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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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Linsell, Chris, and n/a. "Learning algebra in an activity-based mathematics programme." University of Otago. Department of Mathematics & Statistics, 2005. http://adt.otago.ac.nz./public/adt-NZDU20061016.161725.
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This thesis presents the findings of a research project which explored students� learning during an activity-based mathematics programme. The research investigated what students learnt about solving linear equations and examined the role of activities in this learning.
The investigation of learning in the classroom was guided by the principles of naturalistic enquiry. A longitudinal study was used to investigate students� learning during a unit of work that that made extensive use of activities and contexts. The longitudinal design of the study allowed the development of algebraic thinking to be investigated. The ideas of both Piaget and Vygotsky suggest that it is necessary to study the process of change in order to understand the thinking of students. A group of four students, two girls and two boys, were studied for twenty-seven lessons with each student interviewed individually within six days of each lesson, using the technique of stimulated recall. All lessons and interviews were recorded for subsequent transcription and analysis.
Learning to solve equations formally, using inverse operations, proved to be difficult for all the students. For two of them, their poor understandings of arithmetic structure and inverse operations were impediments that prevented them from doing more than attempt to follow procedures. Two of the students did succeed in using inverse operations to solve equations, but were still reasoning arithmetically. There was little evidence in the data that any of the students got to the point of regarding equations as objects to act on. They consistently focussed on the arithmetic procedures required for inverse operations. Even by the end of the topic the most able student, like the others, was still struggling to write algebraic statements. One of the most striking features of the results was the slow progress of the students. For at least two of the students, lack of prerequisite numeracy skills provided a good explanation of why this was so. However for the other two, poor numeracy did not appear to be a reason. The findings are, however, perhaps not too surprising. For children learning about arithmetic, the change from a process to an object view, from counting strategies to part/whole strategies, seems a particularly difficult transition to make. To move from a process to an object view of equations appears to be a similarly difficult transition.
The way in which the students made use of the contexts showed that the activities did not directly facilitate the students to develop an understanding of formal solution processes. The students did not usually make use of the contexts when solving equations, working at the abstract symbolic level instead. Although it was hoped that, by engaging students in meaningful activities, the students would construct understandings of formal solution processes, this did not occur. None of the activities used in the study provided a metaphor for the formal method of solving equations. It is suggested that, for a context to be of great value for teaching a mathematical concept, the physical activity should act as a metaphor for the intended mathematical activity.
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Pijeira, Díaz H. J. (Héctor Javier). "Electrodermal activity and sympathetic arousal during collaborative learning." Doctoral thesis, Oulun yliopisto, 2019. http://urn.fi/urn:isbn:9789526222196.
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Abstract This dissertation investigates high school students’ individual and interpersonal physiology of electrodermal activity (EDA) during collaborative learning in naturalistic settings. EDA is an index of sympathetic arousal, which is concomitant with cognitive and affective processes. Two data collections were organized with students working collaboratively in triads. The first one took place during the performance of a science task, and the second during two runs of a six-week advanced physics course. The data collected included EDA (measured unobtrusively using Empatica® E3 and E4 wristbands), performance measures (pre- and post-tests, task solutions, and course exam), and questionnaires on cognitive, affective, and collaborative aspects of learning. The work was reported in three articles. The results indicate that, on average, students spent more than half (60%) of the class at a low arousal level, possibly signaling relaxation, disengagement, or boredom. Most of the time (≈60–95% of the lesson), triad members were at a different arousal level, which might indicate that students took turns (alternating task-doers) in executing the task or applied some division of labor rather than truly collaborating. In terms of achievement, sympathetic arousal during the exam was a predictor of the exam grades, and pairwise directional agreement of EDA was positively and highly correlated to the dual learning gain. Arousal contagion could have occurred in up to 41% of the high arousal intervals found. The possible arousal contagion cases took place mostly on a 1:1 basis (71.3%), indicating that interactions in a collaborative learning triad seem to occur mainly between two members rather than among the three. The findings provide an ecologically-valid picture of the students’ EDA responses in the classroom, both individually and collaboratively, benefiting from the connection of arousal to cognitive and affective processes to increase the saliency of otherwise elusive phenomena. Methodologically, the study contributes to the exploration and exploitation of psychophysiological approaches for collaborative learning research. On a practical level, it provides physiological indices that could be incorporated into learning analytics dashboards to support students’ awareness and reflection, and teachers’ pedagogical practicesTiivistelmä Tässä väitöstutkimuksessa tarkastellaan elektrodermaalista aktiivisuutta (EDA) ja tästä johdettua sympaattista vireystilaa ja fysiologisia indeksejä, samanaikaisesti yksilöiden ja yksilöiden välisten kognitiivisten ja affektiivisten prosessien kanssa. Tutkimusaineisto kerättiin yhteisöllisen oppimisen tilanteista, joissa oppilaat työskentelivät kolmen hengen ryhmissä. Ensimmäinen osa aineistosta kerättiin oppilaiden suorittaessa luonnontieteiden alan tehtävää ja toinen kahden fysiikan syventävän kurssin aikana. Aineistoon sisältyi EDA (Empatica® E3- ja E4-rannekkeista), oppimisen mittaukset (alku- ja lopputestit, tehtävien ratkaisut ja kurssikokeet) sekä kyselylomakkeet oppimisen kognitiivisista, affektiivisista ja yhteisöllisen työskentelyn näkökulmista. Tutkimus on raportoitu kolmessa artikkelissa. Tulokset osoittavat, että opiskelijoiden sympaattisen hermoston vireystila oli keskimäärin yli puolet (60 %) luokkatyöskentelystä alhainen, mikä viittaa mahdolliseen rentoutumiseen, osallistumisen puutteeseen tai tylsistymiseen. Ryhmänjäsenet olivat suurimman osan ajasta (≈60-95 %) eri vireystilan tasoilla, mikä voi tarkoittaa, että he suorittivat tehtävää vuorotellen (tehtävän suorittajaa vaihdellen) tai jonkinlaista työnjakoa käyttäen, yhteisöllisen työskentelyn sijaan. Sympaattinen vireystila kurssikokeessa ennusti kokeen arvosanoja. Lisäksi oppilasparien EDA:n samansuuntaisuus korreloi vahvasti oppimistulosten kanssa. Yksilöiden välillä tapahtuvaa sympaattisen vireystilan ”tarttumista” on voinut esiintyä jopa 41 prosentissa todetuista korkean vireystilan intervalleista. Mahdolliset ”tarttumiset” ilmenivät enimmäkseen (71,3%) 1:1 suhteessa, mikä viittaa siihen, että vuorovaikutus yhteisöllisessä oppimisessa näyttäisi tapahtuvan pääasiassa kahden yksilön välillä kaikkien kolmen sijaan. Tulokset tarjoavat ekologisesti validin kuvan opiskelijoiden EDA-reaktioista luokkahuoneessa sekä yksilöllisesti että yhteisöllisesti tarkasteltuna, selventäen samalla kuvaa sympaattisen vireystilan yhteydestä kognitiivisiin ja affektiivisiin prosesseihin. Menetelmällisesti tutkimus kartoittaa psykofysiologisen lähestymistavan mahdollisuuksia yhteisöllisen oppimisen tutkimuksessa. Se esittelee fysiologisia indeksejä, jotka voitaisiin visualisoida oppimisen analytiikan sovelluksissa opiskelijoiden tietoisuuden ja reflektion sekä opettajien pedagogisten käytäntöjen tukemiseksi
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Dong, Shuonan. "Unsupervised learning and recognition of physical activity plans." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42195.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.Includes bibliographical references (p. 125-129).
