Where are the Large N Studies in Education?: Introducing a Dataset of Scientific Articles and NLP Techniques

Research, especially in Education, is hampered by scale and our aim is to help shift this dynamic by tracking relevant studies from major scientific venues. The current version of our dataset considers top conferences and journals from the domain (N = 33,531). Several heuristics using advanced text searches and regular expressions, together with NLP techniques ranging from part of speech tagging, syntactic dependency parsing, to semantic models, are employed to extract relevant studies. Our method achieves an F1 score of .633 when employed on a manually annotated subset of 1,000 articles. When applied to the entire dataset, the total number of articles with large N was around 10%, with a positive trend in the last years. L@S was by far the venue with the highest density of identified articles, thus arguing for its emphasis on scale. Further filtering of the articles is required when focusing only on learning, as the articles span across multiple domains and have multiple interests. Nonetheless, the order of magnitude raises current problems, namely that these studies are scarce and future endeavors should emphasize the importance of scale. Our dataset, including the validation subset, and the corresponding code for data crawling, PDF processing, and large N extraction mechanisms, have been open-sourced to further support the initiative and stimulate new analyses.