BibSonomyburst/tag/learningBibSonomy RSS feed for /tag/learning2012-02-15T14:17:41+01:00http://www.bibsonomy.org/bibtex/2888f9372bb7c35ca9900c311afd892ae/yishyish2012-02-15T13:25:29+01:00design ld learning typologies <span class="authorEditorList"><a href="/author/Dobozy">E. Dobozy</a> </span><em>eLearning Papers</em> (<em>December 2011</em>)<em>ISSN: 1887-1542 . </em>Wed Feb 15 13:25:29 CET 2012eLearning PapersDecember ISSN: 1887-154227Typologies of Learning Design and the introduction of a "LD-Type 2" case example2011design ld learning typologies This paper explores the need for greater clarity in the conceptualisation of Learning Design (LD). Building on Cameron’s (2010) work, a three-tiered LD architecture is introduced. It is argued that this conceptualisation is needed in order to advance the emerging field of LD as applied to education research. This classification differentiates between LD as a concept (LD Type 1), LD as a process (LD Type 2), and LD as a product (LD Type 3). The usefulness of the three types is illustrated by a case example of a virtual history fieldtrip module constructed in LAMS as Type 2 LD. This case shows the workflow from LD Type 1 to LD Type 2, followed by LD Type 3 research and development data. History as a learning area was chosen in this paper for its ability to illustrate LD concepts and the interrelationship of LD types. The case serves to illustrate the foundations, scope and ambitions of this learning design project, which was underpinned by an educational psychology framework and firmly linked to the goals of the new Australian curriculum. The purpose of LD as process is to inform other teachers of the affordance of LD, providing contextualised data and to invite critique of particular TEL practices.http://www.bibsonomy.org/bibtex/21d1eea7e494d880f1c3eee5e97daa382/yishyish2012-02-15T13:20:15+01:00design educational learning <span class="authorEditorList"><a href="/author/Goodyear">Peter Goodyear</a>, and <a href="/author/Ellis">Robert Ellis</a> </span><em>Proceedings of the ASCILITE 2007 conference, Singapore, </em>(<em>2007</em>)Wed Feb 15 13:20:15 CET 2012Proceedings of the ASCILITE 2007 conference, SingaporeStudents' interpretations of learning tasks: Implications for educational design2007design educational learning This paper is concerned with the issues that arise when one sees teaching as a process of design, and students as co-constructors of their learning environments. The dominant models of design, we argue, tend to either configure the learner as a compliant consumer of educational designs and a well-behaved user of educational technologies, or they tend to romanticise learners as media savvy experts on managing their own learning. In our view, ‘teaching-as-design’ needs to be supported with intellectual resources that avoid these extremes. To get a better sense of how design should be informed by a knowledge of student perspectives, we present the outcomes of some recent research into the ways in which students on ‘blended learning’ courses interpret the requirements of learning through discussion and learning through inquiry.http://www.bibsonomy.org/bibtex/29eee9f892c80e5965cdc0321ef27dc21/yishyish2012-02-15T12:17:58+01:00cognitive design education instructional learning load <span class="authorEditorList"><a href="/author/Sweller">John Sweller</a> </span><em>Learning and instruction</em> <em>4(4):295--312</em> (<em>1994</em>)Wed Feb 15 12:17:58 CET 2012Learning and instruction4295--312Cognitive load theory, learning difficulty, and instructional design41994cognitive design education instructional learning load This paper is concerned with some of the factors that determine the difficulty of material that needs to be learned. It is suggested that when considering intellectual activities, schema acquisition and automation are the primary mechanisms of learning. The consequences of cognitive load theory for the structuring of information in order to reduce difficulty by focusing cognitive activity on schema acquisition is briefly summarized. It is pointed out that cognitive load theory deals with learning and problem solving difficulty that is artificial in that it can be manipulated by instructional design. Intrinsic cognitive load in contrast, is constant for a given area because it is a basic