2023 ASEE Annual Conference & Exposition

The ACRL Framework for Information Literacy in Higher Education describes desirable information literacy competencies in terms of novice and expert behaviors. One may reasonably argue that it is outside of the scope of a university education to fully achieve the expert level of behavior described. However, choices in designing information literacy instruction can improve chances of a measure of expertise being developed even prior to graduation.
One element of expertise that is often overlooked in instructional programs is the framing of the conditions under which certain methods of inquiry or analysis are to be used. Often, the conditions of use are obvious inside of the academy, given the context of the assignment or unit of study. However, when that context is removed and students are given a real-world problem, they may struggle to identify the proper tools to use because they have yet to develop a schema that guides this kind of decision making. This understanding or “conditional knowledge” – knowledge of when to use the tools at one’s disposal – is one of the key distinguishing attributes of experts. One method for helping students explicitly develop conditional knowledge is called Decision-based Learning (DBL).
This paper describes continuing efforts to employ DBL techniques in undergraduate information literacy instruction, in furtherance of expert literacy skill levels identified in ACRL’s Framework (a work-in-progress). It summarizes results of recently published studies in this area and explores different areas within the domain of college-level information literacy where developing conditional knowledge may provide the largest gains in information literacy education. Focus is placed on concepts of particular interest to engineering undergraduate students. Finally, the paper provides examples of possible ways of incorporating DBL to teach these principles and provides observations from a pilot implementation of these example DBL models.