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Dissertations / Theses on the topic 'Progressive learning'
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Cox, Graham. "Communities of practice : learning in progressive ensembles." Thesis, University of Huddersfield, 2003. http://eprints.hud.ac.uk/id/eprint/4711/.
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This study examines the learning of ensemble skills by young musicians in progressive ensembles. Data collection took place in three music centres which form part of an LEA music service's ensemble provision. This study uniquely approaches ensembles as'socio-musical' phenomena. It finds description and explanation in the constantly changing and developing socio-musical interactions that form ensembles. Using an ethnographic approach it examines the practice of ensembles that are part of musical learning pyramids, through the eyes and actions of the ensemble members. There is an examination of ensemble membership and the social structures and interactions that form ensembles. This study explores, and for the first time identifies, a set of ensemble specific skills. These are the skills that a musician uses to negotiate, integrate and cooperate with other participants in the production of ensemble performance. It goes on to examine how ensemble specific skills are acquired and suggests that the learning process is one of serial performative responsibility transfer created within stratified centripetal progression. New, or novice, members of an ensemble start by participating at a peripheral level leaving it to others to take performative responsibility for the production of a negotiated collaborative realization of the musical intentions of the composer. This study has been influenced by the work of Lave and Wenger and social theories of learning. However, it departs from these theories by suggesting that the learning process within an ensemble is responsibility led and stratified.
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Yang, Boo Ho. "Progressive learning and its application to robotic assembly." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/35038.
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Yu, Jyu-fang. "Learning and teaching the English Progressive in Taiwan /." Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3008480.
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Archibald, Douglas Bollon. "Global education, an alternative program of study for progressive learning." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0013/MQ53397.pdf.
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Li, Shih-Hung. "Progressive learning of endpoint feedback systems with model uncertainty and sensor noise." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/38158.
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Wan, Lisa Chi Yan. "Implementation of Professional Learning Communities at the Progressive Academy of Southeast Asia." Thesis, University of Southern California, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10749206.
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There is a large body of research that suggests the concept of a professional learning community (PLC) can promote improved student learning by increasing collective teacher capacity to meet the diverse learning needs of students (Reeves, 2016; Battersby & Verdi, 2013; Marzano & DuFour, 2011; Fullan 2010, Senge, 2006; Morrissey, 2000; Hord, 1997). Many schools have implemented PLCs around the world. Despite the well-documented benefits of PLCs, there is inconsistency to the extent with which PLCs are implemented within schools and districts. The purpose of this study was to (a) understand how the elementary division at Progressive Academy of Southeast Asia (PASA), an independent private school, has implemented PLCs; (b) learn about promising practices utilized by high performing PLCs to promote teacher collaboration and high productivity; and (c) understand barriers confronted by PLCs during the PLC process. A qualitative research design was used to understand the implementation process through focus group discussions. Focus group discussions were conducted with all grade levels, kindergarten to grade 5 in the elementary division. Ten themes emerged from the study (1) an alignment of belief in the mission and vision and purpose of PLCs by faculty and administration, (2) the elementary school has implemented systems and structures to support the PLC process, (3) there is a collaborative culture for the PLC process, (4) collective responsibility for student learning is still at its infancy stage, (5) PLCs are results oriented and have clearly established SMART goals that align with the elementary school’s strategic plan, (6) instructional assistants are a strong system of support for learning for students, (7) trust is a key to high performing PLCs, (8) there is a low level of trust in some PLCs, (9) there is a transient faculty, and (10) there are an overwhelming number of initiatives that require the time of PLCs at the school.
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Sah, Shubham. "A Virtual Reality Based Progressive Learning Paradigm For Supply Chain Management Education." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282059998.
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Wu, Wenbo. "Object Recognition with Progressive Refinement for Collaborative Robots Task Allocation." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41581.
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With the rapid development of deep learning techniques, the application of Convolutional Neural Network (CNN) has benefited the task of target object recognition. Several state-of-the-art object detectors have achieved excellent performance on the precision for object recognition. When it comes to applying the detection results for the real world application of collaborative robots, the reliability and robustness of the target object detection stage is essential to support efficient task allocation. In this work, collaborative robots task allocation is based on the assumption that each individual robotic agent possesses specialized capabilities to be matched with detected targets representing tasks to be performed in the surrounding environment which impose specific requirements. The goal is to reach a specialized labor distribution among the individual robots based on best matching their specialized capabilities with the corresponding requirements imposed by the tasks. In order to further improve task recognition with convolutional neural networks in the context of robotic task allocation, this thesis proposes an innovative approach for progressively refining the target detection process by taking advantage of the fact that additional images can be collected by mobile cameras installed on robotic vehicles. The proposed methodology combines a CNN-based object detection module with a refinement module. For the detection module, a two-stage object detector, Mask RCNN, for which some adaptations on region proposal generation are introduced, and a one-stage object detector, YOLO, are experimentally investigated in the context considered. The generated recognition scores serve as input for the refinement module. In the latter, the current detection result is considered as the a priori evidence to enhance the next detection for the same target with the goal to iteratively improve the target recognition scores. Both the Bayesian method and the Dempster-Shafer theory are experimentally investigated to achieve the data fusion process involved in the refinement process. The experimental validation is conducted on indoor search-and-rescue (SAR) scenarios and the results presented in this work demonstrate the feasibility and reliability of the proposed progressive refinement framework, especially when the combination of adapted Mask RCNN and D-S theory data fusion is exploited.
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Friedle, Robert E. "Assessment of learning potential using a modified version of the coloured progressive matrices." Virtual Press, 1986. http://liblink.bsu.edu/uhtbin/catkey/468076.
