The Writing Mentor TM (WM) application is a Google Docs add-on designed to help students improve their writing in a principled manner and to promote their writing success in postsecondary settings. WM provides automated writing evaluation (AWE) feedback using natural language processing (NLP) methods and linguistic resources. AWE features in WM have been informed by research about postsecondary student writers often classified as developmental (Burstein et al., 2016b), and these features address a breadth of writing sub-constructs (including use of sources, claims, and evidence; topic development; coherence; and knowledge of English conventions). Through an optional entry survey, WM collects self-efficacy data about writing and English language status from users. Tool perceptions are collected from users through an optional exit survey. Informed by language arts models consistent with the Common Core State Standards Initiative and valued by the writing studies community, WM takes initial steps to integrate the reading and writing process by offering a range of textual features, including vocabulary support, intended to help users to understand unfamiliar vocabulary in coursework reading texts. This paper describes WM and provides discussion of descriptive evaluations from an Amazon Mechanical Turk (AMT) usability task situated in WM and from users-in-the-wild data. The paper concludes with a framework for developing writing feedback and analytics technology.

Background: Employing natural language processing and latent semantic analysis, the current work was completed as a constituent part of a larger research project for designing and launching artificial intelligence in the form of deep artificial neural networks. The models were evaluated on a proprietary corpus retrieved from a data warehouse, where it was extracted from MyReviewers, a sophisticated web application purposed for peer review in written communication, which was actively used in several higher education institutions. The corpus of laboratory reports in STEM annotated by instructors and students was used to train the models. Under the Common Rule, research ethics were ensured by protecting the privacy of subjects and maintaining the confidentiality of data, which mandated corpus de-identification.Literature Review: De-identification and pseudonymization of textual data remains an actively studied research question for several decades. Its importance is stipulated by numerous laws and regulations in the United States and internationally with HIPAA Privacy Rule and FERPA.Research Question: Text de-identification requires a significant amount of manual post-processing for eliminating faculty and student names.  This work investigated automated and semi-automated methods for de-identifying student and faculty entities while preserving author names in cited sources and reference lists. It was hypothesized that a natural language processing toolkit and an artificial neural network model with named entity recognition capabilities would facilitate text processing and reduce the amount of manual labor required for post-processing after matching essays to a list of users’ names. The suggested techniques were applied with supplied pre-trained models without additional tagging and training. The goal of the study was to evaluate three approaches and find the most efficient one among those using a users’ list, a named entity recognition toolkit, and an artificial neural network.Research Methodology: The current work studied de-identification of STEM laboratory reports and evaluated the performance of the three techniques: brute forth search with a user lists, named entity recognition with the OpenNLP machine learning toolkit, and NeuroNER, an artificial neural network for named entity recognition built on the TensorFlow platform. The complexity of the given task was determined by the dilemma, where names belonging to students, instructors, or teaching assistants must be removed, while the rest of the names (e.g., authors of referenced papers) must be preserved.Results: The evaluation of the three selected methods demonstrated that automating de-identification of STEM lab reports is not possible in the setting, when named entity recognition methods are employed with pre-trained models. The highest results were achieved by the users’ list technique with 0.79 precision, 0.75 recall, and 0.77 F1 measure, which significantly outweighed OpenNLP with 0.06 precision, 0.14 recall, and 0.09 F1, and NeuroNER with 0.14 precision, 0.56 recall, and 0.23 F1.Discussion: Low performance of OpenNLP and NeuroNER toolkits was explained by the complexity of the task and unattainability of customized models due to imposed time constraints. An approach for masking possible de-identification errors is suggested.Conclusion: Unlike multiple cases described in the related work, de-identification of laboratory reports in STEM remained a non-trivial labor-intensive task. Applied out of the box, a machine learning toolkit and an artificial neural network technique did not enhance performance of the brute forth approach based on user list matching.Directions for Future Research: Customized tagging and training on the STEM corpus were presumed to advance outcomes of machine learning and predominantly artificial intelligence methods. Application of other natural language toolkits may lead to deducing a more effective solution.