Using Machine Learning to Support Quality Judgments
Abstract
Resource and collection “quality” is becoming an increasingly important topic for educational digital libraries. Computational models of quality and automated approaches for computing the quality of digital resources are necessary components of next generation cognitive tools aimed at supporting collection curators in making quality decisions. This research identifies and computes metrics for 16 quality indicators (e.g., cognitive authority, resource currency, cost, and advertising) and employs machine-learning techniques to classify resources into different quality bands based on these indicators. Two experiments were conducted to determine if these indicators could be used to accurately classify resources into different quality bands and to determine which indicators positively or negatively influenced resource classification. The results suggest that resources can be automatically classified into quality bands, and that focusing on a subset of the identified indicators can increase classification accuracy.