This thesis desires to enable a new kind of interaction between humans and computational agents, such as robots or computers, by allowing the agent to anticipate and adapt to human intent. In the future, more robots may be deployed in situations that require collaboration with humans, such as scientific exploration, search and rescue, hospital assistance, and even domestic care. These situations require robots to work together with humans, as part of a team, rather than as a stand-alone tool. The intent recognition capability is necessary for computational agents to play a more collaborative role in human-robot interactions, moving beyond the standard master-slave relationship of humans and computers today. We provide an innovative capability for recognizing human intent, through statistical plan learning and online recognition. We approach the plan learning problem by employing unsupervised learning to automatically determine the activities in a plan based on training data. The plan activities are described by a mixture of multivariate probability densities. The number of distributions in the mixture used to describe the data is assumed to be given. The training data trajectories are fed again through the activities' density distributions to determine each possible sequence of activities that make up a plan. These activity sequences are then summarized with temporal information in a temporal plan network, which consists of a network of all possible plans. Our approach to plan recognition begins with formulating the temporal plan network as a hidden Markov model. Next, we determine the most likely path using the Viterbi algorithm. Finally, we refer back to the temporal plan network to obtain predicted future activities. Our research presents several innovations:
(cont.) First, we introduce a modified representation of temporal plan networks that incorporates probabilistic information into the state space and temporal representations. Second, we learn plans from actual data, such that the notion of an activity is not arbitrarily or manually defined, but is determined by the characteristics of the data. Third, we develop a recognition algorithm that can perform recognition continuously by making probabilistic updates. Finally, our recognizer not only identifies previously executed activities, but also pre-dicts future activities based on the plan network. We demonstrate the capabilities of our algorithms on motion capture data. Our results show that the plan learning algorithm is able to generate reasonable temporal plan networks, depending on the dimensions of the training data and the recognition resolution used. The plan recognition algorithm is also successful in recognizing the correct activity sequences in the temporal plan network corresponding to the observed test data.
by Shuonan Dong.
S.M.
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Hamid, Muhammad Raffay. "Unsupervised Activity Discovery and Characterization for Sensor-Rich Environments." Thesis, Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/14131.
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This thesis presents an unsupervised method for discovering and analyzing the different
kinds of activities in an active environment. Drawing from natural language processing, a
novel representation of activities as bags of event n-grams is introduced, where the global
structural information of activities using their local event statistics is analyzed. It is demonstrated how maximal cliques in an undirected edge-weighted graph of activities, can be used in an unsupervised manner, to discover the different activity-classes. Taking on some work done in computer networks and bio-informatics, it is shown how to characterize these discovered activity-classes from a wholestic as well as a by-parts view-point. A definition of anomalous activities is formulated along with a way to detect them based on the difference of an activity instance from each of the discovered activity-classes. Finally, an information theoretic method to explain the detected anomalies in a human-interpretable form is presented. Results over extensive data-sets, collected from multiple active environments are
presented, to show the competence and generalizability of the proposed framework.
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Kling, Mattias. "Developing a Source Criticism Learning Activity for a Digital Learning Environment in History." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-118311.
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Source criticism is an important part of the national curriculum in the history subject in middle grade schools. This master thesis presents the development of a digital learning activity for teaching students about source criticism concepts. The learning activity was developed by first researching the history curriculum and how source criticism is taught within the subject. A conceptual design was drafted based on these findings and the design of an existing framework for teaching students within the history subject. The existing framework, and the developed learning activity, is based on learning-by-teaching implemented in the form of a teachable agent. The teachable agent act as a tutee that students have to teach themselves, increasing students' motivation and learning. By teaching the agent, students improve their own learning. The final implementation of the activity is integrated into both the technical framework and the narrative of the existing environment.
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Coupland, Mary. "Learning with new tools." Access electronically, 2004. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20041221.111821/index.html.
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Parker, Abigail B. "Teaching Envisionment: Activity Settings for Learning to Teach Literature." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1218493014.
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Zhang, Chenyang. "Human Activity Analysis using Multi-modalities and Deep Learning." Thesis, The City College of New York, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10159927.
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With the successful development of video recording devices and sharing platforms, visual media has become a significant component of everyone's life in the world. To better organize and understand the tremendous amount of visual data, computer vision and machine learning have become the key technologies to resolve such a huge problem. Among the topics in computer vision research, human activity analysis is one of the most challenging and promising areas. Human activity analysis is dedicated to detecting, recognizing, and understanding the context and meaning of human activities in visual media. This dissertation focuses on two aspects in human activity analysis: 1) how to utilize multi-modality approach, including depth sensors and traditional RGB cameras, for human action modeling. 2) How to utilize more advanced machine learning technologies, such as deep learning and sparse coding, to address more sophisticated problems such as attribute learning and automatic video captioning.
