52 research outputs found

    Games for and by Teachers and Learners

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    With the advent of social media it is widely accepted that teachers and learners are not only consumers but also may have an active role in contributing and co-creating lesson materials and content. Paradoxically one strand of technology enhanced learning, i.e. game-based learning, aligns only slightly to this development. Games while there to experience, explore and collaborate are almost exclusively designed by professionals. Despite, or maybe because, games are the exclusive domain of professional developers, the general impression is that games require complex technologies and that games are difficult to organise and to embed in a curriculum. This chapter will make a case that games are not necessarily the exclusive domain of game professionals. Rather than enforcing teachers to get acquainted with and use complex, technically demanding games, we will discuss approaches that teachers themselves can use to build games, make use of existing games and even one step beyond use tools or games that can be used by learners to create their own designs, e.g. games or virtual worlds

    Keep it simple : Lowering the Barrier for Authoring Serious Games

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    Background. Despite the continuous and abundant growth of the game market the uptake of serious games in education has been limited. Games require complex technologies and are difficult to organise and to embed in the curriculum. Aim. This article explores to what extent game templates and game authoring processes can be designed that can be easily adopted and adapted by teachers while only using openly available tools. Method. It discusses the design and first evaluation of two game platforms: ARGUMENT, based on a wiki, and ARLEARN, a toolkit based on openly available Google technologies. ARGUMENT is a text-based game challenging students to take a position on a given topic. ARLEARN offers an explicit mobile and virtual gameplay environment and a defined authoring process to create game scripts. Results. ARGUMENT and ARLEARN have been evaluated in four small-scale studies, where educators designed game scenarios and students played the resulting games. Conclusions. The results indicate that both tools are useful instruments that can be operated by teachers to build games and game-alike educational activities and, additionally, are a valuable step to gain experience with serious games.SURFnet/Kennisnet (ARGUMENT, StreetLearn, ARLearn), UNHC

    INCREASING THE WILLINGNESS TO COLLABORATE ONLINE: AN ANALYSIS OF SENTIMENT-DRIVEN INTERACTIONS IN PEER CONTENT PRODUCTION

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    We investigate mechanisms that trigger collaborative work behavior in online peer communities. We regard the collaboration among Wikipedia editors as a social process influenced by specific communication practices. We analyze and quantify the way Wikipedia editors communicate their feedback and support towards each others’ work in form of sentiments and opinions, and explore to what extent this influences online trust among them. We show that peer content production in Wikipedia is influenced by sharing sentiments during discussions among editors. At the global level, sharing sentiments positively influences the level of online trust. We also find a significant difference in the amount of online trust among editors who share mainly positive or mainly negative sentiments. We further suggest that providing and receiving especially supportive feedback expressed in form of positive sentiments and opinions may be beneficial in terms of virtual teamwork

    ΠšΠΎΠ»ΠΈΡ‡Π΅ΡΡ‚Π²Π΅Π½Π½Ρ‹ΠΉ Π°Π½Π°Π»ΠΈΠ· лСксики английского языка Π² викисловарях ΠΈ Wordnet.

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    A quantitative analysis of the English lexicon was performed in the paper. The three electronic dictionaries are under examination: the English Wiktionary, WordNet, and the Russian Wiktionary. The quantity of English words and their meanings (senses) are calculated. The distribution of words for each part of speech, the quantity of monosemous and polysemous words and the distribution of words by number of meanings were calculated and compared across these dictionaries. The analysis shows that the average polysemy, the number and the distribution of word senses follow similar patterns in both expert and collaborative resources with relatively minor differences.Π’ Ρ€Π°Π±ΠΎΡ‚Π΅ Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½ количСствСнный Π°Π½Π°Π»ΠΈΠ· лСксики английского языка ΠΏΠΎ Π΄Π°Π½Π½Ρ‹ΠΌ трѐх элСктронных словарСй: Английского Викисловаря, WordNet ΠΈ Русского Викисловаря. БравниваСтся объѐм словарСй ΠΈ распрСдСлСниС слов английского языка ΠΏΠΎ частям Ρ€Π΅Ρ‡ΠΈ. ΠŸΡ€ΠΈΠ²ΠΎΠ΄ΠΈΡ‚ΡΡ ΡΠΎΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠ΅ ΠΌΠ½ΠΎΠ³ΠΎΠ·Π½Π°Ρ‡Π½Ρ‹Ρ… слов ΠΈ слов с ΠΎΠ΄Π½ΠΈΠΌ Π·Π½Π°Ρ‡Π΅Π½ΠΈΠ΅ΠΌ, Π° Ρ‚Π°ΠΊΠΆΠ΅ распрСдСлСниС английских слов ΠΏΠΎ числу Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΉ. ЭкспСримСнты ΠΏΠΎΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‚, Ρ‡Ρ‚ΠΎ лингвистичСскиС рСсурсы, созданныС ΠΊΠ°ΠΊ экспСртами, Ρ‚Π°ΠΊ ΠΈ энтузиастами, ΠΏΠΎΠ΄Ρ‡ΠΈΠ½ΡΡŽΡ‚ΡΡ ΠΎΠ±Ρ‰ΠΈΠΌ Π·Π°ΠΊΠΎΠ½Π°ΠΌ

