Cross Language Text Classification via Subspace Co-Regularized Multi-View Learning

In many multilingual text classification problems, the documents in different languages often share the same set of categories. To reduce the labeling cost of training a classification model for each individual language, it is important to transfer the label knowledge gained from one language to another language by conducting cross language classification. In this paper we develop a novel subspace co-regularized multi-view learning method for cross language text classification. This method is built on parallel corpora produced by machine translation. It jointly minimizes the training error of each classifier in each language while penalizing the distance between the subspace representations of parallel documents. Our empirical study on a large set of cross language text classification tasks shows the proposed method consistently outperforms a number of inductive methods, domain adaptation methods, and multi-view learning methods.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

SURVEY DEVELOPMENT AND VALIDATION: STUDENT SELF EXPECTATIONS OF THE FIRST-YEAR COLLEGE EXPERIENCE SURVEY (THE SE-FYE SCALE)

First year at a university is often a transitional stage for students where they have opportunities to grow and develop in various areas and get familiar with a collegiate environment. Understanding what first-year students expect to navigate and succeed in their first year at the university is crucial. Their expectations reflect how well they prepared for university, affecting their behaviors and eventually influencing their intentions to continue higher education. One of the primary purposes of this study was to design a survey to measure student self-expectations of the first-year college experience (the SE-FYE scale) by applying a structured survey development process and consulting various sources. Moreover, this study aimed to validate and improve the items of the original SE-FYE survey based on examined psychometric properties by applying Rasch measurement analysis. The target population of this study was first-year undergraduate students at a public four-year university in the United States. The sample consisted of 40 first-year students who responded as the pre-group at the beginning of Fall 2022 and 21 first-year students who responded close to the end of the semester as the post-group. The final SE-FYE scale was formed after several modifications, including 22 items to measure five primary variables: student self-expectations for their first year, self-expectations of academic readiness, self-expectations of academic engagement, self-expectations of personal development, and expectations about career preparation. Student persistence and characteristic items were included in the survey. The findings suggested that the final scale items established a reasonable unidimensionality, fit, separation, reliability, and category functionality. Interpretations and suggestions of the results were made from the perspectives of survey development and student success in higher education