To explore the utilization of the depth cameras, we first present a depth camera-based image descriptor called histogram of 3D facets (H3DF) and its utilization in human action and hand gesture recognition and a holistic depth video representation for human actions. To unify both the inputs from depth cameras and RGB cameras, this dissertation first discusses a joint framework to model human affections from both facial expressions and body gestures with a multi-modality fusion framework. Then we present deep learning-based frameworks for human attribute learning and automatic video captioning tasks. Compared to human action detection recognition, automatic video captioning is more challenging because it includes complex language models and visual context. Extensive experiments have also been conducted on several public datasets to demonstrate that our proposed frameworks in this dissertation outperform the state-of-the-art approaches in this research area.
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Gentek, Anna. "Activity Recognition Using Supervised Machine Learning and GPS Sensors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295600.
Full textAbstract:
Human Activity Recognition has become a popular research topic among data scientists. Over the years, multiple studies regarding humans and their daily motion habits have been investigated for many different purposes. This fact is not surprising when we look at all the opportunities and applications that can be applied and utilized thanks to the results of these algorithms. In this project we implement a system that can effectively collect sensor data from mobile devices, process it and by using supervised machine learning successfully predict the class of a performed activity. The project was executed based on datasets and features extracted from GPS sensors. The system was trained using various machine learning algorithms and Python SciKit to guarantee optimal solutions with accurate predictions. Finally, we applied a majority vote rule to secure the best possible accuracy of the activity classification process. As a result we were able to identify various activities including walking, cycling, driving and public transportation methods bus and metro with 90+% accuracy.Att utföra aktivitetsigenkänning på människor har blivit ett populärt forskningsämne bland datavetare, där flertalet studier rörande människor och deras dagliga rörelsevanor undersökts för många olika syften. Detta är inte förvånande när man ser till de möjligheter och användningsområden som kan tillämpas och utnyttjas tack vare resultaten från dessa system. Detta projekt går ut på att implementera ett system som mha samlad sensordata från mobila enheter, kan bearbeta den och genom s.k övervakad maskininlärning med goda resultat bestämma den aktivitet som utförts. Projektet genomfördes baserat på dataset och egenskaper extraherade från GPS-data. Systemet tränades med olika maskininlärningsalgoritmer genom Python SciKit för att välja den bäst lämpade metoden för detta projekt. Slutligen tillämpade vi majority votemetoden för att säkerställa bästa möjliga noggrannhet i aktivitetsklassificeringsprocessen. Resultatet blev ett system som framgångsrikt kan identifiera aktiviteterna gå, cykla, köra bil samt med ett ytterligare fokus på kollektivtrafikmetoderna buss och tunnelbana, med en noggrannhet på över 90%.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
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Brown, Jamie Branam, Mary R. Langenbrunner, and Teresa Brooks-Taylor. "Oppression Activity Using the Mechanism of Social Service Learning." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etsu-works/5867.
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Bhavaraju, Srilaya. "Using machine learning for analysis of neuronal network activity." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129131.
Full textAbstract:
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 75-77).
Analyzing neuronal activity in developing neuronal networks can improve our understanding of neuronal dysfunctions underlying conditions such as Rett syndrome. Two-photon calcium imaging is used to capture neuronal network activity over time. This method produces large sets of images that are typically manually analyzed by skilled neuroscientists. Because this process is both time-consuming and subject to error, discovery of therapies that ameliorate network dysfunction may be slowed. We improve an existing, semi-autonomous machine learning pipeline for two-photon calcium imaging sequence analysis. We introduce to the pipeline neuron detection methods using supervised learning models, heuristic filtering of pixels for signal extraction, and event detection using deconvolution. With these methods, we improve neuron detection performance, alter signal-to-noise ratio of extracted calcium signals, and allow for integration of methods that infer action potential firing underlying these signals.
by Srilaya Bhavaraju.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Greenall, John Patrick. "High-level activity learning and recognition in structured environments." Thesis, University of Leeds, 2012. http://etheses.whiterose.ac.uk/3231/.
Full textAbstract:
Automatic recognition of events in video is an immensly challenging problem. If solved, the number of potential domains in which such a system could be deployed is vast and growing; including traffic monitoring, surveillance, security, elderly care and semantic video search to name but a few. Much prior research in the area has focused on producing a solution that is tailored towards one of these applications, applying methods which are most appropriate given the constraints of the target domain. For the moment, this remains to some extent the only practical way to approach the problem. The aim in this thesis is to build a high-level framework for event recognition which is in the main generic and widely transferrable, yet allows domain-appropriate elements to be incorporated. A detector is constructed for low-level events which is based on dense extraction of Histograms of Optical Flow. This descriptor has only recently been adopted by the event detection community, and as such there are aspects of the features which have not been optimized. This thesis performs extensive experimentation on normalization scheme and finds that the strategy most widely in use is suboptimal compared to one of the alternatives proposed. The detector is then trained on a challenging real world domain to run in a sliding window fashion on continuous video input. A high level model which exploits temporal relations between different event types is constructed. The model is designed with transferrability and computational tractability in mind. Several methods are benchmarked for learning the distributions over time differences between pairs of events. Three different connection strategies are proposed and evaluated for creating a tree structured prior that permits fast, exact inference. An efficient iterative optimization scheme is presented for handling scenarios which contain unknown numbers of event instances. Finally, the model is extended in a Conditional Random Field framework that allows weights to be learned to balance the response from independent detectors with the pairwise temporal relationships.
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Nguyen, Dieu My Thanh. "OLFACTORY LEARNING AND BRAIN ACTIVITY IN NOVOMESSOR COCKERELLI ANTS." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/613353.
Full textAbstract:
In this study, an olfactory associative conditioning paradigm was developed to study the
learning and memory capabilities of Novomessor cockerelli ants. When the antennae of the
ant come into contact with sucrose solution, the ant extends its tongue to consume the
sucrose. The tongue reflex was conditioned by pairing an odor (conditioned stimulus) with
sucrose (unconditioned stimulus) over ten trials. The ant’s tongue reflex in response to
odor indicates that an association between the odor and the sucrose has been made in the
ant’s brain. The second part of the study includes analyzing the regional brain variations of
cytochrome oxidase (COX) staining after olfactory conditioning. The antennal lobe and
mushroom body are major brain regions in the insect olfactory pathway, and are regions of
interest in this analysis. Results show that there are significant differences in metabolic
activity across brain regions (antennal lobe, glomeruli, central boxy complex, mushroom
body, and lateral protocerebrum), but the differences do not correlate with the learning
status of the ants (learned vs non-learned).