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An assessment procedure to measure the learning potential of mildly and moderately mentally handicapped adults was investigated in this study. The procedure was developed to overcome the limitations of previously established learning potential assessment procedures by meeting the specific orientation necessary when assessing the mentally handicapped, by avoiding practice and mechanical memory effects, and by requiring the subjects to show the ability to retain and apply new cognitive skills. Thirty institutionalized mentally handicapped adults were matched on IG and then randomly assigned to an assessment or control group. For the assessment group the intervention procedure involved the Coloured Progressive Matrices test as a pretest measure, in four treatment sessions each two to three days apart, and as a posttest measure. Verbal and/or figural aids were provided when an incorrect response was given on a test item during the treatment sessions. The control group was provided the same number of administrations of the test but without the treatment intervention. Significant differences were found between the pre and post test performance levels of the assessment group. The control group showed no significant gains. The learning potential assessment procedure used in this study provided a measure of the learning potential of mentally handicapped institutionalized adults.The results indicate that the mentally handicapped can perform at much higher levels than those assessed by standardly used intellectual measures. Implications are that through the use of this approach a clearer discrimination of intellectual ability within the broad classifications of the mentally handicappped can be gained. Such information could be used to aid in the selection of peers, developmental programming decisions, and provide prescriptive information about the most efficacious modes of learning for an individual.
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Lai, Matteo. "Conditional MR image synthesis with Auxiliary Progressive Growing GANs." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
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L'addestramento di algotritmi di deep learning (DL) richiede una grande quantità di dati, che però spesso non sono disponibili in ambito medico. In questa tesi viene proposto un modello per la generazione di dataset sintetici etichettati nell'ambito dell'imaging medico ad alta risoluzione. Dopo aver presentato vantaggi e limiti dell'uso delle tecniche di DL in radiologia, vengono proposte le Generative Adversarial Networks (GANs) come possibile soluzione per superare tali limiti. Illustrando lo stato dell'arte relativo alle GAN, viene focalizzata l'attenzione sulle Progressive Growing GAN, capaci di generare immagini ad alta risoluzione, e sulle Auxiliary Classifier GAN (ACGAN), capaci di generare immagini target.
Sulla base di questi modelli, vengono proposte le innovative Progressive ACGAN (PACGAN), progettate per generare immagini target ad elevata risoluzione. L'obiettivo di questo lavoro di tesi è sfruttare la capacità delle GAN di creare una rappresentazione nello spazio latente dei dati del training set, sia per generare immagini target ad alta risoluzione (256 x 256), che per effettuare una classificazione. Il modello proposto viene testato su un dataset contenente 200 immagini di risonanza magnetica (RM) cerebrale di soggetti sani e pazienti con malattia di Alzheimer.
I risultati del modello sono molto promettenti.
La qualità delle immagini generate è stata valutata sia visivamente che quantitativamente, tramite FID (Fréchet Inception Distance) e MS-SSIM (Multi-Scale Structural Similarity Index), evidenziando una maggiore capacità delle PACGAN di rappresentare immagini target ad alta risoluzione rispetto alle ACGAN. Le performance di classificazione risultano ottime nel training set, con discreta capacità di generalizzare su nuovi dati.
Il modello proposto consente quindi di generare immagini target ad alta risoluzione che possono essere usate per ottenere dataset sintetici.
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Makopoulou, Kyriaki. "Continuing Professional Development for Physical Education teachers in Greece : towards situated, sustained and progressive learning?" Thesis, Loughborough University, 2009. https://dspace.lboro.ac.uk/2134/35339.
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The aim of this study was to build upon recent PE-CPD (Physical Education Continuing Professional Development) research by exploring the nature and quality of PE-CPD provision in Greece; the spectrum of teachers' professional learning experiences in both formal and informal learning contexts; and examining the features of effective CPD provision. The study employed qualitative research methods. Data were collected in three overlapping phases in one borough in Athens from repeat interviews with nine individual PE teacher case studies, interviews with six key CPD stakeholders with a range of responsibilities; and an open-ended profile questionnaire distributed to other PE teachers in the same locality.
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Kassoy, Felice Robbins. "When it all adds up, you feel good that you helped someone:Prosocial Skills in the Context of Service-Learning." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1385744417.
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Lindström, Clare. "Making a difference : Exploring the teaching and learning of the English progressive aspect among Swedish 6th grade students." Licentiate thesis, Högskolan i Jönköping, Högskolan för lärande och kommunikation, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-29038.
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The aim of this study is to generate knowledge about what 6th grade students (12-13 years old) need to discern in order to be able to use the English progressive aspect (PROG) in a syntactically and semantically accurate way. This object of learning is not only complex, but it is unmarked grammatically in Swedish, which poses considerable difficulties for English language learners. The theoretical framework has been the Variation Theory of learning which was used to design and analyse teaching and learning. A basic assumption is that learning is being able to discern critical aspects by seeing and experiencing variation and not sameness. The method chosen to answer the research question was Learning Study, an interventionist, iterative classroom-based approach, characterized by the double aim of improving teaching and at the same time developing theory. Three secondary school English teachers and the teacher researcher, collaborated to plan, teach, evaluate and analyze a series of six lessons with the Variation Theory as the pedagogical principle. Empirical data consisted of interviews, pre- and post-lesson assessments, and video recordings of the lessons. The results show that the students in this study needed to discern the following four critical aspects: (1) to differentiate between tense and grammatical aspect, (2) to differentiate between simple aspect and progressive aspect, (3) to discern the concept of ongoingness and (4) to differentiate between stative and non-stative meanings. Furthermore, the findings also suggest that (1) the necessity of separation, (2) treating the PROG as an undivided whole and (3) the use of carefully cho-sen and powerful examples encourage an enhanced understanding of the PROG.
