Relevant bibliographies by topics / Learning activity

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Learning activity'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 May 2022

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Journal articles on the topic "Learning activity"

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R. D. Gomathi, R. D. Gomathi, and P. Kiruthika P. Kiruthika. "Activity Based Language Learning – an Effective Learning Method." Indian Journal of Applied Research 3, no. 11 (October 1, 2011): 254–55. http://dx.doi.org/10.15373/2249555x/nov2013/82.

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Kulsum, Umi. "HYBRID LEARNING TIME MODIFICATION CAN IMPROVE LEARNING ACTIVITY AND LEARNING OUTCOMES." SCHOOL EDUCATION JOURNAL PGSD FIP UNIMED 11, no. 3 (December 23, 2021): 263–68. http://dx.doi.org/10.24114/sejpgsd.v11i3.27922.

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The purpose of this study was to determine the effectiveness of hybrid learning time modification in terms of learning outcomes; knowing the relationship between learning activities and learning outcomes and knowing the effect of hybrid and one other group is the conventional group (face-to-face only), this group is the control group.Collecting data using a learning activity questionnaire and a knowledge test to determine learning outcomes. Data analysis technique with Ancova. The results of the study: (1) hybrid learning time modification is effective in improving learning outcomes (2) significant relationship between learning activity and learning outcomes, significance 0.000; (3) there is a significant difference in the effect of variations in hybrid learning time modification on learning activity and learning outcomes, the significance of 0.037 Keywords: Time Modification, Hybrid Learning, Active Learning, Learning Outcomes
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Bridjeshpappula and Geetha narayanankannaiyan. "Assessment of students learning capability adapting activity based learning – STAD." International Journal of Psychosocial Rehabilitation 24, no. 04 (February 28, 2020): 2982–88. http://dx.doi.org/10.37200/ijpr/v24i4/pr201410.

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Liu, Qingzhong, Zhaoxian Zhou, Sarbagya Ratna Shakya, Prathyusha Uduthalapally, Mengyu Qiao, and Andrew H. Sung. "Smartphone Sensor-Based Activity Recognition by Using Machine Learning and Deep Learning Algorithms." International Journal of Machine Learning and Computing 8, no. 2 (April 2018): 121–26. http://dx.doi.org/10.18178/ijmlc.2018.8.2.674.

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Giddens, Jean Foret. "Innovative Learning Activity." Journal of Nursing Education 47, no. 4 (April 1, 2008): 196. http://dx.doi.org/10.3928/01484834-20080401-08.

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MacLeod, Martha L. P. "Innovative Learning Activity." Journal of Nursing Education 48, no. 6 (June 1, 2009): 356. http://dx.doi.org/10.3928/01484834-20090515-10.

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Horsley, Trisha Leann. "Innovative Learning Activity." Journal of Nursing Education 49, no. 6 (June 1, 2010): 363–64. http://dx.doi.org/10.3928/01484834-20090521-02.

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Noone, Joanne, Stephanie A. Sideras, and Amy Miner Ross. "Innovative Learning Activity." Journal of Nursing Education 48, no. 7 (July 1, 2009): 416. http://dx.doi.org/10.3928/01484834-20090615-11.

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Noone, Joanne. "Innovative Learning Activity." Journal of Nursing Education 48, no. 8 (August 1, 2009): 472. http://dx.doi.org/10.3928/01484834-20090717-04.

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Ross, Amy Miner, and Donna Markle. "Innovative Learning Activity." Journal of Nursing Education 48, no. 10 (October 1, 2009): 592. http://dx.doi.org/10.3928/01484834-20090918-02.

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Dissertations / Theses on the topic "Learning activity"

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Lompscher, Joachim. "Learning strategies : an essential component of learning activity." Universität Potsdam, 1994. http://opus.kobv.de/ubp/volltexte/2005/450/.

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Smith, Raymond. "MULTIZOOM ACTIVITY RECOGNITION USING MACHINE LEARNING." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2162.

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In this thesis we present a system for detection of events in video. First a multiview approach to automatically detect and track heads and hands in a scene is described. Then, by making use of epipolar, spatial, trajectory, and appearance constraints, objects are labeled consistently across cameras (zooms). Finally, we demonstrate a new machine learning paradigm, TemporalBoost, that can recognize events in video. One aspect of any machine learning algorithm is in the feature set used. The approach taken here is to build a large set of activity features, though TemporalBoost itself is able to work with any feature set other boosting algorithms use. We also show how multiple levels of zoom can cooperate to solve problems related to activity recognition.
Ph.D.
School of Computer Science
Engineering and Computer Science
Computer Science
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Makris, Dimitrios. "Learning an activity-based semantic scene model." Thesis, City University London, 2004. http://eprints.kingston.ac.uk/7781/.

