By Charles Lang, Columbia University 

 

 

Regardless of the specific algorithmic methodology employed, building an intelligent agent involves the reduction of complex data into a lower dimensional representation that the machine can use to make predictions about the world. For an autonomous vehicle the machine must reduce complex sensor inputs into representations about the physical surroundings. Within education technology applications this means creating representations of students from student data. As these models become more sophisticated and begin modeling cognitive ability and other psycho-social constructs it is important that we ask critical questions about the use and meaning of these machine representations within educational contexts. Machine representations have substantial similarities with older data representations of students such as grades, standardized tests and rubric scoring, but differ in one important way, the number of dimensions that inferences may be based on. High dimensional representations may create problems for educational organizations (for example, they are not human interpretable) while at the same time the new representations do not solve any of the well documented social, cultural, political and pedagogical tensions inherent in older formats.

 

 

Charles Lang is Visiting Assistant Professor in Learning Analytics at Teachers College, Columbia University where he is co-Director of the Masters of Science in Learning Analytics. His research interests center on the use of big data in education and the role of online assessment data in understanding student learning. Specifically, Charles studies innovative methodologies for assessing student learning (predictive analytics, personalization and graphical models of knowledge) and how these new tools can be incorporated into instructional workflow.