The discussion is focused on the difference of data driven vs. data informed decisions (Chapter 7, 8, and online resources below)

The difference between data driven and data informed decisions were discussed. The data driven decision are entirely evidence-based. The importance that is set to the data will also encourage information to be up to date and well examined. However, the negative aspect of it is that data is trusted blindly and little focus is given to qualitative data. Trusting data blindly may lead to negative effects when data is low quality or inaccurate.

On the other hand, making data informed decisions means that data is used as an important factor in decision but not the only factor. It means that qualitative and subjective information is used. A data informed educational institution would take other factors like subjective student experience into consideration in addition to data when making decisions. The advantages of this approach is that it takes into account the limitations of available data, uses multiple sources to make decisions, rather than just relying on data, and encourages creative and “out-of-the-box” decision making process, potentially leading to better results.

We will continue this discussion in Spring 2020.

Resources on data driven v. data informed decisions

https://youtu.be/uK9DQTkN0l0

The discussion is focused on the following:

–        Leading and Lagging Indicators (Chapter 4, 5, and 6 and online resources below)

–        Discussing current indicators used at KCC

–        Other colleges & suggestions.

Resources on Lagging & Leading Indicators of Student Performance

–        Authors’ blog about the book:  Creating a Data-Informed Culture at Community Colleges, by Brad C. Phillips and Jordan Horowitz on August 30,2017. https://www.hepg.org/blog/moving-the-needle-on-community-college-student-suc

–        Authors’ presentation http://www.linkedlearning.org/wp-content/uploads/2017/02/Measuring-Success-Leading-and-Lagging-Indicators-for-Linked-Learning.pdf

–        Data First: Leading and Lagging Indicators. [Video file.] Retrieved from Center for Public Education, https://vimeo.com/15739964

Other Resources for Current & Future Discussion

–        McComb, B. E., & Lyddon, J. W. (2016). Understanding the effectiveness and impact of student success interventions on campus: Understanding the effectiveness and impact of student success. New Directions for Community Colleges, 2016(175), 83-94.

–        Carales, V. D., Garcia, C. E., & Mardock‐Uman, N. (2016). Key resources for community college student success programming. New Directions for Community Colleges, 2016(175), 95-102.

–        Calculating Cost-Return on Investments in Student Success https://jfforg-prod-prime.s3.amazonaws.com/media/documents/ISS_brief_010510.pdf

–        “This pilot project tied program-level cost data to student outcomes and explored the extent to which the additional revenue that colleges and universities generate by increasing student retention offsets the additional cost of first-year programs. The project’s goal was to develop, test, and standardize tools that document the relationship between program costs and student results. Armed with this information, institutions will be better able to make informed, data-driven decisions about how to invest limited dollars in ways that help students succeed.”
https://www.jff.org/resources/calculating-cost-return-investments-student-success/

Additional attachments:

McComb_et_al-2016-New_Directions_for_Community_Colleges (1)

Carales_et_al-2016-New_Directions_for_Community_Colleges (1)

Faculty Interest Group “Using Data to Support Teaching and Learning”

Source: Creating Data-Informed Culture in Community Colleges: A New Model for Educators

Meeting 1: Chapter 1 to 3

This book shows how to use and present data at the local level to inform decision making and to help local CC to use existing data in ways that lead to improvements in student success. The first meeting will cover the first three chapters which elaborate on analytics block of the Figure below.

The challenge is not getting more data; getting the right data, or having access to data. While they are important, the effective data use to make reframing analytics to make data useful, usable, and actionable.

Data Accuracy

• Data entry
  • “…the US Department of Education estimates that “at least 70% and often 80 to 85% of the effort in data analytics is devoted to data cleaning, formatting, and alignment.”
  • Data that is send out both internal and external sources needs to be consistently reviewed… (e.g., judgement about how to code data is on IT staffer…)

• Data reporting
  • Data needs to be transformed in to tables, charts, and other displays in order to help educators understand the meaning behind numbers (p. 24).
  • Putting a good data-use practice to work
    • Rule 1: Focus
    • Rule 2: Only report data needed for compliance or making a decision

To avoid information overload, focusing on the issue at hand is crucial (37)

• Limit the number of displays to two for each point being made… two different displays
• Reduce the amount of information in any single display
• Tell a story – stories help us connect emotionally as well as cognitively with data. “Numbers, tables, and charts in and of themselves are not enough to inform and influence. Rather, it is the story behind the numbers that has the ability to impact educators.” (p18).
  • (10% or 10,000 students did not achieve a goal).
  • Put a face on the numbers
• Rule 3: Use telling headlines (p. 25 – 29)
• Rule 4: Use template for engaging in data conversations
  • Template – if information is presented the same way in each report, in the same order, with the same heading, it is easier to identify trends within the report.
  • …establishes a pattern for presenting information repeatedly, in in that way, capitalizes on the people desire for familiarity, is known as template.
  • Provides a natural way to organize information that makes new ideas and are more familiar.
  • Templates allow the audience to focus on content and not on the image in front of them (p39).

• Coordination of Reporting
  • How does a CC meet the needs of the user and produce data that is not ultimately discounted? We recommended that A&R, IS and IR come together and decide who provides what data to whom… We suggest creating a grid that details who does what for whom and when they do so.

Questions

• How is coordination of reporting in KCC?
• Which model does KCC follow?
  • Model 1: The college does not appreciate data and its sues
  • Model 2: The college wants to do something with data but is unsure what to do
  • Model 3: The college has embraced the use of data but has et to realize its potential.
• Relevant experience and suggestions