130 research outputs found

    TOWARDS BUILDING INTELLIGENT COLLABORATIVE PROBLEM SOLVING SYSTEMS

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    Historically, Collaborative Problem Solving (CPS) systems were more focused on Human Computer Interaction (HCI) issues, such as providing good experience of communication among the participants. Whereas, Intelligent Tutoring Systems (ITS) focus both on HCI issues as well as leveraging Artificial Intelligence (AI) techniques in their intelligent agents. This dissertation seeks to minimize the gap between CPS systems and ITS by adopting the methods used in ITS researches. To move towards this goal, we focus on analyzing interactions with textual inputs in online learning systems such as DeepTutor and Virtual Internships (VI) to understand their semantics and underlying intents. In order to address the problem of assessing the student generated short text, this research explores firstly data driven machine learning models coupled with expert generated as well as general text analysis features. Secondly it explores method to utilize knowledge graph embedding for assessing student answer in ITS. Finally, it also explores a method using only standard reference examples generated by human teacher. Such method is useful when a new system has been deployed and no student data were available.To handle negation in tutorial dialogue, this research explored a Long Short Term Memory (LSTM) based method. The advantage of this method is that it requires no human engineered features and performs comparably well with other models using human engineered features.Another important analysis done in this research is to find speech acts in conversation utterances of multiple players in VI. Among various models, a noise label trained neural network model performed better in categorizing the speech acts of the utterances.The learners\u27 professional skill development in VI is characterized by the distribution of SKIVE elements, the components of epistemic frames. Inferring the population distribution of these elements could help to assess the learners\u27 skill development. This research sought a Markov method to infer the population distribution of SKIVE elements, namely the stationary distribution of the elements.While studying various aspects of interactions in our targeted learning systems, we motivate our research to replace the human mentor or tutor with intelligent agent. Introducing intelligent agent in place of human helps to reduce the cost as well as scale up the system

    Pedagogy for a Plugged-in Age, Independent Study 2017

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    This is an independent study for Hladkyj\u27s graduate studies in Strategic Foresight and Innovation (MDes) at OCAD University. The study’s goal is to establish a “lay of the land” informing how technology and pedagogy might be designed in the Web Design Graduate Certificate program Hladkyj spearheaded at Sheridan College. The research consists of a literature review validated by primary sources, composed of four 30-minute, semi-structured expert interviews with college-level design educators, administrators, and students (both current and former). A modified, “human-centred” STEEP V (Social, Technological, Economic, Environmental, Political, Values) framework provides context to the findings.The output is actionable and threefold: (1) High-level motivations / truths on which to base curriculum design principles, (2) Levers and tensions that designers must capitalize on in curriculum development, and (3) Initial “design dilemmas” or points of view that can spark reframing of digital pedagogy for a Major Research Project (MRP)

    The student-produced electronic portfolio in craft education

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    The authors studied primary school students’ experiences of using an electronic portfolio in their craft education over four years. A stimulated recall interview was applied to collect user experiences and qualitative content analysis to analyse the collected data. The results indicate that the electronic portfolio was experienced as a multipurpose tool to support learning. It makes the learning process visible and in that way helps focus on and improves the quality of learning. © ISLS.Peer reviewe

    Proceedings of the 15th International Conference on Technology in Mathematics Teaching (ICTMT 15)

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    Harvesting and summarizing user-generated content for advanced speech-based human-computer interaction

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-164).There have been many assistant applications on mobile devices, which could help people obtain rich Web content such as user-generated data (e.g., reviews, posts, blogs, and tweets). However, online communities and social networks are expanding rapidly and it is impossible for people to browse and digest all the information via simple search interface. To help users obtain information more efficiently, both the interface for data access and the information representation need to be improved. An intuitive and personalized interface, such as a dialogue system, could be an ideal assistant, which engages a user in a continuous dialogue to garner the user's interest and capture the user's intent, and assists the user via speech-navigated interactions. In addition, there is a great need for a type of application that can harvest data from the Web, summarize the information in a concise manner, and present it in an aggregated yet natural way such as direct human dialogue. This thesis, therefore, aims to conduct research on a universal framework for developing speech-based interface that can aggregate user-generated Web content and present the summarized information via speech-based human-computer interaction. To accomplish this goal, several challenges must be met. Firstly, how to interpret users' intention from their spoken input correctly? Secondly, how to interpret the semantics and sentiment of user-generated data and aggregate them into structured yet concise summaries? Lastly, how to develop a dialogue modeling mechanism to handle discourse and present the highlighted information via natural language? This thesis explores plausible approaches to tackle these challenges. We will explore a lexicon modeling approach for semantic tagging to improve spoken language understanding and query interpretation. We will investigate a parse-and-paraphrase paradigm and a sentiment scoring mechanism for information extraction from unstructured user-generated data. We will also explore sentiment-involved dialogue modeling and corpus-based language generation approaches for dialogue and discourse. Multilingual prototype systems in multiple domains have been implemented for demonstration.by Jingjing Liu.Ph.D
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