https://www.bibsonomy.org/user/yish/analysisBibSonomy RSS Feed for /user/yish/analysisToday, speech technology is only available for a small fraction of the thousands of languages spoken around the world because traditional systems need to be trained on large amounts of annotated speech audio with transcriptions. Obtaining that kind of data for every human language and dialect is almost impossible. Wav2vec works around this limitation by requiring little to no transcribed data. The model uses self-supervision to push the boundaries by learning from unlabeled training data. This enables speech recognition systems for many more languages and dialects, such as Kyrgyz and Swahili, which don’t have a lot of transcribed speech audio. Self-supervision is the key to leveraging unannotated data and building better systems.https://ai.meta.com/research/impact/wav2vec/yish2023-07-17T15:00:32+02:00AI analysis speech speech2text text <span itemprop="description">Today, speech technology is only available for a small fraction of the thousands of languages spoken around the world because traditional systems need to be trained on large amounts of annotated speech audio with transcriptions. Obtaining that kind of data for every human language and dialect is almost impossible. Wav2vec works around this limitation by requiring little to no transcribed data. The model uses self-supervision to push the boundaries by learning from unlabeled training data. This enables speech recognition systems for many more languages and dialects, such as Kyrgyz and Swahili, which don’t have a lot of transcribed speech audio. Self-supervision is the key to leveraging unannotated data and building better systems.</span>ATLAS.ti helps you uncover actionable insights with intuitive research tools and best-in-class technology. Try it for free today!https://atlasti.com/yish2023-04-18T11:50:02+02:00ai analysis data qualitative research tool <span itemprop="description">ATLAS.ti helps you uncover actionable insights with intuitive research tools and best-in-class technology. Try it for free today!</span>In the Developmental Intelligence Laboratory, we are interested in understanding fundamental cognitive mechanisms of human intelligence, human learning, and human interaction and communication in everyday activities. To do so, we collect and analyze micro-level multimodal behavioral data using state-of-the-art sensing and computational techniques. One of our primary research aims is to understand human learning and early development. How do young children acquire fundamental knowledge of the world? How do they select and process the information around them and learn from scratch? How do they learn to move their bodies and to communicate and interact with others? Learning this kind of knowledge and skills is the core of human intelligence. To understand how human learners achieve the learning goal, the primary approach in our research is to attach GoPro-like cameras on the head of young children to record egocentric video from their point of view. Using this innovative approach, we've been collecting video data of children’s everyday activities, such as playing with their parents and their peers, reading books with parents and caregivers, and playing outside. We've been using state-of-the-art machine learning and data mining approaches to analyze high-density behavioral data. This research line will ultimately solve the mystery on why human children are such efficient learners. Moreover, the findings from our research will be used to help improve learning of children with developmental deficits. A complimentary research line is to explore how human learning can teach us about how machines can learn. Can we model and simulate how a human child learns and develops? To this end, our research aims at bridging and connecting developmental science in psychology and machine learning and computer vision in computer science.https://www.la.utexas.edu/users/dil/index.htmlyish2022-11-10T16:41:48+01:00analysis analytics behavioural cognition cognitive development multimodal social <span itemprop="description">In the Developmental Intelligence Laboratory, we are interested in understanding fundamental cognitive mechanisms of human intelligence, human learning, and human interaction and communication in everyday activities. To do so, we collect and analyze micro-level multimodal behavioral data using state-of-the-art sensing and computational techniques. One of our primary research aims is to understand human learning and early development. How do young children acquire fundamental knowledge of the world? How do they select and process the information around them and learn from scratch? How do they learn to move their bodies and to communicate and interact with others? Learning this kind of knowledge and skills is the core of human intelligence. To understand how human learners achieve the learning goal, the primary approach in our research is to attach GoPro-like cameras on the head of young children to record egocentric video from their point of view. Using this innovative approach, we&#039;ve been collecting video data of children’s everyday activities, such as playing with their parents and their peers, reading books with parents and caregivers, and playing outside. We&#039;ve been using state-of-the-art machine learning and data mining approaches to analyze high-density behavioral data. This research line will ultimately solve the mystery on why human children are such efficient learners. Moreover, the findings from our research will be used to help improve learning of children with developmental deficits. A complimentary research line is to explore how human learning can teach us about how machines can learn. Can we model and simulate how a human child learns and develops? To this end, our research aims at bridging and connecting developmental science in psychology and machine learning and computer vision in computer science.