Active learning provides an engaging alternative to the typical onesided lecture in which students passively listen to the instructor. In this paper, I discuss a collection of active learning strategies for designing a course in Information Visualization. The primary strategy, design games, consists of a goal-oriented session during class in which students are given a task and directed to solve...

The thesis aims to find an efficient algorithm for solving parity games. Parity games are graph-based, 0-sum, 2-person games with infinite plays. It is known that these games are determined: all nodes in these games are won by exactly one player. Solving parity games is equivalent to the model checking problem of modal mu-calculus; an efficient solution has important implications to program ver...

Autism can be a debilitating condition that affects a person's personal and social affairs throughout their lifetime. With 1 in 110 people diagnosed with an Autism Spectrum Disorder (ASD) [49], it is important that we develop assistive and learning technologies to help them achieve their potential. In this work I describe the development of a new technologymediated therapeutic game, Frame It, a...

Friedrich Froebel, the German educator and founder of the Kindergarten Movement, developed a series of play materials including geometric building blocks and pattern activity blocks designed to teach children about forms and relationships found in nature. Froebel’s notions about using activity and play in preschool education complement many principles of early childhood education used in contem...

Video games are supposed to provide a good learning environment. We study a large-scale use (260 students and 20 teachers) of Prog&Play, a serious game specially designed to teach computer programming fundamentals. In earlier quantitative studies, we found, through a students’ motivation survey, that the students’ interest for Prog&Play was not only related to the intrinsic game quality, it was...

We present the discriminative recurrent sparse auto-encoder model, comprising a recurrent encoder of rectified linear units, unrolled for a fixed number of iterations, and connected to two linear decoders that reconstruct the input and predict its supervised classification. Training via backpropagation-through-time initially minimizes an unsupervised sparse reconstruction error; the loss functi...