NEURAL NETWORKS TO SIMULATE HUMAN LEARNING: A SHIFT TOWARDS MODULAR ARCHITECTURES

Size: px
Start display at page:

Download "NEURAL NETWORKS TO SIMULATE HUMAN LEARNING: A SHIFT TOWARDS MODULAR ARCHITECTURES"

Transcription

1 NEURAL NETWORKS TO SIMULATE HUMAN LEARNING: A SHIFT TOWARDS MODULAR ARCHITECTURES Syed Sibte Raza Abidi School of Computer Sciences Universiti Sains Malaysia Penang, Malaysia. sraza@cs.usm.my ABSTRACT Neural networks have a natural propensity for learning - learning from being instructed or from experience. We believe that neural networks provide substantial opportunities for simulating various human learning activities in that these networks emphasise an inherent adaptive learning ability, either supervised (based on instructions) or unsupervised (based on observations). However, to use neural networks for simulating aspects of human cognition and development it remains of interest to examine the parallels, if any, between human learning and neural network learning mechanisms. For that matter, we suggest a possible interpretation of traditional psychological notions of human learning in a neural network terminology. We argue that in order to simulate aspects of human learning, it is important to use a modular neural network architecture that integrates a variety of neural networks in some principled fashion. We propose a framework for developing modular neural network architectures, and present ACCLAIM, an exemplar modular neural network architecture for simulating the development of early child language. 1. INTRODUCTION Learning is an extremely studied subject, particularly in psychology and more recently in Artificial Intelligence and Neural s (or Connectionist s). Neural networks emphasise an inherent adaptive learning ability, either supervised (based on instructions) or unsupervised (based on observations). Both the neural network community and psychologists widely suggest that due to their learning capabilities, neural networks provide substantial opportunities for simulating various human learning activities. However, it remains of interest to examine the parallels, if any, between human learning and neural network learning mechanisms. The influence of psychology on neural network learning has been always very direct: Hebbian learning, that is, to reinforce the connection weights between simultaneously active units, was inspired by early Pavlovian learning models. More recently, some neural network researchers and philosophers of science, including McClelland (1988), Bechtal & Abrahamsen (1991) and Seidenberg (1993) have suggested a neural network based interpretation of psychological notions of learning, in particular notions of human learning proposed by the eminent Swiss psychologist Jean Piaget. In this paper, we firstly provide an interpretation of psychological notions of human learning in a neural network terminology. This exercise leads towards establishing the aptness of neural networks for simulating aspects of human learning and cognition. Next, we argue that to perform realistic simulations of human learning activities it is important to use modular neural network architectures, so as to incorporate the variety of constraints that need to be addressed by a realistic simulation. In this regard, we propose a framework for developing modular neural network architectures. Finally, we demonstrate how neural networks can be used to simulate, or more accurately trained, to mimic the development of language amongst children during infancy. We present an exemplar modular neural network architecture -- ACCLAIM, which not only simulates aspects of the development of child language both at the oneword and two-word stage, but also produces child-like one-word utterances and two-word sentences.

2 2. AN INTERPRETATION OF HUMAN LEARNING IN TERMS OF NEURAL NETWORK TERMINOLOGY According to eminent developmental psychologist Jean Piaget, learning is the acquisition of knowledge due to some particular information provided by the environment. Learning is inconceivable without a theoretically prior interior structure of equilibrium which provides the capacity to learn and the structuring of the learning process; in a wide sense, it includes both (Furth, 1969: 294). Furthermore, the so-called cognitive development is made possible through an interaction between what Piaget s calls assimilation - a process by which perceptual stimuli are absorbed and interpreted, and accommodation - a co-occurring process whereby the internal structures are adjusted to facilitate the assimilation of the new perceptual stimuli. It may be noted that Piaget s definition of human learning includes references to environment, prior interior structure, capacity to learn and a learning process. Piagetian notions of learning, synthesising biological growth and environmental influence, have a computational interpretation, albeit rather a simplistic one. We argue that a computational interpretation of psychological notions of learning need to incorporate data structures that can learn. By learning we mean that the data structures should have the tendency to modify or expand to incorporate new information acquired by way of continuous interaction with the environment. Traditional AI structures may suffice to represent knowledge, but they lack the ability to learn in a developmental, time-varying manner. On the contrary, we find neural networks having a natural propensity to learn - either from experience or from being told, and that their learning mechanisms have some empathy with Piaget s notions of learning. Our attempt to reinterpret Piaget s notion of learning in neural network terminology takes into account James McClelland s (1989) seminal paper, in which he provides an introductory exposition of connectionism s 1 influence on modelling aspects of cognition. McClelland s essay includes the description of the learning principle governing cognitive development: adjust the parameters of the mind in proportion to the extent to which their adjustments can produce a reduction in the discrepancy between the expected and observed events (1989:20). It is of interest to note that, from McClelland s learning principle, which claims to capture the residue of Piaget s notions of human learning, emerges a neural network based interpretation of Piaget s notions of learning, as noted in table 1. Piagetian Constructs Analogous Neural Notions Parameters of the Connections among units. Both entities are amenable to alteration mind due to experience. Expected event Desired pattern of activation over the network's output units. Observed event Actual pattern of activation produced over the network's output units. Adjustment of the Connectionist learning processes that involve adjustment of parameters connections. Discrepancy 'Error minimisation' process during connectionist learning, reducing reduction error between expected and observed pattern of activation. Table 1: Correspondence between Piagetian constructs and analogous neural network notions. We believe that, an advantage of suggesting a neural network based interpretation of Piagetian notions of learning is that these notions can now be implemented into neural networks and can be observed by simulating a variety of cognition oriented scenarios, for instance the simulation of concept development, language development, language production and so on. 3. A SHIFT TOWARDS MODULAR NEURAL NETWORK ARCHITECTURES 1 We would use the wordconnectionism or Connectionist as a synonym of neural networks

