Directing students to profound open-book test preparation: The relationship between deep learning and open-book test time

M. Heijne-Penninga*, J.B. Kuks, W.H. Hofman, J. Cohen-Schotanus

*Corresponding author for this work

Research output: Contribution to journalArticleAcademic

11 Citations (Scopus)


Background: Considering the growing amount of medical knowledge and the focus of medical education on acquiring competences, using open-book tests seems inevitable. A possible disadvantage of these tests is that students underestimate test preparation.

Aims: We examined whether students who used a deep learning approach needed less open-book test time, and how students performed on open-book questions asked in a closed-book setting.

Method: Second- (N = 491) and third-year students (N = 325) prepared half of the subject matter to be tested closed-book and half to be tested open-book. In agreement with the Board of Examiners, some questions in the closed-book test concerned open-book subject matter, and vice versa. Data were gathered about test time, deep learning and preparation time. Repeated measurement analysis, t-tests and partial correlations were used to analyse the data.

Results: We found a negative relationship between deep learning and open-book test time for second-year students. Students scored the lowest on closed-book questions about open-book subject matter.

Conclusions: Reduction of the available test time might force students to prepare longer and deeper for open-book tests. Further research is needed to identify variables that influence open-book test time and to determine how restrictive this time should be.

Original languageEnglish
Pages (from-to)E16-E21
Number of pages6
JournalMedical Teacher
Issue number1
Publication statusPublished - 2011

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