Visualizations are nowadays appearing in popular media and are used everyday in the workplace. This democratisation of visualization challenges educators to develop effective learning strategies, in order to train the next generation of creative visualization specialists. There is high demand for skilled individuals who can analyse a problem, consider alternative designs, develop new visualizations, and be creative and innovative. Our three-stage framework, leads the learner through a series of tasks, each designed to develop different skills necessary for coming up with creative, innovative, effective, and purposeful visualizations. For that, we get the learners to create an explanatory visualization of an algorithm of their choice. By making an algorithm choice, and by following an active-learning and project-based strategy, the learners take ownership of a particular visualization challenge. They become enthusiastic to develop good results and learn different creative skills on their learning journey.

OBJECTIVE: To examine the relationships among different performance scores for each of four diagnostic decision support systems (DDSSs). DESIGN: Intercorrelations among seven performance scores on a set of 105 cases for each of four DDSSs (DXplain, Iliad, Meditel, QMR) were computed. METHODS: The performance scores for each case reflected: 1) presence or absence of the case diagnosis in the DDSS knowledge base; 2) presence or absence of the correct diagnosis anywhere on the DDSS diagnosis list; 3) presence or absence of the correct diagnosis in the top ten diagnoses; 4) relevance of the DDSS diagnosis list; 5) comprehensiveness of the DDSS diagnosis list; 6) whether the DDSS suggested additional diagnoses to the experts' list; and 7) the length of the DDSS diagnosis list. RESULTS: For all DDSSs, the two Correct Diagnosis scores (top ten and total list) were significantly related: 1) to the presence of the correct diagnosis in the knowledge base; 2) to the Comprehensiveness score; and 3) to each other. There were significant differences among the four DDSSs on the magnitude and/or direction of the relationships between: 1) the two Correct Diagnosis scores; 2) the Relevance and Length scores; and 3) the Relevance and Additional Diagnoses scores. CONCLUSION: The production of a correct diagnosis for a given case is not related to the number of diagnoses suggested by the DDSS and, across different DDSSs, is not consistently related to other measures of performance. These data indicate that multiple measures are needed to fully describe the performance of a DDSS.