An adaptation algorithm for an intelligent natural language tutoring system

The focus of computerised learning has shifted from content delivery towards personalised online learning with Intelligent Tutoring Systems (ITS). Oscar Conversational ITS (CITS) is a sophisticated ITS that uses a natural language interface to enable learners to construct their own knowledge through discussion. Oscar CITS aims to mimic a human tutor by dynamically detecting and adapting to an individual's learning styles whilst directing the conversational tutorial. Oscar CITS is currently live and being successfully used to support learning by university students. The major contribution of this paper is the development of the novel Oscar CITS adaptation algorithm and its application to the Felder–Silverman learning styles model. The generic Oscar CITS adaptation algorithm uniquely combines the strength of an individual's learning style preference with the available adaptive tutoring material for each tutorial question to decide the best fitting adaptation. A case study is described, where Oscar CITS is implemented to deliver an adaptive SQL tutorial. Two experiments are reported which empirically test the Oscar CITS adaptation algorithm with students in a real teaching/learning environment. The results show that learners experiencing a conversational tutorial personalised to their learning styles performed significantly better during the tutorial than those with an unmatched tutorial

Uncovering the Hidden Cognitive Processes and Underlying Dynamics of Deception

This dissertation examines the processing demands associated with motor responding and verbal statements during deceptive (or deceptive-like) behavior. In the first set of studies presented in Chapter 2, participants motor movements in a false response paradigm revealed signatures of competition with the truth. In a second set of studies presented in Chapter 3, deceptive participants used language that reflected cognitive and social demands inherent to various types of deception. In evaluating both motor and verbal cues, this dissertation provides a comprehensive, multi-modal approach to better understanding the cognitive processes underlying deception. in conducting the motor responding studies, participants\u27 arm movements were analyzed as they navigated a motor tracking device (computer-mouse, Nintendo Wiimote). To visually co-present response options, where the true option acts as a competitor to a false target. In an initial study, competition during deceptive responding was shown to be much greater than during truthful responding. In two follow-up studies, the introduction of various task-based cognitive demands was shown to systematically modulate response performance. Specifically, these studies suggest that an intention to false respond early in question presentation will amplify competition effects, and that false responding to information in autobiographical memory is much more difficult than responding to information in general semantic memory. In the studies analyzing verbal statements, the focus is turned to large-scale linguistic analyses using automated natural language processing tools. In the first study, changes in language use were identifed between deceptive and truthful narratives using six psychologically relevant categories. A major finding was that the language of deception is adapted to faciliate ease of cognitive processing. In a second study, the indicative phrasing and semantic content of deceptive texts was extracted using a contrastive corpus analysis, whereby indicative features are defined by frequent use in one corpus while being infrequent in a comparative corpus. Two contexts of deception were evaluated. In the first context of computer-mediated conversations, decievers used a range of unique thematic elements, as in avoiding personal involvement in their narrative accounts. In the second context of attitudes towards abortion, unique thematic elements once again emerged; for example, participants tended to position their arguments in terms of formal law

Personalising Learning with Dynamic Prediction and Adaptation to Learning Styles in a Conversational Intelligent Tutoring System

This thesis presents research that combines the benefits of intelligent tutoring systems (ITS), conversational agents (CA) and learning styles theory by constructing a novel conversational intelligent tutoring system (CITS) called Oscar. Oscar CITS aims to imitate a human tutor by implicitly predicting individuals’ learning style preferences and adapting its tutoring style to suit them during a tutoring conversation. ITS are computerised learning systems that intelligently personalise tutoring based on learner characteristics such as existing knowledge and learning style. ITS are traditionally student-led, hyperlink-based learning systems that adapt the presentation of learning resources by reordering or hiding links. Research suggests that students learn more effectively when instruction matches their learning style, which is typically modelled explicitly using questionnaires or implicitly based on behaviour. Learning is a social process and natural language interfaces to ITS, such as CAs, allow students to construct knowledge through discussion. Existing CITS adapt tutoring according to student knowledge, emotions and mood, however no CITS adapts to learning styles. Oscar CITS models a human tutor by directing a tutoring conversation and automatically detecting and adapting to an individual’s learning styles. Original methodologies and architectures were developed for constructing an Oscar Predictive CITS and an Oscar Adaptive CITS. Oscar Predictive CITS uses knowledge captured from a learning styles model to dynamically predict learning styles from an individual’s tutoring dialogue. Oscar Adaptive CITS applies a novel adaptation algorithm to select the best tutoring style for each tutorial question. The Oscar CITS methodologies and architectures are independent of the learning styles model and subject domain. Empirical studies involving real students have validated the prediction and adaptation of learning styles in a real-world teaching/learning environment. The results show that learning styles can be successfully predicted from a natural language tutoring dialogue, and that adapting the tutoring style significantly improves learning performance