Automatic detection of accommodation steps as an indicator of knowledge maturing
► This paper measures accommodation steps as indicator of knowledge maturing. ► We applied a task detection approach and observed users contributing to a wiki. ► An empirical study was conducted to set up and test the machine learning algorithms. ► The results show that we could identify accommodati...
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Veröffentlicht in: | Interacting with computers 2011-05, Vol.23 (3), p.247-255 |
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creator | Moskaliuk, Johannes Rath, Andreas Devaurs, Didier Weber, Nicolas Lindstaedt, Stefanie Kimmerle, Joachim Cress, Ulrike |
description | ► This paper measures accommodation steps as indicator of knowledge maturing. ► We applied a task detection approach and observed users contributing to a wiki. ► An empirical study was conducted to set up and test the machine learning algorithms. ► The results show that we could identify accommodation with a rate of 79.12%.
Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed. |
doi_str_mv | 10.1016/j.intcom.2011.03.006 |
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Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed.</description><identifier>ISSN: 0953-5438</identifier><identifier>EISSN: 1873-7951</identifier><identifier>DOI: 10.1016/j.intcom.2011.03.006</identifier><identifier>CODEN: INTCEE</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Accommodation ; Algorithms ; Automatic ; Co-evolution ; Computer Science ; Context-awareness ; Educational psychology ; Empirical analysis ; Engineering Sciences ; Indicators ; Knowledge ; Knowledge development ; Knowledge maturing ; Learning ; Machine learning ; On-line systems ; Robotics ; Task detection ; Tasks ; Wikis</subject><ispartof>Interacting with computers, 2011-05, Vol.23 (3), p.247-255</ispartof><rights>2011 Elsevier B.V.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c450t-baaf047eda06fa90bb50f953a9003660fdfd7d992f7271feb8c592c0fc1499183</citedby><cites>FETCH-LOGICAL-c450t-baaf047eda06fa90bb50f953a9003660fdfd7d992f7271feb8c592c0fc1499183</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27901,27902</link.rule.ids><backlink>$$Uhttps://hal.science/hal-00861580$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Moskaliuk, Johannes</creatorcontrib><creatorcontrib>Rath, Andreas</creatorcontrib><creatorcontrib>Devaurs, Didier</creatorcontrib><creatorcontrib>Weber, Nicolas</creatorcontrib><creatorcontrib>Lindstaedt, Stefanie</creatorcontrib><creatorcontrib>Kimmerle, Joachim</creatorcontrib><creatorcontrib>Cress, Ulrike</creatorcontrib><title>Automatic detection of accommodation steps as an indicator of knowledge maturing</title><title>Interacting with computers</title><description>► This paper measures accommodation steps as indicator of knowledge maturing. ► We applied a task detection approach and observed users contributing to a wiki. ► An empirical study was conducted to set up and test the machine learning algorithms. ► The results show that we could identify accommodation with a rate of 79.12%.
Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. 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Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.intcom.2011.03.006</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Accommodation Algorithms Automatic Co-evolution Computer Science Context-awareness Educational psychology Empirical analysis Engineering Sciences Indicators Knowledge Knowledge development Knowledge maturing Learning Machine learning On-line systems Robotics Task detection Tasks Wikis |
title | Automatic detection of accommodation steps as an indicator of knowledge maturing |
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