Browsing by Subject "Data-driven"

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    A case study of a first-grade teacher team collaboratively planning literacy instruction for English learners
    (2013-06) Frederick, Amy Rae
    Teachers, researchers and policy-makers in the areas of literacy and language development have recently touted teacher collaboration as an innovative approach to better serving elementary English learners. Collaborative planning and instructional approaches are becoming widespread in educational practice. And though research seems promising in terms of benefits for teachers and students, there is scant information about the collaborative meaning-making practices of teacher teams and the instructional decisions that result. In the current study I explore the practices and perceptions of one first-grade team as they collaborate to plan instruction for their English-learning students. My study is situated within a sociocultural framework (Vygotsky, 1978; Johnson, 2009) and uses case study methodology to provide an in-depth exploration of the actions and perceptions of teachers within a unique context (Merriam, 2009). I highlight three major findings: the team's planning practices were significantly influenced by multi-layered policies in both supportive and restrictive ways; the team's collaborative planning promoted practices that may improve the teachers' understandings of English learners and support improved classroom practice; and the participants, though dissatisfied with aspects of their collaborative practices, felt that overall they were better teachers because of them. Findings from this project add to the fields of professional development and literacy instruction for English learners by identifying the influences and tensions embedded in the teachers' work and exposing the everyday negotiations of complicated issues that teachers undertake.
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    Enhancing Data-Driven Decision Support with Multi-Perspective Solutions
    (2020-08) Wang, Yaqiong
    As digital systems become ubiquitous, providing all-around support for decision makers has become a significant part of contemporary information systems. To this end, numerous data-driven analytics techniques have been widely adopted by various platforms to facilitate decision making in a wide variety of application domains, e.g., product choice, employee recruitment, and medical diagnosis. The appropriate application of various data-driven methodologies for decision support in complex real-world contexts is crucial to gain benefits and to avoid unexpected consequences and, thus, the ability take into account multiple perspectives for better decision support represents an important challenge. In order to provide insights into this question, this thesis focuses on investigating some of the problems existing in decision support applications and attempts to provide various solutions and empirical evidence of the effectiveness of these solutions. Specifically, my thesis proposes to provide more nuanced decision support in different application domains by balancing different aspects of decision support models or by providing complementary sources of information for decision makers, e.g., balancing accuracy and long-tailness to address popularity bias in recommender systems; using individual prediction reliability to complement outcome prediction to support decision making in highly risk-sensitive domains like medical diagnosis or financial markets; providing complementary channels to fulfill online consumption decision support in the retailing industry. Solutions and findings provided by my thesis advance the understanding of decision support problems in multifaceted contexts, and have practical implications for information systems that adopt data-driven methods.