</span>This paper provides a summary account of Activity-Centred Analysis and Design (ACAD). ACAD offers a practical approach to analysing complex learning situations, in a way that can generate knowledge that is reusable in subsequent (re)design work. ACAD has been developed over the last two decades. It has been tested and refined through collaborative analyses of a large number of complex learning situations and through research studies involving experienced and inexperienced design teams. The paper offers a definition and high level description of ACAD and goes on to explain the underlying motivation. The paper also provides an overview of two current areas of development in ACAD: the creation of explicit design rationales and the ACAD toolkit for collaborative design meetings. As well as providing some ideas that can help teachers, design teams and others discuss and agree on their working methods, ACAD has implications for some broader issues in educational technology research and development. It questions some deep assumptions about the framing of research and design thinking, in the hope that fresh ideas may be useful to people involved in leadership and advocacy roles in the field.https://petergoodyear.net/blog/yish2022-04-12T20:44:23+02:00ACAD Activity-Centred Analysis Design LD LDF and design framework learning <span itemprop="description">This paper provides a summary account of Activity-Centred Analysis and Design (ACAD). ACAD offers a practical approach to analysing complex learning situations, in a way that can generate knowledge that is reusable in subsequent (re)design work. ACAD has been developed over the last two decades. It has been tested and refined through collaborative analyses of a large number of complex learning situations and through research studies involving experienced and inexperienced design teams. The paper offers a definition and high level description of ACAD and goes on to explain the underlying motivation. The paper also provides an overview of two current areas of development in ACAD: the creation of explicit design rationales and the ACAD toolkit for collaborative design meetings. As well as providing some ideas that can help teachers, design teams and others discuss and agree on their working methods, ACAD has implications for some broader issues in educational technology research and development. It questions some deep assumptions about the framing of research and design thinking, in the hope that fresh ideas may be useful to people involved in leadership and advocacy roles in the field.</span>Task analysis is the systematic study of how users complete tasks to achieve their goals. This knowledge ensures products and services are designed to efficiently and appropriately support those goals.https://www.nngroup.com/articles/task-analysis/yish2021-07-20T19:02:55+02:00UX analysis design task <span itemprop="description">Task analysis is the systematic study of how users complete tasks to achieve their goals. This knowledge ensures products and services are designed to efficiently and appropriately support those goals.</span>To assist European universities to become more mature users and custodians of digital data about their students as they learn online, the SHEILA project will build a policy development framework that promotes formative assessment and personalized learning, by taking advantage of direct engagement of stakeholders in the development process.https://sheilaproject.eu/yish2019-11-06T17:27:53+01:00advocacy analysis analytics assessment eu learning meital needs policy project readiness tool <span itemprop="description">To assist European universities to become more mature users and custodians of digital data about their students as they learn online, the SHEILA project will build a policy development framework that promotes formative assessment and personalized learning, by taking advantage of direct engagement of stakeholders in the development process.</span>http://www.statistiklabor.de/en/yish2018-06-11T09:46:08+02:00analysis data inquiry statistics <a itemprop="url" data-versiondate="2018-06-11T09:46:08+02:00" href="http://www.statistiklabor.de/en/" rel="nofollow" class="description-link">http://www.statistiklabor.de/en/</a>JASP is an open-source statistics program that is free, friendly, and flexible. Armed with an easy-to-use GUI, JASP allows both classical and Bayesian analyses.https://jasp-stats.org/yish2018-06-11T09:44:34+02:00SPSS analysis data inquiry science software statistics <span itemprop="description">JASP is an open-source statistics program that is free, friendly, and flexible. Armed with an easy-to-use GUI, JASP allows both classical and Bayesian analyses.