Reviews approaches to machine learning (development of techniques to automate acquisition of new information, skills, and ways of organizing existing information) in symbolic domains. Four categorical tasks addressed in machine learning literature are examined: learning from examples, learning search heuristics, learning by observation, and…

Describes an exploratory study on the acquisition and use of knowledge representation skills and structures by sixth-graders, supported by a computer-based learning environment. Results indicated that symbolic structures can be taught successfully, and that students using them in the context of instructional tasks perform at the higher band of…

Twenty-eight hearing college students were trained on 30 signs from American Sign Language that had been classified as iconic, opaque, or abstract. Students learned and retained iconic signs better than opaque or abstract. A videotaped presentation mode produced greater consistency in scores than computer-assisted instruction. (Author/JDD)

Computer-based learning environments commonly comprise symbolic as well as static and dynamic pictorial representations, frequently combined with the possibility of modifying them interactively. While multiple, dynamic and interactive external representations have the potential to improve learning in various ways, they also place specific demands…