Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space
This work provides an incremental step towards automating qualitative analysis of student writing, which could help learning sciences researchers analyze larger datasets more efficiently.
This paper developed a machine learning method to automatically score undergraduate biology lab reports for argumentation sophistication. The method, which combines word embeddings and an LSTM model with a novel contrastive learning setup, achieved a high Quadratic Weighted Kappa (QWK) score, approaching human inter-rater reliability.
Qualitative analysis of verbal data is of central importance in the learning sciences. It is labor-intensive and time-consuming, however, which limits the amount of data researchers can include in studies. This work is a step towards building a statistical machine learning (ML) method for achieving an automated support for qualitative analyses of students' writing, here specifically in score laboratory reports in introductory biology for sophistication of argumentation and reasoning. We start with a set of lab reports from an undergraduate biology course, scored by a four-level scheme that considers the complexity of argument structure, the scope of evidence, and the care and nuance of conclusions. Using this set of labeled data, we show that a popular natural language modeling processing pipeline, namely vector representation of words, a.k.a word embeddings, followed by Long Short Term Memory (LSTM) model for capturing language generation as a state-space model, is able to quantitatively capture the scoring, with a high Quadratic Weighted Kappa (QWK) prediction score, when trained in via a novel contrastive learning set-up. We show that the ML algorithm approached the inter-rater reliability of human analysis. Ultimately, we conclude, that machine learning (ML) for natural language processing (NLP) holds promise for assisting learning sciences researchers in conducting qualitative studies at much larger scales than is currently possible.