Controlling and Deriving Value from Educational Data

by | Feb 6, 2018

As we are at the very beginning of this new calendar year, it is a perfect time to add a 2018 milestone and develop an action plan to begin realizing value from your educational data.

We introduced and outlined the potential value in our Educational Intelligence Benefits and Getting Started posts as it relates to both what the value can be to key stakeholders and how to begin to mobilize and operationalize analytics initiatives. Also, the value of educational data and its derived EI is increasingly moving from a discretionary “nice-to-have” to an imperative. This is emphasized in an excellent ACE paper, The Data Enabled Executive: Using Analytics for Student Success and Sustainability, but some data access challenges still exist despite the progress made in mobilizing key data sources.

As discussed in the Getting Started post for mobilizing an analytics initiative, it is best to begin with a small and manageable project scope and timeline. It is essential that it has some immediate targeted stakeholder value and then incrementally layer on additional capabilities as needed. Bottom line, deriving EI analytics insights does not have to start with a comprehensive or complex analytics project to deliver value.

In the spirit of getting something with EI driven value underway, note that the rest of this post has subtlely suggested timeline tips to help guide what may be a very doable initial EI project in 2018. You can cross reference these tags with high level timeline recap at the end of the post. (Hover or tap the icon to see the timeline tips)

Timeline Tip January-February: initial access to collect and explore data; identify a small set (2-5) institutional / stakeholder questions/metrics to gain insight from; share with stakeholders for input It is essential for institutions to first and foremost control all of the invaluable data “exhaust” they own that is generated by their students and stakeholders which is siloed across many different specialized platforms and applications. Having a solid, scalable and unified data management foundation is a cornerstone for beginning and extending an institution’s analytics capabilities. Great value can be realized from this milestone alone by prioritizing and implementing this foundation inclusive of enabling some basic EI capabilities as a first step.

Timeline Tip March: refine EI data and insight requirements; establish plan and budget for initial and incremental analytics milestones These capabilities typically take the form of stakeholder specific descriptive dashboards to replace / upgrade any standard reporting. These dashboards unify real-time and historical data along with basic statistical and comparative correlations to yield more comprehensive analytics insights than traditional reporting. Also, this enhanced capability is a tremendous step up from isolated reports and dashboards embedded within each individual learning platform and tool.

As for identifying data sources to leverage, we did a bit of an isolated drill down in our LMS Data post on the importance of the LMS data as a source of EI not only in Teaching and Learning (T&L) but across other lifecycle areas. Timeline Tip March: refine EI data and insight requirements; establish plan and budget for initial and incremental analytics milestones Factoring in additional data sources in the academic enterprise will enhance the depth and breadth of the EI and increase stakeholder value for the T&L lifecycle area. These sources can be 3rd party LTI-integrated tools (e.g., assessment, collaboration, media, e-readers, homework and remediation) which are commonly leveraged for online curriculum. In addition, incorporating operational data sources such as the SIS and CRM platforms that contain contextual data that spans the academic as well as the operational dimension of the student lifecycle, can further enhance EI insights and establish the groundwork to expand EI to address other student lifecycle needs.

Timeline Tip April: finalize initial data source and EI insight requirements; finalize the fall term “go live” target; stakeholder input checkpoint The best approach for identifying and prioritizing data sources is to focus and scope the EI insights required for a specific student lifecycle area of interest. Then, within each lifecycle area, identify the list of questions or intended data informed capability required for the key stakeholders consuming this EI to better inform their understanding and actions. Several of our clients see Institutional Effectiveness and Teaching & Learning areas as key areas to begin.

Timeline Tip May-July: implement finalized data collection and EI insight capabilities
June-July: triage/defer initial EI “go live” capabilities; implementation continues
July-August: finalize / lock-in initial capabilities; stakeholder input checkpoint; implementation continues
August: “go live” with initial EI capabilities, target stakeholder rollout / training
In determining EI requirements, Intellify client engagements have yielded some common EI solutions and capabilities across selective lifecycle areas. Some of the more common areas of interest, capabilities, and required data sources are outlined below.

