Case-based comparative evaluation appears to be an important strat?egy for addressing problems in weak analytic domains, such as the law and practical ethics. Comparisons to paradigm, hypothetical, or past cases may help a reasoner make decisions about a current dilemma. W e are investigating the uses of comparative evaluation in practical ethical reasoning, and whether recent philosophical models of casuistic rea?somng in medical ethics may contribute to developing models of com?parative evaluation. A good comparative reasoner, we believe, should be able to integrate abstract knowledge of reasons and principles into its analysis and still take a problem's context and details adequately into account. TRUTH-TELLER is a program we have developed that compares pairs of cases presentmg ethical dilemmas about whether to tell the truth by marshaling relevant similarities and differences in a context sensitive manner. The program has a variety of methods for reasoning about reasons. These include classifying reasons as prin?cipled or altruistic, comparing the strengths of reasons, and qualifying reasons by participants' roles and the criticality of consequences. W e describe a knowledge representation and comparative evaluation pro?cess for this domain. In an evaluation of the program, five professional ethicists scored the program's output for randomly-selected pairs of cases. The work contributes to context sensitive similarity assessment and to models of argumentation in weak analytic domains.

When teaching at scale in the physical classroom or online classroom of a MOOC, the scarce resource of personal instructor communication becomes a differentiating factor between the quality of learning experience available in smaller classrooms. In this paper, through real-time predictive modeling of engagement analytics, we augment a MOOC platform with personalized communication affordances, allowing the instructional staff to direct communication to learners based on individual predictions of three engagement analytics. The three model analytics are the current probability of earning a certificate, of submitting enough materials to pass the class, and of leaving the class and not returning. We engineer an interactive analytics interface in edX which is populated with real-time predictive analytics from a backend API service. The instructor can target messages to, for example, all learners who are predicted to complete all materials but not pass the class. Our approach utilizes the state-of-the-art in recurrent neural network classification, evaluated on a MOOC dataset of 20 courses and deployed in one. We provide evaluation of these courses, comparing a manual feature engineering approach to an automatic feature learning approach using neural networks. Our provided code for the front-end and back-end allows any instructional team to add this personalized communication dashboard to their edX course granted they have access to the historical clickstream data from a previous offering of the course, their course’s daily provided log data, and an external machine to run the model service API.