The explication of quality standards in self-evaluation

Education aiming at students’ competence development asks for new assessment methods. The quality of these methods needs to be assured using adapted quality criteria and accompanying standards. As such standards are not widely available, this study sets out to examine what level of compliance with quality criteria stakeholders consider satisfactory. Two professional education programmes specified the implicit standards they applied in a self-evaluation procedure designed to evaluate the quality of their Competence Assessment Programs (CAPs). They specified similar cut-off scores, but different descriptive standards. Analysis revealed that this was due to theIR experience with competence-based education and the quality of their own CAP, but influences of the selected method and the understanding of the quality criteria were also found. As such, the specified standards are local, but meaningful for the programmes’ quality assurance. Implications for self-evaluation and standard-setting procedures are discussed

Reflection during portfolio-based conversations

This study aims to explore the relationship between the occurrence of reflection (and non-reflection) and thinking activities (e.g., orientating, selecting, analysing) during portfolio-based conversations. Analysis of 21 transcripts of portfolio-based conversations revealed that 20% of the segments were made up of reflection (content reflection (6%), process reflection (10%), and premise reflection (4%)). The thinking activities comparing, analysing and concluding occurred significantly more often during reflection than during non-reflection. Orientating on the task, selecting and describing, occurred significantly less often during reflection. The outcomes show that the occurrence of certain thinking activities can be an indication of reflection

Overview and State-of-the-Art of Uncertainty Visualization

International audienceThe goal of visualization is to effectively and accurately communicate data. Visualization research has often overlooked the errors and uncertainty which accompany the scientific process and describe key characteristics used to fully understand the data. The lack of these representations can be attributed, in part, to the inherent difficulty in defining, characterizing, and controlling this uncertainty, and in part, to the difficulty in including additional visual metaphors in a well designed, potent display. However, the exclusion of this information cripples the use of visualization as a decision making tool due to the fact that the display is no longer a true representation of the data. This systematic omission of uncertainty commands fundamental research within the visualization community to address, integrate, and expect uncertainty information. In this chapter, we outline sources and models of uncertainty, give an overview of the state-of-the-art, provide general guidelines, outline small exemplary applications, and finally, discuss open problems in uncertainty visualization