</span>V-Note is Video Analysis Software V-Note Video Analysis Software helps you get more from your videos through analysis and easy collaboration. V-Note is used by researchers, students, athletes, coaches, doctors, and consumer researchers around the world. (Who?) What Can V-Note Do? V-Note is powerful and flexible. It can be used in many ways: Do video-based research Collaborate around video to improve practice Make player/team histories and highlight videos Analyze players and teams Compare questions asked by student teachers across a semester Compare teaching and teachers Look for growth and change Analyze live while recording or use existing videos Link rubrics, playbooks, or other documents to videos Juxtapose video clips Propose and discuss categories for human behavior and interaction Transcribe easily yourself or automatically (U.S. English https://www.v-note.org/yish2017-09-08T10:07:37+02:00analysis free tool video <span itemprop="description">V-Note is Video Analysis Software V-Note Video Analysis Software helps you get more from your videos through analysis and easy collaboration. V-Note is used by researchers, students, athletes, coaches, doctors, and consumer researchers around the world. (Who?) What Can V-Note Do? V-Note is powerful and flexible. It can be used in many ways: Do video-based research Collaborate around video to improve practice Make player/team histories and highlight videos Analyze players and teams Compare questions asked by student teachers across a semester Compare teaching and teachers Look for growth and change Analyze live while recording or use existing videos Link rubrics, playbooks, or other documents to videos Juxtapose video clips Propose and discuss categories for human behavior and interaction Transcribe easily yourself or automatically (U.S. English </span>iNZight - a free, easy to use software for statistical data analysishttps://www.stat.auckland.ac.nz/~wild/iNZight/index.phpyish2017-06-27T11:50:09+02:00analysis analytics data levinsky opensource platform statistics tool <span itemprop="description">iNZight - a free, easy to use software for statistical data analysis</span>https://concord.org/projects/codapyish2017-06-27T11:49:09+02:00analysis analytics data education inquiry online opensource platform statistics tool <a itemprop="url" data-versiondate="2017-06-27T11:49:09+02:00" href="https://concord.org/projects/codap" rel="nofollow" class="description-link">https://concord.org/projects/codap</a>Download Transana for free. Transana is a cross-platform tool for the transcription and qualitative analysis of audio and video data. It also provides the ability to identify and organize analytically interesting portions of videos, as well as attach keywords to those video clips.https://sourceforge.net/projects/transana/yish2017-05-17T21:37:53+02:00analysis qualitative video <span itemprop="description">Download Transana for free. Transana is a cross-platform tool for the transcription and qualitative analysis of audio and video data. It also provides the ability to identify and organize analytically interesting portions of videos, as well as attach keywords to those video clips.</span>https://ant.umn.edu/welcomeyish2017-03-15T09:08:02+01:00analysis annotation discussion video <a itemprop="url" data-versiondate="2017-03-15T09:08:02+01:00" href="https://ant.umn.edu/welcome" rel="nofollow" class="description-link">https://ant.umn.edu/welcome</a>http://mashe.hawksey.info/2013/02/twitter-archive-tagsv5/yish2014-01-14T13:43:25+01:00analysis archive google hawksey martin online spreadsheets tool twitter <a itemprop="url" data-versiondate="2014-01-14T13:43:25+01:00" href="http://mashe.hawksey.info/2013/02/twitter-archive-tagsv5/" rel="nofollow" class="description-link">http://mashe.hawksey.info/2013/02/twitter-archive-tagsv5/</a>http://www.casos.cs.cmu.edu/projects/automap/yish2013-09-18T15:28:07+02:00analysis analytics concept conceptmaps free map mapping mining open text tool <a itemprop="url" data-versiondate="2013-09-18T15:28:07+02:00" href="http://www.casos.cs.cmu.edu/projects/automap/" rel="nofollow" class="description-link">http://www.casos.cs.cmu.edu/projects/automap/</a>NodeXL is a free, open-source template for Microsoft® Excel® 2007 and 2010 that makes it easy to explore network graphs. With NodeXL, you can enter a network edge list in a worksheet, click a button and see your graph, all in the familiar environment of the Excel window.http://nodexl.codeplex.com/yish2012-09-12T15:39:06+02:00analitics analysis discovery exploration network nodexl visualisation <span itemprop="description">NodeXL is a free, open-source template for Microsoft® Excel® 2007 and 2010 that makes it easy to explore network graphs. With NodeXL, you can enter a network edge list in a worksheet, click a button and see your graph, all in the familiar environment of the Excel window.</span>http://code.google.com/p/tatiana/yish2011-03-30T10:24:12+02:00analysis education interaction research text tool <a itemprop="url" data-versiondate="2011-03-30T10:24:12+02:00" href="http://code.google.com/p/tatiana/" rel="nofollow" class="description-link">http://code.google.com/p/tatiana/</a>Analysis of Competing Hypotheses http://competinghypotheses.org/yish2010-09-10T13:53:11+02:00Analysis Competing Hypotheses analysis decision evidence software support tool <span itemprop="description">Analysis of Competing Hypotheses </span>http://www.elsevierdirect.com/BrainNavigator/yish2010-02-15T14:05:06+01:00analysis brain education mapping navigator research software <a itemprop="url" data-versiondate="2010-02-15T14:05:06+01:00" href="http://www.elsevierdirect.com/BrainNavigator/" rel="nofollow" class="description-link">http://www.elsevierdirect.com/BrainNavigator/</a>