“Killer courses” in distance education (#OMDE)


Studying through distance education (DE) is, for many students, like entering a minefield. Whether it is studying on their own, or encountering unexpected difficulties in courses or balancing work, studies and personal lives, DE remains a challenge for most students. DE is furthermore infamous for its many casualties in the form of high dropout rates, whether in individual courses or in cohorts of students as they progress through a programme. It is generally accepted that DE students take approximately one-and-a-half times as long (compared to residential institutions) to complete programmes due to different course loads, work-life balance, etc. The concern is not necessarily that DE students take longer to complete ‘whole’ programmes, but that many of them, may actually never complete a full programme. The reasons for this high dropout rate in DE are complex. This blog shares some thoughts on those modules which are known to have notoriously high failure rates resulting in students often repeating the course more than 3 times, and those courses that create barriers to students’ completion of their studies, the so-called “killer courses”.

Any attempt to understand the various factors impacting on student retention and success in programmes and courses should take cognisance of the fact that student retention and success is, per se, complex. The student journey consists of mostly non-linear, multidimensional, interdependent interactions and variables at different phases in the nexus between student, institution and broader societal factors. Some of the variables impacting on student success originate in the demographic detail, life-worlds, aspirations, schooling background of students. Students’ understanding of why they want to study and how their studies ‘fit’ their profiles are furthermore dynamic and may change as their life circumstances change.

Another set of variables impacting on student success originate in the unique characteristics, values and culture of the delivering institution such as institutional efficiencies or inefficiencies, policy, admission requirements, appropriate use of technology, etc. The fact that both students and institution are also impacted by variables outside their locus of control complicates matters even further. Factors such as the global economic downturn resulting in downsizing, unemployment, increasing labour unrest, etc., have major impacts on student and institutional success. When we think about the so-called “killer courses,” let us not forget that what happens (or does not happen) in a particular course is the result of many interdependent and often mutually constitutive variables (see Subotzky & Prinsloo, 2011).

Let us now turn to the so-called “killer courses”. How should we understand these courses with high dropout or failure rates? The one danger in identifying a course as a “high risk” or even a “killer” course is that we may think the problem lies only in the content and/or pedagogic structure of the course. We then forget that many factors impacting on student success lie outside the content and/or pedagogic structures of courses. It is so easy to blame academics  responsible for these courses for  not doing more to prevent student  failure  or dropout, while many of the variables impacting on dropout and failure fall outside of the loci of control of academics.

What is the difference between a “high risk” course which students repeatedly fail and so-called “killer courses”? The latter are those courses which prevent students from completing their qualifications or programmes. I personally don’t like the notion of a “killer course” although the term does not leave any doubt what these courses do to students’ aspirations and resources. A more appropriate term would be to call these courses “barriers to registration.”

So how do we then respond to high risk courses and those courses which are barriers to completion? My suspicion is that the problem (mostly) does not lie with the course content at all.  The problem lies in the alchemy or lack of alchemy between individual students’ and the course where there is no ‘fit’ between student and module. Though it is always possible to redesign courses and assessments to specifically address difficult threshold concepts, I don’t think this is a silver bullet. It is also not effective to send out yet another tutorial letter to all students. We have to drill down and analyse data to get a sense of where students who repeatedly fail a module go wrong or miss the plot.  Once we know where student miss the plot we can plan accordingly. Possible options for interventions include offering individualized e-tutor support to these students. How scalable this option is with regard to student numbers and cost will be determined by the context. A more appropriate option may be to identify “at risk” students earlier. Surely we have access to data which provide us crucial information of which students, if they register for particular courses or follow-up courses may be at risk? Research in the context of first-level accounting at Unisa (Du Plessis, Müller & Prinsloo, 2005) showed that students who repeat the course have a greater chance of failing it again. If students failed a module for more than three times, even with help, we must seriously consider allowing these students to register for an alternative curriculum whether in the same subject or, where possible and appropriate, in another discipline.

In conclusion:

There is enough evidence that indicates that some students are at higher risk to fail certain modules than other students. There is also ample evidence that some modules are, for some students, high risk environments or “killer modules.”  We should however be careful to think that the problem lies only in the module itself. The inability of some students to pass some modules, often repeatedly, is more probably found in a lack of ‘fit’ between students and module. Institutions have masses of student data which allow us to put systems and processes in place to prevent courses from becoming defined as “high risk” or “barriers to graduation”. The question is not whether we know, but whether we care enough to signpost the minefield, and where possible, provide alternative routes…

[Image retrieved from http://pixtale.net/2012/03/30-years-since-the-falklands-war/  ]

References

Du Plessis, A., Müller, H., & Prinsloo, P. (2005). Determining the profile of the successful first-year accounting student. SAJHE 19 (4), 684-698.

Subotzky, G., & Prinsloo, P. (2011): Turning the tide: a socio-critical model and framework for improving student success in open distance learning at the University of South Africa. Distance Education, 32(2), 177-193.

Advertisements

About opendistanceteachingandlearning

Research professor in Open Distance and E-Learning (ODeL) at the University of South Africa (Unisa). Interested in teaching and learning in networked and open distance and e-learning environments. I blog in my personal capacity and the views expressed in the blog does not reflect or represent the views of my employer, the University of South Africa (Unisa).
This entry was posted in OMDE601 and tagged , , , , , , , . Bookmark the permalink.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s