Web-based learning through mixed-initiative interactions : design and implimentation

Mixed-initiative interaction is a naturally-occurring feature of human-human interactions. It characterize by turn-taking, frequent change of focus, agenda and control among the “speakers”. This human-based mixed-initiative interaction can be implemented through a mixed-initiative systems which are a popular approach to building intelligent systems that can collaborate naturally and effectively with people. Mixed-initiative systems exhibit various degrees of involvement in regards to the initiatives taken by the user or the system. In any discourse, the initiative may be shared between either, a learner and a system agent, or between two independent system agents. Both the parties in question establish and maintain a common goal and context, and proceed with an interaction mechanism involving initiative taking that optimizes their progress towards the goal. However, the application of mixed-initiative interaction in web-based learning is very much limited. In this paper, we discuss the design and implementation of a web-based learning system through mixedinitiative system known as JavaLearn. JavaLearn allows the interaction between the system (in the form of a software agent) and the individual learner. Here, the system supports the learning through a problem solving activity by demanding active learning behaviour from the learner with minimal natural language understanding by the agent and embodies the application-dependent aspects of the discourse. It guides the learner to solve the problem by giving adaptive advice, hints and engage the learner in the real time interaction in the form of “conversation”. The principal features of this system are: It is adaptive and are based on reflection, observation and relation. The system acquires its intelligence through the finite state machine and rule-based agents. (Abstract by authors

A Multi-Agent Architecture for An Intelligent Web-Based Educational System

An intelligent educational system must constitute an adaptive system built on multi-agent system architecture. The multi-agent architecture component provides self-organization, self-direction, and other control functionalities that are crucially important for an educational system. On the other hand, the adaptiveness of the system is necessary to provide customization, diversification, and interactional functionalities. Therefore, an educational system architecture that integrates multi-agent functionality [50] with adaptiveness can offer the learner the required independent learning experience. An educational system architecture is a complex structure with an intricate hierarchal organization where the functional components of the system undergo sophisticated and unpredictable internal interactions to perform its function. Hence, the system architecture must constitute adaptive and autonomous agents differentiated according to their functions, called multi-agent systems (MASs). The research paper proposes an adaptive hierarchal multi-agent educational system (AHMAES) [51] as an alternative to the traditional education delivery method. The document explains the various architectural characteristics of an adaptive multi-agent educational system and critically analyzes the system’s factors for software quality attributes

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks

Modelling human teaching tactics and strategies for tutoring systems

One of the promises of ITSs and ILEs is that they will teach and assist learning in an intelligent manner. Historically this has tended to mean concentrating on the interface, on the representation of the domain and on the representation of the student’s knowledge. So systems have attempted to provide students with reifications both of what is to be learned and of the learning process, as well as optimally sequencing and adjusting activities, problems and feedback to best help them learn that domain. We now have embodied (and disembodied) teaching agents and computer-based peers, and the field demonstrates a much greater interest in metacognition and in collaborative activities and tools to support that collaboration. Nevertheless the issue of the teaching competence of ITSs and ILEs is still important, as well as the more specific question as to whether systems can and should mimic human teachers. Indeed increasing interest in embodied agents has thrown the spotlight back on how such agents should behave with respect to learners. In the mid 1980s Ohlsson and others offered critiques of ITSs and ILEs in terms of the limited range and adaptability of their teaching actions as compared to the wealth of tactics and strategies employed by human expert teachers. So are we in any better position in modelling teaching than we were in the 80s? Are these criticisms still as valid today as they were then? This paper reviews progress in understanding certain aspects of human expert teaching and in developing tutoring systems that implement those human teaching strategies and tactics. It concentrates particularly on how systems have dealt with student answers and how they have dealt with motivational issues, referring particularly to work carried out at Sussex: for example, on responding effectively to the student’s motivational state, on contingent and Vygotskian inspired teaching strategies and on the plausibility problem. This latter is concerned with whether tactics that are effectively applied by human teachers can be as effective when embodied in machine teachers

Bayesian Knowledge Tracing for Navigation through Marzano’s Taxonomy

In this paper we propose a theoretical model of an ITS (Intelligent Tutoring Systems) capable of improving and updating computer-aided navigation based on Bloom’s taxonomy. For this we use the Bayesian Knowledge Tracing algorithm, performing an adaptive control of the navigation among different levels of cognition in online courses. These levels are defined by a taxonomy of educational objectives with a hierarchical order in terms of the control that some processes have over others, called Marzano’s Taxonomy, that takes into account the metacognitive system, responsible for the creation of goals as well as strategies to fulfill them. The main improvements of this proposal are: 1) An adaptive transition between individual assessment questions determined by levels of cognition. 2) A student model based on the initial response of a group of learners which is then adjusted to the ability of each learner. 3) The promotion of metacognitive skills such as goal setting and self-monitoring through the estimation of attempts required to pass the levels. One level of Marzano's taxonomy was left in the hands of the human teacher, clarifying that a differentiation must be made between the tasks in which an ITS can be an important aid and in which it would be more difficult