measure 8th grade students’ understanding of physical science concepts taught via an experimental problem-based curriculum, SLIDER (Science Learning Integrating Design Engineering and Robotics). In SLIDER, students use LEGO robotics to complete a series of investigations and engineering design challenges designed to deepen their understanding of key force and motion concepts (net force, acceleration, friction, balanced forces, and inertia). The simulation-based performance assessment consisted of 4 tasks in which students engaged with video simulations illustrating physical science concepts aligned to the SLIDER curriculum. The performance assessment was administered to a stratified sample of 8th grade students (N=24) in one school prior to and following implementation of the SLIDER curriculum. In addition to providing an illustration of the use of simulation- based performance assessment in the context of design-based implementation research (DBIR), the results of the study indicate preliminary evidence of student learning over the course of curriculum implementation. 
    more » « less
  5. Web-browsing histories, online newspapers, streaming music, and stock prices all show that we live in an age of data. Extracting meaning from data is necessary in many fields to comprehend the information flow. This need has fueled rapid growth in data science education aiming to serve the next generation of policy makers, data science researchers, and global citizens. Initially, teaching practices have been drawn from data science's parent disciplines (e.g., computer science and mathematics). This project addresses the early stages of developing a concept inventory of student difficulty within the newly emerging field of data science. In particular this project will address three primary research objectives: (1) identify student misconceptions in data science courses; (2) document students’ prior knowledge and identify courses that teach early data science concepts; and (3) confirm expert identification of data science concepts, and their importance for introductory-level data science curricula. During the first year of this grant, we have collected approximately 200 responses for a survey to confirm concepts from an existing body of knowledge presented by the Edison Project. Survey respondents are comprised of faculty and industry practitioners within data science and closely related fields. Preliminary analysis of these results will be presented with respect to our third research objective. In addition, we developed and launched a pilot assessment for identifying student difficulties within data science courses. The protocol includes regular responses to reflective questions by faculty, teaching assistants, and students from selected data science courses offered at the three participating institutions. Preliminary analyses will be presented along with implications for future data collection in year two of the project. In addition to the anticipated results, we expect that the data collection and analysis methodologies will be of interest to many scholars who have or will engage in discipline-based educational research. 
    more » « less