EDUCAUSE 2016: Analytics & Insight

The upcoming EDUCAUSE 2016 conference is known for pushing the boundaries and exploring the most progressive ideas in higher education IT. Inspired by a growing focus on the advancement of data analytics and its potential for higher education, the Data Science team at Campus Labs took a closer look at the conference sessions and unlocked some interesting insight.

Outcomes Matter: Bloom’s Taxonomy of Learning

Outcome statements have the power to provide valuable insight, but much like a campus, there are even more pieces of data available for understanding the learning taking place at EDUCAUSE. By removing the verb from the outcome statements and analyzing the remaining content, the Data Science team was able to tag the text with appropriate topics using a non-negative matrix factorization, a common text clustering technique. What emerged were 17 clear topics and themes - identifying areas outside of the scope of the traditional tracks and categories. Applying these same techniques on a campus would have the power to potentially illuminate gaps and even unintended areas of learning.

All Tracks

Creating
16%
Evaluating
16%
Analyzing
2%
Applying
14%
Understanding
32%
Remembering
2%

Emerging Topics

Outcome statements have the power to provide valuable insight, but much like a campus, there are even more pieces of data available for understanding the learning taking place at EDUCAUSE. Analyzing the descriptions of 300+ events, the Data Science team found 17 clear topics and themes - identifying areas outside of the scope of the traditional tracks and categories. Unlocking these same insights on a campus can be valuable for illuminating gaps and even unintended areas of learning.

Insights

As institutions continue to refine and improve the student learning experience, the attention is rightly focused on outcomes. And to properly assess the progress of learning, the outcomes themselves have to be measurable. It is not uncommon for learning outcomes to be written in such a way that they are difficult to assess. Applying the Campus Labs Bloom’s Taxonomy algorithm to this data, we discovered the same challenges experienced by campuses when they are authoring learning outcomes. Institutions are often looking for streamlined ways to give multiple stakeholders feedback on crafting meaningful, measurable, and manageable outcomes. Applying this same algorithm to institutional outcomes, the Data Science team is quickly able to identify immeasurable outcomes early-on, allowing instructors plenty of time to act quickly in order to redesign or realign learning outcomes to optimize for success.

Why Couldn’t They Be Tagged?

Ambiguous Verb Choice • Wrong Verb Tense • Non-cognitive Verbs

Common Immeasureable Verbs

Will appreciate • Gain familiarity • Have exposure to

And That’s Just the Start

What our Data Science team has done with the data from the EDUCAUSE 2016 just scratches the surface of the analytics and insight the Campus Labs platform has the power to bring to an institution.

To learn more, schedule a 1:1 demo and let’s talk data at EDUCAUSE