Mind vs. Machine: A comparison between human analysis and autonomous natural language processing in the classification of learning outcomes
School of Arts and Humanities
This paper reports on research and development of an automated system to classify instructional activities to help assure student achievement. Using the Instructional Activity Matrix, a synthesized taxonomy to identify types of knowledge and levels of cognitive processing, human classification of high school curriculum learning outcomes was compared to the prototype system. Findings pointed to areas where human analysis and natural language processing had unique abilities and flaws, indicating the potential for such systems as tools for teachers in calibrating their understandings when developing learning tasks for students.