3 Over the years neural network technology has certainly matured, in a theoretical sense new neural network architectures and learning algorithms have been formulated, also the philosophical implications of neural networks are seemingly now more well-grounded. We believe that now when the efficacy of neural networks is widely accepted, the scope of cognitive modelling using neural networks need to be expanded. Previously, many neural network researchers have attempted to simulate aspects of human learning, in particular linguistic behaviour, using a single neural network and learning mechanism -- the multilayered backpropagation network, a controlled feedback loop, implementing a supervised learning algorithm. The success of such simulations was determined in terms of the ability of the neural network to associate a set of input patterns with a corresponding set of output patterns. Although, the strategy employed by early researchers is seemingly valid for simulating low-level cognitive activities, however if one needs to simulate a high level cognitive activity, which involves an interplay of a variety of cognitive aspects, the single neural network approach would certainly prove inadequate. Developmental psychologists have consistently argued that human development, which may include the development of language, sensori-motor control, visual recognition, and object permanence, etc. is achieved through different learning mechanisms, for instance error correction, classical conditioning, self-organisation, pattern classification and so on. Therefore, to perform a realistic simulation of some aspect of human cognitive development, in our case language development, one at least needs a unified framework that (a) incorporates a variety of learning mechanisms; (b) manipulates a variety of inputs - perceptual, verbal, functional, etc.; (c) includes both localist and distributed representation schemes; and (d) satisfies multiple simultaneous constraints. This brings into relief the need for a modular neural network architecture: an architecture that integrates both supervised and unsupervised learning algorithms in a unified framework, thus exploiting the collective strengths of a variety of neural networks to provide a more realistic simulation. In a modular neural network architecture the individual neural networks retain their structural and functional distinctness and can be viewed as independent 'modules' of a model. Development of modular neural network architectures, in simple terms, requires the mixing and matching of the relative strengths of a variety of neural networks. We propose a framework for developing modular neural network architectures that distinguishes candidate neural networks on the basis of their intrinsic characteristics such as learning mechanisms, input/output representation schemes, environmental considerations and so on (Abidi, 1994 & 1996). Our framework mainly emphasises (i) psychological and neurobiological distinctions between various neural networks when selecting neural networks to simulate certain tasks; (ii) architectural specifications - determining the number of layers, number of units in a layer, activation update functions and learning parameters; (iii) a plausible connectivity scheme by which various neural networks can efficiently communicate with each other; and (iv) variety of training strategies, including (a) one neural network learning its training data independently; (b) two or more neural networks learning their specified training data simultaneously; and (c) a co-operative training strategy where one or more neural networks transform the training data to a representation scheme that is interpretable by the principal neural network being trained. We present below a seven phase strategy for developing modular neural network architectures: I. identify the sub-tasks constituting a complex cognitive activity. Use an individual neural network to simulate a sub-task. Such a neural network can be regarded as an independent module of the modular architecture. II. design appropriate neural networks that can simulate the sub-tasks. The design metrics are the number of layers, number of units in each layer, connectivity pattern of the layers and the activation function of the units. III. develop a knowledge representation scheme that can be shared by other neural networks, i.e., the knowledge stored in one network is accessible to other networks in the modular architecture. IV. establish a communication mechanism among the neural networks so that information is accessible throughout the modular architecture. V. train each neural network with its respective stimuli either separately or if needed in conjunction with other related networks.

4 VI. represent explicitly the knowledge learnt by each neural network, such that it is understandable and has some significance to an external observer. VII. formulate a processing scheme that may synchronise the overall operation of the modular architecture. The processing scheme should retain concurrency, enhance the processing strengths of various networks and at the same time avoid unnecessary cross-talk (influence) between the neural networks. 4. ACCLAIM - A MODULAR NEURAL NETWORK ARCHITECTURE Language development is an exemplar high level human cognitive development; a complex activity that seems improbable to simulate using just a single neural network. Rather, a realistic simulation of child language development would require a variety of multi-layered neural networks: for instance, one to learn to process lexical input and output, yet another to learn phonology and more networks to learn concepts, semantic relations and word-order. Based on earlier proposals advocating the modularity approach for simulating high-level cognitive activities, we present a modular neural network model - ACCLAIM (A Connectionist Child LAnguage Development & Imitation Model), which simulates child language development within the age group 9-24 months. ACCLAIM systematically synthesises both supervised and unsupervised learning neural networks (including Kohonen Maps, Backpropagation networks, Hebbian Connections and the Spreading Activation mechanism), based on our psycholinguistic model of child language development. ACCLAIM (see figure 1b) has been used to simulate the development and categorisation of concepts amongst children together with the lexicalisation of these concepts: the 'concept memory' and 'word lexicon' have been simulated using two independent Kohonen Maps that are linked together through a Hebbian Connection based naming connection network. Backpropagation networks have been used to implement a 'conceptual relation network (for one-word sentence production) and to implement a 'wordorder network (for two-word sentence production). Children's evolving semantic performance has been simulated by a semantic relation network using an Hebbian Connection. The training data used for our simulation is based on 'realistic' child language data acquired from various child language studies. Perceptual Stimuli Concepts Perceptual Stimuli Unitary Concept Module Concept Memory Naming Connection Module Concept Memory Semantic Relation Module Concept Memory concepts Naming Connections concept categories Conceptual Relations Word Lexicon Semantic Relations One-word utterance Words Learnt Semantic Relations PERCEPTUAL INPUT (Semantic Features) Concept Memory Kohonen Map Conceptual Relations Backpropagation Naming Connection Hebbian Connections Semantic Relations Hebbian Connections Word-Order Testing LINGUISTIC INPUT (Adult two-word collocations) Word Lexicon Kohonen Map Two-word collocation Word Lexicon Word Order Module words Word-order Learnt Word-order ONE-WORD UTTERANCES Backpropagation TWO-WORD SENTENCES (a) (b) Figure 1: (a) Four neural network modules each comprising more than one neural network and simulating some aspect of child language development; (b) The modular architecture of ACCLAIM - an integration of various neural networks each simulating an aspect of child language development