Lifecycle Areas Analytics Capabilities Data Sources
Teaching and Learning

Identify students at risk for not successfully completing the program/course

Identify curriculum, tools, and practices that improve student performance and engagement

Inform instructors, students, curriculum designers and academic advisors

  • LMS
  • Learning activity tools (LTI)
Timeline Tip January-February: initial access to collect and explore data; identify a small set (2-5) institutional / stakeholder questions/metrics to gain insight from; share with stakeholders for input
March: refine EI data and insight requirements; establish plan and budget for initial and incremental analytics milestones
Institutional Effectiveness

Measure current progress against institutional objectives and metrics, to inform administrators, program directors and instructors

  • LMS
  • Learning activity tools (LTI)
  • SIS
  • CRM
  • Financials
Student Readiness

Monitor student readiness levels, and engagement via student readiness remediation modules and courses

Identify students that would benefit from advising and help inform advising activities

  • Readiness diagnostic tool
  • LMS
  • Learning activity tools (LTI)
Student Success / Retention and Persistence

Identify students at-risk for not completing the program and help inform advising activities involving success coaches, academic advisors, and instructors

  • LMS
  • Learning activity tools (LTI)
  • SIS
  • CRM
  • Academic advising & scheduling tools
Degree Planning

Monitor and analyze initial and ongoing course/program level selections made, performance and outcomes

Inform advisors and students of challenges and alternatives affecting both the current degree path as well as alternative paths

  • LMS
  • SIS
  • Alumni community
  • Employment / Job industry data
Enrollment and Admissions

Analysis of student admission and enrollment profiles, along with ongoing monitoring and analysis of their progress toward expected outcomes, to derive insights to inform and optimize the admissions and enrollment criteria and process

  • Admissions
  • CRM
  • SIS
Marketing and Recruiting

Analysis of applicant lead and marketing channel profiles and effectiveness. Inform and optimize the alignment of channel and applicant profile with target admissions and enrollment criteria

  • Marketing / Lead gen channels
  • Applicant tracking
  • Admissions
  • SIS
  • CRM

Timeline Tip September: deliver initial EI insights to stakeholders; monitor/respond to stakeholder feedback
October: monitor/respond to stakeholder feedback
November: retrospective and determine necessary adjustments
As you can see, tremendous value can be gained by incrementally enabling EI analytics for each of the lifecycle areas. EI continuously informs and optimizes insights and actions specific to a lifecycle area and across the board. It may be obvious, but it is important to note that there is quite a bit of overlap and interdepencies across the lifecycle areas in terms of insights required and leveraged from other areas as well as the underlying analytics and data required. This is all the more reason to build a strong centralized data and analytics foundation to support and sustain the range and scale of the data and insights needed.

Timeline Recap

  1. January-February: initial access to collect and explore data; identify a small set (2-5) institutional / stakeholder questions/metrics to gain insight from; share with stakeholders for input
  2. March: refine EI data and insight requirements; establish plan and budget for initial and incremental analytics milestones
  3. April: finalize initial data source and EI insight requirements; finalize the fall term “go live” target; stakeholder input checkpoint
  4. May-July: implement finalized data collection and EI insight capabilities
  5. June-July: triage/defer initial EI “go live” capabilities; implementation continues
  6. July-August: finalize / lock-in initial capabilities; stakeholder input checkpoint; implementation continues
  7. August: “go live” with initial EI capabilities, target stakeholder rollout / training
  8. September: deliver initial EI insights to stakeholders; monitor/respond to stakeholder feedback
  9. October: monitor/respond to stakeholder feedback
  10. November: retrospective and determine necessary adjustments
  11. December: lay groundwork for adjustments to factor into the following annual implementation cycle

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