IEEE Access (Jan 2024)
Education Quality Ranker: A Novel Internet-Scale, Graph-Learning-Based, and Multi-View Quality Model for College/Univeristy Ranking
Abstract
In the realm of higher education, accurately assessing and ranking the quality of educational offerings is crucial. The paper introduces “Education Quality Ranker,” an innovative framework designed to rank colleges and universities on an Internet-scale by leveraging graph-learning techniques and a multi-view quality model. This model effectively categorizes and evaluates teaching approaches and preferences by identifying peer circles of instructors with similar attributes within educational contexts. The primary challenges addressed include the need for a model flexible enough to adapt to various instructional features across different educational environments and the significant variability in data availability concerning each instructor’s documented teaching practices. To overcome these challenges, the framework incorporates a geometry-based feature selector that identifies high-quality features indicative of each instructor’s teaching genre. Utilizing a sophisticated probabilistic model, it represents each instructor’s attributes as a distribution within a latent space, enabling a nuanced understanding of instructional styles. Furthermore, the framework constructs a graph that mirrors the instructional similarities among educators, facilitating the identification of densely connected subgraphs or “circles” of instructors with shared teaching attributes. By mining these instructor circles, the Education Quality Ranker can not only score each university’s education performance accurately but also optimize educational quality holistically. The efficacy of this approach is underscored by experiments conducted on a dataset encompassing a vast number of education instructors from 33 well-known colleges/universities, demonstrating the model’s capability to delineate distinct instructional genres accurately and enhance university rankings.
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