Student and Teacher Learning:

Teacher-Student Curriculum Learning

We propose Teacher-Student Curriculum Learning (TSCL), a framework for automatic curriculum learning, where the Student tries to learn a complex task and the Teacher automatically chooses subtasks from a given set for the Student to train on. We describe a family of Teacher algorithms that rely on the intuition that the Student should practice more those tasks on which it makes the fastest prog...

Teacher-Student Framework: A Reinforcement Learning Approach

We propose a reinforcement learning approach to learning to teach. Following Torrey and Taylor’s framework [18], an agent (the “teacher”) advises another one (the “student”) by suggesting actions the latter should take while learning a specific task in a sequential decision problem; the teacher is limited by a “budget” (the number of times such advice can be given). Our approach assumes a princ...

Teacher Training, Teacher Quality, and Student Achievement

We study the effects of various types of education and training on the ability of teachers to promote student achievement. Previous studies on the subject have been hampered by inadequate measures of teacher training and difficulties addressing the non-random selection of teachers to students and of teachers to training. We address these issues by estimating models that include detailed measure...

Improving Mathematics Teacher Practice and Student Learning through Professional Development

We propose to study how to provide, on a large scale, the professional development and continuing support teachers need to improve student achievement in mathematics. Over five years of a longitudinal study, we will examine how the use of specific research-based instructional strategies in the classroom— supported by professional development and highly rated curriculum materials—relate to lasti...

Law Learning, Teacher-Student Relations, and the Legal Profession