Abstract: Automated scoring engines are increasingly being used to score the free-form text responses that students give to questions. Such engines are not designed to appropriately deal with responses that a human reader would find alarming such as those that indicate an intention to self-harm or harm others, responses that allude to drug abuse or sexual abuse or any response that would elicit concern for the student writing the response. Our neural network models have been designed to help identify these anomalous responses from a large collection of typical responses that students give. The responses identified by the neural network can be assessed for urgency, severity, and validity more quickly by a team of reviewers than otherwise possible. Given the anomalous nature of these types of responses, our goal is to maximize the chance of flagging these responses for review given the constraint that only a fixed percentage of responses can viably be assessed by a team of reviewers.

Introduction: Automated Essay Scoring (AES) and Automated Short Answer Scoring (ASAS) has become more prevalent among testing agencies BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . These systems are often designed to address one task and one task alone; to determine whether a written piece of text addresses a question or not. These engines were originally based on either hand-crafted features or term frequency–inverse document frequency (TF-IDF) approaches BIBREF4 . More recently, these techniques have been superseded by the combination of word-embeddings and neural networks BIBREF5 , BIBREF6 , BIBREF7 . For semantically simple responses, the accuracy of these approaches can often be greater than accuracy of human raters, however, these systems are not trained to appropriately deal with the anomalous cases in which a student writes something that elicits concern for the writer or those around them, which we simply call an `alert'. Typically essay scoring systems do not handle alerts, but rather, separate systems must be designed to process these types of responses before they are sent to the essay scoring system. Our goal is not to produce a classification, but rather to use the same methods developed in AES, ASAS and sentiment analysis BIBREF8 , BIBREF9 to identify some percentage of responses that fit patterns seen in known alerts and send them to be assessed by a team of reviewers. Assessment organizations typically perform some sort of alert detection as part of doing business. In among hundreds of millions of long and short responses we find cases of alerts in which students have outlined cases of physical abuse, drug abuse, depression, anxiety, threats to others or plans to harm themselves BIBREF10 . Such cases are interesting from a linguistic, educational, statistical and psychological viewpoint BIBREF11 . While some of these responses require urgent attention, given the volume of responses many testing agencies deal with, it is not feasible to systematically review every single student response within a reasonable time-frame. The benefits of an automated system for alert detection is that we can prioritize a small percentage which can be reviewed quickly so that clients can receive alerts within some fixed time period, which is typically 24 hours. Given the prevalence of school shootings and similarly urgent situations, reducing the number of false positives can effectively speed up the review process and hence optimize our clients ability to intervene when necessary. As a classification problem in data science, our problem has all the hallmarks of the most difficult problems in natural language processing (NLP) BIBREF12 ; alerts are anomalous in nature making training difficult, the data is messy in that it contains misspellings (both misused real words and incorrectly spelled words) BIBREF13 , students often use student specific language or multi-word colloquialisms BIBREF14 and the semantics of alerts can be quite complex and subtle, especially when the disturbing content is implicit rather than explicit. The responses themselves are drawn from a wide range of free-form text responses to questions and student comments from a semantically diverse range of topics, including many that are emotive in nature. For example, the semantic differences between an essay on gun-control and a student talking about getting a gun can be very subtle. Sometimes our systems include essays on emotive topics because the difference in language between such essays and alerts can be very small. Students often use phrases like “kill me now" as hyperbole out of frustration rather than a genuine desire to end ones life, e.g., "this test is so boring, kill me now". To minimize false positives, the engine should attempt to evaluate context, not just operate on key words or phrases. When it comes to neural network design, there are two dominant types of neural networks in NLP; convolutional neural networks (CNN) and recurrent neural networks (RNN) BIBREF15 . Since responses may be of an arbitrary length different recurrent neural networks are more appropriate tools for classifying alerts BIBREF16 . The most common types of cells used in the design of recurrent neural networks are Gated Recurrent Units (GRU)s BIBREF17 and Long-Short-Term-Memory (LSTM) units BIBREF18 . The latter were originally designed to overcome the vanishing gradient problem BIBREF19 . The GRU has some interesting properties which simplify the LSTM unit and the two types of units can give very similar results BIBREF20 . We also consider stacked versions, bidirectional variants BIBREF21 and the effect of an attention mechanism BIBREF22 . This study has been designed to guide the creation of our desired final production model, which may include higher stacking, dropouts (both regular and recurrent) and may be an ensemble of various networks tuned to different types of responses BIBREF23 . Similar comparisons of architectures have appeared in the literature BIBREF24 , BIBREF7 , however, we were not able to find similar comparisons for detecting anomalous events. In section SECREF2 we outline the nature of the data we have collected, a precise definition of an alert and how we processed the data for the neural network. In section SECREF3 we outline the definition of the models we evaluate and how they are defined. In section SECREF4 we outline our methodology in determining which models perform best given representative sensitivities of the engine. We attempt to give an approximation of the importance of each feature of the final model.

Question: What baseline model is used?
Answer: Logistic regression with TF-IDF with latent semantic analysis representations