The need to identify student cognitive engagement in online-learning settings has increased with our use of online learning approaches because engagement plays an important role in ensuring student success in these environments. Engaged students are more likely to complete online courses successfully, but this setting makes it more difficult for instructors to identify engagement. In this study, we developed predictive models for automating the identification o f cognitive engagement in online discussion posts. We adapted the Interactive, Constructive, Active, and Passive (ICAP) Engagement theory [15] by merging ICAP with Bloom's taxonomy. We then applied this adaptation of ICAP to label student posts (N = 4,217), thus capturing their level of cognitive engagement. To investigate the feasibility of automatically identifying cognitive engagement, the labelled data were used to train three machine learning classifiers (i.e., decision tree, random forest, and support vector machine). Model inputs included features extracted by applying CohMetrix to student posts and non-linguistic contextual features (e.g., number of replies). The support vector machine model outperformed the other classifiers. Our findings suggest it is feasible to automatically identify cognitive engagement in online learning environments. Subsequent analyses suggest that new language features (e.g., AWL use) should be included because they support the identification o f cognitive engagement. Such detectors could be used to help identify students who are in need of support or help adapt teaching practices and learning materials. [For the full proceedings, see ED623995.]