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Kim, Juho Ph D. Massachusetts Institute of Technology. "Learnersourcing : improving learning with collective learner activity." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101464.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages [199]-213).
Millions of learners today are watching videos on online platforms, such as Khan Academy, YouTube, Coursera, and edX, to take courses and master new skills. But existing video interfaces are not designed to support learning, with limited interactivity and lack of information about learners' engagement and content. Making these improvements requires deep semantic information about video that even state-of-the-art AI techniques cannot fully extract. I take a data-driven approach to address this challenge, using large-scale learning interaction data to dynamically improve video content and interfaces. Specifically, this thesis introduces learnersourcing, a form of crowdsourcing in which learners collectively contribute novel content for future learners while engaging in a meaningful learning experience themselves. I present learnersourcing applications designed for massive open online course videos and how-to tutorial videos, where learners' collective activities 1) highlight points of confusion or importance in a video, 2) extract a solution structure from a tutorial, and 3) improve the navigation experience for future learners. This thesis demonstrates how learnersourcing can enable more interactive, collaborative, and data-driven learning.
by Juho Kim.
Ph. D.
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Olnén, Johanna, and Julia Sommarlund. "Activity Recognition Using IoT and Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295603.

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Internet of Things devices, such as smartphonesand smartwatches, are currently becoming widely accessible andprogressively advanced. As the use of these devices steadilyincreases, so does the access to large amounts of sensory data.In this project, we developed a system that recognizes certainactivities by applying a linear classifier machine learning modelto a data set consisting of examples extracted from accelerometersensor data. We obtained the data set by collecting data from amobile device while performing commonplace everyday activities.These activities include walking, standing, driving, and ridingthe subway. The raw accelerometer data was then aggregatedinto data points, consisting of several informative features. Thecomplete data set was subsequently split into 80% training dataand 20% test data. A machine learning algorithm, in this case,a support vector machine, was presented with the training dataset and finally classified all test data with a precision higher than90%. Hence, meeting our set objective to build a service with acorrect classification score of over 90%.Human activity recognition has a large area of application,including improved health-related recommendations and a moreefficiently engineered system for public transportation.
Internet of Things-enheter, så som smarta telefoner och klockor, blir numera allt mer tillgängliga och tekniskt avancerade. Eftersom användningen av dessa smarta enheter stadigt ökar, ökar också tillgången till stora mängder data från sensorer i dessa enheter. I detta projekt utvecklade vi ett system som känner igen vissa aktiviteter genom att tillämpa en linjär klassificerande maskininlärningsmodell på en uppsättning data som extraherats från en accelerometer, en sensor i en smart telefon. Datauppsättningen skapades genom att samla in data från en smart telefon medan vi utförde vardagliga aktiviteter, så som promenader, stå stilla, köra bil och åka tunnelbana. Rå accelerometerdata samlades in och gjordes om till datavektorer innehållandes statistiska mått. Den totala datauppsättningen delades sedan upp i 80% träningsdata och 20% testdata. En maskininlärningsalgoritm, i detta fall en supportvektormaskin, introducerades med träningsdatan och klassificerade slutligen testdatan med en precision på över 90%. Därmed uppfylldes vårt uppsatta mål med att bygga en tjänst med en korrekt klassificering på över 90%. Igenkänning av mänsklig aktivitet har ett stort användningsområde, och kan bidra till förbättrade hälsorekommendationer och en mer effektiv kollektivtrafik.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
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Pang, Jinyong. "Human Activity Recognition Based on Transfer Learning." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7558.

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Human activity recognition (HAR) based on time series data is the problem of classifying various patterns. Its widely applications in health care owns huge commercial benefit. With the increasing spread of smart devices, people have strong desires of customizing services or product adaptive to their features. Deep learning models could handle HAR tasks with a satisfied result. However, training a deep learning model has to consume lots of time and computation resource. Consequently, developing a HAR system effectively becomes a challenging task. In this study, we develop a solid HAR system using Convolutional Neural Network based on transfer learning, which can eliminate those barriers.
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Axelsson, Henrik, and Daniel Wass. "Machine Learning for Activity Recognition of Dumpers." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260256.

