Influences of deep learning, need for cognition and preparation time on open- and closed-book test performance.

OBJECTIVES: The ability to master discipline-specific knowledge is one of the competencies medical students must acquire. In this context, 'mastering' means being able to recall and apply knowledge. A way to assess this competency is to use both open- and closed-book tests. Student performance on both tests can be influenced by the way the student processes information. Deep information processing is expected to influence performance positively. The personal preferences of students in relation to how they process information in general (i.e. their level of need for cognition) may also be of importance. In this study, we examined the inter-relatedness of deep learning, need for cognition and preparation time, and scores on open- and closed-book tests. METHODS: This study was conducted at the University Medical Centre Groningen. Participants were Year 2 students (n = 423). They were asked to complete a questionnaire on deep information processing, a scale for need for cognition on a questionnaire on intellectualism and, additionally, to write down the time they spent on test preparation. We related these measures to the students' scores on two tests, both consisting of open- and closed-book components and used structural equation modelling to analyse the data. RESULTS: Both questionnaires were completed by 239 students (57%). The results showed that need for cognition positively influenced both open- and closed-book test scores (beta-coefficients 0.05 and 0.11, respectively). Furthermore, study outcomes measured by open-book tests predicted closed-book test results better than the other way around (beta-coefficients 0.72 and 0.11, respectively). CONCLUSIONS: Students with a high need for cognition performed better on open- as well as closed-book tests. Deep learning did not influence their performance. Adding open-book tests to the regularly used closed-book tests seems to improve the recall of knowledge that has to be known by heart. Need for cognition may provide a valuable addition to existing theories on learning.

Influence of open- and closed-book tests on medical students' learning approaches.

CONTEXT: Two learning approaches are consistently distinguished in the literature: deep and surface learning. The deep learning approach is considered preferable. Open-book tests are expected to stimulate deep learning and to offer a possible way of handling the substantial growth in medical knowledge. In this study we test the hypothesis that open-book tests stimulate deep learning more than closed-book tests. METHODS: Medical students in Years 2 (n = 423) and 3 (n = 306) participated in this study. They evaluated their preparation for open- and closed-book tests using the test for Deep Information Processing (DIP). This questionnaire consists of 24 items divided into three subscales: Critical Reading; Broaden One's Context, and Structuring. A paired t-test was used to analyse the data. RESULTS: Both cohorts scored significantly higher when preparing for closed-book tests for the overall DIP score and on the Broaden One's Context and Structuring scales. Year 3 students also scored significantly higher on the Critical Reading scale when preparing for closed-book tests. Gender differences were found: women used deeper learning approaches than men. CONCLUSIONS: Our hypothesis was not supported. In fact, the opposite was found: closed-book tests stimulated a deep learning approach more than open-book tests. Three possible explanations are: deep learning is particularly necessary for remembering and recalling knowledge; students feel more confident when preparing for closed-book tests, and students are more motivated to study for closed-book tests. The debate on the concept of deep learning in higher education should probably be renewed.