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Background: Educational leadership perspectives are missing from existing literature related to school shootings, which have been dominated instead by experts in criminal justice, law enforcement, and psychology. Purpose: In this article, we systematically review the literature base on educational leadership related to school shootings in the United States to identify gaps and develop an education-specific, leadership-specific research agenda for the United States. Methods: This exploratory-topographical review follows standards for systematic research reviews in educational leadership. Through reviews of 16 core educational leadership journals, and online scholarly search engines for research and keywords, we identify gaps in the current inter-disciplinary literature. Findings: We learned that the research base on school shootings is multidisciplinary, with scholars across seven different fields taking different approaches. Second, we found that while many scholars are addressing the problem of school shootings, the research base on school shootings from education researchers and specifically within the field of educational leadership are limited. Implications: We discuss three ways in which educational leaders and leadership scholars can inform school shooting research via emphasizing relationships, school–community partnerships, and meeting the needs of the marginalized. We propose preliminary recommendations for an education-specific, educational leadership U.S. research agenda, and suggestions for preparation programs. 

In this paper we propose a new framework—MoViLan (Modular Vision and Language) for execution of visually grounded natural language instructions for day to day indoor household tasks. While several data-driven, end-to-end learning frameworks have been proposed for targeted navigation tasks based on the vision and language modalities, performance on recent benchmark data sets revealed the gap in developing comprehensive techniques for long horizon, compositional tasks (involving manipulation and navigation) with diverse object categories, realistic instructions and visual scenarios with non reversible state changes. We propose a modular approach to deal with the combined navigation and object interaction problem without the need for strictly aligned vision and language training data (e.g., in the form of expert demonstrated trajectories). Such an approach is a significant departure from the traditional end-to-end techniques in this space and allows for a more tractable training process with separate vision and language data sets. Specifically, we propose a novel geometry-aware mapping technique for cluttered indoor environments, and a language understanding model generalized for household instruction following. We demonstrate a significant increase in success rates for long horizon, compositional tasks over recent works on the recently released benchmark data set -ALFRED.