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Goutham, Mithun. "Machine learning based user activity prediction for smart homes." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595493258565743.
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Solis, John D. "The relationship between preservice teachers' social learning style preferences and learning activity role choices." Laramie, Wyo. : University of Wyoming, 2006. http://proquest.umi.com/pqdweb?did=1225152311&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.
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Rambusch, Jana. "Embodiment and situated learning." Thesis, University of Skövde, School of Humanities and Informatics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-904.
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Cognition has for a long time been viewed as a process that can be described in terms of computational symbol manipulation, i.e. a process that takes place inside people’s heads and is largely unaffected by contextual aspects. In recent years, however, there has been a considerable change in the way researchers look at and study human cognition. These changes also have far-reaching implications for education and educational research. Situated learning is a theoretical framework in which sociocultural aspects of cognition and learning are strongly emphasised, that is, the context in which learning takes place is an important part of learning activity. The concept of activity is central to situated learning theories, but activity has been considered an exclusively sociocultural process in which the body only plays a minor role. In embodied cognition research, on the other hand, there is an increasing awareness that mind and body are inextricably intertwined and cannot be viewed in isolation. Findings in cognitive neuroscience provide additional evidence that cognition is tightly linked to perception and action. The aim of this thesis has been to investigate the role of the body in situated learning activity by integrating these different perspectives on cognition and learning. The analysis suggests that, like individual human conceptualization and thought, situated learning is in fact deeply rooted in bodily activity. In social interactions the body provides individuals with a similar perspective on the world, it functions as a means of signalling to others what cannot (yet) be expressed verbally, and it serves as a resonance mechanism in the understanding of others.
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Pietsch, James Roderick. "Collaborative learning in mathematics." University of Sydney, 2005. http://hdl.handle.net/2123/1088.
Full textAbstract:
Doctor of Philosophy (PhD)This study looked at the implementation of a collaborative learning model at two schools in Sydney designed to realise the principles recommended by reform documents such as the Principles and Standards for School Mathematics (NCTM, 2000) and policy documents including Numeracy, A Priority for All (DETYA, 2000). A total of 158 year seven and year eight students ranging in age from 12 to 15 years old from two schools participated in the study. In all, seven classroom teachers participated in the study each completing two topics using the collaborative learning model. Four research questions were the focus of the current study. Three research questions were drawn from eight principles identified in the literature regarding what constitutes effective mathematics learning. These questions related to the nature of collaboration evident in each classroom, the level of motivation and self-regulation displayed by students in the different types of classrooms and the relationship between learning mathematics within the collaborative learning model and real-world mathematics. A final research question examined the degree to which the concerns of teachers relating to preparing students for examinations are met within the collaborative learning model. Several different data collection strategies were adopted to develop a picture of the different forms of activity evident in each classroom and the changes that took place in each classroom during and after the implementation of the collaborative learning model. These included classroom observations, interviews with student and teacher participants, questionnaires and obtaining test results. Both exploratory and confirmatory factor analysis were used to reduce the data collected. Factor scores and test results were compared using t-tests, ANOVAs and Mann Whitney nonparametric tests. Data collected from interviews and classroom observations were analysed using a grounded approach beginning with the open coding of phenomena. Leont’ev’s theoretical approach to activity systems (1972; 1978) was then used to describe the changing nature of classroom activity with the introduction of the collaborative learning model. Within the collaborative classrooms there were a greater number of mathematical voices participating in classroom discussions, a breaking down of traditional roles held by teachers and students, and dominant patterns of collaboration evident in each classroom reflecting pre-existing cultural ways of doing. Furthermore, there was some quantitative evidence suggesting that student levels of critical thinking, self-regulation and help seeking increased and students were also observed regulating their own learning as well as the learning of others. Classroom practice was also embedded in the cultural practice of preparing topic tests, enabling students to use mathematics within the context of a work group producing a shared outcome. Finally, there was quantitative evidence that students in some of the collaborative classes did not perform as well as students in traditional classrooms on topic tests. Comments from students and teachers, however, suggested that for some students the collaborative learning model enabled them to learn more effectively, although other students were frustrated by the greater freedom and lack of direction. Future research could investigate the effectiveness of strategies to overcome this frustration and the relationship between different types of collaboration and developing mathematical understanding.
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Bygrave, Patricia, and n/a. "Music as a cognitive developing activity : implications for learning and for the learning disabled child." University of Canberra. Education, 1985. http://erl.canberra.edu.au./public/adt-AUC20060622.143654.
Full textAbstract:
Various cognitive learning theories have been examined for the purpose of considering music
as an activity developing cognitive processes.
It was felt that an exploration of these
theories could offer insights into how music
can be used to demonstrate cognitive
development in learning and in children with
learning disabilities. In an attempt to
illustrate the relationship between music and
cognition, concepts in the theories of Piaget,
the Neo-Piagetians (Case, Pascaul-Leone, Biggs
and Collis), Bruner, Ausubel, Vygotsky, Luria
and Leont'ev have been discussed in association
with music activities. It is argued that
music can be identified as a cognitive activity
and applied to learning and to learning
disabilities through recognised special
education approaches; these include
perceptual-motor, multi-sensory, languagedevelopment
related, developmental and
behavioural.
The theories of Vygotsky, Luria and Leont'ev -
the 'troika' - are considered in greater
detail. Their theories, although focussing on
concepts of language in cognitive development,
appear to offer a means for the wider
application of music to cognitive development.
Vygotsky's developmental hierarchy, Luria's
brain functional system, and Leont'ev's theory
of activity have been amalgamated into a
theoretical framework demonstrating the
processing of information through music
activity leading to cognitive development.
This framework provides for an investigation
into learning capacities and learning
potential, relevant to cognitive development in
learning and in the learning disabled child.
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Malmberg, J. (Jonna). "Tracing the process of self-regulated learning – students’ strategic activity in g/nStudy learning environment." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526204703.
