EFFECTS OF TWO SELECTED INTELLIGENT TUTORS ON PRE-SERVICE PHYSICS TEACHERS’ ACADEMIC PERFORMANCE IN COLLEGES OF EDUCATION ON SOUTHWESTERN NIGERIA

This study investigated the effects of the developed and adapted intelligent tutoring systems on academic performance of pre-service physics teachers in colleges of educations, southwestern Nigeria. It determines the effect of the intelligent tutors on retention of Physics concepts at the colleges of education. This is with the ultimate goal of proffering solution to the challenges of achieving some of the objectives of Physics education in the nation. The study adopted non-equivalent pretest - posttest control group design. The population comprised Physics students in colleges of education in Southwestern Nigeria. Three colleges of education were purposively selected from three states in Southwestern Nigeria based on proximity, willingness to participate in the experiment, availability of ICT facilities and a favourable academic calendar. Three intact classes of 200 Level Physics students who were randomly assigned to the locally developed intelligent tutoring systems labelled (ITPA), adapted intelligent tutoring systems labelled (ITPB) and the control groups were taught the topics Vector and Circular Motion. Intelligent Tutoring Performance Test (ITPT) an achievement test was used as pre and posttests for the academic performance. Data collected were analyzed using mean, t-test, one way Analysis of Variance (ANOVA), Scheffé and Bonferroni post-hoc analyses. The results showed significant difference in the posttest as well as in the retention test of those taught using the developed tutor, the adapted tutor and control (F = 144.32; p < 0.05).  Article visualizations

Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.

We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts

A review on massive e-learning (MOOC) design, delivery and assessment

MOOCs or Massive Online Open Courses based on Open Educational Resources (OER) might be one of the most versatile ways to offer access to quality education, especially for those residing in far or disadvantaged areas. This article analyzes the state of the art on MOOCs, exploring open research questions and setting interesting topics and goals for further research. Finally, it proposes a framework that includes the use of software agents with the aim to improve and personalize management, delivery, efficiency and evaluation of massive online courses on an individual level basis.Peer ReviewedPostprint (author's final draft

A group learning management method for intelligent tutoring systems

In this paper we propose a group management specification and execution method that seeks a compromise between simple course design and complex adaptive group interaction. This is achieved through an authoring method that proposes predefined scenarios to the author. These scenarios already include complex learning interaction protocols in which student and group models use and update are automatically included. The method adopts ontologies to represent domain and student models, and object Petri nets to specify the group interaction protocols. During execution, the method is supported by a multi-agent architecture

Teaching Construction in the Virtual University: the WINDS project

This paper introduces some of the Information Technology solutions adopted in Web based INtelligent Design Support (WINDS) to support education in A/E/C design. The WINDS project WINDS is an EC-funded project in the 5th Framework, Information Society Technologies programme, Flexible University key action. WINDS is divided into two actions: ·The research technology action is going to implement a learning environment integrating an intelligent tutoring system, a computer instruction management system and a set of co-operative supporting tools. ·The development action is going to build a large knowledge base supporting Architecture and Civil Engineering Design Courses and to experiment a comprehensive Virtual School of Architecture and Engineering Design. During the third year of the project, more than 400 students all over Europe will attend the Virtual School. During the next three years the WINDS project will span a total effort of about 150 man-years from 28 partners of 10 European countries. The missions of the WINDS project are: Advanced Methodologies in Design Education. WINDS drives a breakdown with conventional models in design education, i.e. classroom or distance education. WINDS implements a problem oriented knowledge transfer methodology following Roger Schank's Goal Based Scenario (GBS) pedagogical methodology. GBS encourages the learning of both skills and cases, and fosters creative problem solving. Multidisciplinary Design Education. Design requires creative synthesis and open-end problem definition at the intersection of several disciplines. WINDS experiments a valuable integration of multidisciplinary design knowledge and expertise to produce a high level standard of education. Innovative Representation, Delivery and Access to Construction Education. WINDS delivers individual education customisation by allowing the learner access through the Internet to a wide range of on-line courses and structured learning objects by means of personally tailored learning strategies. WINDS promotes the 3W paradigm: learn What you need, Where you want, When you require. Construction Practice. Construction industry is a repository of ""best practices"" and knowledge that the WINDS will profit. WINDS system benefits the ISO10303 and IFC standards to acquire knowledge of the construction process directly in digital format. On the other hand, WINDS reengineers the knowledge in up-to-date courses, educational services, which the industries can use to provide just-in-time rather than in-advance learning. WINDS IT Solutions The missions of the WINDS project state many challenging requirements both in knowledge and system architecture. Many of the solutions adopted in these fields are innovative; others are evolution of existing technologies. This paper focuses on the integration of this set of state-of-the-art technologies in an advanced and functionally sound Computer Aided Instruction system for A/E/C Design. In particular the paper deals with the following aspects: Standard Learning Technology Architecture The WINDS system relies on the in progress IEEE 1484.1 Learning Technology Standard Architecture. According to this standard the system consists of two data stores, the Knowledge Library and the Record Database, and four process: System Coach, Delivery, Evaluation and the Learner. WINDS implements the Knowledge Library into a three-tier architecture: 1.Learning Objects: ·Learning Units are collections of text and multimedia data. ·Models are represented in either IFC or STEP formats. ·Cases are sets of Learning Units and Models. Cases are noteworthy stories, which describes solutions, integrate technical detail, contain relevant design failures etc. 2.Indexes refer to the process in which the identification of relevant topics in design cases and learning units takes place. Indexing process creates structures of Learning Objects for course management, profile planning procedures and reasoning processes. 3.Courses are taxonomies of either Learning Units or a design task and Course Units. Knowledge Representation WINDS demonstrates that it is possible and valuable to integrate a widespread design expertise so that it can be effectively used to produce a high level standard of education. To this aim WINDS gathers area knowledge, design skills and expertise under the umbrellas of common knowledge representation structures and unambiguous semantics. Cases are one of the most valuable means for the representation of design expertise. A Case is a set of Learning Units and Product Models. Cases are noteworthy stories, which describe solutions, integrate technical details, contain relevant design failures, etc. Knowledge Integration Indexes are a medium among different kind of knowledge: they implement networks for navigation and access to disparate documents: HTML, video, images, CAD and product models (STEP or IFC). Concept indexes link learning topics to learning objects and group them into competencies. Index relationships are the base of the WINDS reasoning processes, and provide the foundation for system coaching functions, which proactively suggest strategies, solutions, examples and avoids students' design deadlock. Knowledge Distribution To support the data stores and the process among the partners in 10 countries efficiently, WINDS implements an object oriented client/server as COM objects. Behind the DCOM components there is the Dynamic Kernel, which dynamically embodies and maintains data stores and process. Components of the Knowledge Library can reside on several servers across the Internet. This provides for distributed transactions, e.g. a change in one Learning Object affects the Knowledge Library spread across several servers in different countries. Learning objects implemented as COM objects can wrap ownership data. Clear and univocal definition of ownerships rights enables Universities, in collaboration with telecommunication and publisher companies, to act as "education brokers". Brokerage in education and training is an innovative paradigm to provide just-in-time and personally customised value added learning knowledg