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Designing Research for Meaningful Results in Educational Leadership  

Karen Moran Jackson and Ric Brown

Making appropriate methodological and analytic decisions in educational research requires a thorough grounding in the literature and a thorough understanding of the chosen methodology. Detailed preplanning is important for all method types and includes an understanding of the assumptions, limitations, and delimitations of the study. For quantitative research, researchers should be cautious with data analysis decisions that give preference to statistically significant results, noting that quantitative research can proceed with intents other than confirmatory hypothesis testing. Decisions and procedures that are used to search for low p values, rather than answer the driving research question, are especially problematic. Presentation of quantitative results should include components that clarify and account for analytic choices, that report all relevant statistical results, and that provide sufficient information to replicate the study. Consideration should also be given to joining recent initiatives for more transparency in research with the use of preregistered studies and open data repositories. For qualitative research, researchers should be thoughtful about choosing a specific method for their project that appropriately matches the method’s framework and analytic procedures with the research aim and anticipated sample. Qualitative researchers should also strive for transparency in their method description by allowing for a view of the analytic process that drove the data collection and iterative dives into the data. Presentation of qualitative results requires a balance between providing a compelling narrative that establishes the trustworthiness of results with the judicious use of participant voices. Mixed methods research also requires appropriate integration of different data types.


Computing in Precollege Science, Engineering, and Mathematics Education  

Amy Voss Farris and Gözde Tosun

Computing is essential to disciplinary practices and discourses of science, engineering, and mathematics. In each of these broad disciplinary areas, technology creates new ways of making sense of the world and designing solutions to problems. Computation and computational thinking are synergistic with ways of knowing in mathematics and in science, a relationship known as reflexivity, first proposed by Harel and Papert. In precollege educational contexts (e.g., K-12 schooling), learners’ production of computational artifacts is deeply complementary to learning and participating in science, mathematics, and engineering, rather than an isolated set of competencies. In K-12 contexts of teaching and learning, students’ data practices, scientific modeling, and modeling with mathematics are primary forms through which computing mediates the epistemic work of science, mathematics, and engineering. Related literature in this area has contributed to scholarship concerning students’ development of computational literacies––the multiple literacies involved in the use and creation of computational tools and computer languages to support participation in particular communities. Computational thinking is a term used to describe analytic approaches to posing problems and solving them that are based on principles and practices in computer science. Computational thinking is frequently discussed as a key target for learning. However, reflexivity refocuses computational thinking on the synergistic nature between learning computing and the epistemic (knowledge-making) work of STEM disciplines. This refocusing is useful for building an understanding of computing in relation to how students generate and work with data in STEM disciplines and how they participate in scientific modeling and modeling in mathematics, and contributes to generative computational abstractions for learning and teaching in STEM domains. A heterogeneous vision of computational literacies within STEM education is essential for the advancement of a more just and more equitable STEM education for all students. Generative computational abstractions must engage learners’ personal and phenomenological recontextualizations of the problems that they are making sense of. A democratic vision of computing in STEM education also entails that teacher education must advance a more heterogeneous vision of computing for knowledge-making aims. Teachers’ ability to facilitate authentic learning experiences in which computing is positioned as reflexive, humane, and used authentically in service of learning goals in STEM domains is of central importance to learners’ understanding of the relationship of computing with STEM fields.


Research Challenges and Innovative Methodologies, Approaches, and Processes  

S. Anthony Thompson

Investigative practices, including research methodologies, approaches, processes, as well as knowledge dissemination efforts continue to evolve within inclusive or special education. So too do such practices evolve within related fields such as nursing, psychology, community-based care, health promotion, etc. There are several research approaches that promote the tools required to effect inclusive education, such as: evidence-based practice (EBP), EBP in practice, creative secondary uses of (anonymous) data, collective impact, qualitative evidence synthesis (QES), and lines of action (LOA). Other approaches that promote a more inclusive education research agenda more generally, include action research and participatory action research, inclusive research, appreciative inquiry, and arts-based educational research.