Understanding and Supporting Vocabulary Learners via Machine Learning on Behavioral and Linguistic Data

This dissertation presents various machine learning applications for predicting different cognitive states of students while they are using a vocabulary tutoring system, DSCoVAR. We conduct four studies, each of which includes a comprehensive analysis of behavioral and linguistic data and provides data-driven evidence for designing personalized features for the system. The first study presents how behavioral and linguistic interactions from the vocabulary tutoring system can be used to predict students' off-task states. The study identifies which predictive features from interaction signals are more important and examines different types of off-task behaviors. The second study investigates how to automatically evaluate students' partial word knowledge from open-ended responses to definition questions. We present a technique that augments modern word-embedding techniques with a classic semantic differential scaling method from cognitive psychology. We then use this interpretable semantic scale method for predicting students' short- and long-term learning. The third and fourth studies show how to develop a model that can generate more efficient training curricula for both human and machine vocabulary learners. The third study illustrates a deep-learning model to score sentences for a contextual vocabulary learning curriculum. We use pre-trained language models, such as ELMo or BERT, and an additional attention layer to capture how the context words are less or more important with respect to the meaning of the target word. The fourth study examines how the contextual informativeness model, originally designed to develop curricula for human vocabulary learning, can also be used for developing curricula for various word embedding models. We identify sentences predicted as low informative for human learners are also less helpful for machine learning algorithms. Having a rich understanding of user behaviors, responses, and learning stimuli is imperative to develop an intelligent online system. Our studies demonstrate interpretable methods with cross-disciplinary approaches to understand various cognitive states of students during learning. The analysis results provide data-driven evidence for designing personalized features that can maximize learning outcomes. Datasets we collected from the studies will be shared publicly to promote future studies related to online tutoring systems. And these findings can also be applied to represent different user states observed in other online systems. In the future, we believe our findings can help to implement a more personalized vocabulary learning system, to develop a system that uses non-English texts or different types of inputs, and to investigate how the machine learning outputs interact with students.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162999/1/sjnam_1.pd

Formação do significado perceptual das palavras através de interacção

Doutoramento em Engenharia InformáticaThis thesis addresses the problem of word learning in computational agents. The motivation behind this work lies in the need to support language-based communication between service robots and their human users, as well as grounded reasoning using symbols relevant for the assigned tasks. The research focuses on the problem of grounding human vocabulary in robotic agent’s sensori-motor perception. Words have to be grounded in bodily experiences, which emphasizes the role of appropriate embodiments. On the other hand, language is a cultural product created and acquired through social interactions. This emphasizes the role of society as a source of linguistic input. Taking these aspects into account, an experimental scenario is set up where a human instructor teaches a robotic agent the names of the objects present in a visually shared environment. The agent grounds the names of these objects in visual perception. Word learning is an open-ended problem. Therefore, the learning architecture of the agent will have to be able to acquire words and categories in an openended manner. In this work, four learning architectures were designed that can be used by robotic agents for long-term and open-ended word and category acquisition. The learning methods used in these architectures are designed for incrementally scaling-up to larger sets of words and categories. A novel experimental evaluation methodology, that takes into account the openended nature of word learning, is proposed and applied. This methodology is based on the realization that a robot’s vocabulary will be limited by its discriminatory capacity which, in turn, depends on its sensors and perceptual capabilities. An extensive set of systematic experiments, in multiple experimental settings, was carried out to thoroughly evaluate the described learning approaches. The results indicate that all approaches were able to incrementally acquire new words and categories. Although some of the approaches could not scale-up to larger vocabularies, one approach was shown to learn up to 293 categories, with potential for learning many more.Esta tese aborda o problema da aprendizagem de palavras em agentes computacionais. A motivação por trás deste trabalho reside na necessidade de suportar a comunicação baseada em linguagem entre os robôs de serviço e os seus utilizadores humanos, bem como suportar o raciocínio baseado em símbolos que sejam relevantes no contexto das tarefas atribuídas e cujo significado seja definido com base na experiência perceptiva. Mais especificamente, o foco da investigação é o problema de estabelecer o significado das palavras na percepção do robô através da interacção homemrobô. A definição do significado das palavras com base em experiências perceptuais e perceptuo-motoras enfatiza o papel da configuração física e perceptuomotora do robô. Entretanto, a língua é um produto cultural criado e adquirido através de interacções sociais. Isso destaca o papel da sociedade como fonte linguística. Tendo em conta estes aspectos, um cenário experimental foi definido no qual um instrutor humano ensina a um agente robótico os nomes dos objectos presentes num ambiente visualmente partilhado. O agente associa os nomes desses objectos à sua percepção visual desses objectos. A aprendizagem de palavras é um problema sem objectivo pré-estabelecido. Nós adquirimos novas palavras ao longo das nossas vidas. Assim, a arquitectura de aprendizagem do agente deve poder adquirir palavras e categorias de uma forma semelhante. Neste trabalho foram concebidas quatro arquitecturas de aprendizagem que podem ser usadas por agentes robóticos para aprendizagem e aquisição de novas palavras e categorias, incrementalmente. Os métodos de aprendizagem utilizados nestas arquitecturas foram projectados para funcionar de forma incremental, acumulando um conjunto cada vez maior de palavras e categorias. É proposta e aplicada uma nova metodologia da avaliação experimental que leva em conta a natureza aberta e incremental da aprendizagem de palavras. Esta metodologia leva em consideração a constatação de que o vocabulário de um robô será limitado pela sua capacidade de discriminação, a qual, por sua vez, depende dos seus sensores e capacidades perceptuais. Foi realizado um extenso conjunto de experiências sistemáticas em múltiplas situações experimentais, para avaliar cuidadosamente estas abordagens de aprendizagem. Os resultados indicam que todas as abordagens foram capazes de adquirir novas palavras e categorias incrementalmente. Embora em algumas das abordagens não tenha sido possível atingir vocabulários maiores, verificou-se que uma das abordagens conseguiu aprender até 293 categorias, com potencial para aprender muitas mais