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Rönnåsen, Berit. "Aspekter på lärande vid dövblindhet : möjligheter och begränsningar för personer med Alström syndrom." Licentiate thesis, Örebro universitet, Institutionen för hälsovetenskap och medicin, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-44667.
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Banda, Emmanuel. "Transformative Learning and Student Empowerment: Zimbabwean Graduate Students' Immersion into United States Higher Education." Miami University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=miami1406820972.
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Hayes, Taylor Ray. "A Novel Method for Analyzing Sequential Eye Movements Reveals the Relationship Between Learning and Strategy on Raven's Advanced Progressive Matrices." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1299420177.
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Senft, Emmanuel. "Teaching robots social autonomy from in situ human supervision." Thesis, University of Plymouth, 2018. http://hdl.handle.net/10026.1/13077.
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Traditionally the behaviour of social robots has been programmed. However, increasingly there has been a focus on letting robots learn their behaviour to some extent from example or through trial and error. This on the one hand excludes the need for programming, but also allows the robot to adapt to circumstances not foreseen at the time of programming. One such occasion is when the user wants to tailor or fully specify the robot's behaviour. The engineer often has limited knowledge of what the user wants or what the deployment circumstances specifically require. Instead, the user does know what is expected from the robot and consequently, the social robot should be equipped with a mechanism to learn from its user. This work explores how a social robot can learn to interact meaningfully with people in an efficient and safe way by learning from supervision by a human teacher in control of the robot's behaviour. To this end we propose a new machine learning framework called Supervised Progressively Autonomous Robot Competencies (SPARC). SPARC enables non-technical users to control and teach a robot, and we evaluate its effectiveness in Human-Robot Interaction (HRI). The core idea is that the user initially remotely operates the robot, while an algorithm associates actions to states and gradually learns. Over time, the robot takes over the control from the user while still giving the user oversight of the robot's behaviour by ensuring that every action executed by the robot has been actively or passively approved by the user. This is particularly important in HRI, as interacting with people, and especially vulnerable users, is a complex and multidimensional problem, and any errors by the robot may have negative consequences for the people involved in the interaction. Through the development and evaluation of SPARC, this work contributes to both HRI and Interactive Machine Learning, especially on how autonomous agents, such as social robots, can learn from people and how this specific teacher-robot interaction impacts the learning process. We showed that a supervised robot learning from their user can reduce the workload of this person, and that providing the user with the opportunity to control the robot's behaviour substantially improves the teaching process. Finally, this work also demonstrated that a robot supervised by a user could learn rich social behaviours in the real world, in a large multidimensional and multimodal sensitive environment, as a robot learned quickly (25 interactions of 4 sessions during in average 1.9 minutes) to tutor children in an educational game, achieving similar behaviours and educational outcomes compared to a robot fully controlled by the user, both providing 10 to 30% improvement in game metrics compared to a passive robot.
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Vieira, Kléber Mendes. "O ensino do conceito de ângulo: limites e possibilidades." Universidade Estadual da Paraíba, 2010. http://tede.bc.uepb.edu.br/tede/jspui/handle/tede/1968.
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Previous issue date: 2010-12-17The aim of this research was to develop a pedagogical approach on angle concept development, based on the theoretical proposal called progressive abstraction developed by Michelmore and White (2000). An exploratory and experimental investigation was accomplished through a questionnaire and observations in loco, posing on how the teaching of this content is processed in Municipal Schools of Campina Grande (MSCG), in the sixth year of elementary schools and its approach in textbooks, having as reference the descriptors of Brazil Examination. We found out that most of the teachers do not approach angle concept in the year recommended by the oficial documents, not considering the idea of angle as a turn or changing direction. While the analysis of the three books most used in schools showed that only one was according to the descriptors of Brazil Examination. Taking those aspects for granted, we elaborated an initial pedagogical approach, applied in a 6th year class of MSCG, whose result was evaluated through a pre-test and a post-test, as well as participant observations were made, verifying the adequacy of the pedagogical proposal related to cognitive and affective aspects, showing that it is viable, both with regard to costs and to time and its application in the available physical spaces in traditional classrooms. After the application in the classroom, we made minor adjustments in the initial pedagogical approach. We presented the results attached.
Esta pesquisa teve como objetivo desenvolver uma abordagem didática, sobre o desenvolvimento do conceito de ângulo, fundamentada na proposta teórica denominada de abstração progressiva desenvolvida por Michelmore e White (2000). Nesta direção, foi realizada uma investigação de caráter exploratório e experimental por meio de questionário e observações in loco, levantando como se processa o ensino deste conteúdo na Rede Municipal de Ensino de Campina Grande (RMECG), em turmas do 6º ano do ensino fundamental e a sua abordagem nos livros didáticos, tendo como referência os descritores da Prova Brasil. Constatou-se que a maioria dos professores não aborda o conceito de ângulo no ano recomendado pelos documentos oficiais, deixando de lado a ideia de ângulo como giro ou mudança de direção, enquanto a análise dos três livros didáticos mais utilizados na rede verificou que apenas um estava de acordo com os descritores da Prova Brasil. Levando em conta estes aspectos, foi elaborada uma abordagem didática inicial, aplicada em uma turma de 6º Ano da RMECG, cujo resultado foi avaliado por meio de um pré- teste e de um pós-teste, bem como foram efetuadas observações participantes, verificando-se a adequação da proposta didática no que se refere aos aspectos cognitivos e afetivos, mostrando que ela é viável, tanto no que se refere aos custos, quanto ao tempo e à sua aplicação nos espaços físicos disponibilizados em salas de aulas tradicionais. Após a aplicação em sala de aula, foram efetuados pequenos ajustes na abordagem didática inicial, sendo o seu resultado apresentado em anexo.
