Identifying the Supports and Challenges of High School Latino English Language Learners

Two out of three English Language Learners (ELLs) graduate from secondary schools nationwide. Of the nearly five million ELLs in public schools, more than 70% of these students’ first language is Spanish. In order to understand and resolve this phenomena and in an effort to increase the number of graduates, this research examined what high school Latino ELLs identified as the major external and internal factors that support or challenge them on the graduation pathway. The study utilized a 32 quantitative and qualitative question student survey, as well as student focus groups. Both the survey and the focus groups were conducted in English and Spanish. The questions considered the following factors: 1) value of education; 2) expectations in achieving their long-term goals; 3) current education levels; 4) expectations before coming to the United States; 5) family obligations; and 6) future aspirations. The survey was administered to 159 Latino ELLs enrolled in grades 9-12. Research took place at three high schools that provide English for Speakers of Other Languages (ESOL) classes in a large school system in the Mid-Atlantic region. The three schools involved in the study have more than 1,500 ELLs. Two of the schools had large ESOL instructional programs, and one school had a comparatively smaller ESOL program. The majority of students surveyed were from El Salvador (72%) and Guatemala (12.6%). Using Qualtrics, an independent facilitator and a bilingual translator administered the online survey tool to the students during their ESOL classes. Two weeks later, the researcher hosted three follow-up focus groups, totaling 37 students from those students who took the survey. Each focus group was conducted at the three schools by the lead researcher and the translator. The purpose of the focus group was to obtain deeper insight on how secondary age Latino ELLs defined success in school, what they identified to be their support factors, and how previous and present experiences helped or hindered their goals. From the research findings, ten recommendations range from suggested policy updates to cross-cultural/equity training for students and staff; they were developed, stemming from the findings and what the students identified.

Structured Approaches for Exploring Interpersonal Relationships in Natural Language Text

Human relationships have long been studied by scientists from domains like sociology, psychology, literature, etc. for understanding people's desires, goals, actions and expected behaviors. In this dissertation we study inter-personal relationships as expressed in natural language text. Modeling inter-personal relationships from text finds application in general natural language understanding, as well as real-world domains such as social networks, discussion forums, intelligent virtual agents, etc. We propose that the study of relationships should incorporate not only linguistic cues in text, but also the contexts in which these cues appear. Our investigations, backed by empirical evaluation, support this thesis, and demonstrate that the task benefits from using structured models that incorporate both types of information. We present such structured models to address the task of modeling the nature of relationships between any two given characters from a narrative. To begin with, we assume that relationships are of two types: cooperative and non-cooperative. We first describe an approach to jointly infer relationships between all characters in the narrative, and demonstrate how the task of characterizing the relationship between two characters can benefit from including information about their relationships with other characters in the narrative. We next formulate the relationship-modeling problem as a sequence prediction task to acknowledge the evolving nature of human relationships, and demonstrate the need to model the history of a relationship in predicting its evolution. Thereafter, we present a data-driven method to automatically discover various types of relationships such as familial, romantic, hostile, etc. Like before, we address the task of modeling evolving relationships but don't restrict ourselves to two types of relationships. We also demonstrate the need to incorporate not only local historical but also global context while solving this problem. Lastly, we demonstrate a practical application of modeling inter-personal relationships in the domain of online educational discussion forums. Such forums offer opportunities for its users to interact and form deeper relationships. With this view, we address the task of identifying initiation of such deeper relationships between a student and the instructor. Specifically, we analyze contents of the forums to automatically suggest threads to the instructors that require their intervention. By highlighting scenarios that need direct instructor-student interactions, we alleviate the need for the instructor to manually peruse all threads of the forum and also assist students who have limited avenues for communicating with instructors. We do this by incorporating the discourse structure of the thread through latent variables that abstractly represent contents of individual posts and model the flow of information in the thread. Such latent structured models that incorporate the linguistic cues without losing their context can be helpful in other related natural language understanding tasks as well. We demonstrate this by using the model for a very different task: identifying if a stated desire has been fulfilled by the end of a story.