We will explore current frameworks and research around data use in schools. Topics will include data literacy, student-involved data use, and classroom “look fors.”
These instructions will guide our work in developing a district framework for effective data use. The armature below comes from the Schildkamp article reference in the Literature Review section, but many other pieces of educational research over the last decade point to similar categories. Remember, this is a starting point. We can add, edit, or delete to support our own vision of data use for our district.
- Danielson 1f - Using student assessments
- Danielson 3d - Using assessment in instruction
- Danielson 4b - Maintaining accurate records
- AWSP Criterion 3 - Planning with data
- WASA Criterion 3 - System-wide improvement
- Open data and e-skills (full article)
- Summary of data skills
Datnow, A., & Hubbard, L. (2016). Teacher capacity for and beliefs about data-driven decision making: A literature review of international research. Journal of Educational Change, 17(1), 7-28.
Farrell, C. C., & Marsh, J. A. (2016). Contributing conditions: A qualitative comparative analysis of teachers’ instructional responses to data. Teaching and Teacher Education, 60, 398-412.
Hoogland, I., Schildkamp, K., van der Kleij, F., Heitink, M., Kippers, W., Veldkamp, B., & Dijkstra, A. M. (2016). Prerequisites for data-based decision making in the classroom: Research evidence and practical illustrations. Teaching and teacher education, 60, 377-386.
Mandinach, E. B., & Gummer, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education, 60, 366-376.
Mandinach, E. B., & Jimerson, J. B. (2016). Teachers learning how to use data: A synthesis of the issues and what is known. Teaching and Teacher Education, 60, 452-457.
Park, V., Daly, A. J., & Guerra, A. W. (2013). Strategic framing: How leaders craft the meaning of data use for equity and learning. Educational Policy, 27(4), 645-675.
Schildkamp, K., & Kuiper, W. (2010). Data-informed curriculum reform: Which data, what purposes, and promoting and hindering factors. Teaching and teacher education, 26(3), 482-496.
Vanlommel, K., Vanhoof, J., & Van Petegem, P. (2016). Data use by teachers: The impact of motivation, decision-making style, supportive relationships and reflective capacity. Educational Studies, 42(1), 36-53.
Zadra, J. R., & Clore, G. L. (2011). Emotion and perception: The role of affective information. Wiley interdisciplinary reviews: cognitive science, 2(6), 676-685.
- Learn more about the history of data visualization in journalism as told by Scott Klein of ProPublica. You can watch his presentation at the 2016 Tapestry conference at the link above.
- Watch the 2016 Eyeo presentation, Setting Tangents around a Circle, from Josh Begley and consider the impact of our choices about which data to include or exclude.
- Have you ever tried to imagine how a fish soup tastes whose recipe is based on publicly available local fishing data? Or what a pizza would be like if it was based on Helsinki’s population mix? Data Cuisine, led by Moritz Stefaner, explores food as a means of data expression - or, if you like – edible diagrams.
- Students in Civic Art & Design Studio (IN123), with professor Catherine DʼIgnazio, visualized 100 of the more than 6,000 citizen questions collected by the City of Boston as part of the GoBoston 2030 transportation planning process. This video is realized as a large, projected public art installation. Are you interested in making your own gifs? There are lots of great resources on the web. Check out the repo on making data-related animated gifs from Lena Groeger of ProPublica.