Artificial intelligence research, as a part of computer science, has produced a variety of programs of experimental and applications interest: programs for scientific inference, chemical synthesis, planning robot control, extraction of meaning from English sentences, speech understanding, interpretation of visual images, and so on. The symbolic manipulation techniques used in artificial intelligence provide a framework for analyzing and coding the knowledge base of a problem independently of an algorithmic implementation. A possible application of artificial intelligence methodology to protein crystallography is described.
It is commonly accepted that there are various teaching and learning styles, and that different combinations are more effective depending on the participants and environment. Moreover, learning which involves more than listening, such as teamwork and labwork, will benefit comprehension and retention. Most materials textbooks do not have an abundance of these educational activities. This paper introduces a cooperative learning activity supporting the topic of micrographs and microstructural characterization. It takes at least half an hour, and is flexible in scope of content. It starts with a short introduction on outcomes, and then has the students discuss terminology with their neighbors. After another short discussion, the students pair off and follow a procedure for verbally describing a known microstructure, and having their partner listen and recreate it. To conclude the activity, the class has a discussion of the student's experiences. A critical thinking question is provided as an assessment tool.
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