Instruction facilitating the abstraction and transfer processes necessary for deep conceptual understanding is notoriously difficult. Individual differences in learning strategies are one possible explanation for variation in transfer outcomes. Prior work has shown that rule-abstraction learning strategies facilitate knowledge transfer better than…

We are currently in a century of data where massive amount of data are collected and processed every day, and machine learning plays a critical role in automatically processing the data and mining useful information from it for making decisions. Despite the wide and successful applications of machine learning in different fields, the robustness of…

To meet the national and international call for creative and innovative engineers, many engineering departments and classrooms are striving to create more authentic learning spaces where students are actively engaging with design and innovation activities. For example, one model for teaching innovation is Innovation-Based Learning (IBL) where…

The aim of this dissertation is to investigate the mechanisms involved in phonetic learning of an unfamiliar accent, focusing on understanding what processes underlie changes to phonetic category structure, how such learning affects subsequent online lexical processing, and whether the same mechanisms that underlie learning for a single speaker…

Dialogue act (DA) classification plays an important role in understanding, interpreting and modeling dialogue. Dialogue acts (DAs) represent the intended meaning of an utterance, which is associated with the illocutionary force (or the speaker's intention), such as greetings, questions, requests, statements, and agreements. In natural language…

This research captures the experiences of high school graduates who were labeled English Learners while they were enrolled in a public high school in the greater Sacramento area of California. The study examined what these bilingual high school graduates report as beneficial and detrimental experiences faced while completing the course…

An ongoing debate in phonology concerns the extent to which the phonological typology is shaped by synchronic learning biases. The two best-studied types of synchronic bias are complexity bias, a bias against formally complex patterns, and substantive bias, a bias against phonetically unnatural patterns. While most previous work has focused on…

This dissertation examines the question of how phonological alternations are learnt. In constraint-based models of phonological learning, it is hypothesized that prior learning of phonotactics from the lexicon facilitates the learning of alternations. While this is an influential assumption, the empirical evidence for it is equivocal. In this…

This dissertation begins with the observation of a typological asymmetry within phonological patterns related to phonetic naturalness. Patterns that are rooted within existing tendencies of perception and/or production--in other words, patterns that are phonetically "natural"--are common in phonological typology and seen in a variety of…

This research examines issues regarding model estimation and robustness in the use of Bayesian Inference Networks (BINs) for measuring Learning Progressions (LPs). It provides background information on LPs and how they might be used in practice. Two simulation studies are performed, along with real data examples. The first study examines the case…

The Virtual Chemical Vapor Deposition (CVD) Process Development Project provides the context for the two areas of the research presented in this dissertation. The first area, generally referred to as feedback in this dissertation, focuses on student learning and the interactions of students and instructors that take place in the project,…

Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

Representational fluency (RF) includes an ability to interpret, create, move within and among, and connect tool-based representations of mathematical objects. Taken as an indicator of conceptual understanding, there is a need to better support school algebra students' RF in learning environments that utilize both computer algebra systems…

A basic and classical assumption in the machine learning research area is "randomness assumption" (also known as i.i.d assumption), which states that data are assumed to be independent and identically generated by some known or unknown distribution. This assumption, which is the foundation of most existing approaches in the literature, simplifies…

In the preparation and education of civil engineers it is essential to both increase student knowledge of the world (conceptual understanding), but also to establish and develop new ways of thinking (epistemology). Both of these processes of change can be considered learning, but they are vastly different in the time, energy and resources they…