Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

D-WISE Tool Suite for the Sociology of Knowledge Approach to Discourse

Under the umbrella of the D-WISE project, manual and digital approaches to discourse analysis are combined to develop a prototypical working environment for digital qualitative discourse analysis. This new qualitative data analysis tool, called D-WISE Tool Suite, is built up in a process of close exchange by the two teams from humanities and informatics and focuses on developing central innovations regarding the availability of relevant Digital Humanities (DH) applications. Bridging the gap between structural patterns detected with digital methods and interpretative processes of human meaning making is at the core of the collaborative approach of anthropological studies and computer linguistics in the D-WISE project, which innovates both informatics technology of contextoriented embedding representations and hermeneutic methodologies for discourse analysis in the Sociology of Knowledge Approach to Discourse (SKAD). In this paper, the intertwining of the two paradigms Human-in-the-loop and AI-in-theloop will be presented by outlining the concept of Human Computer Interaction (HCI) in the D-WISE Tool Suite with its AI-empowered features and established modes of feedback-loops and the supported functions for facilitating SKAD

A Cross-Cultural Analysis of Sentiment in “COVID-19” Reportage of CCTV News and The New York Times

Drawing support from the artificial intelligence platform of Baidu Cloud and the natural language processing approach, this paper provides an empirically-grounded micro-analysis of Sino-American news discourses on “COVID-19” pandemic in China 2020 by using keyword wordcloud analysis on sentiment expressions, namely the discourses from the websites of CCTV News and The New York Times. The authors analyzed the media’s intended attitudes expressed with sentiment, and found that the attitude of the Chinese people and China’s media towards the epidemic was mostly positive; while New York Times was mostly negative about the epidemic, especially at the peak of the outbreak. Such a difference presents a prevalent manifestation of recognition towards the epidemic led by either government or media institutions while people face uncertainties caused by corona virus, which may further influence the public opinion and attitudes towards the epidemic, which in turn has broader social/political-interactional purposes and public cognitive construction.

Interactive Machine Learning with Applications in Health Informatics

Recent years have witnessed unprecedented growth of health data, including millions of biomedical research publications, electronic health records, patient discussions on health forums and social media, fitness tracker trajectories, and genome sequences. Information retrieval and machine learning techniques are powerful tools to unlock invaluable knowledge in these data, yet they need to be guided by human experts. Unlike training machine learning models in other domains, labeling and analyzing health data requires highly specialized expertise, and the time of medical experts is extremely limited. How can we mine big health data with little expert effort? In this dissertation, I develop state-of-the-art interactive machine learning algorithms that bring together human intelligence and machine intelligence in health data mining tasks. By making efficient use of human expert's domain knowledge, we can achieve high-quality solutions with minimal manual effort. I first introduce a high-recall information retrieval framework that helps human users efficiently harvest not just one but as many relevant documents as possible from a searchable corpus. This is a common need in professional search scenarios such as medical search and literature review. Then I develop two interactive machine learning algorithms that leverage human expert's domain knowledge to combat the curse of "cold start" in active learning, with applications in clinical natural language processing. A consistent empirical observation is that the overall learning process can be reliably accelerated by a knowledge-driven "warm start", followed by machine-initiated active learning. As a theoretical contribution, I propose a general framework for interactive machine learning. Under this framework, a unified optimization objective explains many existing algorithms used in practice, and inspires the design of new algorithms.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147518/1/raywang_1.pd