Educational Games for Learning Programming Languages

A concept of educational game for learning programming languages is presented. The idea of learning programming languages and improving programming skills through programming game characters’ behavior is described. The learning course description rules for using in games are suggested. The concept is implemented in a game for learning C# programming language. A common game architecture is modified for using in the educational game. The game engine is built on the base of the graphical engine Ogre3D and extended with game logic. The game has been developed as an industry level commercial product and is planned for sale to educational institutions.

Integriertes Informationsmanagement für Bildungseinrichtungen

Der CampusSource Workshop fand vom 10. bis 12. Oktober 2006 an der Westfälischen Wilhelms Universität (WWU) in Münster statt. Kernpunkte der Veranstaltung waren die Entwicklung einer Engine zur Verknüpfung von e-Learning Anwendungen mit Systemen der HIS GmbH und die Erstellung von Lehr- und Lerninhalten mit dem Ziel der Wiederverwendung. Im zweiten Kapitel sind Vorträge der Veranstaltung im Adobe Flash Format zusammengetragen. Zur Betrachtung der Vorträge ist der Adobe Flash Player, mindestens in der Version 6 erforderlich

Induction by a Hilbert hypercube representation

This thesis describes a novel connectionist machine utilizing induction by a Hilbert hypercube representation. This representation offers a number of distinct advantages which are described. We construct a theoretical and practical learning machine which lies in an area of overlap between three disciplines - neural nets, machine learning and knowledge acquisition - hence it is refered to as a "coalesced" machine. To this unifying aspect is added the various advantages of its orthogonal lattice structure as against less structured nets. We discuss the case for such a fundamental and low level empirical learning tool and the assumptions behind the machine are clearly outlined. Our theory of an orthogonal lattice structure the Hilbert hypercube of an n-dimensional space using a complemented distributed lattice as a basis for supervised learning is derived from first principles on clearly laid out scientific principles. The resulting "subhypercube theory" was implemented in a development machine which was then used to test the theoretical predictions again under strict scientific guidelines. The scope, advantages and limitations of this machine were tested in a series of experiments. Novel and seminal properties of the machine include: the "metrical", deterministic and global nature of its search; complete convergence invariably producing minimum polynomial solutions for both disjuncts and conjuncts even with moderate levels of noise present; a learning engine which is mathematically analysable in depth based upon the "complexity range" of the function concerned; a strong bias towards the simplest possible globally (rather than locally) derived "balanced" explanation of the data; the ability to cope with variables in the network; and new ways of reducing the exponential explosion. Performance issues were addressed and comparative studies with other learning machines indicates that our novel approach has definite value and should be further researched.

Using genetic programming and decision trees for generating structural descriptions of four bar mechanisms

Four bar mechanisms are basic components of many important mechanical devices. The kinematic synthesis of four bar mechanisms is a difficult design problem. A novel method that combines the genetic programming and decision tree learning methods is presented. We give a structural description for the class of mechanisms that produce desired coupler curves. Constructive induction is used to find and characterize feasible regions of the design space. Decision trees constitute the learning engine, and the new features are created by genetic programming.

Motivation, the Engine of Learning

This essay centers in motivation as a fundamental aspect of learning and in the double way sense that this relation must have. It defines the word “motivation” and the manner how relationship between students and teachers come about in the game of helping out or reduce motivation, and thus learning. It also defines the reason why teachers must help build up the so called “intrinsic motivation”, ending with the importance this has and how it can be achieved.

Learning Physics with the Unreal Tournament Engine

Computer games such as Unreal Tournament (UT2004 and UT3) contain a 'physics engine' responsible for producing believable dynamic interactions between players and objects in the three-dimensional (3D) virtual world of a game. Through a series of probing experiments we have evaluated the fidelity and internal consistency of the UT2004 physics engine. These experiments have then led to the production of resources which may be used by learners and teachers of secondary-school physics. We also suggest an approach to learning, where both teachers and pupils may produce learning materials using the Unreal Tournament editor 'UnrealEd'.

