BCIT Citations Collection | BCIT Institutional Repository

BCIT Citations Collection

Improving students' engagement with large-team software development projects
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.
Predicting academic performance
Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work., Peer reviewed, Conference paper, Published.
Taxonomizing features and methods for identifying at-risk students in computing courses
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.