This article from Inside Higher Ed reports on some initial findings from the Gates Foundation data mining project in the US. The key finding reported in the article is
New students are more likely to drop out of online colleges if they take full courseloads than if they enroll part time
This article gives me pause to reflect on two observations – starvation and tell us what we already know – and talk a bit about patterns.
What we already know
Online students are for all intents and purposes distance education students. Long before online learning many universities across the world had long experience (and lots of research) with distance education. One of the reasons I started in e-learning was that I taught at one of these institutions and print-based distance education just didn’t cut it.
Here’s an excerpt from a paper I wrote in 1996 titled Computing by distance education: Problems and solutions”
Most distance students are either returning to study after a prolonged absence or studying seriously for the first time. The initial period of readjustment to the requirements of study can cause problems for some students.
The circumstances under which distance students study can also generate problems that lead to poor performance. These circumstances include workload, family commitments, illness, economic situation, geographic location and general lack of time.
For these and other reasons, anyone with time in a distance education institution new that a large percentage of distance education students would drop out in the first year because they weren’t all that familiar with the requirements of DE study and over-extended themselves in some way.
I don’t find it at all surprising that students of “online colleges” would suffer the same problem. Especially if they took on more courses. All we’ve done is exchange the platform/medium.
I really don’t think this particular finding is, as suggested in the article,
challenging conventional wisdom about student success.
It does, as suggested further into the article, raise questions about some of the assumptions built into financial aid practices, but doesn’t really challenge wisdom about student success.
This sounds like a big project. A $1 million grant, 6 institutions, 640,000 students, and 3 million course-level records. All focused on at-risk students or student success. Given the other rhetoric around learning analytics it isn’t hard to see what management are really interested in learning analytics. A perfect tool for them to see what is going on, be informed and subsequently take action. Leaving aside all of the likely problems arising from management taking action, my biggest worry is that this approach to learning analytics is going to starve the other uses of “learning analytics”.
Taking a higher ed focus, there are (at least) four roles at universities for whom learning analytics might provide benefits, including:
- Administration – retention, success, at-risk, efficiencies etc.
- Students – seeing their performance in the context of others (somewhat related to success, retention etc)
- Teachers – knowing what’s going on in their courses and what happens when they make changes.
- Researchers – as a research method that complements other quantitative and qualitative methods for figuring out the why, why, how, who etc with e-learning.
So which roles do you think learning analytics, as implemented at universities, is most likely to serve?
Who holds the purse strings?
Over the last 16 years or so I have observed universities spend tens of millions of dollars on administrative information systems. Enterprise Resource Plannings (ERP) systems like Peoplesoft etc have consumed vasts amounts of resources.
At the same time learning and teaching systems have had comparatively little spent on them. And that’s before you factor in staffing. Compare the number of analysts, programmers and associated support staff an institution employs around its ERP system with the number it employs around its LMS.
For various reasons, administrative systems tend to starve learning and teaching systems (let alone research systems) of funds and resources.
I can see the same thing happening with analytics.
These sorts of patterns are interesting. We’ve started documenting some of these patterns over here. The trouble is that all too often the answer to “why” is provided via the schemata of the people involved. Rather than research.
For example, Col published yesterday about another pattern
The later a student first accesses the LMS course site, the lower their final grade will be
Now does this mean that the keener, better organised students are those that access the course site early in term? Or is it because these are the students who don’t have disruptive life situations to deal with?