component of the material. Intrinsic cognitive load is characterized in terms of element interactivity. The elements of most schemas must be learned simultaneously because they interact and it is the interaction that is critical. If, as in some areas, interactions between many elements must be learned, then intrinsic cognitive load will be high. In contrast, in different areas, if elements can be learned successively rather than simultaneously because they do not interact, intrinsic cognitive load will be low. It is suggested that extraneous cognitive load that interferes with learning only is a problem under conditions of high cognitive load caused by high element interactivity. Under conditions of low element interactivity, re-designing instruction to reduce extraneous cognitive load may have no appreciable consequences. In addition, the concept of element interactivity can be used to explain not only why some material is difficult to learn but also, why it can be difficult to understand. Understanding becomes relevant when high element interactivity material with a naturally high cognitive load must be learned.http://www.bibsonomy.org/bibtex/203f74a32e09a85175c4c08e49d434405/yishyish2012-02-13T17:04:22+01:00cloudworks design education instructional learning <span class="authorEditorList"><a href="/author/Siedlaczek">Kathy Siedlaczek</a>, <a href="/author/Conole">Gráinne Conole</a>, <a href="/author/Castañeda">Linda Castañeda</a>, <a href="/author/Galley">Rebecca Galley</a>, <a href="/author/Passos">Rosario Passos</a>, <a href="/author/Owen">Martin Owen</a>, <a href="/author/Ryberg">Thomas Ryberg</a>, <a href="/author/Low">Alfred Low</a>, <a href="/author/Hill">LeRoy Hill</a>, and <a href="/author/Cross">Simon Cross</a> </span>(<em>2009</em>)Mon Feb 13 17:04:22 CET 2012Learning Design vs. Instructional DesignCloudworks discussion2009cloudworks design education instructional learning What are the differences between learning design and instructional design? Is it the approach? The breadth? The focus? The audience? Or is a just a new term for the same thing?http://www.bibsonomy.org/bibtex/217899f3317b5b73aa321066da55b17e8/schmidt2schmidt22012-02-09T23:09:32+01:00exams higher_education learning teaching <span class="authorEditorList"><a href="/author/MacKay">David MacKay</a> </span> (<em>2005</em>)Thu Feb 09 23:09:32 CET 2012Everyone Should Get an A2005exams higher_education learning teaching http://www.bibsonomy.org/bibtex/203cdf152fb5df8a2196983b6bcb16ef9/schmidt2schmidt22012-02-09T22:47:33+01:00ability abstraction indicator learning programming toread <span class="authorEditorList"><a href="/author/Bennedssen">Jens Bennedssen</a>, and <a href="/author/Caspersen">Michael E. Caspersen</a> </span><em>Proceedings of the Fourth international Workshop on Computing Education Research, </em><em>page 15--26. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Thu Feb 09 22:47:33 CET 2012New York, NY, USAProceedings of the Fourth international Workshop on Computing Education Research15--26ICER '08Abstraction ability as an indicator of success for learning computing science?2008ability abstraction indicator learning programming toread Computing scientists generally agree that abstract thinking is a crucial component for practicing computer science.</p> <p>We report on a three-year longitudinal study to confirm the hypothesis that general abstraction ability has a positive impact on performance in computing science.</p> <p>Abstraction ability is operationalized as stages of cognitive development for which validated tests exist. Performance in computing science is operationalized as grade in the final assessment of ten courses of a bachelor's degree programme in computing science. The validity of the operationalizations is discussed.</p> <p>We have investigated the positive impact overall, for two groupings of courses (a content-based grouping and a grouping based on SOLO levels of the courses' intended learning outcome), and for each individual course.