In this session I described a classical, iterative version of PBL in which the case discussion stimulates learning. All material is discussed twice,  first without prior preparation and then after researching the questions raised in first session (called learning issues). Next I outlined seven learning outcome categories according to Fink’s (2003) taxonomy of significant learning that guide our options for evaluating student learning in PBL. These categories are: learning how to learn, motivation/ interest/values/respect for others, human dimension, integration/connection, application/problem solving/ critical thinking, knowledge, and skills.  As I went through the PBL process I identified specific embedded assessments that are congruent with this taxonomy of learning that can be used at each step. For example, the summaries of learning issues can be evaluated for: deep-learning (learning for understanding and meaning, and many connections are formed among concepts learned) , use of evidence-based decision making to evaluate information, synthesis of knowledge, evidence of self-directed learning, information literacy skills, and written communication. Many different types of categories of outcomes can be evaluated throughout all in-class PBL activities including: professional behaviors, leadership effective team behaviors, and management of complex projects.  These evaluations are based on repeated observations of in-class interactions.  Faculty, peers and the students can assess themselves on these dimensions.  I discussed a few examples of non-embedded, authentic evaluation tools that are consistent with the PBL process, such as the triple jump.

I proposed an evaluation framework for selecting what to evaluate and how that considers the outcome category, the rationale for selection, the specific outcome to be evaluated, how the outcome should be measured and how to collect data to measure the outcome.  Finally, I applied the framework to examples of how to evaluate deep learning and information processing.  Deep learning falls in the categories of learning to learn, application and problem solving.  Problem solving is hard to measure directly, but evidence of deep learning is a prerequisite for problem solving.  Deep learning can be evaluated from the student discussions of cases, particularly on the second go around with the material.  Students collectively can create concept maps of their understanding of the case and the underlying basic science that explains the disease process.  Scoring rubrics can be used to evaluate students’ concept maps.  Usually a group grade is given and then individual students can earn more or less than the group grade for performance that was markedly above or below the standard performance.  Peer feedback is helpful in determining the individual points. Information literacy standards for higher education have been established by the Association of College and Research Libraries including: determination of information needs, acquisition of information effectively and efficiently, critical evaluation of information and its sources, incorporation of selected information into one’s knowledge base and use of information legally and ethically.  The process of generating, researching and reporting on learning issues allows us to evaluate students on information literacy

Association of College and Research Libraries  www.ala.org/acrl/ilintr.html

Fink, LD. (2003) Creating Significant Learning Experiences.  San Francisco: Jossey-Bass

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