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To have a good image contrast is an important issue in medical images. This paper introduces a feedback-type image processing architecture that can enhance image contrast without further digital image processing technique, e.g. histogram equalization. Compared with the conventional open-loop imaging system, the images derived by the proposed method has a full-range histogram without causing image distortion, and this is difficult to attain for open-loop imaging system.

When students are asked to examine their understanding individually or in small groups, information can become part of a feedback process that supports students’ learning. As designers of technology to support learning, we are interested in supporting such feedback processes in the context of guided inquiry instruction. This paper explores the potential of automatically associating mathematical descriptions with student submissions created with interactive diagrams. The paper focuses on the feedback processes that occur when students use the descriptions provided by the technology as resources for reflection and learning. We discuss the design of personal feedback processes where students reflect on and communicate their own learning, utilizing individually-reported multi-dimensional automatic analysis of their submissions in response to example-eliciting tasks. While there is much research and development work to be done, we consider mathematical descriptions of student work as an important contribution to broader developments in learning analytics.