</p> <p>Surprisingly, our study shows that there is hardly any correlation between stage of cognitive development (abstraction ability) and final grades in standard CS courses, neither for the various group-ings, nor for the individual courses. Possible explanations are discussed.Abstraction ability as an indicator of success for learning computing science?http://www.bibsonomy.org/bibtex/2658dcf8a86184ac2f70317f04828763a/sidyrsidyr2012-02-09T06:41:59+01:00bayesian learning tree-width <span class="authorEditorList"><a href="/author/Chechetka">Anton Chechetka</a>, and <a href="/author/Guestrin">Carlos Guestrin</a> </span><em>NIPS, </em><em>Curran Associates, Inc., </em>(<em>2007</em>)Thu Feb 09 06:41:59 CET 2012NIPSconf/nips/2007Efficient Principled Learning of Thin Junction Trees.2007bayesian learning tree-width http://www.bibsonomy.org/bibtex/21bacff4727755b0d74610da3b373a30e/sidyrsidyr2012-02-08T12:50:45+01:00cognition learning models probabilistic <span class="authorEditorList"><a href="/author/Griffiths">Thomas L. Griffiths</a>, <a href="/author/Chater">Nick Chater</a>, <a href="/author/Kemp">Charles Kemp</a>, <a href="/author/Perfors">Amy Perfors</a>, and <a href="/author/Tenenbaum">Joshua B. Tenenbaum</a> </span><em>Trends in Cognitive Sciences</em> <em>14(8):357-364</em> (<em>Jun 23, 2010</em>)Wed Feb 08 12:50:45 CET 2012Trends in Cognitive SciencesJune8357-364Probabilistic models of cognition: exploring representations and inductive biases142010cognition learning models probabilistic 23Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind faces involve induction. The probabilistic approach to modeling cognition begins by identifying ideal solutions to these inductive problems. Mental processes are then modeled using algorithms for approximating these solutions, and neural processes are viewed as mechanisms for implementing these algorithms, with the result being a top-down analysis of cognition starting with the function of cognitive processes. Typical connectionist models, by contrast, follow a bottom-up approach, beginning with a characterization of neural mechanisms and exploring what macro-level functional phenomena might emerge. We argue that the top-down approach yields greater flexibility for exploring the representations and inductive biases that underlie human cognition.http://www.bibsonomy.org/bibtex/2019b90333189d1f4729b03df53c848dc/sidyrsidyr2012-02-08T09:50:37+01:00dependency grammar learning unsupervised <span class="authorEditorList"><a href="/author/Klein">Dan Klein</a>, and <a href="/author/Manning">Christopher D. Manning</a> </span><em>ACL, </em><em>page 478-485. </em><em>ACL, </em>(<em>2004</em>)Wed Feb 08 09:50:37 CET 2012ACLconf/acl/2004478-485Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency.2004dependency grammar learning unsupervised http://www.bibsonomy.org/bibtex/2873c1a387c202cecd2567b26fe9d0402/sidyrsidyr2012-02-08T09:22:36+01:00grammar language learning machine universal <span class="authorEditorList"><a href="/author/Lappin">Shalom Lappin</a>, and <a href="/author/Shieber">Stuart M. Shieber</a> </span><em>Journal of Linguistics</em> <em>43(02):393--427</em> (<em>2007</em>)Wed Feb 08 09:22:36 CET 2012Journal of Linguistics02393--427Machine learning theory and practice as a source of insight into universal grammar432007grammar language learning machine universal http://www.bibsonomy.org/bibtex/2fef51a34106d013137eabb1c566cfd88/sidyrsidyr2012-02-08T06:59:37+01:00language learning model unsupervised <span class="authorEditorList"><a href="/author/Solan"> Solan</a>, <a href="/author/Horn">D Horn</a>, <a href="/author/Ruppin">E Ruppin</a>, and <a href="/author/Edelman">S Edelman</a> </span><em>Proc Natl Acad Sci U S A</em> <em>102(33):11629-11634</em> (<em>August 2005</em>)Wed Feb 08 06:59:37 CET 2012Proc Natl Acad Sci U S Aaug3311629-11634Unsupervised learning of natural languages1022005language learning model unsupervised We address the problem, fundamental to linguistics, bioinformatics, and certain other disciplines, of using corpora of raw symbolic sequential data to infer underlying rules that govern their production. Given a corpus of strings (such as text, transcribed speech, chromosome or protein sequence data, sheet music, etc.), our unsupervised algorithm recursively distills from it hierarchically structured patterns. The adios (automatic distillation of structure) algorithm relies on a statistical method for pattern extraction and on structured generalization, two processes that have been implicated in language acquisition. It has been evaluated on artificial context-free grammars with thousands of rules, on