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The construction industry has lagged behind other industries in productivity growth rate. Earth-moving sites, and other practices where dumpers are used, are no exceptions. Such projects lack convenient and accurate solutions for utilization mapping and tracking of mass flows, which both currently and mainly rely on manual activity tracking. This study intends to provide insights of how autonomous systems for activity tracking of dumpers can contribute to the productivity at earthmoving sites. Autonomous systems available on the market are not implementable to dumper fleets of various manufacturers and model year, whereas this study examines the possibilities of using activity recognition by machine learning for a system based on smartphones mounted in the driver’s cabin. Three machine learning algorithms (naive Bayes, random forest and feed-forward backpropagation neural network) are trained and tested on data collected by smartphone sensors. Conclusions are that machine learning models, and particularly the neural network and random forest algorithms, trained on data from a standard smartphone, are able to estimate a dumper’s activities at a high degree of certainty. Finally, a market analysis is presented, identifying the innovation opportunity for a potential end-product as high.
Byggnadsbranschen har halkat efter andra branscher i produktivitetsökning. Markarbetesprojekt och andra arbeten där dumprar används är inga undantag. Sådana projekt saknar användarvänliga system för att kartlägga maskinutnyttjande och massaflöde. Nuvarande lösningar bygger framförallt på manuellt arbete. Denna studie syftar skapa kännedom kring hur autonoma system för aktivitetsspårning av dumprar kan öka produktiviteten på markarbetesprojekt. Befintliga autonoma lösningar är inte implementerbara på maskinparker med olika fabrikat eller äldre årsmodeller. Denna studie undersöker möjligheten att applicera aktivitetsigenkänning genom maskininlärning baserad på smartphones placerade i förarhytten för en sådan autonom lösning. Tre maskininlärningsalgoritmer (naive Bayes, random forest och backpropagation neuralt nätverk) tränas och testas på data från sensorer tillgängliga i vanliga smartphones. Studiens slutsatser är att maskininlärningsmodeller, i synnerhet neuralt nätverk och random forest-algoritmerna, tränade på data från vanliga smartphones, till hög grad kan känna igen en dumpers aktiviteter. Avslutningsvis presenteras en marknadsanalys som bedömer innovationsmöjligheten för en eventuell slutprodukt som hög.
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Albert, Florea George, and Filip Weilid. "Deep Learning Models for Human Activity Recognition." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20201.

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AMI Meeting Corpus (AMI) -databasen används för att undersöka igenkännande av gruppaktivitet. AMI Meeting Corpus (AMI) -databasen ger forskare fjärrstyrda möten och naturliga möten i en kontorsmiljö; mötescenario i ett fyra personers stort kontorsrum. För attuppnågruppaktivitetsigenkänninganvändesbildsekvenserfrånvideosoch2-dimensionella audiospektrogram från AMI-databasen. Bildsekvenserna är RGB-färgade bilder och ljudspektrogram har en färgkanal. Bildsekvenserna producerades i batcher så att temporala funktioner kunde utvärderas tillsammans med ljudspektrogrammen. Det har visats att inkludering av temporala funktioner både under modellträning och sedan förutsäga beteende hos en aktivitet ökar valideringsnoggrannheten jämfört med modeller som endast använder rumsfunktioner[1]. Deep learning arkitekturer har implementerats för att känna igen olika mänskliga aktiviteter i AMI-kontorsmiljön med hjälp av extraherade data från the AMI-databas.Neurala nätverks modellerna byggdes med hjälp av KerasAPI tillsammans med TensorFlow biblioteket. Det finns olika typer av neurala nätverksarkitekturer. Arkitekturerna som undersöktes i detta projektet var Residual Neural Network, Visual GeometryGroup 16, Inception V3 och RCNN (LSTM). ImageNet-vikter har använts för att initialisera vikterna för Neurala nätverk basmodeller. ImageNet-vikterna tillhandahålls av Keras API och är optimerade för varje basmodell [2]. Basmodellerna använder ImageNet-vikter när de extraherar funktioner från inmatningsdata. Funktionsextraktionen med hjälp av ImageNet-vikter eller slumpmässiga vikter tillsammans med basmodellerna visade lovande resultat. Både Deep Learning användningen av täta skikt och LSTM spatio-temporala sekvens predikering implementerades framgångsrikt.
The Augmented Multi-party Interaction(AMI) Meeting Corpus database is used to investigate group activity recognition in an office environment. The AMI Meeting Corpus database provides researchers with remote controlled meetings and natural meetings in an office environment; meeting scenario in a four person sized office room. To achieve the group activity recognition video frames and 2-dimensional audio spectrograms were extracted from the AMI database. The video frames were RGB colored images and audio spectrograms had one color channel. The video frames were produced in batches so that temporal features could be evaluated together with the audio spectrogrames. It has been shown that including temporal features both during model training and then predicting the behavior of an activity increases the validation accuracy compared to models that only use spatial features [1]. Deep learning architectures have been implemented to recognize different human activities in the AMI office environment using the extracted data from the AMI database.The Neural Network models were built using the Keras API together with TensorFlow library. There are different types of Neural Network architectures. The architecture types that were investigated in this project were Residual Neural Network, Visual Geometry Group 16, Inception V3 and RCNN(Recurrent Neural Network). ImageNet weights have been used to initialize the weights for the Neural Network base models. ImageNet weights were provided by Keras API and was optimized for each base model[2]. The base models uses ImageNet weights when extracting features from the input data.The feature extraction using ImageNet weights or random weights together with the base models showed promising results. Both the Deep Learning using dense layers and the LSTM spatio-temporal sequence prediction were implemented successfully.
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Sabzpoushan, Maryam. "Play to learn : children learning and activity space." Thesis, KTH, Arkitektur, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-96485.

