Heritage Language Maintenance and Biliteracy Development for Immigrants' Children: A Study of Chinese Immigrants' Family Language Policy and Biliteracy Practices

This study reports on research that examines the family language policy (FLP) and biliteracy practices of middle-class Chinese immigrant families in a metropolitan area in the southwest of the U.S. by exploring language practices pattern among family members, language and literacy environment at home, parents’ language management, parents’ language attitudes and ideologies, and biliteracy practices. In this study, I employed mixed methods, including survey and interviews, to investigate Chinese immigrant parents’ FLP, biliteracy practices, their life stories, and their experience of raising and nurturing children in an English-dominant society. Survey questionnaires were distributed to 55 Chinese immigrant parents and interviews were conducted with five families, including mothers and children. One finding from this study is that the language practices pattern at home shows the trend of language shift among the Chinese immigrants’ children. Children prefer speaking English with parents, siblings, and peers, and home literacy environment for children manifests an English-dominant trend. Chinese immigrant parents’ language attitudes and ideologies are largely influenced by English-only ideology. The priority for learning English surpasses the importance of Chinese learning, which is demonstrated by the English-dominant home literacy practices and an English-dominant language policy. Parents invest more in English literacy activities and materials for children, and very few parents implement Chinese-only policy for their children. A second finding from this study is that a multitude of factors from different sources shape and influence Chinese immigrants’ FLP and biliteracy practices. The factors consist of family-related factors, social factors, linguistic factors, and individual factors. A third finding from this study is that a wide variety of strategies are adopted by Chinese immigrant families, which have raised quite balanced bilingual children, to help children maintain Chinese heritage language (HL) and develop both English and Chinese literacy. The close examination and comparison of different families with English monolingual children, with children who have limited knowledge of HL, and with quite balanced bilingual children, this study discovers that immigrant parents, especially mothers, play a fundamental and irreplaceable role in their children’s HL maintenance and biliteracy development and it recommends to immigrant parents in how to implement the findings of this study to nurture their children to become bilingual and biliterate. Due to the limited number and restricted area and group of participant sampling, the results of this study may not be generalized to other groups in different contexts.

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.

Language-based Techniques for Practical and Trustworthy Secure Multi-party Computations

Secure Multi-party Computation (MPC) enables a set of parties to collaboratively compute, using cryptographic protocols, a function over their private data in a way that the participants do not see each other's data, they only see the final output. Typical MPC examples include statistical computations over joint private data, private set intersection, and auctions. While these applications are examples of monolithic MPC, richer MPC applications move between "normal" (i.e., per-party local) and "secure" (i.e., joint, multi-party secure) modes repeatedly, resulting overall in mixed-mode computations. For example, we might use MPC to implement the role of the dealer in a game of mental poker -- the game will be divided into rounds of local decision-making (e.g. bidding) and joint interaction (e.g. dealing). Mixed-mode computations are also used to improve performance over monolithic secure computations. Starting with the Fairplay project, several MPC frameworks have been proposed in the last decade to help programmers write MPC applications in a high-level language, while the toolchain manages the low-level details. However, these frameworks are either not expressive enough to allow writing mixed-mode applications or lack formal specification, and reasoning capabilities, thereby diminishing the parties' trust in such tools, and the programs written using them. Furthermore, none of the frameworks provides a verified toolchain to run the MPC programs, leaving the potential of security holes that can compromise the privacy of parties' data. This dissertation presents language-based techniques to make MPC more practical and trustworthy. First, it presents the design and implementation of a new MPC Domain Specific Language, called Wysteria, for writing rich mixed-mode MPC applications. Wysteria provides several benefits over previous languages, including a conceptual single thread of control, generic support for more than two parties, high-level abstractions for secret shares, and a fully formalized type system and operational semantics. Using Wysteria, we have implemented several MPC applications, including, for the first time, a card dealing application. The dissertation next presents Wys*, an embedding of Wysteria in F*, a full-featured verification oriented programming language. Wys* improves on Wysteria along three lines: (a) It enables programmers to formally verify the correctness and security properties of their programs. As far as we know, Wys* is the first language to provide verification capabilities for MPC programs. (b) It provides a partially verified toolchain to run MPC programs, and finally (c) It enables the MPC programs to use, with no extra effort, standard language constructs from the host language F*, thereby making it more usable and scalable. Finally, the dissertation develops static analyses that help optimize monolithic MPC programs into mixed-mode MPC programs, while providing similar privacy guarantees as the monolithic versions.

SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING

Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP.