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

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Goal Reasoning: Papers from the ACS Workshop

This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013

The Augmented Learner : The pivotal role of multimedia enhanced learning within a foresight-based learning model designed to accelerate the delivery of higher levels of learner creativity

The central theme for this dissertation lies at the intersection of multisensory technology enhanced learning, the field of foresight and transformative pedagogy and their role in helping to develop greater learner creativity. These skills will be key to meeting the needs of the projected growing role of the creative class within the emerging global workforce structure and the projected growth in R&D and the advancement of human-machine resource management. Over the past two decades, we have traversed from the Industrial Age through the Information Age into what we now call postnormal times, manifested partly in Industry 4.0. It is widely considered that the present education system in countries with developed economies is not optimised for delivering the much-needed creative skills, which are prominent amongst the critical 21st C skills required by the creative class, (also known as creatives), which will be increasingly dominant in terms of near future employability. Consequently, there will be a potential shortfall of creatives unless this issue is rapidly addressed. To ensure that the creative skills I aimed to enhance were relevant and aligned with emerging demands of the changing landscape, I deconstructed the critical dimensions, context, and concept of creativity in postnormal times as well as undertaking in-depth research on the potential future workscape and the future of education and learning, applying a comprehensive foresight approach to the latter using a 2030-2040 horizon. Based upon the outcomes of these studies I designed an experimental integrative learning system that I have applied, researched, and evolved over the past 4 years with over 150 students at PhD and master’s level. The system is aimed at generating higher levels of creative engagement and development through a focus on increased immersion and creativity-inducing approaches. The system, which I call the Living Learning System, is based upon eight integrated elements, supported by course development pillars aimed at optimizing learner future skill competencies and levels of creativity for which I apply severalevaluation techniques and metrics. Accordingly, as the central hypothesis of this dissertation, I argue that by integrating the critical elements of the Living Learning System, such as emerging multisensory technology enhanced learning coupled with optimised transformative and experiential learning approaches, framed within the field of foresight, with its futures focus and decentralised thinking approaches, students increase their ability to be creative. This increased ability is based on the student attaining a richer level of personal ambience through deeper immersion generated through higher incidence of self-direction, constructivism-based blended pedagogy, futures literacy, and a balance of decentralised and systems-based thinking, as well as cognitive and social platforms aimed at optimizing learner creative achievement. This dissertation demonstrates how the application of the combined elements of the Living Learning System, with its futures focus and its ensuing transdisciplinary curricula and courses, can provide a clear path towards significantly increased learner creativity. The findings of the quantitative, questionnaire-based research set out in detail in Chapter 9, together with the performance and creativity evaluation models applied against the selected case studies of student projects substantiate the validity of the hypothesis that the application of the Living Learning System with its futures focus leads to increased creativity in line with the needs of the postnormal era.publishedVersio