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Abstract This study focuses on the process of self-regulated learning by investigating in detail how learners engage in self-regulated and strategic learning when studying in g/nStudy learning environments. The study uses trace methods to enable recognition of temporal patterns in learners’ activity that can signal strategic and self-regulated learning. The study comprises three data sets. In each data set, g/nStudy technology was used to support and trace self-regulated learning. In the analysis, micro-analytical protocols along with qualitative approach were favoured to better understand the process of self-regulated and strategic learning in authentic classroom settings. The results suggested that the specific technological tools used to support strategic and self-regulated learning can also be used methodologically to investigate patterns emerging from students’ cognitive regulation activity. The advantage of designing specific tools to trace and support self-regulated learning also helps to interpret the way in which the learning patterns actually inform SRL theoretically and empirically. Depending on how the tools are used, they can signal the typical patterns existing in the learning processes of students or student groups. The learning patterns found in the students’ cognitive regulation activity varied in terms of how often the patterns emerged in their learning, how the patterns were composed and when the patterns were used. Moreover, there were intra-individual differences – firstly, in how students with different learning outcomes allocated their study tactic use, and secondly, how self-regulated learning was used in challenging learning situations perceived by students. These findings indicate log file traces can reveal differences in self-regulated learning between individuals and between groups of learners with similar characteristics based on the learning patterns they used. However, learning patterns obtained from log file traces can sometimes be complex rather than simple. Therefore, log file traces need to be combined with other situation-specific measurements to better understand how they might elucidate self-regulated learning in the learning contextTiivistelmä Tässä väitöskirjassa tutkitaan oppilaiden itsesäätöisen ja strategisen oppimisen ilmenemistä oppimisprosessin aikana. Tutkimuksessa hyödynnetään g/nStudy- oppimisympäristöä, jonka avulla on mahdollista tukea ja jäljittää oppimisen strategista toimintaa. g/nStudy-oppimisympäristö tallentaa lokidataa, joka on tarkkaa ajallista informaatiota siitä toiminnasta, jota oppilas tekee työskentelynsä aikana. Toisin sanoen, lokidatasta on mahdollista jäljittää ne tiedot, jotka reflektoivat strategista – ja itsesäätöistä oppimista. Erityisenä mielenkiinnon kohteena oli selvittää miten lokidatasta voi löytää strategisia oppimisen toimintamalleja, ja miten nämä strategiset oppimisen toimintamallit vaihtelevat oppilaiden, oppilasryhmien ja erilaisten oppimisen tilanteiden aikana. Väitöstutkimus muodostuu kolmesta erillisestä tutkimusaineistosta. Jokaisessa kolmessa aineistossa on hyödynnetty g/nStudy-teknologian mahdollisuuksia tukea ja jäljittää itsesäätöistä oppimista. Tutkimusaineiston analyysissä hyödynnetään mikroanalyyttista lähestymistapaa sekä laadullista tutkimusotetta. Tutkimuksen analyyttinen lähestymistapa antaa mahdollisuuden ymmärtää itsesäätöisen- ja strategisen oppimisen ilmenemistä aidossa oppimistilanteessa. Tutkimustulokset osoittavat, että oppimisympäristöön sisällytettyjä teknologisia työkaluja voidaan käyttää tukemaan itsesäätöistä ja strategista toimintaa. Sen lisäksi samoja työkaluja voidaan käyttää myös menetelmällisenä välineenä tutkittaessa itsesäätöistä – ja strategista toimintaa erilaisissa oppimistilanteissa. Tutkimus -tulokset osoittavat, että oppimisen strategiset toimintamallit vaihtelivat oppilaiden – ja oppimistilanteiden välillä. Oppimisen strategisissa toimintamalleissa oli myös laadullisia eroja sen suhteen, miten usein ne ilmenivät oppimisprosessin aikana ja mistä strategisista toiminnoista ne koostuivat. Johtopäätöksenä voi todeta, että lokidatan käyttäminen tutkimusmenetelmänä edesauttaa paljastamaan opiskelun strategisia toimintamalleja oppilaiden – ja oppilasryhmien välillä. Tutkimuksen perusteella voidaan todeta, että strategiset toimintamallit voivat olla hyvinkin monimuotoisia. On tärkeää tunnistaa, missä tilanteissa ja milloin näitä toimintamalleja käytetään ja erityisesti mikä on niiden vaikutus oppimisen laatuun
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Li, Huiyong. "Enhancing Students' Self-Direction Skill with Learning and Physical Activity Data." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263776.
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Chung, Chak. "The implementation of the activity approach in Hong Kong." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18540296.
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Mahdaviani, Maryam. "Semi-supervised and active training of conditional random fields for activity recognition." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/346.
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Automated human activity recognition has attracted increasing attention in the past decade. However, the application of machine learning and probabilistic methods for activity recognition problems has been studied only in the past couple of years. For the first time, this thesis explores the application of semi-supervised and active learning in activity recognition. We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs),a probabilistic graphical model. In real-world applications such as activity recognition, unlabeled sensor traces are relatively easy to obtain whereas labeled examples are expensive and tedious to collect. Furthermore, the ability to automatically select a small subset of discriminatory features from a large pool can be advantageous in terms of computational speed as well as accuracy. We introduce the semi-supervised virtual evidence boosting (sVEB)algorithm for training CRFs — a semi-supervised extension to the recently developed virtual evidence boosting (VEB) method for feature selection and parameter learning. sVEB takes advantage of the unlabeled data via mini-mum entropy regularization. The objective function combines the unlabeled conditional entropy with labeled conditional pseudo-likelihood. The sVEB algorithm reduces the overall system cost as well as the human labeling cost required during training, which are both important considerations in building real world inference systems. Moreover, we propose an active learning algorithm for training CRFs is based on virtual evidence boosting and uses entropy measures. Active virtual evidence boosting (aVEB) queries the user for most informative examples, efficiently builds up labeled training examples and incorporates unlabeled data as in sVEB. aVEB not only reduces computational complexity of training CRFs as in sVEB, but also outputs more accurate classification results for the same fraction of labeled data. Ina set of experiments we illustrate that our algorithms, sVEB and aVEB, benefit from both the use of unlabeled data and automatic feature selection, and outperform other semi-supervised and active training approaches. The proposed methods could also be extended and employed for other classification problems in relational data.