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Maddox, Winston H. "Adapting to a Virtual Learning Environment." Antioch University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=antioch1429557429.
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Madeland, Jonathan, and Olle Mattsson. "Progressiv Fritid : Meningsfull fritid ur ett post-progressivt jämlikhetsperspektiv." Thesis, Linnéuniversitetet, Institutionen för utbildningsvetenskap (UV), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-65329.
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I denna studie undersöker vi fritidspedagogers förhållningssätt till meningsfull fritid för barn, hur dessa förhållningssätt står emot föräldrar inom medel- och arbetarklassen, och om fritidspedagoger använder verksamheten för att skapa likvärdiga möjligheter för barn att generera utbildningsfrämjande resurser. Barn rustas olika inför framtiden beroende på vilket hem och vilka uppväxtvillkor de föds in i vilket föreslår en inbyggd orättvisa i vårt samhälle. Föräldrars tillgång till socialt, kulturellt och ekonomiskt kapital skapar ett värderande av utbildning som överförs till deras barn genom den primära socialisationen. Föräldrar inom medelklassen odlar till skillnad från föräldrar inom arbetarklassen aktivt handplockade färdigheter hos sina barn genom organiserade fritidsaktiviteter som föräldrarna anser blir gynnande ur ett framtidsperspektiv. Med skilda ambitioner om sina barns framtida utbildningsval fortsätter de högre klasserna att ta avstånd från de lägre. Genom deltagande observation och intervjuer undersöker vi fritidspedagogers eget förhållningssätt. Vi ser en potential hos fritidshemmet att bli en jämlikhetsfrämjande institution genom att förse barn inom arbetarklassen med likvärdiga möjligheter att generera samma typ av resurser på fritiden som barn inom medelklassen gör. Fritidspedagoger värderar fri lek där eleverna får utvecklas naturligt i en trygg, pedagogisk miljö och menar att vad som är meningsfullt utgår ifrån vad eleverna själva upplever. Fritidspedagogers förhållningssätt till meningsfull fritid skiljer sig därför från föräldrar inom medelklassen. Att barns fritidsvanor ser olika ut är inte ett jämlikhets-problem som uppmärksammas på fritidshemmet.
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Dwight, James Scutt III. "Hyperpedagogy: Intersections among poststructuralist hypertext theory, critical inquiry, and social justice pedagogies." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/11132.
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Hyperpedagogy seeks to actualize social justice pedagogies and poststructuralist theorizing in digitally enhanced and online learning environments. Hyperpedagogy offers ways to incorporate transactional pedagogies into digital curricula so that learners throughout the United States' pluralistic culture can participate in e-learning. Much of the hyperbole promoting e-learning is founded on social-efficiency pedagogies (i.e. preparing tomorrow's workers for the information-based, new global economy) that tend to homogenize culturally pluralistic learners. The premium placed on a strict adherence to rigid learning systems inculcated within standards-based reform movements typically, moreover, discriminate against historically marginalized learners. Hyperpedagogy seeks to elucidate the closeting of privilege in e-learning so that learners of color, female learners, and homosexual learners can be better represented in the literature than is currently practiced.Ph. D.
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Lund, William B. "Ensemble Methods for Historical Machine-Printed Document Recognition." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4024.
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The usefulness of digitized documents is directly related to the quality of the extracted text. Optical Character Recognition (OCR) has reached a point where well-formatted and clean machine- printed documents are easily recognizable by current commercial OCR products; however, older or degraded machine-printed documents present problems to OCR engines resulting in word error rates (WER) that severely limit either automated or manual use of the extracted text. Major archives of historical machine-printed documents are being assembled around the globe, requiring an accurate transcription of the text for the automated creation of descriptive metadata, full-text searching, and information extraction. Given document images to be transcribed, ensemble recognition methods with multiple sources of evidence from the original document image and information sources external to the document have been shown in this and related work to improve output. This research introduces new methods of evidence extraction, feature engineering, and evidence combination to correct errors from state-of-the-art OCR engines. This work also investigates the success and failure of ensemble methods in the OCR error correction task, as well as the conditions under which these ensemble recognition methods reduce the Word Error Rate (WER), improving the quality of the OCR transcription, showing that the average document word error rate can be reduced below the WER of a state-of-the-art commercial OCR system by between 7.4% and 28.6% depending on the test corpus and methods. This research on OCR error correction contributes within the larger field of ensemble methods as follows. Four unique corpora for OCR error correction are introduced: The Eisenhower Communiqués, a collection of typewritten documents from 1944 to 1945; The Nineteenth Century Mormon Articles Newspaper Index from 1831 to 1900; and two synthetic corpora based on the Enron (2001) and the Reuters (1997) datasets. The Reverse Dijkstra Heuristic is introduced as a novel admissible heuristic for the A* exact alignment algorithm. The impact of the heuristic is a dramatic reduction in the number of nodes processed during text alignment as compared to the baseline method. From the aligned text, the method developed here creates a lattice of competing hypotheses for word tokens. In contrast to much of the work in this field, the word token lattice is created from a character alignment, preserving split and merged tokens within the hypothesis columns of the lattice. This alignment method more explicitly identifies competing word hypotheses which may otherwise have been split apart by a word alignment. Lastly, this research explores, in order of increasing contribution to word error rate reduction: voting among hypotheses, decision lists based on an in-domain training set, ensemble recognition methods with novel feature sets, multiple binarizations of the same document image, and training on synthetic document images.
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Jank, Werner. "Lernende: Objekte des Lehrens? Subjekte ihres Lernens?" Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-87562.