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Play To Learn is an architectural project that aims to create an interwoven series of formal and informal, experimental learning and activity spaces for children between the ages of 3-12 years. Play To Learn creates a new place in an inner city and seaside location where children can come to play, experience, experiment and learn.
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Gordon, Susan Eve. "Understanding Students Learning Statistics: An Activity Theory Approach." University of Sydney. School of Development and Learning, 1998. http://hdl.handle.net/2123/353.

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In this project I investigate university students orientations to learning statistics. The students who participated in my research were studying statistics as a compulsory component of their psychology course. My central thesis is that learning develops in the relationship between the thinking, feeling and acting person and the social, institutional and cultural contexts surrounding him or her. How students orient themselves or position themselves to learn statistics is reflected in their engagement with the learning task and their activities. These activities determine the quality of their learning and emerging knowledge. To understand student learning I draw on the powerful theories of Vygotsky (1962, 1978) and Leontev (1978, 1981). In particular, I extend and apply Leontev's construct of activity (Leontev, 1981). This suggests that individuals act in accordance with their purposes and needs which are shaped by and reflect histories and resources, both personal and cultural. My investigation consists of two studies. Study One is a qualitative exploration of the orientations to learning statistics of five older students. These students sought help with statistics at the Mathematics Learning Centre where I work. My case studies of these students are inseparable from my efforts to help them learn statistics. Study Two is grounded in Study One. The main source of data for this broader study is a survey which was completed by 279 psychology students studying statistics. In keeping with the theoretical framework, my methodology involves a holistic analysis of students and the milieu in which they act. My findings suggest relationships among students affective appraisals; their conceptions of statistics; their approaches to learning it; their evaluations and the outcomes of their actions. In Study One the relationships emerged from the students' descriptions. In Study Two I quantified the ways in which variables related to each other. Structure for the data was provided by means of correlations, factor analysis and cluster analysis. For this study I also interviewed students and teachers of statistics. My data support the systemic view of teaching and learning in context afforded by my theoretical perspective. Learning statistics involves the whole person (Semenov, 1978) and is inseparable from the arena of his or her actions. The goal of statistics education is surely to enable students to develop useful, meaningful knowledge. My findings suggest that for many of the participants in my project this goal was not being met. Most of these students reported their reluctance to learn statistics and described adopting primarily surface approaches to learning it. A range of conceptions of the subject was expressed, but for many of the students statistical meaning was evidently reduced to performance on assessment tasks. Such orientations to learning statistics may lead to it becoming irrelevant and inert information. For a few students, however, the experience of learning statistics led to self development and enhanced perspectives on the world in which we live. My project indicates the diversity of students' experiences. It raises issues as to why we teach statistics today and how the teaching and learning of statistics is being supported at university. //REFERENCES Leontev, A. N. (1978). Activity, Consciousness, and Personality. (M. J. Hall, Trans.). Englewood Cliffs, New Jersey: Prentice-Hall. Leontev, A. N. (1981). The problem of activity in psychology. In J. V. Wertsch (Ed.), The Concept of Activity in Soviet Psychology, (pp. 37-71). New York: M. E. Sharpe. Semenov, N. (1978). An empirical psychological study of thought processes in creative problem-solving from the perspective of the theory of activity. Soviet Psychology, 16(1), 3-46. Vygotsky, L. S. (1962). Thought and Language. Cambridge, Massachusetts: The M.I.T. Press. Vygotsky, L. S. (1978). Mind in Society. Cambridge, MA: Harvard University Press.
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Books on the topic "Learning activity"

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Ireson, Judith. Learners, learning and educational activity. New York, NY: Routledge, 2008.

