Educational Intelligence Benefits Stakeholders Across the Student Lifecycle
In our initial post introducing our perspective on Educational Intelligence or “EI,” we set out to address the nature and state of analytics in the education space. We defined EI and discussed its evolution as it relates to BI / Business Analytics, including some differentiating considerations when applying analytics to education.
In education, the student experience and supporting functions drive analytics needs. The student experience lifecycle is comprised of three overarching areas: teaching and learning, student success, and recruitment and enrollment. In this post, we’ll take a closer look at how EI impacts key stakeholders across the student lifecycle, including the student, instructor, curriculum designer, administrator, success coach, and researcher.
Students generate a wealth of real-time activity data that should be at their disposal. A recent survey revealed that 82% of students expect their data to be used in ways that will transform the college experience in the next decade. In other words, students expect educational intelligence. With data analytics, students can see status and progress down to specific learning activities, allowing them to identify strengths and weaknesses. They can also benchmark themselves against anonymous peer activity and leverage the patterns of students to influence their own learning path.
In addition, advanced analytic capabilities can be introduced to predict or suggest alternate paths and results as scenarios for the student to consider. Students can use suggested scenarios to adjust their level of academic engagement or their overall program of study to achieve the best course of action for their goals. This is a powerful way to enhance the learning experience and outcomes for students in their course work and overall program of study. Furthermore, the cumulative achievements, competencies and skills driven by insights along the way equip students with higher value qualifications needed for increasingly skill-demanding career and job market.
With instructors, we commonly hear a desire to access timely insights about students. These instructors seek EI to optimize student performance as well as curriculum efficacy. While curriculum and teaching interactions are underway, EI facilitates the intervention with struggling students and making the necessary curriculum adjustments to improve results before the course is complete.
Knowing which students are at-risk in particular areas of the course enables an instructor to better intervene and assist their students in a more timely and targeted way. EI lets instructors use granular activity measurements such as engagement, time spent, effort, peer and instructor communication and assignment results to identify at-risk students and assist them in a much more informed way.
Similarly, EI can help instructors determine the strengths and weaknesses of the curriculum itself. An instructor may change his/her approach based on real-time insights on the curriculum structure or engagement with and results from discrete learning activities or content elements. This curriculum efficacy insight also enables instructors to determine which instructional materials and activities are delivering the most positive results and where any deficiencies might hinder performance. This is especially helpful when many of these activities and content elements are purchased from 3rd parties.
For Curriculum Designers/Developers
I have had many recent discussions and interactions with curriculum designers, developers and instructional designers within the standards communities and Intellify’s publisher/digital courseware provider clients, who are both excited as well as challenged by the volume of and access to the real-time granular learning data. For them, the data from a predominantly digital courseware environment is a valuable byproduct that opens new possibilities.
The EI from the data provides these stakeholders with the real-time insights needed to enhance their analysis of the curriculum structure and its discrete learning activity elements at a detailed level. These insights determine the strengths and weaknesses of individual learning activities that then inform any adjustments needed for current and future versions of the curriculum to deliver the best results. Such timely adjustments to the curriculum can optimize the experience and outcomes for students before they’ve completed the course. Also, such insights identify effective learning activities and facilitate more informed purchase and usage of instructional materials.
At the program or higher level of administration, stakeholders are typically interested in starting with a broader, enterprise perspective aligned with the different student lifecycle dimensions. At a basic level, insights are provided via descriptive analytics that are similar to more traditional business intelligence and surface as much information as possible in a consolidated but simplified form to help expedite administrator analysis, decisions, and actions. At more advanced levels, administrators can benefit from more predictive and prescriptive capabilities depending on the size, scope and broad interdependencies of student lifecycle dimensions. This can help automate the identification of effective options and decision-making scenarios based on forecasted results.
Together, these basic and advanced capabilities can help administrators take informed actions in areas such as marketing, recruitment, enrollment, program/curriculum efficacy, student retention, as well as academic and career advising in a more effective and timely fashion.
For Advisors and Success Coaches
Gone are the days when the only data advisors had at their disposal were past grades along with instructor and/or peer review input. Within their capacity of guiding students to define, attain, and align their academic goals with continued education or career goals, advisors need access to holistic views of students. Specifically, advisors can benefit from EI for course/degree selections and performance, comparative insights relative to historical and current peer cohorts, academic paths taken and available alternatives, as well as how to achieve the best outcomes.
As an example, one Intellify client is leveraging EI derived from multiple academic and operational data sources to create comprehensive, multi-dimensional online student profiles that inform success coaches.
Lastly, insights derived from the core academic experience, combined with current labor market data, can enhance the advisor’s ability to intervene and assist, ensuring a student’s readiness and fit for the increasingly demanding employment landscape.
Both independent and institutionally-sponsored learning science research initiatives are fast emerging as the data rich, digital learning delivery environment continues to evolve and expand. These research initiatives aim to leverage the enormous and expanding volume of historical and current data and apply data science to derive a deeper understanding of instruction and learning. This greatly improves existing research as well as surface new research opportunities across the academic and operational dimensions of a single or multiple institution ecosystem.
The EI capabilities required for researchers is less about having canned insights, but rather providing a comprehensive data management and analytics toolset that supports standards-based interoperability and transparency. Furthermore, researchers would have the ability to aggregate, manage and iteratively analyze large volumes of granular data. With this foundation, researchers can then design and develop their own computational or advanced algorithmic models needed to complete their project work more effectively to deliver their own EI insights.
My discussions with researchers in communities such as SOLAR and IMS Global indicate that the majority of researchers today are still spending most of their time collecting and managing data from different learning activity platform and application sources. They are also spending time selecting and deploying a variety of different tools to collect, manage, and process this data. This prevents them from spending most of their time and resources on modeling and interpreting new insights.
As EI continues to expand in practice and get more advanced in capability, the benefits provided to stakeholders will continue to expand and elevate the impact on each stakeholder’s ability to enhance the educational experience. Furthermore, as each individual stakeholder’s contribution and impact is enhanced by EI, the academic and operational impact on the overall educational experience and outcomes will continue to improve incrementally.
The next posts will address various perspectives on the how, what, and when as it relates to the design and implementation of educational analytics to actually realize EI in practice.