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

Predicting Off-task Behaviors in an Adaptive Vocabulary Learning System

ABSTRACT In many studies, engagement has been considered as an important aspect of effective learning. Retaining student engagement is thus an important goal in intelligent tutoring systems (ITS). My current studies with collaborators on Dynamic Support of Contextual Vocabulary Acquisition for Reading (DSCoVAR) include building prediction models for students' off-task behaviors. By extracting linguistically meaningful features and historical context information from interaction log data, these studies illustrate how some types of off-task behavior can be modeled from behavioral logs. The results of this research contribute to existing studies by providing examples of how to extract behavioral measures and predict off-task behaviors within a vocabulary learning system. Identifying off-task behaviors can improve students' learning by providing personalized learning materials: for example, off-task behavior classifiers can be used to achieve more accurate predictions of the student's vocabulary mastery level, which in turn can improve the system's adaptive performance. Toward our goal of developing highly effective personalized vocabulary learning systems, this research would benefit from expert feedback on issues that include: principled approaches for adaptive assessment and feedback in a vocabulary learning system; and alternative methods for defining and generating off-task labels

An Attention-Based Model for Predicting Contextual Informativeness and Curriculum Learning Applications

Both humans and machines learn the meaning of unknown words through contextual information in a sentence, but not all contexts are equally helpful for learning. We introduce an effective method for capturing the level of contextual informativeness with respect to a given target word. Our study makes three main contributions. First, we develop models for estimating contextual informativeness, focusing on the instructional aspect of sentences. Our attention-based approach using pre-trained embeddings demonstrates state-of-the-art performance on our single-context dataset and an existing multi-sentence context dataset. Second, we show how our model identifies key contextual elements in a sentence that are likely to contribute most to a reader's understanding of the target word. Third, we examine how our contextual informativeness model, originally developed for vocabulary learning applications for students, can be used for developing better training curricula for word embedding models in batch learning and few-shot machine learning settings. We believe our results open new possibilities for applications that support language learning for both human and machine learner

Serially Connected Micro Amorphous Silicon Solar Cells for Compact High-Voltage Sources

We demonstrate a compact amorphous silicon (a-Si) solar module to be used as high-voltage power supply. In comparison with the organic solar module, the main advantages of the a-Si solar module are its compatibility with photolithography techniques and relatively high power conversion efficiency. The open circuit voltage of a-Si solar cells can be easily controlled by serially interconnecting a-Si solar cells. Moreover, the a-Si solar module can be easily patterned by photolithography in any desired shapes with high areal densities. Using the photolithographic technique, we fabricate a compact a-Si solar module with noticeable photovoltaic characteristics as compared with the reported values for high-voltage power supplies

Characterization and Zoonotic Potential of Uropathogenic Escherichia coli Isolated from Dogs

The aim of this study was to investigate the characteristics of canine uropathogenic Escherichia coli (UPEC) and the interaction between canine UPEC and human bladder epithelial cells. Ten E. coli isolates collected from dogs with cystitis were analyzed for antimicrobial resistance patterns, the presence of virulence factors, and biofilm formation. The ability of these isolates to induce cytotoxicity, invade human bladder epithelial cells, and stimulate an immune response was also determined. We observed a high rate of antimicrobial resistance among canine UPEC isolates. All virulence genes tested (including adhesins, iron acquisition, and protectin), except toxin genes, were detected among the canine UPEC isolates. We found that all isolates showed varying degrees of biofilm formation (mean, 0.26; range, 0.07 to 0.82), using a microtiter plate assay to evaluate biofilm formation by the isolates. Cytotoxicity to human bladder epithelial cells by the canine UPEC isolates increased in a time-dependent manner, with a 56.9% and 36.1% reduction in cell viability compared with the control at 6 and 9 h of incubation, respectively. We found that most canine UPEC isolates were able to invade human bladder epithelial cells. The interaction between these isolates and human bladder epithelial cells strongly induced the production of proinflammatory cytokines such as IL-6 and IL-8. We demonstrated that canine UPEC isolates can interact with human bladder epithelial cells, although the detailed mechanisms remain unknown. The results suggest that canine UPEC isolates, rather than dogspecific pathogens, have zoonotic potential.OAIID:oai:osos.snu.ac.kr:snu2013-01/102/0000051105/2SEQ:2PERF_CD:SNU2013-01EVAL_ITEM_CD:102USER_ID:0000051105ADJUST_YN:NEMP_ID:A077262DEPT_CD:551CITE_RATE:1.381FILENAME:2013-3 jmb 23(3)422-429.pdfDEPT_NM:수의학과EMAIL:[email protected]_YN:YCONFIRM:

An auxin-mediated ultradian rhythm positively influences root regeneration via EAR1/EUR1 in Arabidopsis

Ultradian rhythms have been proved to be critical for diverse biological processes. However, comprehensive understanding of the short-period rhythms remains limited. Here, we discover that leaf excision triggers a gene expression rhythm with ~3-h periodicity, named as the excision ultradian rhythm (UR), which is regulated by the plant hormone auxin. Promoter–luciferase analyses showed that the spatiotemporal patterns of the excision UR were positively associated with de novo root regeneration (DNRR), a post-embryonic developmental process. Transcriptomic analysis indicated more than 4,000 genes including DNRR-associated genes were reprogramed toward ultradian oscillation. Genetic studies showed that EXCISION ULTRADIAN RHYTHM 1 (EUR1) encoding ENHANCER OF ABSCISIC ACID CO-RECEPTOR1 (EAR1), an abscisic acid signaling regulator, was required to generate the excision ultradian rhythm and enhance root regeneration. The eur1 mutant exhibited the absence of auxin-induced excision UR generation and partial failure during rescuing root regeneration. Our results demonstrate a link between the excision UR and adventitious root formation via EAR1/EUR1, implying an additional regulatory layer in plant regeneration

Funding structures for Build-to-Suit developments in Brazil: advantages and risks

Empreendimentos build-to-suit são aqueles em que o locador desenvolve um imóvel sob medida para o locatário, que o ocupará pelo prazo previsto em contrato. Dadas as peculiaridades desse tipo de contrato no contexto do real estate, o objetivo deste artigo é analisar as diferentes origens de recursos (fontes de funding) e a forma como eles são empregados (estruturas de funding) para desenvolver os empreendimentos, e discutir as vantagens e riscos dessas estruturas de funding do ponto de vista do empreendedor, que também é o locador. De forma a desenvolver este estudo e formatar as estruturas de funding apresentadas, parte-se de uma revisão das\ud práticas atuais do mercado imobiliário brasileiro (através de notícias veiculadas\ud na mídia e de prospectos de negócios realizados), da literatura brasileira sobre o tema e do conhecimento gerado no Grupo de Real Estate da Escola Politécnica da USP. De maneira a verificar a validade legal das soluções, é realizada uma checagem com\ud base na legislação brasileira e nas normas da Comissão de Valores Mobiliários.\ud Considera-se fontes de funding aquelas tratadas (1) como equity: capital próprio do empreendedor, capital de parceiros (e sócios) no empreendimento na forma de dinheiro ou imóveis (notadamente, o terreno onde será construído o empreendimento), ou investimento de Fundo de Investimento Imobiliário (FII); e (2) como dívida: financiamento bancário, securitização dos recebíveis de aluguéis com CRI ou debêntures. As estruturas de funding apresentadas serão combinações dessas fontes. A análise evidencia que estruturas com financiamento por securitização e emissão de CRI são as mais adequadas de forma geral para os negócios, assim como o investimento completo por FII para negócios de maior porte e nos quais o FII é proprietário direto do empreendimento. \ud Palavras-chave: real estate, build-to-suit, locação, funding, project financeBuild-to-suit real estate assets are tailor made developments for the tenant purposes, who occupies and operates the property for the duration agreed. Given the peculiarities of these contracts and the specificities of the property, this article aims at analyzing the sources of capital and how these funds are mixed and structured for the developments. The article discusses the risks and benefits of each of these funding\ud structures assuming the role of developer. In order to do this study and establish the funding structures shown, the research starts with a review of the current practices in Brazilian real estate market (based on press releases and prospects of deals), of local research papers, and will use the knowledge created at the Real Estate Research Group at Escola Politécnica at Universidade de São Paulo. Since it’s necessary to validate\ud the solutions proposed, Brazilian laws and Comissão de Valores Mobiliários (CVM) norms\ud are reviewed. Funding sources considered will be treated as (1) equity: developers own funds, partnership (via capital or real state – mainly land – investment), or Fundo de Investimento Imobiliário (Brazilian investment structure comparable to REITs); or as (2) debt: banks traditional credit lines, securitization of receivables with CRI emissions\ud , and debt bond emissions. The funding structures presented are mixes of these sources. The analysis shows that the structures best suited for this purpose are those with debt by securitization with CRI emissions, along with the complete investment by a FII but only with large emissions and having the FII as the sole owner of the real estate. \ud Keywords: real estate, build-to-suit, rent, funding, project financ