Search for: All records

K-12 Computer Science (CS) education is developing rapidly but still lacks a comprehensive measure for CS teachers’ pedagogical content knowledge (PCK). We respond to this need by describing the design of a CS-PCK instrument for ‘Algorithms and Programming’ that measures three broad constructs: (a) teachers’ understanding of standards and standards alignment, (b) teachers’ formative assessment practices, and (c) teachers’ self-efficacy for teaching and assessing CS. 

Background: While the nasopharynx is initially the dominant upper airway infection site for SARS-CoV-2, the physiologic mechanism launching the infection at the lower airway is still not well-understood. Based on the rapidity of infection progression to the lungs, it has been hypothesized that the nasopharynx may be acting as the primary seeding zone for subsequent contamination of the lower airway via aspiration of virus-laden boluses of nasopharyngeal fluids. Methodology: To examine the plausibility of the aspiration-driven mechanism, we have computationally tracked the inhalation process in three anatomic airway reconstructions and have quantified the nasopharyngeal liquid volume transmitted to the lower airspace during each aspiration. Results: Extending the numerical trends on aspiration volume to earlier records on aspiration frequencies indicates a total aspirated nasopharyngeal liquid volume of 0.3 – 0.76 ml/day. Subsequently, for mean sputum viral load, our modeling projects that the number of virions reaching the lower airway will range over 2.1×106 – 5.3×106 /day; for peak viral load, the corresponding number hovers between 7.1×108 – 1.8×109. Conclusions: The virion transmission findings fill in a key piece of the mechanistic puzzle on the systemic progression of SARS-CoV-2, and subjectively point to health conditions like dysphagia, with proclivity to increased aspiration, as some of the potential underlying risk factors for aggressive lung infections. 

We articulate a framework for characterizing student learning trajectories as they progress through a scientific modeling curriculum. By maintaining coherence between modeling representations and leveraging key design principles including evidence-centered design, we develop mechanisms to evaluate student science and computational thinking (CT) proficiency as they transition from conceptual to computational modeling representations. We have analyzed pre-post assessments and learning artifacts from 99 6th grade students and present three contrasting vignettes to illustrate students’ learning trajectories as they work on their modeling tasks. Our analysis indicates pathways that support the transition and identify domain-specific support needs. Our findings will inform refinements to our curriculum and scaffolding of students to further support the integrated learning of science and CT. 

We articulate a framework for characterizing student learning trajectories as they progress through a scientific modeling curriculum. By maintaining coherence between modeling representations and leveraging key design principles including evidence-centered design, we develop mechanisms to evaluate student science and computational thinking (CT) proficiency as they transition from conceptual to computational modeling representations. We have analyzed pre-post assessments and learning artifacts from 99 6th grade students and present three contrasting vignettes to illustrate students’ learning trajectories as they work on their modeling tasks. Our analysis indicates pathways that support the transition and identify domain-specific support needs. Our findings will inform refinements to our curriculum and scaffolding of students to further support the integrated learning of science and CT. 

We articulate a framework for characterizing student learning trajectories as they progress through a scientific modeling curriculum. By maintaining coherence between modeling representations and leveraging key design principles including evidence-centered design, we develop mechanisms to evaluate student science and computational thinking (CT) proficiency as they transition from conceptual to computational modeling representations. We have analyzed pre-post assessments and learning artifacts from 99 6th grade students and present three contrasting vignettes to illustrate students’ learning trajectories as they work on their modeling tasks. Our analysis indicates pathways that support the transition and identify domain-specific support needs. Our findings will inform refinements to our curriculum and scaffolding of students to further support the integrated learning of science and CT. 

Computational Thinking (CT) can play a central role in fostering students' integrated learning of science and engineering. We adopt this framework to design and develop the Water Runoff Challenge (WRC) curriculum for lower middle school students in the USA. This paper presents (1) the WRC curriculum implemented in an integrated computational modeling and engineering design environment and (2) formative and summative assessments used to evaluate learner’s science, engineering, and CT skills as they progress through the curriculum. We derived a series of performance measures associated with student learning from system log data and the assessments. By applying Path Analysis we found significant relations between measures of science, engineering, and CT learning, indicating that they are mutually supportive of learning across these disciplines. 

We articulate a framework for using computational modeling to coherently integrate the design of science and engineering curricular experiences. We describe how this framework informs the design of the Water Runoff Challenge (WRC), a multi-week curriculum unit and modeling environment that integrates Earth science, engineering, and computational modeling for upper elementary and lower middle school students. In the WRC, students develop conceptual and computational models of surface water runoff, then use simulations incorporating their models to develop, test, and optimize solutions to the runoff problem. We conducted a classroom pilot study where we collected students’ learning artifacts and data logged from their use of the computational environment. We illustrate opportunities students had to integrate science, engineering, and computational thinking during the unit in a pair of contrasting vignettes.