Search engine content analysis

Search engines have forever changed the way people access and discover knowledge, allowing information about almost any subject to be quickly and easily retrieved within seconds. As increasingly more material becomes available electronically the influence of search engines on our lives will continue to grow. This presents the problem of how to find what information is contained in each search engine, what bias a search engine may have, and how to select the best search engine for a particular information need. This research introduces a new method, search engine content analysis, in order to solve the above problem. Search engine content analysis is a new development of traditional information retrieval field called collection selection, which deals with general information repositories. Current research in collection selection relies on full access to the collection or estimations of the size of the collections. Also collection descriptions are often represented as term occurrence statistics. An automatic ontology learning method is developed for the search engine content analysis, which trains an ontology with world knowledge of hundreds of different subjects in a multilevel taxonomy. This ontology is then mined to find important classification rules, and these rules are used to perform an extensive analysis of the content of the largest general purpose Internet search engines in use today. Instead of representing collections as a set of terms, which commonly occurs in collection selection, they are represented as a set of subjects, leading to a more robust representation of information and a decrease of synonymy. The ontology based method was compared with ReDDE (Relevant Document Distribution Estimation method for resource selection) using the standard R-value metric, with encouraging results. ReDDE is the current state of the art collection selection method which relies on collection size estimation. The method was also used to analyse the content of the most popular search engines in use today, including Google and Yahoo. In addition several specialist search engines such as Pubmed and the U.S. Department of Agriculture were analysed. In conclusion, this research shows that the ontology based method mitigates the need for collection size estimation.

HyperCard test engine using Rasch Analysis

This paper will focus on the development of an interactive test engine using Rasch analysis of item responses for question selection and reporting of results. The Rasch analysis is used to determine student ability and question difficulty. This model is widely used in the preparation of paper-based tests and has been the subject of particular use and development at the Australian Council for Education Research (ACER). This paper presents an overview of an interactive implementation of the Rasch analysis model in HyperCard, where student ability estimates are generated 'on the fly' and question difficulty values updated from time to time. The student ability estimates are used to determine question selection and are the basis of scoring and reporting schemes.

Calculation of engine parameters using reconfigurable hardware

The feasibility of real-time calculation of parameters for an internal combustion engine via reconfigurable hardware implementation is investigated as an alternative to software computation. A detailed in-hardware field programmable gate array (FPGA)-based design is developed and evaluated using input crank angle and in-cylinder pressure data from fully instrumented diesel engines in the QUT Biofuel Engine Research Facility (BERF). Results indicate the feasibility of employing a hardware-based implementation for real-time processing for speeds comparable to the data sampling rate currently used in the facility, with acceptably low level of discrepancies between hardware and software-based calculation of key engine parameters.

Acquiring search expertise : learning experiences and threshold concepts

Expert searchers engage with information as information brokers, researchers, reference librarians, information architects, faculty who teach advanced search, and in a variety of other information-intensive professions. Their experiences are characterized by a profound understanding of information concepts and skills and they have an agile ability to apply this knowledge to interacting with and having an impact on the information environment. This study explored the learning experiences of searchers to understand the acquisition of search expertise. The research question was: What can be learned about becoming an expert searcher from the learning experiences of proficient novice searchers and highly experienced searchers? The key objectives were: (1) to explore the existence of threshold concepts in search expertise; (2) to improve our understanding of how search expertise is acquired and how novice searchers, intent on becoming experts, can learn to search in more expertlike ways. The participant sample drew from two population groups: (1) highly experienced searchers with a minimum of 20 years of relevant professional experience, including LIS faculty who teach advanced search, information brokers, and search engine developers (11 subjects); and (2) MLIS students who had completed coursework in information retrieval and online searching and demonstrated exceptional ability (9 subjects). Using these two groups allowed a nuanced understanding of the experience of learning to search in expertlike ways, with data from those who search at a very high level as well as those who may be actively developing expertise. The study used semi-structured interviews, search tasks with think-aloud narratives, and talk-after protocols. Searches were screen-captured with simultaneous audio-recording of the think-aloud narrative. Data were coded and analyzed using NVivo9 and manually. Grounded theory allowed categories and themes to emerge from the data. Categories represented conceptual knowledge and attributes of expert searchers. In accord with grounded theory method, once theoretical saturation was achieved, during the final stage of analysis the data were viewed through lenses of existing theoretical frameworks. For this study, threshold concept theory (Meyer & Land, 2003) was used to explore which concepts might be threshold concepts. Threshold concepts have been used to explore transformative learning portals in subjects ranging from economics to mathematics. A threshold concept has five defining characteristics: transformative (causing a shift in perception), irreversible (unlikely to be forgotten), integrative (unifying separate concepts), troublesome (initially counter-intuitive), and may be bounded. Themes that emerged provided evidence of four concepts which had the characteristics of threshold concepts. These were: information environment: the total information environment is perceived and understood; information structures: content, index structures, and retrieval algorithms are understood; information vocabularies: fluency in search behaviors related to language, including natural language, controlled vocabulary, and finesse using proximity, truncation, and other language-based tools. The fourth threshold concept was concept fusion, the integration of the other three threshold concepts and further defined by three properties: visioning (anticipating next moves), being light on one's 'search feet' (dancing property), and profound ontological shift (identity as searcher). In addition to the threshold concepts, findings were reported that were not concept-based, including praxes and traits of expert searchers. A model of search expertise is proposed with the four threshold concepts at its core that also integrates the traits and praxes elicited from the study, attributes which are likewise long recognized in LIS research as present in professional searchers. The research provides a deeper understanding of the transformative learning experiences involved in the acquisition of search expertise. It adds to our understanding of search expertise in the context of today's information environment and has implications for teaching advanced search, for research more broadly within library and information science, and for methodologies used to explore threshold concepts.

