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Qualitative analysis of verbal data is of central importance in the learningsciences. It is labor-intensive and time-consuming, however, which limits theamount of data researchers can include in studies. This work is a step towardsbuilding a statistical machine learning (ML) method for achieving an automatedsupport for qualitative analyses of students' writing, here specifically inscore laboratory reports in introductory biology for sophistication ofargumentation and reasoning. We start with a set of lab reports from anundergraduate biology course, scored by a four-level scheme that considers thecomplexity of argument structure, the scope of evidence, and the care andnuance of conclusions. Using this set of labeled data, we show that a popularnatural language modeling processing pipeline, namely vector representation ofwords, a.k.a word embeddings, followed by Long Short Term Memory (LSTM) modelfor capturing language generation as a state-space model, is able toquantitatively capture the scoring, with a high Quadratic Weighted Kappa (QWK)prediction score, when trained in via a novel contrastive learning set-up. Weshow that the ML algorithm approached the inter-rater reliability of humananalysis. Ultimately, we conclude, that machine learning (ML) for naturallanguage processing (NLP) holds promise for assisting learning sciencesresearchers in conducting qualitative studies at much larger scales than iscurrently possible.

Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space