LEARNING CURVE DISAGGREGATION BY STUDENT MASTERY

摘要

Techniques are described for disaggregating learning curves by student mastery for refining and accurately evaluating automated tutoring models. A method comprises receiving performance data for users logging whether a correct response was provided for each opportunity to use a particular skill in a tutoring system, determining a plurality of subpopulations from the users by using the performance data to group by number of opportunities needed for the particular skill to reach a mastery threshold, creating disaggregated learning curves for each of the plurality of subpopulations that map performance opportunities to percentages correct, and evaluating the disaggregated learning curves to identify a suitable adaptation for the tutoring system. The suitable adaptation may then be carried out and may include sending a notification of portions of the tutoring system that need attention and/or adjusting parameters of the tutoring system for a projected learning progression of a particular user.

主权项

1. A method comprising:
receiving performance data for a plurality of users, the performance data logging whether a correct response or an incorrect response was provided for each opportunity to use a particular skill in a tutoring system; determining a plurality of subpopulations from the plurality of users by using the performance data to assign the plurality of users to groups, wherein each user of the plurality of users is assigned to a group based, at least in part, on number of opportunities needed for the user to reach a mastery threshold for the particular skill in the tutoring system; creating disaggregated learning curves for each of the plurality of subpopulations by mapping said each opportunity to use the particular skill to a value based, at least in part, on a number of users in each subpopulation that provided the correct response for said each opportunity; evaluating the disaggregated learning curves to identify a suitable adaptation for the tutoring system; wherein the method is performed by one or more computing devices.