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Ausgangspunkt sind Video-Aufzeichnungen von drei Unterrichtsstunden im Fach Musik. Sie werden zunächst in einen musikdidaktischen Rahmen eingeordnet, der es erlaubt, eine Stunde primär der „Handlungsorientierung“ im Sinn der 1980er Jahre zuzuordnen, die zweite Stunde primär dem musikdidaktischen Modell eines „Aufbauenden Musikunterrichts“ und die dritte Stunde „offenen“ Unterrichtskonzeptionen. Der zweite Abschnitt fragt nach dem Verhältnis von Lernen und Lehren und mündet in die These, dass „natürliches“, informelles Lernen und das Lernen in der Institution Schule in einem unauflösbaren Spannungsverhältnis zueinander stehen. Diese These wird dann an einigen Teilaspekten der aufgezeichneten Schulstunden konkretisiert. Im abschließenden Abschnitt wird die These auf das Spannungsverhältnis zwischen den schulischen Leistungserwartungen und der individuellen Leistungsbereitschaft der Schüler bezogen. Der Beitrag mündet in einem Plädoyer für die Anerkennung und Förderung der grundlegenden Leistungsbereitschaft der Schüler sowie der Selbstwahrnehmung ihrer individuellen Leistungen und ihres individuellen Erfolgs im Sinn einer zunehmenden LeistungsbewusstheitThe point of departure consists of video recordings of three music lessons. These are first of all localised within a music-didactic framework, allowing one lesson to be classified primarily along the lines of “action-oriented” processes in the 1980s sense of the term, the second to be classed primarily with the music-didactic model of “progressive music learning” and the third lesson with “open” teaching concepts. The second section is an enquiry into the relationship between teaching and learning, resulting in the thesis that there is an irresolvable tension between “natural” informal learning and learning within the school institution. In the following section this thesis is concretised along various partial aspects of the recorded lessons. The thesis is then applied in the final section to the tension between the school’s performance expectations and the pupils’ individual willingness to perform. The piece ends by arguing for the recognition and promotion of the pupils’ fundamental willingness to perform as well as of their self-awareness of their individual performance and individual success in the sense of an increasing awareness of performance
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Jank, Werner. "Lernende: Objekte des Lehrens? Subjekte ihres Lernens?" Georg Olms Verlag, 2010. https://slub.qucosa.de/id/qucosa%3A2146.
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Ausgangspunkt sind Video-Aufzeichnungen von drei Unterrichtsstunden im Fach Musik. Sie werden zunächst in einen musikdidaktischen Rahmen eingeordnet, der es erlaubt, eine Stunde primär der „Handlungsorientierung“ im Sinn der 1980er Jahre zuzuordnen, die zweite Stunde primär dem musikdidaktischen Modell eines „Aufbauenden Musikunterrichts“ und die dritte Stunde „offenen“ Unterrichtskonzeptionen. Der zweite Abschnitt fragt nach dem Verhältnis von Lernen und Lehren und mündet in die These, dass „natürliches“, informelles Lernen und das Lernen in der Institution Schule in einem unauflösbaren Spannungsverhältnis zueinander stehen. Diese These wird dann an einigen Teilaspekten der aufgezeichneten Schulstunden konkretisiert. Im abschließenden Abschnitt wird die These auf das Spannungsverhältnis zwischen den schulischen Leistungserwartungen und der individuellen Leistungsbereitschaft der Schüler bezogen. Der Beitrag mündet in einem Plädoyer für die Anerkennung und Förderung der grundlegenden Leistungsbereitschaft der Schüler sowie der Selbstwahrnehmung ihrer individuellen Leistungen und ihres individuellen Erfolgs im Sinn einer zunehmenden Leistungsbewusstheit.The point of departure consists of video recordings of three music lessons. These are first of all localised within a music-didactic framework, allowing one lesson to be classified primarily along the lines of “action-oriented” processes in the 1980s sense of the term, the second to be classed primarily with the music-didactic model of “progressive music learning” and the third lesson with “open” teaching concepts. The second section is an enquiry into the relationship between teaching and learning, resulting in the thesis that there is an irresolvable tension between “natural” informal learning and learning within the school institution. In the following section this thesis is concretised along various partial aspects of the recorded lessons. The thesis is then applied in the final section to the tension between the school’s performance expectations and the pupils’ individual willingness to perform. The piece ends by arguing for the recognition and promotion of the pupils’ fundamental willingness to perform as well as of their self-awareness of their individual performance and individual success in the sense of an increasing awareness of performance.
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Oudainia, Mohamed Radjeb. "Contrôle partagé adaptatif et élaboration de stratégies de conduite personnalisées pour le véhicule automatisé : une approche par apprentissage progressif." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2023. http://www.theses.fr/2023UPHF0038.
