On Trust, Editorial Intent, and Recommender Systems for Supporting Higher Education

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.