It seems like there are new tech and social media tools coming out every day. So what’s out there? And how can these tools be used to enhance your work?
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.
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.
Game-based learning (GBL) has increasingly been used to promote students’ learning engagement. Although prior GBL studies have highlighted the significance of learning engagement as a mediator of students’ meaningful learning, the existing accounts failed to capture specific evidence of how exactly students’ in-game actions in GBL enhance learning engagement. Hence, this mixed-method study was designed to examine whether middle school students’ in-game actions are likely to promote certain types of learning engagement (i.e., content and cognitive engagement).
This mixed-method study was designed to examine whether middle school students’ in-game actions are likely to promote certain types of learning engagement (i.e., content and cognitive engagement).
In science and engineering education, the use of heuristics has been introduced as a way of understanding the world, and as a way to approach problem-solving and design. However, important consequences for the use of heuristics are that they do not always guarantee a correct solution. Learning by Design has been identified as a pedagogical strategy that can guide individuals to properly connect science learning via design challenges.
This article describes the effect of simulation-enabled Learning by Design learning experiences on student-generated heuristics that can lead to solutions to problems.
Gould, R. R., S. Sunbury, & Dussault, M. (2014). In praise of messy data: Lessons from the search for alien worlds. The Science Teacher, 31.
Lessons from the search for alien worlds.
The search for habitable planets offers excellent opportunities to advance students’ understanding of core ideas in physics, including gravity and the laws of motion, the interaction of light and matter, and especially the nature of scientific inquiry. Thanks to the development of online telescopes, students can detect more than a dozen of the known exoplanets from the classroom, using data they gather, assess, and interpret for themselves. We present a suite of activities in which students apply basic physics concepts to their investigations of exoplanets.
Authors present a suite of activities in which students apply basic physics concepts to their investigations of exoplanets. The activities were developed and successfully tested with physics and earth science teachers in secondary schools in 14 states.
Computational algorithmic thinking (CAT) is the ability to design, implement, and assess the implementation of algorithms to solve a range of problems. It involves identifying and understanding a problem, articulating an algorithm or set of algorithms in the form of a solution to the problem, implementing that solution in such a way that the solution solves the problem, and evaluating the solution based on some set of criteria.
This paper explores the CAT Capability Flow, which begins to describe the processes and sub-skills and capabilities involve in computational algorithmic thinking (CAT). To do this, authors engage in an approach which results in an initial flowchart that depicts the processes students are engaging in as an iteratively-refined articulation of the steps involved in computational algorithmic thinking.
Paper from the 2016 Advancing Social Justice from Classroom to Community Conference.
Computational algorithmic thinking (CAT) is the ability to design, implement, and assess the implementation of algorithms to solve a range of problems. Supporting Computational Algorithmic Thinking (SCAT) is a longitudinal project that explores the development of CAT capabilities by guiding African American middle-school girls through the iterative game design cycle, resulting in a set of complex games around broad themes.
This paper explores African American middle-school girls' perspectives of their experience with the Supporting Computational Algorithmic Thinking (SCAT) project and perceptions of themselves as game designers.
Computational algorithmic thinking (CAT) is the ability to design, implement, and assess the implementation of algorithms to solve a range of problems. It involves identifying and understanding a problem, articulating an algorithm or set of algorithms in the form of a solution to the problem, implementing that solution in such a way that the solution solves the problem, and evaluating the solution based on some set of criteria.
This article explores middle school girls' reflections about the difficulties they faced while using computational algorithmic thinking capabilities as they engaged in collaborative game design for social change. Authors focus on how these difficulties changed over the course of three years as well as new difficulties that emerged from year to year as girls become more expert game designers and computational algorithmic thinkers.