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La conduite autonome suscite un intérêt croissant auprès les constructeurs, les chercheurs, les autorités et le grand public en raison de ses promesses en matière de sécurité routière, de mobilité pour les personnes âgées et à mobilité réduite, d'efficacité énergétique et de réduction des émissions. Cependant, le déploiement complet de ces véhicules dépend de leur fiabilité dans toutes les situations, ce qui nécessite une supervision du conducteur. Cela soulève des questions cruciales sur l'interaction homme-machine, en particulier en ce qui concerne le partage de la commande et la résolution des conflits.La thèse s'inscrit dans le cadre du projet ANR-CoCoVeIA (2019-2024) coordonné par le LAMIH (Coopération Conducteur-Véhicule Intelligent Autonome). Son objectif principal est d'incorporer des capacités d'auto-apprentissage dans les véhicules autonomes de niveau 2 pour améliorer leurs compétences en respectant les règles de sécurité routière. La thèse se concentre sur l'optimisation de l'interaction entre le système automatisé et le conducteur pour renforcer l'efficacité, améliorer les performances de conduite et favoriser l'acceptabilité du système.Pour atteindre ces objectifs, une architecture de coopération auto-adaptative à plusieurs niveaux est proposée dans la première partie de la thèse. Cette architecture vise à adapter de manière optimale le comportement du véhicule autonome à un style de conduite préféré des conducteurs tout en garantissant une conduite sécuritaire et efficace. Une deuxième partie de la thèse se penche sur la personnalisation des systèmes d'assistance au changement de voie, utilisant une approche d'apprentissage basée sur la descente de gradient stochastique pour ajuster les paramètres en fonction des préférences du conducteur, en se basant sur la détection de ses intentions de changement de voie.Pour résoudre les conflits entre le conducteur et le système de conduite autonome, la thèse explore trois approches de commande optimale robuste pour les systèmes linéaires à paramètres variants (LPV) représentés sous la forme floue Takagi-Sugeno (T-S). La première approche se concentre sur le contrôle partagé adaptatif en ajustant une fonction de coût multi-objectif en temps réel en fonction de la disponibilité du conducteur et de l'évaluation du risque. La deuxième approche introduit un modèle dynamique du conducteur, dont les paramètres sont identifiés en ligne, permettant une adaptation continue aux caractéristiques du conducteur. Ce modèle est utilisé pour développer un système de contrôle partagé adaptatif pour le maintien de voie en tenant compte de la dynamique des paramètres neuromusculaires du conducteur.La dernière approche vise à éliminer complètement les conflits entre le conducteur et le système de maintien de voie en combinant une fonction de coût adaptative avec un modèle dynamique du comportement du conducteur.Pour la conception du contrôleur partagé LPV, les conditions de stabilité en boucle fermée du contrôle partagé adaptatif (LPV) pour les trois approches sont établies à l'aide d'arguments de stabilité de Lyapunov et formulées sous forme d’un problème d’optimisation d’inégalités matricielles linéaires (LMI) qui peuvent être résolues numériquement grâce à des algorithmes d’optimisation convexe. Des validations expérimentales et des expériences de test utilisateur ont été menées au moyen du simulateur de conduite dynamique SHERPA-LAMIH pour évaluer l’acceptabilité de ces approches, démontrant ainsi leur efficacité pour améliorer la sécurité et le confort de conduite, et validant l’ensemble des approches proposéesAutonomous driving technology is attracting increasing interest from automobile manufacturers, researchers, authorities, and the general public due to its promises in enhancing road safety, providing mobility for the elderly and individuals with reduced mobility, improving energy efficiency, and reducing emissions. However, the full deployment of these vehicles relies on their reliability in all situations, necessitating driver supervision. This raises critical questions about human-machine interaction, particularly concerning sharing control between the automated driving system and the driver, as well as conflict management.The thesis is part of the ANR-CoCoVeIA project (2019-2024), coordinated by LAMIH (Cooperation Driver-Autonomous Intelligent Vehicle). The main objective of this project is to introduce self-learning capabilities into level 2 autonomous vehicles to enhance their skills while adhering to road safety rules. The thesis focuses specifically on managing interactions between the automated system and the driver, with the goal of improving its effectiveness, enhancing driving performance, and promoting the driver's acceptance of the system.To achieve these goals, a multi-level evolutionary cooperation architecture is proposed in the first part of the thesis. This architecture aims to optimally adapt the behavior of the autonomous vehicle to a driver's preferred driving style while ensuring safe and efficient driving. The second part of the thesis delves into personalizing lane change assistance systems, using a stochastic gradient descent-based learning approach to adjust parameters based on the driver's preferences, relying on the detection of their lane change intentions.To address conflicts between the driver and the autonomous driving system, the thesis explores three robust optimal control approaches for linear time-varying parameter systems (LPV) represented in the Takagi-Sugeno (T-S) fuzzy form. The first approach focuses on adaptive shared control by real-time adjustment of a multi-objective cost function based on driver availability and risk assessment. The second approach introduces a driver's dynamic model, with parameters identified online, allowing continuous adaptation to the driver's characteristics. This model is used to develop an adaptive shared control system for lane-keeping, taking into account the neuromuscular dynamics of the driver's parameters. The final approach aims to entirely eliminate conflicts between the driver and the lane-keeping system by combining an adaptive cost function with a dynamic driver behavior model.For the design of the LPV shared controller, stability conditions for closed-loop adaptive shared control (LPV) for all three approaches are established using Lyapunov stability arguments and formulated as a linear matrix inequality (LMI) optimization problem that can be numerically solved using convex optimization algorithms. Experimental validations and user testing experiments were conducted using the SHERPA-LAMIH dynamic driving simulator to assess the acceptability of these approaches, demonstrating their effectiveness in improving safety and driving comfort and validating all of the proposed approaches
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Hameed, Khurram. "Computer vision based classification of fruits and vegetables for self-checkout at supermarkets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2519.
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The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide range of areas such as the development of autonomous vehicles, object recognition, Content Based Image Retrieval (CBIR), image segmentation and terrestrial analysis from space (i.e. crop estimation). Despite significant prior research, the area of object recognition still has many topics to be explored. This PhD thesis focuses on using advanced machine learning approaches to enable the automated recognition of fresh produce (i.e. fruits and vegetables) at supermarket self-checkouts. This type of complex classification task is one of the most recently emerging applications of advanced computer vision approaches and is a productive research topic in this field due to the limited means of representing the features and machine learning techniques for classification. Fruits and vegetables offer significant inter and intra class variance in weight, shape, size, colour and texture which makes the classification challenging.
