TABLE OF CONTENTS QUESTION 1: Definitions of assessment conceptS 2 a. Moderation 2 b. Recording 2 c. Continuous assessment 2 d. Criterion-referenced assessment 3 QUESTION 2: “THE NOBLE PROFESSION” – THE PURPOSE AND IMPORTANCE OF ASSESSMENT IN THE SOUTH AFRICAN CURRICULUM 3 QUESTION 3: COMPARISON OF FORMATIVE AND SUMMATIVE ASSESSMENT 4 QUESTION 4: ASSESSMENT ACTIVITY SCORING RUBRIC 7 QUESTION 5: DESCRIPTION AND IMPLEMENTATION OF ASSESSMENT PRINCIPLES 8 a. Validity 8 b. Fairness 8 c.
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Key words: Episodic Memory; Hippocampus; Binding; Recruitment Abstract The memorization of events and situations (episodic memory) requires the rapid formation of a memory trace consisting of several functional components. A computational model is described that demonstrates how a transient pattern of activity representing an episode can lead to the rapid recruitment of appropriate circuits as a result of long-term potentiation within structures whose architecture and circuitry match those of
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Telescope over the Internet Santhanagobalan Vasanth‚ Sivapalan Sugirthan Department of Electronic and Telecommunication Engineering University of Moratuwa Moratuwa‚ Sri Lanka vasanthcolombo@gmail.com‚ sugi_008@hotmail.com Abstract- This paper describes the design and implementation of a fully automated Telescope that can be controlled over the internet. Telescope over the Internet is novel in the aspect that it will be the first telescope constructed in Sri Lanka which can be accessed
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Comparison of Optimized Backpropagation Algorithms W. Schiffmann‚ M. Joost‚ R. Werner University of Koblenz Institute of Physics Rheinau 3–4 W-5400 Koblenz e-mail: evol@infko.uni-koblenz.de Presented at ESANN 93‚ Br¨ ssel u Abstract Backpropagation is one of the most famous training algorithms for multilayer perceptrons. Unfortunately it can be very slow for practical applications. Over the last years many improvement strategies have been developed to speed up backpropagation. It’s
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INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA END-OF-SEMESTER EXAMINATION SEMESTER 1‚ 2009/2010SESSION KULLIYYAH OF ECONOMICS AND MANAGEMENT SCIENCES Programme : ENMS/BBA/BECS/BACC/BIT/HS Level of Study : 1-4 Time : 9:00 a.m – 12:00 noon Date : 11 Nov 2009 Duration : 3 Hr(s) 0 Min(s) Course Code : MGT 2010 Section(s) : 1 Course Title : Principles and Practice of Management (This Examination Paper Consists of 14 Printed Pages Including A Cover Page With 3 parts) INSTRUCTION(S) TO CANDIDATES:
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‚ Higgins‚ C.‚ & Miller‚ J. (1992). Rule-based neural networks for classification and probabil-ity estimation. Retrieved February 25‚ 2010 from http://id3490.securedata.net/rod/pdf/RG.Paper.JA27.pdf Haryanto‚ I.‚ Setiawan‚ J Haykin‚ S. (2004). Feedforward neural networks: An introduction. Retrieved June 20‚ 2010 from: http://media.wiley.com/product_data/excerpt/19/04713491/0471349119.pdf Joint Admissions Board Joint Board should review mode of admitting university students. (2009‚ August 06). Daily
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Chapter 2 Automated Highway Systems A major long-term element of Intelligent Transportation Systems research and development is Automated highway Systems (AHS). The AHS program is a broad international effort “to provide the basis for‚ and transition to‚ the next major performance upgrade of the vehicle/highway system through the use of automated vehicle control technology” [NAHSC96]. The detailed definition of the Automated Highway System is as follows [Plan93]: The term “fully automated
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INTRODUCTION Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also an academic field of study. Major AI researchers and textbooks define the field as "the study and design of intelligent agents"‚ where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy‚ who coined the term in 1955‚ defines it as "the science and engineering of making intelligent machines". AI research is highly
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Management Accounting Research 20 (2009) 283–295 Contents lists available at ScienceDirect Management Accounting Research journal homepage: www.elsevier.com/locate/mar Performance management systems: A conceptual model Jane Broadbent a‚∗ ‚ Richard Laughlin b a b Vice Chancellor’s Office‚ Roehampton University‚ Roehampton Lane‚ London SW15 5PH‚ United Kingdom Department of Management‚ King’s College London‚ University of London‚ Franklin-Wilkins Building‚ 150 Stamford Street‚ London
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Neural Net. Actually‚ Back Propagation1‚2‚3 is the training or learning algorithm rather than the network itself. The network used is generally of the simple type shown in figure 1.1‚ in chapter 1 and in the examples up until now. These are called FeedForward Networks (we’ll see why in chapter 7 on Hopfield Networks) or occasionally Multi-Layer Perceptrons (MLPs). The network operates in exactly the same way as the others we’ve seen (if you need to remind yourself‚ look at worked example 2.3). Now
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