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Xie, Xiaohui 1972. "Spike-based learning rules and stabilization of persistent neural activity." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86625.
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Fernando, Kayla Dana. "Sex Differences in Serotonin (5-HT) Activity During Safety Learning." Thesis, Boston College, 2018. http://hdl.handle.net/2345/bc-ir:108020.
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Thesis advisor: John P. ChristiansonPatients with posttraumatic stress disorder (PTSD) often show impaired ability to discriminate between “danger” and “safety” cues. Women are more than twice as likely to be diagnosed with PTSD as compared to men; however, translational research has largely relied on the use of male subjects despite evidence of sex differences in fear-motivated behaviors. Serotonergic activity, originating in the dorsal raphe nucleus (DRN) of the central nervous system (CNS), has been found to modulate fear discrimination in males and may contribute to sex differences observed in a Pavlovian fear discrimination paradigm. In this study, male and intact female Sprague-Dawley rats were exposed to fear conditioning with (CS+/CS-) or without (CS+) a safe conditioned stimulus, then subsequently sacrificed for immunohistochemical analysis of serotonergic activity via quantification of tryptophan hydroxylase (TPH) and Fos in the DRN. Females exhibited more rapid and robust discrimination between the CS+ danger cue and CS- safety cue as compared to males. Regardless of condition, females had more double-labeled TPH+Fos cells compared to males, but males had larger variation in TPH+Fos expression compared to females. A parabolic function for TPH+Fos counts predicted fear discrimination in males, but not females, reinforcing the view that serotonin is a modulator of safety-related behavior in males
Thesis (BS) — Boston College, 2018
Submitted to: Boston College. College of Arts and Sciences
Discipline: Arts and Sciences Honors Program
Discipline: Biology
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Bronkhorst, Joseph Victor. "Work-integrated learning in Civil Engineering: an activity theoretical study." Thesis, Cape Peninsula University of Technology, 2013. http://hdl.handle.net/20.500.11838/1979.
Full textAbstract:
THESIS
submitted in fulfilment of the degree
DOCTOR OF EDUCATION
in the
Faculty of Education and Social Sciences
Cape Peninsula University of Technology
2013The aim of this research is to present recommendations for knowledge and practice relations between Further Education and Training (FET) colleges and Civil Engineering (CE) workplaces, and to present a work-integrated learning (WIL) model that could assist with the preparation of CE students for the workplace. Recently, FET colleges have been under the spotlight in terms of student preparedness for the CE workplace. Many questions have been posed by students studying at FET colleges and by CE workplace supervisors in respect of whether the current CE curriculum adequately prepares students for the workplace, or whether the curriculum has become obsolete in terms of knowledge and practice relations. The CE industry is of the opinion that students are insufficiently prepared in terms of skills and knowledge. In the light of this uncertainty, I researched the learning taking place at FET colleges and CE workplaces. I examined similarities and differences in the learning environment of the students. The research provides a theoretical overview of Activity Theory (AT) and its principle of contradictions. The lens of AT and its contradictions provide a versatile tool to enquire into various aspects of WIL, taking into account individual and institutional perspectives, as well as changes over time. Activity Theory and its principle of contradictions provide insights into how transformation may occur within Activity Systems (ASs) in a CE context. The study was conducted over a number of years with participants from three ASs, namely, the classroom, workshop/college yard and workplace. During the research, this study proposed a conceptual framework, rooted in AT, and substantiated by empirical evidence, for describing and analysing the learning taking place in the FET college sector and within the CE workplace environment. The analysis focuses on the perceptions of learning taking place in the ASs. Results reveal a knowledge and practice divide, mediated by AS elements of mediating artefacts, object, subject, division of labour, community and rules. Through a particular focus on the contradictions of the elements of an AS which occur, the objective for this study was to determine ‘knowledge and practice relations’. The components of knowledge and practice are extremely isolated, and by bringing the argument and the empirical findings together, the findings propose: Links between knowledge(‘the classroom’) and practice(‘the workplace’) The surfacing of the disconnect between knowledge and practice between the FET college sector and the CE workplace supports the idea of establishing links between these two sectors. This collaboration could be the turning point in better preparing students for the workplace. Policy formulation and implementation The need for policy review to enhance the integration of knowledge and practice relations in the sector has become apparent. Colleges are expected to undergo a radical transformation and to make major contributions to policy. However, these institutions are new and fragile, and are based on historically weak predecessors. Much of the reform process is oblivious of the connections between college and workplace. The research has established that both CE industries and FET colleges should ensure that they increase their involvement with and participation in the provision of adequately preparing students for the workplace in the Western Cape Province.
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Montoro, Sanjosé Carlos Rubin. "The language learning activity of individual learners using online tasks." Thesis, Open University, 2013. http://oro.open.ac.uk/50081/.
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This study combines an initial interest in private speech (Flavell 1966; Vygostky 1987; Ohta 2001; Ellis 2003), that is, self-addressed speech, used by individual language learners as they interact with online tasks, with a practice-based concern with the introduction of technology in a new self-access centre at the University of Guanajuato, Mexico. This had been done with little concern for the state of preparedness of learners and practitioners, as is often the case elsewhere (Benson 2001; Donaldson and Haggstrom 2006; Levy 2007; Winke and Goertler 2008). Literature on CALL, autonomy and task-based pedagogy revealed the need for an integrated, broad approach beyond technology itself with a special emphasis on the learning context, sociocultural issues and learner background. Often unexplored, the gap between what teachers plan and what learners do with tasks (Nunan 1989; Coughlan and Duff 1994; Roebuck 2000) began to focus the research efforts on investigating the nature of the language learning activity (Beetham 2007) of individual learners. Following suggestions from various authors from different traditions (e.g., Arnold and Ducate 2011; Lantolf and Poehner 2004; Chapelle 2001; Scanlon and Issroff 2005; Kaptelinin and Nardi 2006), activity theory (Vygotsky 1987; Leontiev 1978; Engeström 1987) was chosen as the most suitable theoretical framework and some of its key concepts, such as disturbances (Engeström and Sannino 2011; see also Montoro and Hampel 2011) and contradictions (Engeström 1987), were used to conduct a two-tiered analysis of empirical data gathered electronically during an online experiment followed by stimulated recall (SR) sessions. Findings include the widespread dependence of learners on private speech, memory and oral instruction and their underuse of learning tools (especially text-based ones such as dictionaries and notes), signalling links to literacy issues to be further explored and the prevalence of orality locally. Future research should explore these literacy issues and practical ways to improve the provision of language learning opportunities.
