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Best Practices Report: How Data Analytics Informs District Decision-making

Summary & Recommendations

Summary of findings and new questions for district leaders

A key objective for all of the district leaders within this pilot has been to understand how to effectively leverage data assets to ensure that their technology investments (time, effort, financial resources) support the overall mission and goals of their districts. Education leaders want to improve their decision-making capacities by leveraging quantitative data that can provide new insights into product usage.  

To that end, the CatchOn data adds significant new levels of visibility for the leaders to understand product usage levels and patterns in a variety of settings and across time. The examples provided in this study illustrate just a few of the different ways that the insightful CatchOn data dashboards can support better decision-making in K-12 districts. But there is much more for all of us to learn as we progress on our data journeys. To help with that process, we have developed a short list of questions for education leaders to use to stimulate new conversations within their districts about the types of data needed for effective decision-making and how to translate data into actionable knowledge.  

1

How are you using engagement data to inform your technology product decisions, especially relative to re-purchasing or re-licensing decisions? Do you have a rich set of (up-to-the-minute) data available to inform those decisions including comparative usage data about the use of different products in your classrooms?

2

How are you evaluating the efficacy of the digital products being used in your classrooms, both physical classroom and virtual learning environments? What types of data do you wish you had to inform your evaluation of the impact of technology on learning outcomes?

3

How well are your digital products, tools, and content supporting the overall mission and goals of your district? Are you able to use data assets to see if your product decisions are aligning with the vision of your district for teaching and learning?

4

Where are you on your journey to using data assets to inform better decision-making? What do you need today to advance the overall sophistication of your district in terms of using data more effectively?

Appendix Table 1: Contextual information about the school districts that participated in the pilot.
School District Identifier
State
Community Type
# of Schools
Total Student Population
% of Students in Free or Reduced Lunch Program

A

AL

Rural

44

30,602

38%

B

AL

Rural

3

1,151

67%

C

NE

Urban

73

42,020

44%

D

NJ

Suburban

10

5,216

38%

E

OR

Urban

86

48,710

40%

F

SC

Suburban

31

26,737

38%

G

VA

Suburban

48

40,879

36%

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