From: <abstracts@gsm

AUDITORIUM PRESENTATION

 

CASUS – Implementation in Different Settings and Assessment  of  Key Features through Online Evaluation.

 

Martin Fischer, Inga Hege, Martin Adler, Matthias Holzer, and Veronika Kopp

AG Medizinische Lernprogramme, Munich, Germany

 

Part I:  Settings of e-learning

CASUS is a software package for authoring and delivering case-based learning based on a pedagogical concept developed by the AG Medizinische Lernprogramme at the University of Munich beginning in 1993. The CASUS system is used successfully at numerous faculties in Germany, Israel, the US and Brazil in different settings and content domains. The settings are for example:

 

·        Using CASUS as a “Learning by Teaching” tool: Students in small groups create their own case in a tutorial setting and get credit for the completed case, which is reviewed by an expert.

 

·        Supplementing a lecture with cases matching curricular learning objectives Since 1998 we use CASUS cases in this setting as a voluntary option to gain credits for the lecture in internal medicine.

 

·        Learning cases as preparation for online key feature exams:  Overwhelming numbers of participants make this setting very promising for future curricular implementations.

 

We will report on feedback of the participants, evaluation data gained with online questionnaires and the effects this had on the new implementations of the software.  Moreover we will compare these different settings with each other regarding feasibility, success, interesting evaluation outcomes and advantages/disadvantages.

 

Part II - "Assessment drives the e-learning" when this modified classical saying of George Miller holds true, new valid forms of online assessment have to be established to foster the use of high effort e-learning applications. The key feature approach with special short case studies was developed by Page & Bordage in the mid-nineties for the testing of clinical decision making skills in high stakes licensing exams in Canada. This approach allows for a highly reliable testing of candidates by just using questions focusing on key issues or issues related to frequent errors with respect to the clinical problem. The key feature problems were delivered on paper using MC or short menu answer formats and needed manual scoring.

 

We have created an online key feature assessment tool based on the CASUS learning system using short or long menu answer formats. This approach allowed for fully automated processing of scores and media enriched problem presentations. With 15 cases presented to 4th year medical students (n=40) a reliability of 0.65 was reached. Acceptance and motivation of students to use this kind of assessment was reasonably high. Further elaboration of answer mechanisms and the creation of key feature problems for a variety of domains are planned. It is planned to use the assessment tool routinely at various German medical schools in the summer of 2004. In conclusion, the online key feature assessment approach offers an attractive alternative for the testing of decision making skills in clinical undergraduate training.

 

BENEFIT TO PARTICIPANTS ATTENDING SESSION:

 

Attendees will learn about different settings in which the CASUS system has been introduced during the past ten years. The focus of part II will be on a new form of online assessment. The key feature problems offer an opportunity to support online learning with the various available case-based learning systems. A collaborative effort to jointly create a portfolio of valid key feature problems for an online assessment initiative will be discussed.

 

Martin Fischer

AG Medizinische Lernprogramme, Klinikum der Universitaet Muenchen Ziemssenstr.1, 80336 Muenchen, Germany

Phone: +49 89 5160 2159

Martin.Fischer@lrz.uni-muenchen.de

Website: http://www.casus.net/

 

CO-AUTHORS:

*Inga Hege

**Martin Adler

*Matthias Holzer

*Veronika Kopp

* same as primary author

** Instruct AG, Keferloherstr.109, 80807 Muenchen, Germany

Inga.Hege@instruct.de

Martin Adler@instruct.de

Matthias.Holzer@lrz.uni-muenchen.de

Veronika.Kopp@med.uni-muenchen.de

http://www.instruct.de/