5 Each of ACCLAIM s constituent neural networks can be envisaged as an individual entity, embodying a different kind of knowledge. These neural networks can then be configured based on our psycholinguistic model to realise a variety of neural network modules, where each module simulates an aspect of child language development. For instance, the naming connection module which simulates concept naming, comprises three neural networks - the concept memory, word lexicon and naming connection network. It should be noted that within a neural network module the constituent neural networks retain their identity and interact with each other. In ACCLAIM, four different neural network modules (shown in Figure 1a) relevant to child language development are implemented by integrating various neural networks. One of the advantages of the modular design of ACCLAIM is that knowledge learnt by an individual neural networks is utilised by more than one module, for instance the concepts stored in the concept memory are used by three modules - the one-word module, naming connection module and the semantic relation module. At a deeper level, each module again can be envisaged as an independent model, capable of simulating a psycholinguistic process on its own. For instance, a simulation of the child s development of semantic relations can be performed by just employing the semantic relation module. The modular approach of ACCLAIM makes it possible to work with one module at a time; enabling the simulation of the process with a variety of data without taking into account other modules. More attractively, at a later stage the results of the simulation obtained from one module can be used in simulations involving other modules. Finally, the efficacy of our simulation of child language development carried out using ACCLAIM is demonstrated by the fact that ACCLAIM is able to produce both one-word and two-word sentences in a certain situation, which are similar with the kind of sentences produced by a child in the same real-life situation. Furthermore, ACCLAIM is able to handle novel, noisy and incomplete input situations by generalising to produce adequate and meaningful responses. 5. CONCLUSIONS We have suggested a neural network interpretation of psychological notions of human learning which would assist researchers investigating the role of neural networks in simulating human learning and cognition. The move from single towards modular neural network architectures may form the basis for more elaborate and realistic simulations of human cognitive activities that involve an active interplay between a variety of processes. Furthermore, by way of ACCLAIM we have demonstrated how neural networks can be used to simulate high-level cognitive activities, in particular child language development The architecture of ACCLAIM and the resultant processing capabilities achieved, should be an indicator as to how functionally and structurally divergent neural networks when synthesised together in a meaningful manner, i.e. based on a psycholinguistic model, can simulate a high-level cognitive activity. REFERENCES Abidi, S.S.R. (1996) Neural s and Child Language Development: Towards a Conglomerate Neural Simulation Architecture. To present at International Conference on Neural Information Processing 96, Hong Kong. Abidi, S.S.R. & Ahmad, K. (1996) Child Language Development: A Connectionist Simulation of the Evolving Concept Memory. M. Aldridge (Ed.) Child language. Clevedon: Multilingual Matters Ltd. Abidi, S.S.R. & Ahmad, K. (1994) 'Connectionism as a Model for Child Language Development'. In Artificial Intelligence & Cognitive Science, Seventh Annual Irish Conference, Dublin. Bechtel, W. & Abrahamsen, A. (1991) Connectionism and the Mind. Oxford: Basil Blackwell. Furth, H. G. (1969) Piaget and Knowledge. Chicago: The University of Chicago Press.

6 McClelland, J. (1989) PDP: Implications for Cognition and Development. R. Morris (Ed.) Parallel Distributed Processing: Implications for Psychology and Neurobiology. Oxford: Clarendon Press. Seidenberg, M. (1993) Connectionist Models and Cognitive Theory. Psychological Science, Vol. 4, pp

Evolution of Symbolisation in Chimpanzees and Neural Nets

Evolution of Symbolisation in Chimpanzees and Neural Nets Evolution of Symbolisation in Chimpanzees and Neural Nets Angelo Cangelosi Centre for Neural and Adaptive Systems University of Plymouth (UK) a.cangelosi@plymouth.ac.uk Introduction Animal communication

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should

More information

Proposal of Pattern Recognition as a necessary and sufficient principle to Cognitive Science

Proposal of Pattern Recognition as a necessary and sufficient principle to Cognitive Science Proposal of Pattern Recognition as a necessary and sufficient principle to Cognitive Science Gilberto de Paiva Sao Paulo Brazil (May 2011) gilbertodpaiva@gmail.com Abstract. Despite the prevalence of the

More information

Abstractions and the Brain

Abstractions and the Brain Abstractions and the Brain Brian D. Josephson Department of Physics, University of Cambridge Cavendish Lab. Madingley Road Cambridge, UK. CB3 OHE bdj10@cam.ac.uk http://www.tcm.phy.cam.ac.uk/~bdj10 ABSTRACT

More information

AGENDA LEARNING THEORIES LEARNING THEORIES. Advanced Learning Theories 2/22/2016

AGENDA LEARNING THEORIES LEARNING THEORIES. Advanced Learning Theories 2/22/2016 AGENDA Advanced Learning Theories Alejandra J. Magana, Ph.D. admagana@purdue.edu Introduction to Learning Theories Role of Learning Theories and Frameworks Learning Design Research Design Dual Coding Theory

More information

Concept Acquisition Without Representation William Dylan Sabo

Concept Acquisition Without Representation William Dylan Sabo Concept Acquisition Without Representation William Dylan Sabo Abstract: Contemporary debates in concept acquisition presuppose that cognizers can only acquire concepts on the basis of concepts they already

More information

Document number: 2013/ Programs Committee 6/2014 (July) Agenda Item 42.0 Bachelor of Engineering with Honours in Software Engineering

Document number: 2013/ Programs Committee 6/2014 (July) Agenda Item 42.0 Bachelor of Engineering with Honours in Software Engineering Document number: 2013/0006139 Programs Committee 6/2014 (July) Agenda Item 42.0 Bachelor of Engineering with Honours in Software Engineering Program Learning Outcomes Threshold Learning Outcomes for Engineering

More information

Degeneracy results in canalisation of language structure: A computational model of word learning

Degeneracy results in canalisation of language structure: A computational model of word learning Degeneracy results in canalisation of language structure: A computational model of word learning Padraic Monaghan (p.monaghan@lancaster.ac.uk) Department of Psychology, Lancaster University Lancaster LA1

More information

Evaluation of Learning Management System software. Part II of LMS Evaluation

Evaluation of Learning Management System software. Part II of LMS Evaluation Version DRAFT 1.0 Evaluation of Learning Management System software Author: Richard Wyles Date: 1 August 2003 Part II of LMS Evaluation Open Source e-learning Environment and Community Platform Project

More information

Ling/Span/Fren/Ger/Educ 466: SECOND LANGUAGE ACQUISITION. Spring 2011 (Tuesdays 4-6:30; Psychology 251)