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Ireson, Judith. Learners, learning and educational activity. New York, NY: Routledge, 2008.

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Stevens, Tara. Physical Activity and Student Learning. New York, NY : Routledge, 2019. | Series: Ed psych insights: Routledge, 2019. http://dx.doi.org/10.4324/9780429436567.

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Sannino, Annalisa, Harry Daniels, and Kris D. Gutierrez, eds. Learning and Expanding with Activity Theory. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511809989.

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Li, Xiaoli, Min Wu, Zhenghua Chen, and Le Zhang, eds. Deep Learning for Human Activity Recognition. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0575-8.

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Hansson, Thomas. Contemporary approaches to activity theory: Interdisciplinary perspectives on human behavior. Hershey, PA: Information Science Reference, 2015.

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Abbs, Brian. Jigsaw 3: Activity book 1. Harlow: Longman, 1986.

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Furuno, Setsu. HELP activity guide. Edited by Enrichment Project for Handicapped Infants. 2nd ed. Palo Alto, Calif: VORT Corp., 2005.

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Mitchell, Jenna. Learning about the Bible: An activity book. Brigham City, Utah: Walnut Springs Press, LLC, 2010.

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Books, Priddy. Sticker Activity: Trucks (First Learning Sticker Activity). Tandem Library, 2003.

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Book chapters on the topic "Learning activity"

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Podolskiy, Andrey I. "Learning Activity." In Encyclopedia of the Sciences of Learning, 1761–62. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_314.

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Podolskiy, Andrey. "Activity Theories of Learning." In Encyclopedia of the Sciences of Learning, 83–85. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_310.

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Miller, Robert. "Theta Activity and Learning." In Cortico-Hippocampal Interplay and the Representation of Contexts in the Brain, 189–215. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-21732-0_10.

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Hyndman, Brendon, Matthew Winslade, and Bradley Wright. "Physical Activity and Learning." In Health and Education Interdependence, 179–204. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3959-6_10.

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Ellis, Robert A., and Peter Goodyear. "Learning in activity systems." In The Education Ecology of Universities, 169–92. Abingdon, Oxon ; New York, NY : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9781351135863-9.

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Jia, Chengcheng, and Yun Fu. "Subspace Learning for Action Recognition." In Human Activity Recognition and Prediction, 49–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_3.

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Rintala, Pauli, and Niina Palsio. "Effects of Physical Education Programs on Children with Learning Disabilities." In Adapted Physical Activity, 37–40. Tokyo: Springer Japan, 1994. http://dx.doi.org/10.1007/978-4-431-68272-1_6.

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Haworth, Deborah. "Applying Recovery Through Activity in a secure learning disability service." In Discovery Through Activity, 51–54. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003226109-10.

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Hamid, Raffay. "Classifier Boosting for Human Activity Recognition." In Ensemble Machine Learning, 251–72. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-9326-7_9.

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Lin, Liang, Dongyu Zhang, Ping Luo, and Wangmeng Zuo. "Human Activity Understanding." In Human Centric Visual Analysis with Deep Learning, 135–56. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2387-4_10.

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Conference papers on the topic "Learning activity"

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"Activity Coordination in Collaborative Learning Environments." In 1st International Workshop on Computer Supported Activity Coordination. SciTePress - Science and and Technology Publications, 2004. http://dx.doi.org/10.5220/0002665902270232.

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"Learning virtual project work." In 1st International Workshop on Computer Supported Activity Coordination. SciTePress - Science and and Technology Publications, 2004. http://dx.doi.org/10.5220/0002681500910102.

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Sacher, Patrick, and Thorsten Gattinger. "LEARNING ACTIVITY PROVIDER." In 14th International Technology, Education and Development Conference. IATED, 2020. http://dx.doi.org/10.21125/inted.2020.1526.

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"Ontology and E-Learning." In The 4th International Workshop on Computer Supported Activity Coordination. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002424400870098.

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"Prescribing e-Learning Activities Using Workflow Technologies." In 1st International Workshop on Computer Supported Activity Coordination. SciTePress - Science and and Technology Publications, 2004. http://dx.doi.org/10.5220/0002660500710080.

