MIT App Inventor is an online platform designed to teach computational thinking concepts through development of mobile applications. Students create applications by dragging and dropping components into a design view and using a visual blocks language to program application behavior. In this chapter, we discuss (1) the history of the development of MIT App Inventor, (2) the project objectives of the project and how they shape the design of the system, and (3) the processes MIT uses to develop the platform and how they are informed by computational thinking literature. Key takeaways include use of components as abstractions, alignment of blocks with student mental models, and the benefits of fast, iterative design on learning.
Smartphones are being used for a wide range of activities including messaging, social networking, calendar and contact management as well as location and context-aware applications. The ubiquity of handheld computing technology has been found to be especially useful in disaster management and relief operations [Fajardo and Oppus, 2010]. Our focus is to enable developers to quickly deploy applications that take advantage of key sources that are fundamental for today's networked citizens, including Twitter feeds, Facebook posts, current news releases, and government data. These applications will also have the capability of empowering citizens involved in crisis situations to contribute via crowdsourcing, and to communicate upto-date information to others. We will leverage several technologies to develop this application framework, namely (i) Linked Data principles for structured data, (ii) existing data sources and ontologies for disaster management, and (iii) App Inventor, which is a mobile application development framework for non-programmers. In this paper, we describe our motivating use cases, our architecture, and our prototype implementation.
The Stochastic Activity Network Laboratory for Cognitive Modeling (SANLab-CM) is a new tool that incorporates stochastic operations into activity network modeling (Schweickert, Fisher, & Proctor, 2003). In this article, we discuss the core functionality of SANLab-CM and walk through a case study that expands a previously published single, static path model of telephone operators interacting with customers via a workstation (from Gray, John, & Atwood, 1993) into a stochastic model that generates 55 unique paths with different frequencies and a variety of qualitative properties. Without SANLab-CM, it would have been easy to mistake some of the more frequent critical paths as evidence for alternative strategies for task completion. With SANLab-CM, these critical paths can be shown to be simple emergent properties of variability in elementary cognitive, perceptual, and motor processes.
Increased understanding of developmental disorders of the brain has shown that genetic mutations, environmental toxins and biological insults typically act during developmental windows of susceptibility. Identifying these vulnerable periods is a necessary and vital step for safeguarding women and their fetuses against disease causing agents during pregnancy and for developing timely interventions and treatments for neurodevelopmental disorders. We analyzed developmental time-course gene expression data derived from human pluripotent stem cells, with disease association, pathway, and protein interaction databases to identify windows of disease susceptibility during development and the time periods for productive interventions. The results are displayed as interactive Susceptibility Windows Ontological Transcriptome (SWOT) Clocks illustrating disease susceptibility over developmental time. Using this method, we determine the likely windows of susceptibility for multiple neurological disorders using known disease associated genes and genes derived from RNA-sequencing studies including autism spectrum disorder, schizophrenia, and Zika virus induced microcephaly. SWOT clocks provide a valuable tool for integrating data from multiple databases in a developmental context with data generated from next-generation sequencing to help identify windows of susceptibility.
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