Ling/Span/Fren/Ger/Educ 466: SECOND LANGUAGE ACQUISITION. Spring 2011 (Tuesdays 4-6:30; Psychology 251) Ling/Span/Fren/Ger/Educ 466: SECOND LANGUAGE ACQUISITION Spring 2011 (Tuesdays 4-6:30; Psychology 251) Instructor Professor Joe Barcroft Department of Romance Languages and Literatures Office: Ridgley

More information

P. Belsis, C. Sgouropoulou, K. Sfikas, G. Pantziou, C. Skourlas, J. Varnas

P. Belsis, C. Sgouropoulou, K. Sfikas, G. Pantziou, C. Skourlas, J. Varnas Exploiting Distance Learning Methods and Multimediaenhanced instructional content to support IT Curricula in Greek Technological Educational Institutes P. Belsis, C. Sgouropoulou, K. Sfikas, G. Pantziou,

More information

Seminar - Organic Computing

Seminar - Organic Computing Seminar - Organic Computing Self-Organisation of OC-Systems Markus Franke 25.01.2006 Typeset by FoilTEX Timetable 1. Overview 2. Characteristics of SO-Systems 3. Concern with Nature 4. Design-Concepts

More information

Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments

Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Cristina Vertan, Walther v. Hahn University of Hamburg, Natural Language Systems Division Hamburg,

More information

Paper presented at the ERA-AARE Joint Conference, Singapore, November, 1996.

Paper presented at the ERA-AARE Joint Conference, Singapore, November, 1996. THE DEVELOPMENT OF SELF-CONCEPT IN YOUNG CHILDREN: PRESCHOOLERS' VIEWS OF THEIR COMPETENCE AND ACCEPTANCE Christine Johnston, Faculty of Nursing, University of Sydney Paper presented at the ERA-AARE Joint

More information

Python Machine Learning

Python Machine Learning Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled

More information

Knowledge-Based - Systems

Knowledge-Based - Systems Knowledge-Based - Systems ; Rajendra Arvind Akerkar Chairman, Technomathematics Research Foundation and Senior Researcher, Western Norway Research institute Priti Srinivas Sajja Sardar Patel University

More information

SARDNET: A Self-Organizing Feature Map for Sequences

SARDNET: A Self-Organizing Feature Map for Sequences SARDNET: A Self-Organizing Feature Map for Sequences Daniel L. James and Risto Miikkulainen Department of Computer Sciences The University of Texas at Austin Austin, TX 78712 dljames,risto~cs.utexas.edu

More information

Designing a Rubric to Assess the Modelling Phase of Student Design Projects in Upper Year Engineering Courses

Designing a Rubric to Assess the Modelling Phase of Student Design Projects in Upper Year Engineering Courses Designing a Rubric to Assess the Modelling Phase of Student Design Projects in Upper Year Engineering Courses Thomas F.C. Woodhall Masters Candidate in Civil Engineering Queen s University at Kingston,

More information

Ph.D. in Behavior Analysis Ph.d. i atferdsanalyse

Ph.D. in Behavior Analysis Ph.d. i atferdsanalyse Program Description Ph.D. in Behavior Analysis Ph.d. i atferdsanalyse 180 ECTS credits Approval Approved by the Norwegian Agency for Quality Assurance in Education (NOKUT) on the 23rd April 2010 Approved

More information

Programme Specification

Programme Specification Programme Specification Title: Accounting and Finance Final Award: Master of Science (MSc) With Exit Awards at: Postgraduate Certificate (PG Cert) Postgraduate Diploma (PG Dip) Master of Science (MSc)

More information

A Minimalist Approach to Code-Switching. In the field of linguistics, the topic of bilingualism is a broad one. There are many

A Minimalist Approach to Code-Switching. In the field of linguistics, the topic of bilingualism is a broad one. There are many Schmidt 1 Eric Schmidt Prof. Suzanne Flynn Linguistic Study of Bilingualism December 13, 2013 A Minimalist Approach to Code-Switching In the field of linguistics, the topic of bilingualism is a broad one.

More information

Artificial Neural Networks

Artificial Neural Networks Artificial Neural Networks Andres Chavez Math 382/L T/Th 2:00-3:40 April 13, 2010 Chavez2 Abstract The main interest of this paper is Artificial Neural Networks (ANNs). A brief history of the development

More information

Learning and Teaching

Learning and Teaching Learning and Teaching Set Induction and Closure: Key Teaching Skills John Dallat March 2013 The best kind of teacher is one who helps you do what you couldn t do yourself, but doesn t do it for you (Child,

More information

Learning Methods for Fuzzy Systems

Learning Methods for Fuzzy Systems Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8

More information

Transfer Learning Action Models by Measuring the Similarity of Different Domains

Transfer Learning Action Models by Measuring the Similarity of Different Domains Transfer Learning Action Models by Measuring the Similarity of Different Domains Hankui Zhuo 1, Qiang Yang 2, and Lei Li 1 1 Software Research Institute, Sun Yat-sen University, Guangzhou, China. zhuohank@gmail.com,lnslilei@mail.sysu.edu.cn

More information

Lecture 1: Basic Concepts of Machine Learning

Lecture 1: Basic Concepts of Machine Learning Lecture 1: Basic Concepts of Machine Learning Cognitive Systems - Machine Learning Ute Schmid (lecture) Johannes Rabold (practice) Based on slides prepared March 2005 by Maximilian Röglinger, updated 2010

More information

*** * * * COUNCIL * * CONSEIL OFEUROPE * * * DE L'EUROPE. Proceedings of the 9th Symposium on Legal Data Processing in Europe

*** * * * COUNCIL * * CONSEIL OFEUROPE * * * DE L'EUROPE. Proceedings of the 9th Symposium on Legal Data Processing in Europe *** * * * COUNCIL * * CONSEIL OFEUROPE * * * DE L'EUROPE Proceedings of the 9th Symposium on Legal Data Processing in Europe Bonn, 10-12 October 1989 Systems based on artificial intelligence in the legal

More information

Automating the E-learning Personalization

Automating the E-learning Personalization Automating the E-learning Personalization Fathi Essalmi 1, Leila Jemni Ben Ayed 1, Mohamed Jemni 1, Kinshuk 2, and Sabine Graf 2 1 The Research Laboratory of Technologies of Information and Communication