natural languages as diverse as English and Chinese, and on protein data correlating sequence with function. This unsupervised algorithm is capable of learning complex syntax, generating grammatical novel sentences, and proving useful in other fields that call for structure discovery from raw data, such as bioinformatics.Unsupervised learning of natural languageshttp://www.bibsonomy.org/bibtex/25ef80c896354d96906b6a50b5d40e57e/yishyish2012-02-05T23:35:21+01:00aied inquiry intelligent its learning support tutoring <span class="authorEditorList"><a href="/author/Roll">I. Roll</a>, <a href="/author/Aleven">V. Aleven</a>, and <a href="/author/Koedinger">K. Koedinger</a> </span><em>Intelligent Tutoring Systems, </em><em>page 115--124. </em><em>Springer, </em>(<em>2010</em>)Sun Feb 05 23:35:21 CET 2012Intelligent Tutoring Systems115--124The invention lab: Using a hybrid of model tracing and constraint-based modeling to offer intelligent support in inquiry environments2010aied inquiry intelligent its learning support tutoring http://www.bibsonomy.org/bibtex/26ff3520f5e5ca958c8c5ea1fbe1d7a22/sidyrsidyr2012-02-04T10:41:58+01:00kernel-methods learning machine <span class="authorEditorList"><a href="/author/Hofmann">Thomas Hofmann</a>, <a href="/author/Schölkopf">Bernhard Schölkopf</a>, and <a href="/author/Smola">Alexander J. Smola</a> </span><em>Annals of Statistics</em> <em>36(3):1171--1220</em> (<em>2008</em>)Sat Feb 04 10:41:58 CET 2012Annals of Statistics31171--1220Kernel methods in machine learning362008kernel-methods learning machine We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions defined on nonvectorial data.phd thesis version 2009-10-23http://www.bibsonomy.org/bibtex/216d03a0e8ac507ce0f08605d713885f3/yishyish2012-02-01T13:07:34+01:00LDG design education learning mathematics practice representation representations teaching <span class="authorEditorList"><a href="/author/Herbst">Patricio Herbst</a>, <a href="/author/Chazan">Daniel Chazan</a>, <a href="/author/Chen">Chia-Ling Chen</a>, <a href="/author/Chieu">Vu-Minh Chieu</a>, and <a href="/author/Weiss">Michael Weiss</a> </span><em>ZDM</em> (<em>2011</em>)<em>10.1007/s11858-010-0290-5 . </em>Wed Feb 01 13:07:34 CET 2012ZDM10.1007/s11858-010-0290-591-103Using comics-based representations of teaching, and technology, to bring practice to teacher education courses432011LDG design education learning mathematics practice representation representations teaching This article situates comic-based representations of teaching in the long history of tensions between theory and practice in teacher education. The article argues that comics can be semiotic resources in learning to teach and suggests how information technologies can support experiences with comics in university mathematics methods courses that (a) help learners see the mathematical work of teaching in lessons they observe, (b) allow candidates to explore tactical decision-making in teaching, and (c) support preservice teachers in rehearsing classroom interactions.http://www.bibsonomy.org/bibtex/236ee6b8d66d8c0673dd0de67ac3e4bb2/muhemuhe2012-01-27T14:10:42+01:00Auditory Binaural Hearing; Individual Learning cues; differences; localization; <span class="authorEditorList"><a href="/author/Savel">Sophie Savel</a> </span><em>Hearing Research</em> (<em>2009</em>)Fri Jan 27 14:10:42 CET 2012Hearing Research142-154Individual differences and left-right asymmetries in auditory space perception --Localization of low frequency sounds in free field2552009Auditory Binaural Hearing; Individual Learning cues; differences; localization; The number of subjects in studies on human spatial hearing is generally small. Therefore, individual differences and the factors underlying variability are unknown. In this study, we investigated across-listener variability in auditory localization abilities in a group of 50 naïve adults with normal hearing. Targets were trains of low-frequency noise bursts presented to 1 of 12 hidden speakers in the azimuthal plane. We observed less across-listener variability in the variance of individual responses but more in the root-mean-square and signed errors, which tended to increase with target angle. One third of the listeners demonstrated systematically smaller signed errors with left-sided targets than with right-sided ones. These