Proceedings from the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. Computer science and technology education should provide not only a strong theoretical foundation, but also problem solving, and communication and teamwork skills to prepare the students for careers. Including projects in curricula is a norm in many disciplines. However, projects are generally individual or based on small teams (two to five members). This paper presents my approach to teaching a capstone undergraduate computer technology course at the British Columbia Institute of Technology (BCIT) in the Computer System Technology (CST) Program in which a large class of students (maximum 22), organized into small teams work together and apply Agile software development practices to design, implement, integrate and test a large project. This model provides students with unique learning opportunities and experiences, as well as improving their soft skills, engagement and motivation., Peer reviewed, Conference paper, Published.
Taken from: Basic technical mathematics : with calculus, metric version, seventh edition by Allyn J. Washington, Logic and computer design fundamentals, second edition, updated by M. Morris Mano and Charles R. Kime. Custom edition for British Columbia Institute of Technology., Book, Published., Peer reviewed
Proceedings of the 18th IEEE Symposium on Logic in Computer Science (LICS-03), Ottawa, ON, 2003. The spectrum of a first-order sentence is the set of cardinalities of its finite models. Relatively little is known about the subclasses of spectra that are obtained by looking only at sentences with a specific signature. In this paper, we study natural subclasses of spectra and their closure properties under simple subdiagonal functions. We show that many natural closure properties turn out to be equivalent to the collapse of potential spectrum hierarchies. We prove all of our results using explicit transformations on first-order structures., Conference paper, Published.
Proceedings from the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. Since computing education began, we have sought to learn why students struggle in computer science and how to identify these at-risk students as early as possible. Due to the increasing availability of instrumented coding tools in introductory CS courses, the amount of direct observational data of student working patterns has increased significantly in the past decade, leading to a flurry of attempts to identify at-risk students using data mining techniques on code artifacts. The goal of this work is to produce a systematic literature review to describe the breadth of work being done on the identification of at-risk students in computing courses. In addition to the review itself, which will summarize key areas of work being completed in the field, we will present a taxonomy (based on data sources, methods, and contexts) to classify work in the area., Peer reviewed, Conference paper, Published.