More information

have to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words,

have to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words, A Language-Independent, Data-Oriented Architecture for Grapheme-to-Phoneme Conversion Walter Daelemans and Antal van den Bosch Proceedings ESCA-IEEE speech synthesis conference, New York, September 1994

More information

White Paper. The Art of Learning

White Paper. The Art of Learning The Art of Learning Based upon years of observation of adult learners in both our face-to-face classroom courses and using our Mentored Email 1 distance learning methodology, it is fascinating to see how

More information

Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems

Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems Published in the International Journal of Hybrid Intelligent Systems 1(3-4) (2004) 111-126 Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems Ioannis Hatzilygeroudis and Jim Prentzas

More information

CWIS 23,3. Nikolaos Avouris Human Computer Interaction Group, University of Patras, Patras, Greece

CWIS 23,3. Nikolaos Avouris Human Computer Interaction Group, University of Patras, Patras, Greece The current issue and full text archive of this journal is available at wwwemeraldinsightcom/1065-0741htm CWIS 138 Synchronous support and monitoring in web-based educational systems Christos Fidas, Vasilios

More information

Computerized Adaptive Psychological Testing A Personalisation Perspective

Computerized Adaptive Psychological Testing A Personalisation Perspective Psychology and the internet: An European Perspective Computerized Adaptive Psychological Testing A Personalisation Perspective Mykola Pechenizkiy mpechen@cc.jyu.fi Introduction Mixed Model of IRT and ES

More information

Agent-Based Software Engineering

Agent-Based Software Engineering Agent-Based Software Engineering Learning Guide Information for Students 1. Description Grade Module Máster Universitario en Ingeniería de Software - European Master on Software Engineering Advanced Software

More information

Artificial Neural Networks written examination

Artificial Neural Networks written examination 1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14

More information

Laboratorio di Intelligenza Artificiale e Robotica

Laboratorio di Intelligenza Artificiale e Robotica Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning

More information

Intervening to alleviate word-finding difficulties in children: case series data and a computational modelling foundation

Intervening to alleviate word-finding difficulties in children: case series data and a computational modelling foundation PCGN1003204 Techset Composition India (P) Ltd., Bangalore and Chennai, India 1/20/2015 Cognitive Neuropsychology, 2015 http://dx.doi.org/10.1080/02643294.2014.1003204 5 Intervening to alleviate word-finding

More information

Rule Learning With Negation: Issues Regarding Effectiveness

Rule Learning With Negation: Issues Regarding Effectiveness Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United

More information

Is operations research really research?

Is operations research really research? Volume 22 (2), pp. 155 180 http://www.orssa.org.za ORiON ISSN 0529-191-X c 2006 Is operations research really research? NJ Manson Received: 2 October 2006; Accepted: 1 November 2006 Abstract This paper

More information

Kentucky s Standards for Teaching and Learning. Kentucky s Learning Goals and Academic Expectations

Kentucky s Standards for Teaching and Learning. Kentucky s Learning Goals and Academic Expectations Kentucky s Standards for Teaching and Learning Included in this section are the: Kentucky s Learning Goals and Academic Expectations Kentucky New Teacher Standards (Note: For your reference, the KDE website

More information

Certificate of Higher Education in History. Relevant QAA subject benchmarking group: History

Certificate of Higher Education in History. Relevant QAA subject benchmarking group: History Certificate of Higher Education in History Awarding Institution: The University of Reading Teaching Institution: The University of Reading Relevant QAA subject benchmarking group: History Faculty of Arts

More information

EDITORIAL: ICT SUPPORT FOR KNOWLEDGE MANAGEMENT IN CONSTRUCTION

EDITORIAL: ICT SUPPORT FOR KNOWLEDGE MANAGEMENT IN CONSTRUCTION EDITORIAL: SUPPORT FOR KNOWLEDGE MANAGEMENT IN CONSTRUCTION Abdul Samad (Sami) Kazi, Senior Research Scientist, VTT - Technical Research Centre of Finland Sami.Kazi@vtt.fi http://www.vtt.fi Matti Hannus,

More information

Conditions of study and examination regulations of the. European Master of Science in Midwifery

Conditions of study and examination regulations of the. European Master of Science in Midwifery Conditions of study and examination regulations of the European Master of Science in Midwifery Midwifery Research and Education Unit Department of Obstetrics and Gynaecology Hannover Medical School September

More information

Integrated Science Education in

Integrated Science Education in 5 Integrated Science Education in the Context of the Constructivism Theory: some important issues Vincentas Lamanauskas University of Šiauliai, Lithuania E-mail: v.lamanauskas@ef.su.lt It is obvious that

More information

PROCESS USE CASES: USE CASES IDENTIFICATION

PROCESS USE CASES: USE CASES IDENTIFICATION International Conference on Enterprise Information Systems, ICEIS 2007, Volume EIS June 12-16, 2007, Funchal, Portugal. PROCESS USE CASES: USE CASES IDENTIFICATION Pedro Valente, Paulo N. M. Sampaio Distributed

More information

Ontologies vs. classification systems

Ontologies vs. classification systems Ontologies vs. classification systems Bodil Nistrup Madsen Copenhagen Business School Copenhagen, Denmark bnm.isv@cbs.dk Hanne Erdman Thomsen Copenhagen Business School Copenhagen, Denmark het.isv@cbs.dk

More information

SOFTWARE EVALUATION TOOL

SOFTWARE EVALUATION TOOL SOFTWARE EVALUATION TOOL Kyle Higgins Randall Boone University of Nevada Las Vegas rboone@unlv.nevada.edu Higgins@unlv.nevada.edu N.B. This form has not been fully validated and is still in development.

More information

USER ADAPTATION IN E-LEARNING ENVIRONMENTS

USER ADAPTATION IN E-LEARNING ENVIRONMENTS USER ADAPTATION IN E-LEARNING ENVIRONMENTS Paraskevi Tzouveli Image, Video and Multimedia Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens tpar@image.

More information

Programme Specification

Programme Specification Programme Specification Title: Crisis and Disaster Management Final Award: Master of Science (MSc) With Exit Awards at: Postgraduate Certificate (PG Cert) Postgraduate Diploma (PG Dip) Master of Science

More information

DIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE. Junior Year. Summer (Bridge Quarter) Fall Winter Spring GAME Credits.

DIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE. Junior Year. Summer (Bridge Quarter) Fall Winter Spring GAME Credits. DIGITAL GAMING & INTERACTIVE MEDIA BACHELOR S DEGREE Sample 2-Year Academic Plan DRAFT Junior Year Summer (Bridge Quarter) Fall Winter Spring MMDP/GAME 124 GAME 310 GAME 318 GAME 330 Introduction to Maya

More information

COMPUTER-AIDED DESIGN TOOLS THAT ADAPT

COMPUTER-AIDED DESIGN TOOLS THAT ADAPT COMPUTER-AIDED DESIGN TOOLS THAT ADAPT WEI PENG CSIRO ICT Centre, Australia and JOHN S GERO Krasnow Institute for Advanced Study, USA 1. Introduction Abstract. This paper describes an approach that enables

More information

Axiom 2013 Team Description Paper

Axiom 2013 Team Description Paper Axiom 2013 Team Description Paper Mohammad Ghazanfari, S Omid Shirkhorshidi, Farbod Samsamipour, Hossein Rahmatizadeh Zagheli, Mohammad Mahdavi, Payam Mohajeri, S Abbas Alamolhoda Robotics Scientific Association

More information

Deep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach

Deep search. Enhancing a search bar using machine learning. Ilgün Ilgün & Cedric Reichenbach #BaselOne7 Deep search Enhancing a search bar using machine learning Ilgün Ilgün & Cedric Reichenbach We are not researchers Outline I. Periscope: A search tool II. Goals III. Deep learning IV. Applying

More information

A Pipelined Approach for Iterative Software Process Model

A Pipelined Approach for Iterative Software Process Model A Pipelined Approach for Iterative Software Process Model Ms.Prasanthi E R, Ms.Aparna Rathi, Ms.Vardhani J P, Mr.Vivek Krishna Electronics and Radar Development Establishment C V Raman Nagar, Bangalore-560093,

More information

A Note on Structuring Employability Skills for Accounting Students

A Note on Structuring Employability Skills for Accounting Students A Note on Structuring Employability Skills for Accounting Students Jon Warwick and Anna Howard School of Business, London South Bank University Correspondence Address Jon Warwick, School of Business, London

More information

ECE-492 SENIOR ADVANCED DESIGN PROJECT

ECE-492 SENIOR ADVANCED DESIGN PROJECT ECE-492 SENIOR ADVANCED DESIGN PROJECT Meeting #3 1 ECE-492 Meeting#3 Q1: Who is not on a team? Q2: Which students/teams still did not select a topic? 2 ENGINEERING DESIGN You have studied a great deal

More information

CONCEPT MAPS AS A DEVICE FOR LEARNING DATABASE CONCEPTS

CONCEPT MAPS AS A DEVICE FOR LEARNING DATABASE CONCEPTS CONCEPT MAPS AS A DEVICE FOR LEARNING DATABASE CONCEPTS Pirjo Moen Department of Computer Science P.O. Box 68 FI-00014 University of Helsinki pirjo.moen@cs.helsinki.fi http://www.cs.helsinki.fi/pirjo.moen

More information

Intermediate Computable General Equilibrium (CGE) Modelling: Online Single Country Course

Intermediate Computable General Equilibrium (CGE) Modelling: Online Single Country Course Intermediate Computable General Equilibrium (CGE) Modelling: Online Single Country Course Course Description This course is an intermediate course in practical computable general equilibrium (CGE) modelling

More information

1 NETWORKS VERSUS SYMBOL SYSTEMS: TWO APPROACHES TO MODELING COGNITION

1 NETWORKS VERSUS SYMBOL SYSTEMS: TWO APPROACHES TO MODELING COGNITION NETWORKS VERSUS SYMBOL SYSTEMS 1 1 NETWORKS VERSUS SYMBOL SYSTEMS: TWO APPROACHES TO MODELING COGNITION 1.1 A Revolution in the Making? The rise of cognitivism in psychology, which, by the 1970s, had successfully

More information

Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming

Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming Data Mining VI 205 Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming C. Romero, S. Ventura, C. Hervás & P. González Universidad de Córdoba, Campus Universitario de

More information

Mathematics Program Assessment Plan

Mathematics Program Assessment Plan Mathematics Program Assessment Plan Introduction This assessment plan is tentative and will continue to be refined as needed to best fit the requirements of the Board of Regent s and UAS Program Review

More information

Emotional Variation in Speech-Based Natural Language Generation

Emotional Variation in Speech-Based Natural Language Generation Emotional Variation in Speech-Based Natural Language Generation Michael Fleischman and Eduard Hovy USC Information Science Institute 4676 Admiralty Way Marina del Rey, CA 90292-6695 U.S.A.{fleisch, hovy}

More information

Development of an IT Curriculum. Dr. Jochen Koubek Humboldt-Universität zu Berlin Technische Universität Berlin 2008

Development of an IT Curriculum. Dr. Jochen Koubek Humboldt-Universität zu Berlin Technische Universität Berlin 2008 Development of an IT Curriculum Dr. Jochen Koubek Humboldt-Universität zu Berlin Technische Universität Berlin 2008 Curriculum A curriculum consists of everything that promotes learners intellectual, personal,

More information

DICTE PLATFORM: AN INPUT TO COLLABORATION AND KNOWLEDGE SHARING

DICTE PLATFORM: AN INPUT TO COLLABORATION AND KNOWLEDGE SHARING DICTE PLATFORM: AN INPUT TO COLLABORATION AND KNOWLEDGE SHARING Annalisa Terracina, Stefano Beco ElsagDatamat Spa Via Laurentina, 760, 00143 Rome, Italy Adrian Grenham, Iain Le Duc SciSys Ltd Methuen Park

More information

Programme Specification. MSc in International Real Estate

Programme Specification. MSc in International Real Estate Programme Specification MSc in International Real Estate IRE GUIDE OCTOBER 2014 ROYAL AGRICULTURAL UNIVERSITY, CIRENCESTER PROGRAMME SPECIFICATION MSc International Real Estate NB The information contained