asymmetries were observed less frequently in left-handers and females than in right-handers and males. Performance was not correlated with age. About 4 of 6 listeners trained with sensory feedback showed no reduction of asymmetries with training but rather showed a reduction in errors on their “best” side. Across-listener variability in the asymmetry of brain organization, notably linked to handedness or gender, is discussed.http://www.bibsonomy.org/bibtex/24521f8dd6573972a5a37f0589d4018d0/muhemuhe2012-01-27T14:10:42+01:00Public consistency, judgments, learning preference random reliability, response scaling, time, utility, <span class="authorEditorList"><a href="/author/Brown">Thomas C. Brown</a>, and <a href="/author/Peterson">George L. Peterson</a> </span><em>United States Department of Agriculture</em> (<em>2009</em>)Fri Jan 27 14:10:42 CET 2012United States Department of AgricultureAn Enquiry Into the Method of Paired Comparison- Reliability, Scaling, and Thurstones Law of Comparative Judgment2009Public consistency, judgments, learning preference random reliability, response scaling, time, utility, The method of paired comparisons is used to measure individuals’ preference orderings of items presented to them as discrete binary choices. This paper reviews the theory and application of the paired comparison method, describes a new computer program available for eliciting the choices, and presents an analysis of methods for scaling paired choice data to estimate an interval scale measure of preference. A new procedure for isolating an individual’s inconsistent choices is described. Using data from five empirical studies, the reliability of respondents’ paired choices is assessed using measures of internal reliability, choice consistency, and test-retest reliability.http://www.bibsonomy.org/bibtex/2f01a78e5a23ace2e7a88c2bbe1253a81/khilgenbergkhilgenberg2012-01-26T13:12:53+01:002011 classifier honeypots kde learning machine myspace seminar social spam twitter <span class="authorEditorList"><a href="/author/Lee">Kyumin Lee</a>, <a href="/author/Caverlee">James Caverlee</a>, and <a href="/author/Webb">Steve Webb</a> </span><em>Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, </em><em>page 435--442. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2010</em>)Thu Jan 26 13:12:53 CET 2012New York, NY, USA{Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval}435--442SIGIR '10{Uncovering Social Spammers: Social Honeypots + Machine Learning.}20102011 classifier honeypots kde learning machine myspace seminar social spam twitter {Web-based social systems enable new community-based opportunities for participants to engage, share, and interact. This community value and related services like search and advertising are threatened by spammers, content polluters, and malware disseminators. In an effort to preserve community value and ensure longterm success, we propose and evaluate a honeypot-based approach for uncovering social spammers in online social systems. Two of the key components of the proposed approach are: (1) The deployment of social honeypots for harvesting deceptive spam profiles from social networking communities; and (2) Statistical analysis of the properties of these spam profiles for creating spam classifiers to actively filter out existing and new spammers. We describe the conceptual framework and design considerations of the proposed approach, and we present concrete observations from the deployment of social honeypots in MySpace and Twitter. We find that the deployed social honeypots identify social spammers with low false positive rates and that the harvested spam data contains signals that are strongly correlated with observable profile features (e.g., content, friend information, posting patterns, etc.). Based on these profile features, we develop machine learning based classifiers for identifying previously unknown spammers with high precision and a low rate of false positives.