Learning-based Relevance Feedback for Web-based Relation Completion

In a pilot application based on web search engine calledWeb-based Relation Completion (WebRC), we propose to join two columns of entities linked by a predefined relation by mining knowledge from the web through a web search engine. To achieve this, a novel retrieval task Relation Query Expansion (RelQE) is modelled: given an entity (query), the task is to retrieve documents containing entities in predefined relation to the given one. Solving this problem entails expanding the query before submitting it to a web search engine to ensure that mostly documents containing the linked entity are returned in the top K search results. In this paper, we propose a novel Learning-based Relevance Feedback (LRF) approach to solve this retrieval task. Expansion terms are learned from training pairs of entities linked by the predefined relation and applied to new entity-queries to find entities linked by the same relation. After describing the approach, we present experimental results on real-world web data collections, which show that the LRF approach always improves the precision of top-ranked search results to up to 8.6 times the baseline. Using LRF, WebRC also shows performances way above the baseline.

The Google Online Marketing Challenge: A Transnational Comparison of Classroom Learning with Real Clients, Real Money, and Real Advertising Campaigns

In 2008, a collaborative partnership between Google and academia launched the Google Online Marketing Challenge (hereinafter Google Challenge), perhaps the world’s largest in-class competition for higher education students. In just two years, almost 20,000 students from 58 countries participated in the Google Challenge. The Challenge gives undergraduate and graduate students hands-on experience with the world’s fastest growing advertising mechanism, search engine advertising. Funded by Google, students develop an advertising campaign for a small to medium sized enterprise and manage the campaign over three consecutive weeks using the Google AdWords platform. This article explores the Challenge as an innovative pedagogical tool for marketing educators. Based on the experiences of three instructors in Australia, Canada and the United States, this case study discusses the opportunities and challenges of integrating this dynamic problem-based learning approach into the classroom.

Personalised e-learning, semantic web and learning ontologies

Kurzel(2004) points out that researchers in e-learning and educational technologists, in a quest to provide improved Learning Environments (LE) for students are focusing on personalising the experience through a Learning Management System (LMS) that attempts to tailor the LE to the individual (see amongst others Eklund & Brusilovsky, 1998; Kurzel, Slay, & Hagenus, 2003; Martinez,2000; Sampson, Karagiannidis, & Kinshuk, 2002; Voigt & Swatman; 2003). According to Kurzel (2004) this tailoring can have an impact on content and how it’s accessed; the media forms used; method of instruction employed and the learning styles supported. This project is aiming to move personalisation forward to the next generation, by tackling the issue of Personalised e-Learning platforms as pre-requisites for building and generating individualised learning solutions. The proposed development is to create an e-learning platform with personalisation built-in. This personalisation is proposed to be set from different levels of within the system starting from being guided by the information that the user inputs into the system down to the lower level of being set using information inferred by the system’s processing engine. This paper will discuss some of our early work and ideas.

Semantic web and e-learning

With emergence of "Semantic Web" there has been much discussion about the impact of technologies such as XML and RDF on the way we use the Web for developing e-learning applications and perhaps more importantly on how we can personalise these applications. Personalisation of e-learning is viewed by many authors (see amongst others Eklund & Brusilovsky, 1998; Kurzel, Slay, & Hagenus, 2003; Martinez, 2000; Sampson, Karagiannidis, & Kinshuk, 2002; Voigt & Swatman, 2003) as the key challenge for the learning technologists. According to Kurzel (2004) the tailoring of e-learning applications can have an impact on content and how it's accesses; the media forms used; method of instruction employed and the learning styles supported. This paper will report on a research project currently underway at the eCentre in University of Greenwich which is exploring different approaches and methodologies to create an e-learning platform with personalisation built-in. This personalisation is proposed to be set from different levels of within the system starting from being guided by the information that the user inputs into the system down to the lower level of being set using information inferred by the system's processing engine.