More information

An Empirical and Computational Test of Linguistic Relativity

An Empirical and Computational Test of Linguistic Relativity An Empirical and Computational Test of Linguistic Relativity Kathleen M. Eberhard* (eberhard.1@nd.edu) Matthias Scheutz** (mscheutz@cse.nd.edu) Michael Heilman** (mheilman@nd.edu) *Department of Psychology,

More information

Evolutive Neural Net Fuzzy Filtering: Basic Description

Evolutive Neural Net Fuzzy Filtering: Basic Description Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:

More information

General syllabus for third-cycle courses and study programmes in

General syllabus for third-cycle courses and study programmes in ÖREBRO UNIVERSITY This is a translation of a Swedish document. In the event of a discrepancy, the Swedishlanguage version shall prevail. General syllabus for third-cycle courses and study programmes in

More information

MYCIN. The MYCIN Task

MYCIN. The MYCIN Task MYCIN Developed at Stanford University in 1972 Regarded as the first true expert system Assists physicians in the treatment of blood infections Many revisions and extensions over the years The MYCIN Task

More information

MASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE

MASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE Master of Science (M.S.) Major in Computer Science 1 MASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE Major Program The programs in computer science are designed to prepare students for doctoral research,

More information

Laboratorio di Intelligenza Artificiale e Robotica

Laboratorio di Intelligenza Artificiale e Robotica Laboratorio di Intelligenza Artificiale e Robotica A.A. 2008-2009 Outline 2 Machine Learning Unsupervised Learning Supervised Learning Reinforcement Learning Genetic Algorithms Genetics-Based Machine Learning

More information

Maximizing Learning Through Course Alignment and Experience with Different Types of Knowledge

Maximizing Learning Through Course Alignment and Experience with Different Types of Knowledge Innov High Educ (2009) 34:93 103 DOI 10.1007/s10755-009-9095-2 Maximizing Learning Through Course Alignment and Experience with Different Types of Knowledge Phyllis Blumberg Published online: 3 February

More information

MSc Education and Training for Development

MSc Education and Training for Development MSc Education and Training for Development Awarding Institution: The University of Reading Teaching Institution: The University of Reading Faculty of Life Sciences Programme length: 6 month Postgraduate

More information

Aligning learning, teaching and assessment using the web: an evaluation of pedagogic approaches

Aligning learning, teaching and assessment using the web: an evaluation of pedagogic approaches British Journal of Educational Technology Vol 33 No 2 2002 149 158 Aligning learning, teaching and assessment using the web: an evaluation of pedagogic approaches Richard Hall Dr Richard Hall is the project

More information

Language Acquisition Fall 2010/Winter Lexical Categories. Afra Alishahi, Heiner Drenhaus

Language Acquisition Fall 2010/Winter Lexical Categories. Afra Alishahi, Heiner Drenhaus Language Acquisition Fall 2010/Winter 2011 Lexical Categories Afra Alishahi, Heiner Drenhaus Computational Linguistics and Phonetics Saarland University Children s Sensitivity to Lexical Categories Look,

More information

Merbouh Zouaoui. Melouk Mohamed. Journal of Educational and Social Research MCSER Publishing, Rome-Italy. 1. Introduction

Merbouh Zouaoui. Melouk Mohamed. Journal of Educational and Social Research MCSER Publishing, Rome-Italy. 1. Introduction Acquiring Communication through Conversational Training: The Case Study of 1 st Year LMD Students at Djillali Liabès University Sidi Bel Abbès Algeria Doi:10.5901/jesr.2014.v4n6p353 Abstract Merbouh Zouaoui

More information

USING LEARNING THEORY IN A HYPERMEDIA-BASED PETRI NET MODELING TUTORIAL

USING LEARNING THEORY IN A HYPERMEDIA-BASED PETRI NET MODELING TUTORIAL USING LEARNING THEORY IN A HYPERMEDIA-BASED PETRI NET MODELING TUTORIAL A Paper Submitted to the Graduate Faculty of the North Dakota State University of Agriculture and Applied Science By Vaibhav Kumar

More information

A cautionary note is research still caught up in an implementer approach to the teacher?

A cautionary note is research still caught up in an implementer approach to the teacher? A cautionary note is research still caught up in an implementer approach to the teacher? Jeppe Skott Växjö University, Sweden & the University of Aarhus, Denmark Abstract: In this paper I outline two historically

More information

SPATIAL SENSE : TRANSLATING CURRICULUM INNOVATION INTO CLASSROOM PRACTICE

SPATIAL SENSE : TRANSLATING CURRICULUM INNOVATION INTO CLASSROOM PRACTICE SPATIAL SENSE : TRANSLATING CURRICULUM INNOVATION INTO CLASSROOM PRACTICE Kate Bennie Mathematics Learning and Teaching Initiative (MALATI) Sarie Smit Centre for Education Development, University of Stellenbosch

More information

Chamilo 2.0: A Second Generation Open Source E-learning and Collaboration Platform

Chamilo 2.0: A Second Generation Open Source E-learning and Collaboration Platform Chamilo 2.0: A Second Generation Open Source E-learning and Collaboration Platform doi:10.3991/ijac.v3i3.1364 Jean-Marie Maes University College Ghent, Ghent, Belgium Abstract Dokeos used to be one of

More information

DG 17: The changing nature and roles of mathematics textbooks: Form, use, access

DG 17: The changing nature and roles of mathematics textbooks: Form, use, access DG 17: The changing nature and roles of mathematics textbooks: Form, use, access Team Chairs: Berinderjeet Kaur, Nanyang Technological University, Singapore berinderjeet.kaur@nie.edu.sg Kristina-Reiss,

More information

Course Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE

Course Outline. Course Grading. Where to go for help. Academic Integrity. EE-589 Introduction to Neural Networks NN 1 EE EE-589 Introduction to Neural Assistant Prof. Dr. Turgay IBRIKCI Room # 305 (322) 338 6868 / 139 Wensdays 9:00-12:00 Course Outline The course is divided in two parts: theory and practice. 1. Theory covers

More information

IAT 888: Metacreation Machines endowed with creative behavior. Philippe Pasquier Office 565 (floor 14)

