Document Classification

We present an overview of the document classification process and present research conducted against the newly constructed SBIR-STTR corpus. Specifically, the current methods in use for annotation, corpus construction, feature construction, feature weighting, and classifier algorithms are surveyed. We introduce a new dataset derived from public data downloaded from sbir.gov and the Text Annotation Toolkit (TAT) 1 for use in classification research. TAT is a collection of independent components packaged together into one open source software application. TAT was engineered to support the document classification process and workflow. Tracking of changes in a working corpus, saving data used in the training of classifiers to ensure reproducibility, and providing a mechanism for interacting with copyright protected corpora are all fundamental issues that TAT addresses. TAT is built using the robust Open IDE [35] framework that allows plug-in developers access to standard well tested libraries saving years of development time. The main goal of TAT is to minimize the labor intensive process of creating labelled data that can be used to train, test, and deploy machine learning models for automated text annotation. Additionally, TAT allows researchers an easy method to automatically reproduce prior results. The toolkit can facilitate the annotation of text using different machine learning packages as well as corpora with different metadata specifications. 1TAT is freely available for download from trac.boisestate.ed

Evaluating the Benefits of Team-Based Learning in a Systems Programming Class

In this Research-to-Practice Full Paper, we present the results of adopting Team-Based Learning (TBL) for teaching a Sophomore-level Systems Programming course. The goal of TBL is to provide opportunities for students to apply their knowledge in the classroom to solve problems rather than just covering content. Based on the performance of the students in the course taught with TBL, we found that TBL had a statistically significant impact on student performance in 2 of the 5 programming assignments. Additionally, the end-of-semester student survey indicated that 88% of the students said that the team-based learning activities helped them understand the material better. Students mentioned that they felt like they belonged in Computer Science (fostering a sense of community in large classrooms) and frequently studied with some of their team members for the course assignments. Compared to a previous offering of the course that was purely lecture-based, the class as a whole received higher final grades and performed better on all of the programming assignments