{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/121125"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/121125","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"On Trust, Editorial Intent, and Recommender Systems for Supporting Higher Education","abstract":"Institutional support of higher teaching and learning at scale poses three unique challenges. The first challenge is poor institutional accounting of instructors' use of educational platforms and software, especially the learning management system (LMS). The second challenge is a deficit of trust among stakeholders with unique job roles, prerogatives, and editorial preferences. The third challenge is one-size-fits-all, open-loop, or stopgap support processes. To address these challenges, this three-phase dissertation project proposes a novel sociotechnical framework for institutional support using trustworthy educational recommender systems. This framework accounts for LMS platform contexts, multiple stakeholders, and editorial trust relationships. In its first phase, the project proposes ``Depth of Use\" (DOU): a first-principles framework of frequent LMS use-contexts. DOU is found to highlight low-adoption course cohorts, evaluate course design interventions, and improve IT emergency preparedness. The second phase of this project proposes a novel model of recommendation trustworthiness based in stakeholder allocation of RS editorial tasks. The study discovers a spectrum of faculty intentions about editorial division-of-labor and its frequent rationales, including student expertise, professional curriculum needs, authorship burdens at scale, and learner disengagement. In its third phase, the project investigates how editorial trust might be enhanced by transparency cues (guarantees, social proof, content tags). The dissertation concludes with a set of design guidelines to aid HCI practitioners in enhancing editorial transparency and algorithmic explainability, and increasing process efficacy of institutional support.","abstract_html":"Institutional support of higher teaching and learning at scale poses three unique challenges. The first challenge is poor institutional accounting of instructors' use of educational platforms and software, especially the learning management system (LMS). The second challenge is a deficit of trust among stakeholders with unique job roles, prerogatives, and editorial preferences. The third challenge is one-size-fits-all, open-loop, or stopgap support processes. To address these challenges, this three-phase dissertation project proposes a novel sociotechnical framework for institutional support using trustworthy educational recommender systems. This framework accounts for LMS platform contexts, multiple stakeholders, and editorial trust relationships. In its first phase, the project proposes ``Depth of Use" (DOU): a first-principles framework of frequent LMS use-contexts. DOU is found to highlight low-adoption course cohorts, evaluate course design interventions, and improve IT emergency preparedness. The second phase of this project proposes a novel model of recommendation trustworthiness based in stakeholder allocation of RS editorial tasks. The study discovers a spectrum of faculty intentions about editorial division-of-labor and its frequent rationales, including student expertise, professional curriculum needs, authorship burdens at scale, and learner disengagement. In its third phase, the project investigates how editorial trust might be enhanced by transparency cues (guarantees, social proof, content tags). 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The second phase of this project proposes a novel model of recommendation trustworthiness based in stakeholder allocation of RS editorial tasks. The study discovers a spectrum of faculty intentions about editorial division-of-labor and its frequent rationales, including student expertise, professional curriculum needs, authorship burdens at scale, and learner disengagement. In its third phase, the project investigates how editorial trust might be enhanced by transparency cues (guarantees, social proof, content tags). The dissertation concludes with a set of design guidelines to aid HCI practitioners in enhancing editorial transparency and algorithmic explainability, and increasing process efficacy of institutional support."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["In higher education, supporting faculty effectively can be challenging, especially with technology use at scale. This dissertation reckons with three primary aspects of this challenge: inadequate tracking of how educators use learning platforms, low trust among different institutional stakeholders, and inefficient support processes. To address these challenges, we propose a novel framework to personalize instructional support using learning management systems (LMS) as platforms to reach out to faculty, interpret their technology needs, and deliver interventions using educational recommender systems (ERS). Our framework allows better understanding of faculty's LMS use, editorial intent, and trust of automation. It also highlights structural barriers to trust and process efficacy at universities. 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The second phase of this project proposes a novel model of recommendation trustworthiness based in stakeholder allocation of RS editorial tasks. The study discovers a spectrum of faculty intentions about editorial division-of-labor and its frequent rationales, including student expertise, professional curriculum needs, authorship burdens at scale, and learner disengagement. In its third phase, the project investigates how editorial trust might be enhanced by transparency cues (guarantees, social proof, content tags). The dissertation concludes with a set of design guidelines to aid HCI practitioners in enhancing editorial transparency and algorithmic explainability, and increasing process efficacy of institutional support."],"dc:description.abstractgeneral":["In higher education, supporting faculty effectively can be challenging, especially with technology use at scale. This dissertation reckons with three primary aspects of this challenge: inadequate tracking of how educators use learning platforms, low trust among different institutional stakeholders, and inefficient support processes. To address these challenges, we propose a novel framework to personalize instructional support using learning management systems (LMS) as platforms to reach out to faculty, interpret their technology needs, and deliver interventions using educational recommender systems (ERS). Our framework allows better understanding of faculty's LMS use, editorial intent, and trust of automation. It also highlights structural barriers to trust and process efficacy at universities. 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