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Schott, Alex Hoobie. "Teaching and learning in technical theater: activity, composition and embodiment." Diss., University of Iowa, 2013. https://ir.uiowa.edu/etd/2627.
Full textAbstract:
If not ignored completely, the body has been under theorized in literacy research. However, recent research in cultural studies, linguistics, philosophy, psychology, neuroscience, education, and the arts suggests that the body is implicated in thinking and knowing as well as doing. In this dissertation I examine high school technical theater. In this robustly embodied activity students build sets, rig lights, and paint backdrops in preparation for a theatrical production, as well as run sound and lighting and perform scene changes during the production. I use data from high school technical theater to explore the body in literacy, embodied learning, collaboration, composition processes, and experiential learning.
I gathered data primarily during out of school work sessions over the multi-week production cycles of six plays produced at one high school over two school years. As an experienced theater technician, I used participant observation as the primary method of data collection, supplemented with semi-structured interviews with the technical director, artistic director, and four students. I collected data and analyzed data through iterative processes in which analysis began during data collection, emerging analyses influenced data collection, and constant comparisons to new data influenced emerging analyses.
Observations of student work revealed that student theater technicians employed literacy skills including speaking, reading, writing, drawing, calculating, and interpreting the written text of plays as necessary elements of the normal course of technical theater work. Observations of teaching and learning showed that little explicit instruction was used but that mini-lessons, individual and collective problem solving, and multiple configurations of collaboration among more and less experienced technicians led to the development of critical thinking and physical skills, as well as proficiency in the creation of props through the evaluation and application of building techniques and materials.
I used theories from art making and multimodal literacy to examine technical theater building projects as examples of composition. My findings show that the design of technical theater texts - e.g. props, scenery, lighting - emerges through a recursive process of creation and interpretation and is mediated by the technicians' knowledge of building techniques and materials. Situated learning and activity theory were used to analyze learning in the technical theater community. Results demonstrate that the structure of the community allows for learning through experience, apprenticeship, and collaboration as well as through the creation of texts that balance personal expression with collaborative enterprise.
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Kaykayoglu, Ediz Lutfu. "Cultural Intelligence and Student Activity in a Learning Management System." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent157284718604878.
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Packman, Jill. "Group activity therapy with learning disabled preadolescents exhibiting behavior problems." Thesis, University of North Texas, 2002. https://digital.library.unt.edu/ark:/67531/metadc3326/.
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This study was designed to determine the effectiveness of group activity therapy as a school based intervention with fourth and fifth grade preadolescents with learning disabilities experiencing behavior problems. The group activity therapy intervention followed humanistic principles and was designed to address the cognitive and social emotional needs of this population. The preadolescents were provided a variety of developmental appropriate materials and activities to encourage self expression and group interaction. The 24 volunteer preadolescents were randomly assigned to the experimental group (n=12) and to the control group (n=12). The treatment group preadolescents were divided into groups of three and participated in group activity therapy one hour per week for 12 weeks. The participants were assigned to groups according to individual needs and personality traits. The control group received no treatment during the study. Pre and post test data were collected from parents using the Child Behavior Checklist (CBC) and the Behavior Assessment Scale for Children (BASC).
Analysis of Covariate (ANCOVA) was utilized to determine statistical significance between the treatment group and the control group on the post-test means for each hypothesis. In each case, the post-test specified in each hypothesis was used as the dependent variable and the pre-test as the covariate. Specifically, the preadolescents in the treatment group showed statistically significant decreases in total behavior problems on the BASC (p=.05) and decreases in internalizing problems on both the BASC and CBC (p=.03, p=.05, respectively). While not statistically significant, positive trends were noted on the CBC total behavior scale (p=.08) and on the CBC externalizing scale (p=.09). In addition, Cohen's d effect size was calculated for each hypothesis and post hoc analysis of the subscales to determine practical significance of the treatment on the experimental group when compared to the control group. A large treatment effect size was found on the BASC (d=.91) and CBC (d=.82) total behavior problems scales and on the BASC (d=1.03) and CBC (d=.90) internalizing problems scales. A moderate to large treatment effect size (d=.78) was found on the CBC externalizing problems scale and a medium treatment effect size (d=.53) was found on the BASC externalizing problems scale. Qualitative data was also examined to determine clinical significance of the intervention. This study determined that group activity therapy is an effective intervention for preadolescents diagnosed with a learning disability.
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Igarashi, H. "The development of professional judgement capacity through activity led learning." Thesis, Coventry University, 2015. http://curve.coventry.ac.uk/open/items/9245a038-ced4-4574-9c6e-02f69f802816/1.
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The unique contribution to knowledge of this research is the study of the development of judgement capacity in apprentice and undergraduate engineering learners in Activity Led Learning (ALL) environments. Four case studies of engineering students investigated the learners' experiences of making judgements in various engineering undergraduate and apprenticeship programmes. A phenomenological research methodology was used to infer the learner's judgements from the learners' dialogues and actions that were observed during the learning activity. The findings of the study indicate that the experience and incidence of the learners' exertion of judgement is dependent upon the construct of the ALL environment to provide a problem space with potential for disjuncture, and the intentionality of the learners. The learners did not solve problems by a linear progression but repeatedly re-activated experiences and knowledge, exercising judgements until the states of disjuncture were satisfied leading to the conclusion of the problem. Heuristic judgements that may result in decision making errors tended to dominate the problem spaces though their incidence did not appear to be influenced by the technical or socio-technical demands of the project problem spaces. This thesis concludes that in ALL environments, projects of sufficient length and complexity similar to realistic professional practice, may enable students to acquire the practice of better judgement through disjuncture and by re-activating learning experiences and importing analogies into new problem spaces. However, to acquire skills and knowledge to improve judgement capacity, requires specific and purposeful interventions within ALL that enable the learner to know when heuristic judgements are reliable or otherwise unreliable, and acquiring reasoning strategies to compensate for the effects. It is proposed that in such interventions the learner learns to record their own judgements as they are exerted and to reflect critically on those judgements and their consequences. It also requires that any ALL project that aims to promote judgement capacity has in place assessment instruments that specifically consider the learner effort in the self-development of judgement.