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Bočkor Starc, Barbara. "Cooperative Learning, Playing and Physical Activity." In Developing Effective Learning. University of Primorska Press, 2020. http://dx.doi.org/10.26493/978-961-293-002-8.15.

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"Activity Recognition using Incremental Learning." In Internet and Multimedia Systems and Applications / 747: Human-Computer Interaction. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.747-035.

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Georgievski, Ilche, Prashant Gupta, and Marco Aiello. "Activity Learning for Intelligent Buildings." In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2019. http://dx.doi.org/10.1109/uemcon47517.2019.8993060.

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Hossain, H. M. Sajjad, Nirmalya Roy, and Md Abdullah Al Hafiz Khan. "Active learning enabled activity recognition." In 2016 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 2016. http://dx.doi.org/10.1109/percom.2016.7456524.

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Tao, Xuehong, and Yuan Miao. "Interest Based Learning Activity Negotiation." In 2008 International Conference on Cyberworlds (CW). IEEE, 2008. http://dx.doi.org/10.1109/cw.2008.104.

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Reports on the topic "Learning activity"

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Osentoski, Sarah, Victoria Manfred, and Sridhar Mahadevan. Learning Hierarchical Models of Activity. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada440281.

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Shoham, Yoav, and Moshe Tennenholtz. Co-Learning and the Evolution of Social Activity,. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada325130.

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Pinchuk, Olga P., Oleksandra M. Sokolyuk, Oleksandr Yu Burov, and Mariya P. Shyshkina. Digital transformation of learning environment: aspect of cognitive activity of students. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3243.

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Peculiar features of digital environment include: integration of ICTs; use of local and global networks and resources; support and development of qualitatively new technologies of information processing; active use of modern means, methods and forms of teaching in the educational process. The organization of activities in terms of digital learning environment provides appropriate changes in the interaction between subjects of the educational process. Today, means and technologies of the information and communication networks (ICNs), in particular the Internet, which custom and operational-procedural properties were changed at the initial stage from closed local to open ones at present, become widespread. The development of ICNs (from closed local to open ones) changes the typology of learning environments. The following models of learning environments, which widely use ICT and ICN tools (with basic features that characterize them) are distinguished: using the local communication network for presentation of educational information; using the local communication network and open network resources; using open network resources; for independent use of open network resources directly in the classroom by a student; for use of open network resources by a student in the process of independent learning activity; for use by a student educational resources, specially created by a teacher, as well as resources of an open networks in his independent learning activity.
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Hurwitz, David. An Activity-Based Learning Module for Human Factors in the Introductory Transportation Engineering Course. Portland State University Library, September 2013. http://dx.doi.org/10.15760/trec.48.

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McCann, Michael. Introducing Students to Risk Diversification: Adapting a class activity to the online learning environment. Bristol, UK: The Economics Network, October 2020. http://dx.doi.org/10.53593/n3350a.

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Fedorenko, Elena H., Vladyslav Ye Velychko, Svitlana O. Omelchenko, and Vladimir I. Zaselskiy. Learning free software using cloud services. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3886.

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The article deals with the use of cloud technology services in the study of free software. Free software is a social phenomenon based on the philosophy of freedom and the right to intellectual creative activity. To date, a significant number of software products have been created that are licensed under free software and not used in educational activities. The conducted research revealed the factors promoting and hindering the use of free software in educational activities. Conducted questionnaires, analysis of open data, research of scientists made it possible to conclude on the expediency of using free software in educational activities. Cloud technology is not only a modern trend of effective use of information and communication technologies in professional activity, but also a proven tool for educational activities. To get acquainted with the free software, the use of cloud technologies has been helpful, which is the goal of our research.
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DeWinter, Alun, Arinola Adefila, and Katherine Wimpenny. Jordan Opportunity for Virtual Innovative Teaching and Learning. International Online Teaching and Learning, with Particular Attention to the Jordanian Case. Coventry University, June 2021. http://dx.doi.org/10.18552/jovital/2021/0001.

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Rakestraw, D. Resonant Acoustic Characterization of Coins: An Inquiry-Based Learning Activity for Everyone with a Smartphone. Office of Scientific and Technical Information (OSTI), November 2021. http://dx.doi.org/10.2172/1830948.

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Mpitsos, George J. Parallel Processing and Learning: Variability and Chaos in Self- Organization of Activity in Groups of Neurons. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada264224.

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Freeman, Charles, Phyllis Bell Miller, Caroline Kobia, and Juyoung Lee. What do students really learn from a fashion show? A theoretical approach to a project-based learning activity. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-73.

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