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Diagnosing teachers are teachers who perceive diagnostic information about students’ learning process, interpret these aspects, decide how to respond, and act based on this diagnostic decision. During supervision meetings about the undergraduate thesis supervisors make in-the-moment decisions while interacting with their students. We regarded research supervision as a teaching process for the supervisor and a learning process for the student. We tried to grasp supervisors’ in-the-moment decisions and students’ perceptions of supervisors’ actions. Supervisor decisions and student perceptions were measured with video-stimulated recall interviews and coded using a content analysis approach. The results showed that the in-the-moment decisions our supervisors made had a strong focus on student learning. Supervisors often asked questions to empower students or to increase student understanding. These supervising strategies seemed to be adapted to students’ needs, as the latter had positive perceptions when their control increased or when they received stimuli to think for themselves. published

What makes an expert university teacher? Answers to this question can be found in a multitude of publications, but so far there has been little insight into what these answers have in common. More common ground regarding what teacher expertise entails is necessary for research and support of the professional development of university teachers. To this end, this study aims to find consensus regarding what constitutes teacher expertise in higher education by identifying teacher tasks. We conducted a systematic review in which 46 frameworks for teacher expertise from research and practice contexts were identified, analysed, and synthesised. Six teacher tasks were distinguished: ‘teaching and supporting learning’, ‘educational design’, ‘assessment and feedback’, ‘educational leadership and management’, ‘educational scholarship and research’, and ‘professional development’. Additionally, the following three dimensions for task-related development were found: ‘better task performance’, ‘ability to carry out a greater variety of tasks’, and ‘a larger sphere of influence’. We present and visualise these tasks and task-related dimensions for development as the UNIversity Teacher Expertise (UNITE) synthesis. The synthesis both reflects and contributes to consensus about teacher expertise in higher education, which further research can build on. In academic practice, this synthesis could support teachers' reflection on their professional development and inform faculty development programmes and career policies. Further research is required to validate the results of this study, in particular the dimensions for development. Other suggested areas for future research are to explore how development in different teacher tasks is interrelated, as well as developing and investigating tools and interventions based on the perspective and findings of this study.

Medical image interpretation is moving from using 2D- to volumetric images, thereby changing the cognitive and perceptual processes involved. This is expected to affect medical students' experienced cognitive load, while learning image interpretation skills. With two studies this explorative research investigated whether measures inherent to image interpretation, i.e. human-computer interaction and eye tracking, relate to cognitive load. Subsequently, it investigated effects of volumetric image interpretation on second-year medical students' cognitive load. Study 1 measured human-computer interactions of participants during two volumetric image interpretation tasks. Using structural equation modelling, the latent variable 'volumetric image information' was identified from the data, which significantly predicted self-reported mental effort as a measure of cognitive load. Study 2 measured participants' eye movements during multiple 2D and volumetric image interpretation tasks. Multilevel analysis showed that time to locate a relevant structure in an image was significantly related to pupil dilation, as a proxy for cognitive load. It is discussed how combining human-computer interaction and eye tracking allows for comprehensive measurement of cognitive load. Combining such measures in a single model would allow for disentangling unique sources of cognitive load, leading to recommendations for implementation of volumetric image interpretation in the medical education curriculum. Display Omitted Image interpretation in medicine moved from 2D- to volumetric images.Cognitive load of students interpreting medical images affected.Human computer interaction and time to locate relevant area predict cognitive load.Insights useful for avoiding cognitive overload in medical curriculum.

RATIONALE AND OBJECTIVES: Radiology practice has become increasingly based on volumetric images (VIs), but tests in medical education still mainly involve two-dimensional (2D) images. We created a novel, digital, VI test and hypothesized that scores on this test would better reflect radiological anatomy skills than scores on a traditional 2D image test. To evaluate external validity we correlated VI and 2D image test scores with anatomy cadaver-based test scores. MATERIALS AND METHODS: In 2012, 246 medical students completed one of two comparable versions (A and B) of a digital radiology test, each containing 20 2D image and 20 VI questions. Thirty-three of these participants also took a human cadaver anatomy test. Mean scores and reliabilities of the 2D image and VI subtests were compared and correlated with human cadaver anatomy test scores. Participants received a questionnaire about perceived representativeness and difficulty of the radiology test. RESULTS: Human cadaver test scores were not correlated with 2D image scores, but significantly correlated with VI scores (r = 0.44, P < .05). Cronbach's α reliability was 0.49 (A) and 0.65 (B) for the 2D image subtests and 0.65 (A) and 0.71 (B) for VI subtests. Mean VI scores (74.4%, standard deviation 2.9) were significantly lower than 2D image scores (83.8%, standard deviation 2.4) in version A (P < .001). VI questions were considered more representative of clinical practice and education than 2D image questions and less difficult (both P < .001). CONCLUSIONS: VI tests show higher reliability, a significant correlation with human cadaver test scores, and are considered more representative for clinical practice than tests with 2D images.