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Agnér, Christian, and Anneli Blomqvist. "Evaluating Stress through Machine Learning based on Brain Activity Data." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214709.
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More people are experiencing stressrelatedsymptoms, which is not only causing worsenhealth, but also causing economical drawbacks for thesociety, businesses and individuals. The aim of thisproject is to create a tool that evaluates stress frombrain activity data and can help to avoid develop thesymptoms.An EMOTIV Epoch EEG headset is used to recorddata. The stress level is evaluated from the brainactivity data by the parameters, feeling of pleasure(valence) and the mental workload. k-NN machinelearning is utilized to create a valence classificationalgorithm and the theta power density spectrum is usedto determine the workload. Eye movement disturbancesin the recordings are also addressed.Tests with Stroop word color games as stress stimuliare conducted and the project concludes that it ispossible to determine the stress level correctly, onaverage, 17.56% and when allowing one level difference,48.71% .
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Opushnyev, Serhiy. "Neuronal growth patterns in states of learning and activity blockade." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/61075.
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The role of activity on the formation of neural networks during development is known to be critical. In the research conducted for this dissertation the effect of experience was probed at the single neuron level. First, a method for selecting neurons based on their responses to a visual stimulus and electroporating the selected neurons in a somata dense region was developed. This method was then used to select neurons responsive to a predetermined visual stimulus and the growth behavior of the neuronal arbor was observed in the presence of visual stimuli. When neurons were trained to better discern the visual stimulus the plasticity of the neuron was correlated with the dendritic growth behavior. In general, responsive neurons tended to prune their dendritic arbors while non-responsive neurons tended to grow. Interestingly, neurons that acquired a response with training tended to grow before acquiring a response and prune after. Blockade of NMDA receptors abolished these effects. In a separate set of experiments dendritic growth patterns were observed while all excitatory activity was blocked pharmacologically. These experiments showed that short-term (1.5 hours) excitatory activity blockade does not alter dendritic growth patterns. However, 30 minutes after the start of the activity blockade, the density of filopodia increased, suggesting that the neuron was compensating for the lack of activity.Medicine, Faculty of
Graduate
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Blandon, Alondra Marcela. "Incorporating multicultural education criteria into Project Learning Tree curricula." CSUSB ScholarWorks, 2007. https://scholarworks.lib.csusb.edu/etd-project/3285.
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Many environmental educators have realized that in order to effectively teach environmental sensitivity and literacy, they must approach their audience through a cultural context. Thus, environmental education lessons need to incorporate strategies used in multicultural education in order to be more culturally inclusive. This project includes the identification of multicultural education criteria and the application of these to three lessons from Project Learning Tree: PreK-8 Environmental Education Activity Guide.
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Trent, John Gilbert. "Learning Cantonese in the community an exploration of the role of social activity in language learning /." Click to view the E-thesis via HKUTO, 2003. http://sunzi.lib.hku.hk/hkuto/record/B31945338.
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Trent, John. "Learning Cantonese in the community: an exploration of the role of social activity in language learning." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B31945338.
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Workman, Gayle Jean. "Seniors Learning Sports: A Qualitative Inquiry Regarding the Meanings in Learning and Participating in Physical Activity /." The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487933245537139.
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Zhu, Fan Frank, and 朱凡. "Exploring cortical activity during implicit and explicit processes in motor learning." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45588892.
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Kheir, Abadi Maryam. "The development of a new systematic method based on activity systems that analyses the activity of learning programming." Thesis, Kingston University, 2012. http://eprints.kingston.ac.uk/24602/.
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The activity of learning programming languages is a difficult and complex process. During this complicated procedure, many problems and difficulties might occur. A straightforward and clear approach, which can help to break down the numerous interacting processes into a series of simpler components, would appear useful. Therefore, the main aim of this research is to design and develop an appropriate method that can meet these criteria. The new method offers a new systematic approach for collecting, modelling and analysing data to discover difficulties within the activity of learning programming. Thus, to achieve these aims, the research work commenced with an investigation into the existing variety of frameworks and methodologies, which have been used in Information Technology (IT). The initial research showed that there are many suitable approaches that have been previously used in the IT field. However, most of these do not offer any clear pathway for collecting and analysing the data from beginning to end of the research process. To address these issues, Activity Theory (AT) has been chosen to be used as an initial framework for the study. AT has been selected due to the nature of the topic being examined. There are several communities involved in the process of learning programming, including students, lecturers, technicians and teaching assistants. AT allows for a holistic consideration of the multiple perspectives involved. In addition, the solid ontology of AT assists with the breakdown of complicated environments into simpler units. However, AT does not specify any particular research methodology that should be used. As a result, an appropriate approach has to be identified and coupled with AT in order to create a new systematic method. The following research methodologies are considered: Action Research (AR), Grounded Theory (GT) and Phenomenography (Ph). It is concluded that GT offers the best approach to complement the use of AT in the context of examining the activity of learning programming languages. Consequently, an initial method has been created by combining AT and GT, which has been used to collect and analyse test cases to investigate whether this combination is effective. After using this initial procedure, changes and improvements were made to create a revised method which has been used to collect and analyse a larger set of data. The results of this research, using three type of case studies of responses from the individual students, focus groups including staff, and observation of the activities in workshop sessions, demonstrated the benefits of the method developed. It was found out that this systematic approach facilitated the process of collecting and analysing the data. In turn, this enabled the discovery of contradictions within the activity of learning programming and the proposed of shifts to solve them. Although this method was tested on first-year students at Kingston University, it is potentially generic, allowing it to be considered for use in other similar domains.
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Rodríguez, Pérez Raquel [Verfasser]. "Machine Learning Methodologies for Interpretable Compound Activity Predictions / Raquel Rodríguez Pérez." Bonn : Universitäts- und Landesbibliothek Bonn, 2020. http://d-nb.info/1207923451/34.
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Ng, Yick-mui Emily, and 吳奕梅. "A case study of activity learning in secondary school business subjects." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30290338.
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