    ΠšΠΎΠ»ΠΈΡ‡Π΅ΡΡ‚Π²Π΅Π½Π½Ρ‹ΠΉ Π°Π½Π°Π»ΠΈΠ· лСксики русского WordNet ΠΈ викисловарСй

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    A quantitative analysis of the Russian lexicon was performed in the paper. The thesaurus Russian WordNet and two electronic dictionaries are under examination: the Russian Wiktionary and the English Wiktionary. The quantity of Russian words and their meanings (senses) according to the parts of speech are compared. The distribution of words for each part of speech, the quantity of monosemous and polysemous words and the distribution of words by number of meanings were calculated and compared across these dictionaries. The analysis of the distribution of words by number of meanings revealed a problem that too few or no ambigous Russian words with the number of meanings more than 4 are presented in the English Wiktionary (in comparison with the Russian Wiktionary). The analysis shows that the average polysemy, the number and the distribution of word senses follow similar patterns in both expert and collaborative resources with relatively minor differences.Π’ Ρ€Π°Π±ΠΎΡ‚Π΅ Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½ количСствСнный Π°Π½Π°Π»ΠΈΠ· лСксики русского языка ΠΏΠΎ Π΄Π°Π½Π½Ρ‹ΠΌ тСзауруса Русский WordNet ΠΈ Π΄Π²ΡƒΡ… элСктронных словарСй (Русский Π’ΠΈΠΊΠΈΡΠ»ΠΎΠ²Π°Ρ€ΡŒ ΠΈ Английский Π’ΠΈΠΊΠΈΡΠ»ΠΎΠ²Π°Ρ€ΡŒ). БравниваСтся ΠΎΠ±ΡŠΡ‘ΠΌ словарСй ΠΈ распрСдСлСниС слов русского языка ΠΏΠΎ частям Ρ€Π΅Ρ‡ΠΈ. ΠŸΡ€ΠΈΠ²ΠΎΠ΄ΠΈΡ‚ΡΡ ΡΠΎΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠ΅ ΠΌΠ½ΠΎΠ³ΠΎΠ·Π½Π°Ρ‡Π½Ρ‹Ρ… слов ΠΈ слов с ΠΎΠ΄Π½ΠΈΠΌ Π·Π½Π°Ρ‡Π΅Π½ΠΈΠ΅ΠΌ, Π° Ρ‚Π°ΠΊΠΆΠ΅ распрСдСлСниС русских слов ΠΏΠΎ числу Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΉ. Анализ распрСдСлСния числа Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΉ выявил ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡƒ Английского Викисловаря – отсутствиС ΠΈΠ»ΠΈ нСдостаточная ΠΏΡ€ΠΎΡ€Π°Π±ΠΎΡ‚ΠΊΠ° ΠΌΠ½ΠΎΠ³ΠΎΠ·Π½Π°Ρ‡Π½Ρ‹Ρ… русских слов с числом Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΉ большС Ρ‡Π΅Ρ‚Ρ‹Ρ€Ρ‘Ρ… (ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ со словами Русского Викисловаря). ЭкспСримСнты ΠΏΠΎΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‚, Ρ‡Ρ‚ΠΎ лингвистичСскиС рСсурсы, созданныС энтузиастами, Π΄Π΅ΠΌΠΎΠ½ΡΡ‚Ρ€ΠΈΡ€ΡƒΡŽΡ‚ Ρ‚Π΅ ΠΆΠ΅ закономСрности, Ρ‡Ρ‚ΠΎ ΠΈ Ρ‚Ρ€Π°Π΄ΠΈΡ†ΠΈΠΎΠ½Π½Ρ‹Π΅ словари

    Framework for collaborative knowledge management in organizations

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    Nowadays organizations have been pushed to speed up the rate of industrial transformation to high value products and services. The capability to agilely respond to new market demands became a strategic pillar for innovation, and knowledge management could support organizations to achieve that goal. However, current knowledge management approaches tend to be over complex or too academic, with interfaces difficult to manage, even more if cooperative handling is required. Nevertheless, in an ideal framework, both tacit and explicit knowledge management should be addressed to achieve knowledge handling with precise and semantically meaningful definitions. Moreover, with the increase of Internet usage, the amount of available information explodes. It leads to the observed progress in the creation of mechanisms to retrieve useful knowledge from the huge existent amount of information sources. However, a same knowledge representation of a thing could mean differently to different people and applications. Contributing towards this direction, this thesis proposes a framework capable of gathering the knowledge held by domain experts and domain sources through a knowledge management system and transform it into explicit ontologies. This enables to build tools with advanced reasoning capacities with the aim to support enterprises decision-making processes. The author also intends to address the problem of knowledge transference within an among organizations. This will be done through a module (part of the proposed framework) for domain’s lexicon establishment which purpose is to represent and unify the understanding of the domain’s used semantic
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