Purpose To investigate knowledge and image interpretation skill development in residency by studying scores on knowledge and image questions on radiology tests, mediated by the training environment. Materials and Methods Ethical approval for the study was obtained from the ethical review board of the Netherlands Association for Medical Education. Longitudinal test data of 577 of 2884 radiology residents who took semiannual progress tests during 5 years were retrospectively analyzed by using a nonlinear mixed-effects model taking training length as input variable. Tests included nonimage and image questions that assessed knowledge and image interpretation skill. Hypothesized predictors were hospital type (academic or nonacademic), training hospital, enrollment age, sex, and test date. Results Scores showed a curvilinear growth during residency. Image scores increased faster during the first 3 years of residency and reached a higher maximum than knowledge scores (55.8% vs 45.1%). The slope of image score development versus knowledge question scores of 1st-year residents was 16.8% versus 12.4%, respectively. Training hospital environment appeared to be an important predictor in both knowledge and image interpretation skill development (maximum score difference between training hospitals was 23.2%; P < .001). Conclusion Expertise developed rapidly in the initial years of radiology residency and leveled off in the 3rd and 4th training year. The shape of the curve was mainly influenced by the specific training hospital. (©) RSNA, 2017 Online supplemental material is available for this article.

Rationale and objectives Current radiology practice increasingly involves interpretation of volumetric data sets. In contrast, most radiology tests still contain only 2D images. We introduced a new testing tool that allows for stack viewing of volumetric images in our undergraduate radiology program. We hypothesized that tests with volumetric CT-images enhance test quality, in comparison with traditional completely 2D image-based tests, because they might better reflect required skills for clinical practice. Materials and methods Two groups of medical students (n = 139; n = 143), trained with 2D and volumetric CT-images, took a digital radiology test in two versions (A and B), each containing both 2D and volumetric CT-image questions. In a questionnaire, they were asked to comment on the representativeness for clinical practice, difficulty and user-friendliness of the test questions and testing program. Students’ test scores and reliabilities, measured with Cronbach's alpha, of 2D and volumetric CT-image tests were compared. Results Estimated reliabilities (Cronbach's alphas) were higher for volumetric CT-image scores (version A: .51 and version B: .54), than for 2D CT-image scores (version A: .24 and version B: .37). Participants found volumetric CT-image tests more representative of clinical practice, and considered them to be less difficult than volumetric CT-image questions. However, in one version (A), volumetric CT-image scores (M 80.9, SD 14.8) were significantly lower than 2D CT-image scores (M 88.4, SD 10.4) (p

Rationale and Objectives In current practice, radiologists interpret digital images, including a substantial amount of volumetric images. We hypothesized that interpretation of a stack of a volumetric data set demands different skills than interpretation of two-dimensional (2D) cross-sectional images. This study aimed to investigate and compare knowledge and skills used for interpretation of volumetric versus 2D images. Materials and Methods Twenty radiology clerks were asked to think out loud while reading four or five volumetric computed tomography (CT) images in stack mode and four or five 2D CT images. Cases were presented in a digital testing program allowing stack viewing of volumetric data sets and changing views and window settings. Thoughts verbalized by the participants were registered and coded by a framework of knowledge and skills concerning three components: perception, analysis, and synthesis. The components were subdivided into 16 discrete knowledge and skill elements. A within-subject analysis was performed to compare cognitive processes during volumetric image readings versus 2D cross-sectional image readings. Results Most utterances contained knowledge and skills concerning perception (46%). A smaller part involved synthesis (31%) and analysis (23%). More utterances regarded perception in volumetric image interpretation than in 2D image interpretation (Median 48% vs 35%; z = −3.9; P