IAT 888: Metacreation Machines endowed with creative behavior. Philippe Pasquier Office 565 (floor 14) IAT 888: Metacreation Machines endowed with creative behavior Philippe Pasquier Office 565 (floor 14) pasquier@sfu.ca Outline of today's lecture A little bit about me A little bit about you What will that

More information

The Strong Minimalist Thesis and Bounded Optimality

The Strong Minimalist Thesis and Bounded Optimality The Strong Minimalist Thesis and Bounded Optimality DRAFT-IN-PROGRESS; SEND COMMENTS TO RICKL@UMICH.EDU Richard L. Lewis Department of Psychology University of Michigan 27 March 2010 1 Purpose of this

More information

UML MODELLING OF DIGITAL FORENSIC PROCESS MODELS (DFPMs)

UML MODELLING OF DIGITAL FORENSIC PROCESS MODELS (DFPMs) UML MODELLING OF DIGITAL FORENSIC PROCESS MODELS (DFPMs) Michael Köhn 1, J.H.P. Eloff 2, MS Olivier 3 1,2,3 Information and Computer Security Architectures (ICSA) Research Group Department of Computer

More information

Nottingham Trent University Course Specification

Nottingham Trent University Course Specification Nottingham Trent University Course Specification Basic Course Information 1. Awarding Institution: Nottingham Trent University 2. School/Campus: Nottingham Business School / City 3. Final Award, Course

More information

HARPER ADAMS UNIVERSITY Programme Specification

HARPER ADAMS UNIVERSITY Programme Specification HARPER ADAMS UNIVERSITY Programme Specification 1 Awarding Institution: Harper Adams University 2 Teaching Institution: Askham Bryan College 3 Course Accredited by: Not Applicable 4 Final Award and Level:

More information

Online Marking of Essay-type Assignments

Online Marking of Essay-type Assignments Online Marking of Essay-type Assignments Eva Heinrich, Yuanzhi Wang Institute of Information Sciences and Technology Massey University Palmerston North, New Zealand E.Heinrich@massey.ac.nz, yuanzhi_wang@yahoo.com

More information

YMCA SCHOOL AGE CHILD CARE PROGRAM PLAN

YMCA SCHOOL AGE CHILD CARE PROGRAM PLAN YMCA SCHOOL AGE CHILD CARE PROGRAM PLAN (normal view is landscape, not portrait) SCHOOL AGE DOMAIN SKILLS ARE SOCIAL: COMMUNICATION, LANGUAGE AND LITERACY: EMOTIONAL: COGNITIVE: PHYSICAL: DEVELOPMENTAL

More information

Michael Grimsley 1 and Anthony Meehan 2

Michael Grimsley 1 and Anthony Meehan 2 From: FLAIRS-02 Proceedings. Copyright 2002, AAAI (www.aaai.org). All rights reserved. Perceptual Scaling in Materials Selection for Concurrent Design Michael Grimsley 1 and Anthony Meehan 2 1. School

More information

phone hidden time phone

phone hidden time phone MODULARITY IN A CONNECTIONIST MODEL OF MORPHOLOGY ACQUISITION Michael Gasser Departments of Computer Science and Linguistics Indiana University Abstract This paper describes a modular connectionist model

More information

PROGRAMME SPECIFICATION UWE UWE. Taught course. JACS code. Ongoing

PROGRAMME SPECIFICATION UWE UWE. Taught course. JACS code. Ongoing PROGRAMME SPECIFICATION Section 1: Basic Data Awarding institution/body Teaching institution Delivery Location(s) Faculty responsible for programme Modular Scheme title UWE UWE UWE: St Matthias campus

More information

Outcome Based Education 15/01/2012

Outcome Based Education 15/01/2012 If you are, you breathe. If you breathe, you talk. If you talk, you ASK.. If you ask, you THINK. If you think, you SEARCH.. If you search, you EXPERIENCE. If you experience, you LEARN.. If you learn, you

More information

- «Crede Experto:,,,». 2 (09) (http://ce.if-mstuca.ru) '36

- «Crede Experto:,,,». 2 (09) (http://ce.if-mstuca.ru) '36 - «Crede Experto:,,,». 2 (09). 2016 (http://ce.if-mstuca.ru) 811.512.122'36 Ш163.24-2 505.. е е ы, Қ х Ц Ь ғ ғ ғ,,, ғ ғ ғ, ғ ғ,,, ғ че ые :,,,, -, ғ ғ ғ, 2016 D. A. Alkebaeva Almaty, Kazakhstan NOUTIONS

More information

Timeline. Recommendations

Timeline. Recommendations Introduction Advanced Placement Course Credit Alignment Recommendations In 2007, the State of Ohio Legislature passed legislation mandating the Board of Regents to recommend and the Chancellor to adopt

More information

Understanding student engagement and transition

Understanding student engagement and transition Understanding student engagement and transition Carolyn Mair London College of Fashion University of the Arts London 20 John Prince s Street London http://www.cazweb.info/ Lalage Sanders Cardiff Metropolitan

More information

Knowledge based expert systems D H A N A N J A Y K A L B A N D E

Knowledge based expert systems D H A N A N J A Y K A L B A N D E Knowledge based expert systems D H A N A N J A Y K A L B A N D E What is a knowledge based system? A Knowledge Based System or a KBS is a computer program that uses artificial intelligence to solve problems

More information

An Interactive Intelligent Language Tutor Over The Internet

An Interactive Intelligent Language Tutor Over The Internet An Interactive Intelligent Language Tutor Over The Internet Trude Heift Linguistics Department and Language Learning Centre Simon Fraser University, B.C. Canada V5A1S6 E-mail: heift@sfu.ca Abstract: This

More information

Organising ROSE (The Relevance of Science Education) survey in Finland

Organising ROSE (The Relevance of Science Education) survey in Finland 25.02.2004 1 Organising ROSE (The Relevance of Science Education) survey in Finland Researchers and support The Survey was organised by the following researchers at the Department of Teacher Education,

More information

1. Programme title and designation International Management N/A

1. Programme title and designation International Management N/A PROGRAMME APPROVAL FORM SECTION 1 THE PROGRAMME SPECIFICATION 1. Programme title and designation International Management 2. Final award Award Title Credit value ECTS Any special criteria equivalent MSc

More information