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A main goal of forensic medicine is to document and to translate medical findings to a language and/or visualization that is readable and understandable for judicial persons and for medical laymen. Therefore, in addition to classical methods, scientific cutting-edge technologies can and should be used. Through the use of the Forensic, 3-D/CAD-supported Photogrammetric method the documentation of so-called "morphologic fingerprints" has been realized. Forensic, 3-D/CAD-supported Photogrammetry creates morphologic data models of the injury and of the suspected injury-causing instrument allowing the evaluation of a match between the injury and the instrument. In addition to the photogrammetric body surface registration, the radiological documentation provided by a volume scan (i.e., spiral, multi-detector CT, or MRI) registers the sub-surface injury, which is not visible to Photogrammetry. The new, combined method of merging Photogrammetry and Radiology data sets creates the potential to perform many kinds of reconstructions and postprocessing of (patterned) injuries in the realm of forensic medical case work. Using this merging method of colored photogrammetric surface and gray-scale radiological internal documentation, a great step towards a new kind of reality-based, high-tech wound documentation and visualization in forensic medicine is made. The combination of the methods of 3D/CAD Photogrammetry and Radiology has the advantage of being observer-independent, non-subjective, non-invasive, digitally storable over years or decades and even transferable over the web for second opinion.

Lively research on self-regulated learning has produced a great number of models of self-regulated learning competence and it is still a challenge to integrate them within a single coherent framework. However, such a framework is necessary for, among other reasons, the development of valid assessment methods. We argue that one common characteristic of all models is that they consider the competence to make solid comparisons as a key competence of self-regulated learning. However, the kind of comparisons and the kind of standards used for these comparisons differ between models. The same is true for assessment methods. Valid assessment methods also have implemented comparisons and they also differ concerning the kind of comparison and the kind of standards used for assessment. In order to categorize both, models as well as assessment methods, we propose to distinguish between component models and process models of self-regulated learning. Component models imply the use of offline standards for assessment whereas process models imply the use of online standards. Both offline and online standards can be either quantitative or qualitative. We show that using qualitative standards leads to a higher validity of the assessment than using quantitative standards. This advantage of qualitative standards can be shown for both offline standards as well as online standards.

Training interventions for self-regulated learning foster the use of strategies and skills as well as their transfer to new learning tasks. Because cognitive strategies or motivation regulation strategies are task-specific, their transfer is limited. In contrast, metacognitive skills are task-general and transferable to a wide variety of learning tasks. Questions arise, therefore, as to whether students transfer metacognitive skills spontaneously and how to support metacognitive skill transfer. Previous research shows that hybrid training, which addresses both metacognitive skills and cognitive strategies, supports near transfer. However, it is not clear whether hybrid training also fosters far transfer of metacognitive skills. In investigating this research question, 233 fifth-grade students were randomly assigned to six different conditions: two hybrid-training conditions (metacognitive skills and one out of two cognitive strategies), two non-hybrid training conditions (“only” one out of two cognitive strategies), and two control training conditions (neither metacognitive skills nor cognitive strategies). After 15 weeks of training, transfer of metacognitive skills to learning tasks similar to training tasks (near transfer) was tested. In the following 15 weeks, all students received a second, non-hybrid training involving a new cognitive strategy. Far transfer of metacognitive skills to the new cognitive strategy was tested afterward. The results show that hybrid training, compared to non-hybrid and control training, improved both students’ near and far transfer of metacognitive skills. Moreover, cognitive strategy use increased in at least one of the hybrid-training conditions. However, since the level of metacognitive skills use remained low, further means to support transfer are discussed.