Optimising ITS behaviour with Bayesian networks and decision theory

We propose and demonstrate a methodology for building tractable normative intelligent tutoring systems (ITSs). A normative ITS uses a Bayesian network for long-term student modelling and decision theory to select the next tutorial action. Because normative theories are a general framework for rational behaviour, they can be used to both define and apply learning theories in a rational, and therefore optimal, way. This contrasts to the more traditional approach of using an ad-hoc scheme to implement the learning theory. A key step of the methodology is the induction and the continual adaptation of the Bayesian network student model from student performance data, a step that is distinct from other recent Bayesian net approaches in which the network structure and probabilities are either chosen beforehand by an expert, or by efficiency considerations. The methodology is demonstrated by a description and evaluation of CAPIT, a normative constraint-based tutor for English capitalisation and punctuation. Our evaluation results show that a class using the full normative version of CAPIT learned the domain rules at a faster rate than the class that used a non-normative version of the same system

Virtual reality in the rehabilitation of people with intellectual disabilities

Virtual reality (VR) possesses many qualities that give it rehabilitative potential for people with intellectual disabilities, both as an intervention and an assessment. It can provide a safe setting in which to practice skills that might carry too many risks in the real world. Unlike human tutors, computers are infinitely patient and consistent. Virtual worlds can be manipulated in ways the real world cannot be and can convey concepts without the use of language or other symbol systems. Published applications for this client group have all been as rehabilitative interventions. These are described in three groups: promoting skills for independent living, enhancing cognitive performance, and improving social skills. Five groups of studies are reviewed that utilize virtual technology to promote skills for independent living: grocery shopping, preparing food, orientation, road safety, and manufacturing skills. Fears that skills or habits learnt in a virtual setting would not transfer to the real world setting have not been supported by the available evidence, apart from those studies with people with autistic spectrum disorders. Future directions are in the development of more applications for independent living skills, exploring interventions for promoting motor and cognitive skills, and the developments of ecologically valid forms of assessment

Intelligent tutoring systems for systems engineering methodologies

The general goal is to provide the technology required to build systems that can provide intelligent tutoring in IDEF (Integrated Computer Aided Manufacturing Definition Method) modeling. The following subject areas are covered: intelligent tutoring systems for systems analysis methodologies; IDEF tutor architecture and components; developing cognitive skills for IDEF modeling; experimental software; and PC based prototype

Development of a data model for an Adaptive Multimedia Presentation System (AMPS)

We investigate the requirements and nature of data models for a multimedia learning system that presents adaptable learning objects based on a range of stimuli provided by the student and tutor. A conceptual model is explored together with a proposal for an implementation using the well-known relational data model. We also investigate how to describe the learning objects in the form of hierarchical subject ontology. An ontological calculus is created to allow knowledge metrics to be constructed for evaluation within data models. We further consider the limitations of the relational abstract data model to accurately represent the meaning and understanding of learning objects and contrast this with less structured data models implicit in ontological hierarchies. Our findings indicate that more consideration is needed into how to match traditional data models with ontological structures, especially in the area of database integrity constraints

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

Face-to-face and online interactions - is a task a task?

This study contrasts two different ways of analysing interaction and participation in language learning tutorials: Social network analysis of frequency and QSR analysis of type of interaction. One task from three German beginners' language tutorials (one delivered face-to-face, the other two online) is analysed. A description of the background and method of the study is provided together with some examples of the findings. As this is work in progress, only tentative conclusions can be provided at this stage

The Session Inventory Module within an Intelligent Tutoring System for Data Normalization

The knowledge acquisition of data normalization has been commonly perceived as a hurdle that is challenging students in the beginning database classes. As an effort to provide students with additional assistance while learning this topic, an intelligent tutoring system is proposed and implemented that can be used as a virtual private tutor to teach students in a one-on-one manner. This paper describes the strategically design and management of the tutorial sessions within this system. These sessions are designed and maintained according to their theme topics and difficulty levels so that the virtual tutor can dynamically select sessions based on the assessment of a student’s knowledge level and progress

Technology-enhanced Personalised Learning: Untangling the Evidence

Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves