Using Natural Language as Knowledge Representation in an Intelligent Tutoring System

Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they don’t learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

Metamodel for personalized adaptation of pedagogical strategies using metacognition in Intelligent Tutoring Systems

The modeling process of metacognitive functions in Intelligent Tutoring Systems (ITS) is a difficult and time-consuming task. In particular when the integration of several metacognitive components, such as self-regulation and metamemory is needed. Metacognition has been used in Artificial Intelligence (AI) to improve the performance of complex systems such as ITS. However the design ITS with metacognitive capabilities is a complex task due to the number and complexity of processes involved. The modeling process of ITS is in itself a difficult task and often requires experienced designers and programmers, even when using authoring tools. In particular the design of the pedagogical strategies for an ITS is complex and requires the interaction of a number of variables that define it as a dynamic process. This doctoral thesis presents a metamodel for the personalized adaptation of pedagogical strategies integrating metamemory and self-regulation in ITS. The metamodel called MPPSM (Metamodel of Personalized adaptation of Pedagogical Strategies using Metacognition in intelligent tutoring systems) was synthetized from the analysis of 40 metacognitive models and 45 ITS models that exist in the literature. MPPSMhas a conceptual architecture with four levels of modeling according to the standard Meta- Object Facility (MOF) of Model-Driven Architecture (MDA) methodology. MPPSM enables designers to have modeling tools in early stage of software development process to produce more robust ITS that are able to self-regulate their own reasoning and learning processes. In this sense, a concrete syntax composed of a graphic notation called M++ was defined in order to make the MPPSM metamodel more usable. M++ is a Domain-Specific Visual Language (DSVL) for modeling metacognition in ITS. M++ has approximately 20 tools for modeling metacognitive systems with introspective monitoring and meta-level control. MPPSM allows the generation of metacognitive models using M++ in a visual editor named MetaThink. In MPPSM-based models metacognitive components required for monitoring and executive control of the reasoning processes take place in each module of an ITS can be specified. MPPSM-based models represent the cycle of reasoning of an ITS about: (i) failures generated in its own reasoning tasks (e.g. self-regulation); and (ii) anomalies in events that occur in its Long-Term Memory (LTM) (e.g. metamemory). A prototype of ITS called FUNPRO was developed for the validation of the performance of metacognitive mechanism of MPPSM in the process of the personalization of pedagogical strategies regarding to the preferences and profiles of real students. FUNPRO uses self-regulation to monitor and control the processes of reasoning at object-level and metamemory for the adaptation to changes in the constraints of information retrieval tasks from LTM. The major contributions of this work are: (i) the MOF-based metamodel for the personalization of pedagogical strategies using computational metacognition in ITS; (ii) the M++ DSVL for modeling metacognition in ITS; and (iii) the ITS prototype called FUNPRO (FUNdamentos de PROgramación) that aims to provide personalized instruction in the subject of Introduction to Programming. The results given in the experimental tests demonstrate: (i) metacognitive models generated are consistent with the MPPSM metamodel; (ii) positive perceptions of users with respect to the proposed DSVL and it provide preliminary information concerning the quality of the concrete syntax of M++; (iii) in FUNPRO, multi-level pedagogical model enhanced with metacognition allows the dynamic adaptation of the pedagogical strategy according to the profile of each student.Doctorad

Supporting students in the analysis of case studies for professional ethics education

Intelligent tutoring systems and computer-supported collaborative environments have been designed to enhance human learning in various domains. While a number of solid techniques have been developed in the Artificial Intelligence in Education (AIED) field to foster human learning in fundamental science domains, there is still a lack of evidence about how to support learning in so-called ill-defined domains that are characterized by the absence of formal domain theories, uncertainty about best solution strategies and teaching practices, and learners' answers represented through text and argumentation. This dissertation investigates how to support students' learning in the ill-defined domain of professional ethics through a computer-based learning system. More specifically, it examines how to support students in the analysis of case studies, which is a common pedagogical practice in the ethics domain. This dissertation describes our design considerations and a resulting system called Umka. In Umka learners analyze case studies individually and collaboratively that pose some ethical or professional dilemmas. Umka provides various types of support to learners in the analysis task. In the individual analysis it provides various kinds of feedback to arguments of learners based on predefined system knowledge. In the collaborative analysis Umka fosters learners' interactions and self-reflection through system suggestions and a specifically designed visualization. The system suggestions offer learners the chance to consider certain helpful arguments of their peers, or to interact with certain helpful peers. The visualization highlights similarities and differences between the learners' positions, and illustrates the learners' level of acceptance of each other's positions. This dissertation reports on a series of experiments in which we evaluated the effectiveness of Umka's support features, and suggests several research contributions. Through this work, it is shown that despite the ill-definedness of the ethics domain, and the consequent complications of text processing and domain modelling, it is possible to build effective tutoring systems for supporting students' learning in this domain. Moreover, the techniques developed through this research for the ethics domain can be readily expanded to other ill-defined domains, where argument, qualitative analysis, metacognition and interaction over case studies are key pedagogical practices

