Peer Reviewed Monday – Expertise Reversal Theory
Okay. So I am pretty sure that the actual article I am pointing to here (probably behind a pay wall – sorry) is not peer-reviewed.  It is the editors’ introduction to a special issue of the journal Instructional Science.  In this introduction they tell us that there are five empirical research reports and two commentary pieces in the issue, but this piece is neither empirical research nor peer-reviewed.

So, take a look at the whole issue, if your access to Springer journals is sufficient.  If not, I will summarize!

So, expertise reversal theory.  Sounds fancy.  I have definitely heard much talk about its theoretical context – cognitive load theory.  That shows up all over the place in library instruction circles. I was in an airplane during the opening keynote at WILU last week, but from what I heard (and I heard it a lot – the keynote must have been really good) the “brain guy” talked about the kinds of things that come up in conversations about cognitive load theory.

Expertise reversal theory is a subset of this research.  It suggests that the very same things that reduce cognitive load in novice learners can actually increase cognitive load in expert learners, or in learners with more domain knowledge.

The implications of this for library instruction seem immediate and obvious.

In the classroom.  How many times have we talked about the problem of the class that seems to be equally divided between the students who have never been pointed to a database and students who have been to the library classroom four times this year?  You can’t pitch your presentation to the experts in that scenario, and depending on what you have to do for the novices, you might find yourself saying “well, hearing it again won’t hurt them.”. This theory suggests that maybe it will.

On the web. This is, of course, the really significant place where we have to think about the possibilities this theory raises,  Who makes Beginning and Advanced online help possible?  (okay, maybe Zotero but who else?)

I have been thinking about my tutorial posts from last year, because they were part of a larger process that Hannah and I presented on at WILU.  One of the main takeaways I took from the craft tutorials was the way that they assumed that the people using the tutorials brought with them a body of knowledge, and that idea runs throughout this discussion of expertise reversal theory.  The authors argue that the most important cognitive factor in learning is prior knowledge:

Studies of expert-novice differences in cognitive science have clearly demonstrated that learner knowledge base is the most important and fundamental cognitive characteristic that influences learning and performance.

Basically, the way I read this overview is like this – novices don’t have a set of mental models, body of domain knowledge or prior experiences to structure their interaction with new information, tools, etc.  They need help – and to reduce their cognitive load, we provide  help that gives them that structure, whether it be text presented together with images, detailed step-by-step instructions, or whatever the case may be.  More experienced learners have those mental models in place.  When you give them that structuring information, it becomes something they have to wade through, and it might actually impede their ability to access their own prior knowledge.

At the most basic level, they need the opportunity to “opt out” of the extra help you put in there for novices.

A couple of the papers in the special issue are of interest, though I haven’t read them closely enough to analyze yet – they look at how well expertise reversal theory holds up in messy domains (specifically literary criticism and writing-to-learn in psychology) instead of focusing on “well-structured” domains like math or physics — and as instruction librarians, messy domains are where we usually live, right?

Kalyuga, S., & Renkl, A. (2009). Expertise reversal effect and its instructional implications: introduction to the special issue Instructional Science, 38 (3), 209-215 DOI: 10.1007/s11251-009-9102-0

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