The applications of effective fruit and vegetable classification have significant importance in daily life e.g. crop estimation, fruit classification, robotic harvesting, fruit quality assessment, etc. One potential application for this fruit and vegetable classification capability is for supermarket self-checkouts. Increasingly, supermarkets are introducing self-checkouts in stores to make the checkout process easier and faster. However, there are a number of challenges with this as all goods cannot readily be sold with packaging and barcodes, for instance loose fresh items (e.g. fruits and vegetables). Adding barcodes to these types of items individually is impractical and pre-packaging limits the freedom of choice when selecting fruits and vegetables and creates additional waste, hence reducing customer satisfaction. The current situation, which relies on customers correctly identifying produce themselves leaves open the potential for incorrect billing either due to inadvertent error, or due to intentional fraudulent misclassification resulting in financial losses for the store. To address this identified problem, the main goals of this PhD work are: (a) exploring the types of visual and non-visual sensors that could be incorporated into a self-checkout system for classification of fruits and vegetables, (b) determining a suitable feature representation method for fresh produce items available at supermarkets, (c) identifying optimal machine learning techniques for classification within this context and (d) evaluating our work relative to the state-of-the-art object classification results presented in the literature.
An in-depth analysis of related computer vision literature and techniques is performed to identify and implement the possible solutions. A progressive process distribution approach is used for this project where the task of computer vision based fruit and vegetables classification is divided into pre-processing and classification techniques. Different classification techniques have been implemented and evaluated as possible solution for this problem. Both visual and non-visual features of fruit and vegetables are exploited to perform the classification. Novel classification techniques have been carefully developed to deal with the complex and highly variant physical features of fruit and vegetables while taking advantages of both visual and non-visual features. The capability of classification techniques is tested in individual and ensemble manner to achieved the higher effectiveness.
Significant results have been obtained where it can be concluded that the fruit and vegetables classification is complex task with many challenges involved. It is also observed that a larger dataset can better comprehend the complex variant features of fruit and vegetables. Complex multidimensional features can be extracted from the larger datasets to generalise on higher number of classes. However, development of a larger multiclass dataset is an expensive and time consuming process. The effectiveness of classification techniques can be significantly improved by subtracting the background occlusions and complexities. It is also worth mentioning that ensemble of simple and less complicated classification techniques can achieve effective results even if applied to less number of features for smaller number of classes. The combination of visual and nonvisual features can reduce the struggle of a classification technique to deal with higher number of classes with similar physical features. Classification of fruit and vegetables with similar physical features (i.e. colour and texture) needs careful estimation and hyper-dimensional embedding of visual features. Implementing rigorous classification penalties as loss function can achieve this goal at the cost of time and computational requirements. There is a significant need to develop larger datasets for different fruit and vegetables related computer vision applications. Considering more sophisticated loss function penalties and discriminative hyper-dimensional features embedding techniques can significantly improve the effectiveness of the classification techniques for the fruit and vegetables applications.
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Connolly, Stephen. "Learning progression in secondary students' digital video production." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/10020015/.
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Assessing learning progression in Media Education is an area of study which has been largely neglected in the history of the subject, with very few longitudinal studies of how children learn to become "media literate" over an extended period of time. This thesis is an analysis of data over three years (constituted by the production of digital video work by a small group of secondary school students) which attempts to offer a more extended account of this learning. The thesis views the data through three concepts (or "lenses") which have been key to the development of media education in the UK and abroad. These are Culture, Criticality and Creativity, and the theoretical perspectives that the thesis should be viewed in the light of include the work of Bourdieu, Vygotsky, Heidegger and Hegel. The examination of the student production work carried out in the light of these three lenses suggests that learning progression comes about because of a relationship between all three, the key metaphorical idea put forward by the thesis that describes that relationship is the dialectic of familiarity. This suggests that for media education at least, the learning process is a dialectic one, in which students move from cultural and critical knowledge and experiences that are familiar -or thetic - to ones that are unfamiliar, and hence antithetical. Over time this antithetical knowledge becomes familiar and students synthesise together their popular cultural and critical experiences with the critical experiences that they have in the media classroom. This synthesis is driven by the creative act of production work, which brings together the cultural and the critical, the familiar and unfamiliar. It is this key metaphor then, that offers an account of learning progression in media production, and the relationship of that process to creativity, criticality and popular culture.
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Gupta, Amrita. "Unsupervised learning of disease subtypes from continuous time Hidden Markov Models of disease progression." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54364.
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The detection of subtypes of complex diseases has important implications for diagnosis and treatment. Numerous prior studies have used data-driven approaches to identify clusters of similar patients, but it is not yet clear how to best specify what constitutes a clinically meaningful phenotype. This study explored disease subtyping on the basis of temporal development patterns. In particular, we attempted to differentiate infants with autism spectrum disorder into more fine-grained classes with distinctive patterns of early skill development. We modeled the progression of autism explicitly using a continuous-time hidden Markov model. Subsequently, we compared subjects on the basis of their trajectories through the model state space. Two approaches to subtyping were utilized, one based on time-series clustering with a custom distance function and one based on tensor factorization. A web application was also developed to facilitate the visual exploration of our results. Results suggested the presence of 3 developmental subgroups in the ASD outcome group. The two subtyping approaches are contrasted and possible future directions for research are discussed.
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Tai, Chih-Che. "Learning Progression in Students’ Understanding of Combustion- A Cross- age Study." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etsu-works/3281.
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Suriya, Ramon. "Jaha, såhär går det framåt! –En studie kring vad ABC-klubbens läseböckers A-nivå har för progression." Thesis, Örebro universitet, Institutionen för humaniora, utbildnings- och samhällsvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-83714.
