Finding Black Cat in a Coal Cellar -- Keyphrase Extraction & Keyphrase-Rubric Relationship Classification from Complex Assignments
This work addresses the challenge of improving grading efficiency in online education by automating content analysis, though it is incremental as it builds on existing methods for a specific domain.
The paper tackles the problem of extracting keyphrases and classifying their relationships to rubrics in complex assignments from online graduate programs, finding that unsupervised MultiPartiteRank works best for extraction and supervised SVM with BERT features performs best for classification.
Diversity in content and open-ended questions are inherent in complex assignments across online graduate programs. The natural scale of these programs poses a variety of challenges across both peer and expert feedback including rogue reviews. While the identification of relevant content and associating it to predefined rubrics would simplify and improve the grading process, the research to date is still in a nascent stage. As such in this paper we aim to quantify the effectiveness of supervised and unsupervised approaches for the task for keyphrase extraction and generic/specific keyphrase-rubric relationship extraction. Through this study, we find that (i) unsupervised MultiPartiteRank produces the best result for keyphrase extraction (ii) supervised SVM classifier with BERT features that offer the best performance for both generic and specific keyphrase-rubric relationship classification. We finally present a comprehensive analysis and derive useful observations for those interested in these tasks for the future. The source code is released in \url{https://github.com/manikandan-ravikiran/cs6460-proj}.