Knowledge mentoring as a framework for designing computer-based agents for supporting musical composition learning

An approach to the design of teaching agents in problem-seeking domains - that is based on a systematic relationship between theoretical framework, analysis of empirical data, computational model and computational implementation - has been developed. The theoretical framework, called the Knowledge Mentoring framework (KMf), was developed to investigate how studies of dialogue and interaction can be exploited in a practical way by designers of computer-based teaching agents. A particular focus was the following musical education problem: when interacting with a computer-based music system, many students do not spontaneously reflect on their activity, they often need to be encouraged to do this. The KMf provides a taxonomy and definitions of the pedagogical goals involved in a 'mentoring' style of teaching. Mentoring is an approach to teaching that aims to support learners' creative, metacognitive and critical thinking, these being essential to musical composition and other open-ended, problem-seeking domains. This theoretical framework was used to guide the analysis and modelling of data produced by an empirical study of human teacher-learner interactions. Information on the temporal ordering of teacher-learner interactions was revealed (modelled as. state transition networks and a mentoring script). Findings from the analysis also included a pause taxonomy (that provided evidence of a link between pause length and learner ability) and the occurrence of reciprocal modelling (where participants in learning interactions built up models of the other participants' expectations). The theoretical framework and the analysis findings were then used to develop a computational model for teaching agents in problem-seeking domains. Aspects of our theory, analysis findings and computational model were incorporated into a computational implementation: a pre-prototype teaching agent called MetaMuse. A Cooperative Evaluation of MetaMuse with teacher-composers showed that it had the potential to promote creative reflection in learners

Providing Intelligent and Adaptive Support in Concept Map-based Learning Environments

abstract: Concept maps are commonly used knowledge visualization tools and have been shown to have a positive impact on learning. The main drawbacks of concept mapping are the requirement of training, and lack of feedback support. Thus, prior research has attempted to provide support and feedback in concept mapping, such as by developing computer-based concept mapping tools, offering starting templates and navigational supports, as well as providing automated feedback. Although these approaches have achieved promising results, there are still challenges that remain to be solved. For example, there is a need to create a concept mapping system that reduces the extraneous effort of editing a concept map while encouraging more cognitively beneficial behaviors. Also, there is little understanding of the cognitive process during concept mapping. What’s more, current feedback mechanisms in concept mapping only focus on the outcome of the map, instead of the learning process. This thesis work strives to solve the fundamental research question: How to leverage computer technologies to intelligently support concept mapping to promote meaningful learning? To approach this research question, I first present an intelligent concept mapping system, MindDot, that supports concept mapping via innovative integration of two features, hyperlink navigation, and expert template. The system reduces the effort of creating and modifying concept maps while encouraging beneficial activities such as comparing related concepts and establishing relationships among them. I then present the comparative strategy metric that modes student learning by evaluating behavioral patterns and learning strategies. Lastly, I develop an adaptive feedback system that provides immediate diagnostic feedback in response to both the key learning behaviors during concept mapping and the correctness and completeness of the created maps. Empirical evaluations indicated that the integrated navigational and template support in MindDot fostered effective learning behaviors and facilitating learning achievements. The comparative strategy model was shown to be highly representative of learning characteristics such as motivation, engagement, misconceptions, and predicted learning results. The feedback tutor also demonstrated positive impacts on supporting learning and assisting the development of effective learning strategies that prepare learners for future learning. This dissertation contributes to the field of supporting concept mapping with designs of technological affordances, a process-based student model, an adaptive feedback tutor, empirical evaluations of these proposed innovations, and implications for future support in concept mapping.Dissertation/ThesisDoctoral Dissertation Computer Science 201

The Impact of Online Homework, Time on Homework, Gender, and Metacognition in Improving Student Achievement in Undergraduate Biology Courses

This study sought to address a gap in the literature to determine whether a relationship exists between use of online homework and student achievement in undergraduate biology courses. Previous studies have examined other STEM courses, but few have considered biology. The purpose of the study was to determine whether homework grades in online homework systems can predict student achievement in introductory undergraduate biology courses. This study utilized a correlational analysis by multiple regression using archival data to determine whether final course grades in undergraduate biology courses can be predicted by grades on online homework, time spent on online homework, gender, or participation in a course on metacognition. A second question considered whether performance on a major assignment in a course on metacognition correlated with biology course grades and found a moderate correlation. The study took place at a small private Christian university in the Southeast, with 311 participants. The study found that there is a significant predictive ability to use these variables in determining course grades. Homework scores were most predictive, but time spent on homework and gender were also significant. Participation in a course on metacognition gave inconclusive results and is one suggestion for further research. Keywords: STEM, online homework, academic achievement, metacognitio