}http://www.bibsonomy.org/bibtex/278b3de25f9dfe7ac03e9fb7c245114aa/khilgenbergkhilgenberg2012-01-26T13:12:53+01:002011 artificial intelligence kde learning machine seminar twitter <span class="authorEditorList"><a href="/author/Russell">Stuart Russell</a>, and <a href="/author/Norvig">Peter Norvig</a> </span><em>Prentice-Hall, </em>(<em>1995</em>)Thu Jan 26 13:12:53 CET 2012Artificial {I}ntelligence: {A} {M}odern {A}pproach19952011 artificial intelligence kde learning machine seminar twitter http://www.bibsonomy.org/bibtex/23a5dce655efa6172d4ef01bc4ea0d412/khilgenbergkhilgenberg2012-01-26T13:12:53+01:002011 SVM analysis attribute classifier kde learning machine seminar spam twitter <span class="authorEditorList"><a href="/author/Benevenuto">Fabricio Benevenuto</a>, <a href="/author/Magno">Gabriel Magno</a>, <a href="/author/Rodrigues">Tiago Rodrigues</a>, and <a href="/author/Almeida">Virgilio Almeida</a> </span><em>Proceedings of the Seventh Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference CEAS, </em>(<em>July 2010</em>)Thu Jan 26 13:12:53 CET 2012{Proceedings of the Seventh Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS)}jul{Detecting Spammers on {Twitter}.}20102011 SVM analysis attribute classifier kde learning machine seminar spam twitter {With millions of users tweeting around the world, real time search systems and different types of mining tools are emerging to allow people tracking the repercussion of events and news on Twitter. However, although appealing as mechanisms to ease the spread of news and allow users to discuss events and post their status, these services open opportunities for new forms of spam. Trending topics, the most talked about items on Twitter at a given point in time, have been seen as an opportunity to generate traffic and revenue. Spammers post tweets containing typical words of a trending topic and URLs, usually obfuscated by URL shorteners, that lead users to completely unrelated websites. This kind of spam can contribute to de-value real time search services unless mechanisms to fight and stop spammers can be found. In this paper we consider the problem of detecting spammers on Twitter. We first collected a large dataset of Twitter that includes more than 54 million users, 1.9 billion links, and almost 1.8 billion tweets. Using tweets related to three famous trending topics from 2009, we construct a large labeled collection of users, manually classified into spammers and non-spammers. We then identify a number of characteristics related to tweet content and user social behavior, which could potentially be used to detect spammers. We used these characteristics as attributes of machine learning process for classifying users as either spammers or nonspammers. Our strategy succeeds at detecting much of the spammers while only a small percentage of non-spammers are misclassified. Approximately 70\% of spammers and 96\% of non-spammers were correctly classified. Our results also highlight the most important attributes for spam detection on Twitter.}http://www.bibsonomy.org/bibtex/2e50bafe278b8194bd4b74c2bdf84150c/khilgenbergkhilgenberg2012-01-26T13:12:53+01:002011 bayes classifier kde learning machine seminar spam twitter <span class="authorEditorList"><a href="/author/Wang">Alex Wang</a> </span><em>Data and Applications Security and Privacy XXIV, </em><em>volume 6166 of Lecture Notes in Computer Science, </em><em>chapter 25, </em><em>Springer Berlin / Heidelberg, </em><em>Berlin, Heidelberg, </em>(<em>2010</em>)Thu Jan 26 13:12:53 CET 2012Berlin, Heidelberg{Data and Applications Security and Privacy XXIV}25335--342Lecture Notes in Computer Science{Detecting Spam Bots in Online Social Networking Sites: A Machine Learning Approach.}616620102011 bayes classifier kde learning machine seminar spam twitter {As online social networking sites become more and more popular, they have also attracted the attentions of the spammers. In this paper, Twitter, a popular micro-blogging service, is studied as an example of spam bots detection in online social networking sites. A machine learning approach is proposed to distinguish the spam bots from normal ones. To facilitate the spam bots detection, three graph-based features, such as the number of friends and the number of followers, are extracted to explore the unique follower and friend relationships among users on Twitter. Three content-based features are also extracted from user's most recent 20 tweets. A real data set is collected from Twitter's public available information using two different methods. Evaluation experiments show that the detection system is efficient and accurate to identify spam bots in Twitter.}