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Syftet i den här studien är att undersöka vad ABC-klubben läseböcker på A-nivå (från årkurs 1 till 3) har för progression. För att uppfylla syftet har studien använt sig av både kvantitativa och kvalitativa metoder. Det görs genom att fokusera på ord och meningar i de valda kapitlen i varje bok med hjälp av läsbarhetsindex (LIX-värde). Den kvantitativa metoden kompletterats med en textanalys på meningsbyggnad för att se hur meningarna i varje bok utvecklas. Förhållande mellan text och bild har också analyserats med fokus på vilken funktion de två har och hur de samspelar med varandra. Dessutom har huvudkaraktärerna i läsebokserien analyserats för att finna tecken på utvecklingen över tid. Analysresultatet visar att det finns progression både i och mellan de tre böckerna. Enligt LIX-värdet har bok 1A tydligast progression medan bok 2A har mindre och 3A har minst. Samspelet mellan text och bild har en omvänd funktion. I början är det bilden som bär ansvar för att driva berättelsen framåt sedan är det texten som tar över ansvaret och kan stå på sina egna ben. Progressionen sker även när det gäller huvudkaraktärerna. Genom att undersöka berättelsen så kunde huvudkaraktärernas progression delas in i två teman, emotionell utveckling och utveckling av hur karaktärerna löser komplexa problem. Avslutningsvis diskuteras analysresultatet utifrån didaktiska perspektiv.
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Harrison, Ryan Matthew. "Elementary Teachers’ Understanding and Use of Cognition Based Assessment Learning Progression Materials for Multiplication and Division." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1330296812.
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Hashimoto, Martell Erin. "Using Rasch Models to Develop and Validate an Environmental Thinking Learning Progression." Thesis, Boston College, 2014. http://hdl.handle.net/2345/3838.
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Thesis advisor: Katherine L. McNeillEnvironmental understanding is highly relevant in today's global society. Social, economic, and political structures are connected to the state of environmental degradation and exploitation, and disproportionately affect those in poor or urban communities (Brulle and Pellow, 2006; Executive Order No. 12898, 1994). Environmental education must challenge the way we live, and our social and ecological quality of life, with the goal of responsible action. The development of a learning progression in environmental thinking, along with a corresponding assessment, could provide a tool that could be used across environmental education programs to help evaluate and guide programmatic decisions. This study sought to determine if a scale could be constructed that allowed individuals to be ordered along a continuum of environmental thinking. First, I developed the Environmental Thinking Learning Progression, a scale of environmental thinking from novice to advanced, based on the current available research and literature. The scale consisted of four subscales, each measuring a different aspect of environmental thinking: place consciousness, human connection, agency, and science concepts. Second, a measurement instrument was developed, so that the data appropriately fit the model using Rasch analysis. A Rasch analysis of the data placed respondents along a continuum, given the range of item difficulty for each subscale. Across three iterations of instrument revision and data collection, findings indicated that the items were ordered in a hierarchical way that corresponded to the construct of environmental thinking. Comparisons between groups showed that the average score of respondents who had participated in environmental education programs was significantly higher than those who had not. A comparison between males and females showed no significant difference in average measure, however, there were varied significant differences between how racial/ethnic groups performed. Overall, the results suggest that the Environmental Thinking Learning Progression and instrument are useful and accurate tools to measure individuals along a continuum from novice to advanced. This can be helpful for environmental education programs for use in evaluation and program development within a diverse context
Thesis (PhD) — Boston College, 2014
Submitted to: Boston College. Lynch School of Education
Discipline: Teacher Education, Special Education, Curriculum and Instruction
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Peterson, Kelly(Kelly Nicole). "Personalized Gaussian process-based machine learning models for forecasting Alzheimer's Disease progression." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121678.
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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 81-90).
In this thesis, I address the problem of predicting behavioral and cognitive metrics from highly heterogeneous datasets (e.g. genetic, clinical/patient history, neuropsychological, biohumoral, molecular) with missing or incomplete data, using Personalized Machine Learning (PML) [71, 72]. In specific, my thesis work focuses on exploring the application of personalized machine learning techniques to the problem of predicting behavioral and cognitive metrics given a pre-organized dataset containing multimodal subject data collected from the longitudinal Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Thus, this thesis explores the impact of PML in the context of predicting the progression of Alzheimer's disease (AD) by predicting various cognitive, clinical, and behavioral metrics known to be indicative of AD diagnosis. To do this, we employ Gaussian Process (GP) Regression as a modeling framework. Using this framework, we design and implement two novel methods for personalized prediction of key cognitive metrics associated with the AD progression (e.g., ADAS-Cog13). Our experimental evaluations show that the proposed personalized model yields significant gains in performance over non-personalized ("one size fits all") approaches applied to the target estimation tasks using the ADNI database. The techniques proposed have the potential to advance and revolutionize disease treatment and clinical research in AD and other health-related domains. We also provide an extensive overview of methods that deal with missing data in ADNI dataset, being one of the main challenges when working with real-world data of AD.
by Kelly Peterson.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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McIntyre, Janis. "Learning lifelines : a study of learners' perceptions of participation in and progression from community based adult learning in Scotland." Thesis, University of Strathclyde, 2011. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=15496.
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Edman, Mathias. "Towards disease progression sub-typing via responsibility sampling for robust expectation-maximisation learning." Thesis, KTH, Optimeringslära och systemteori, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266895.
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Most diseases have different heterogeneous effects on patients. Broadly, one may conclude what manifested symptoms correspond to which diagnosis, but usually there is more than one disease progression pattern. Because there is more than one pattern, and because each pattern may require a bespoke (and personalised) therapeutic intervention, time-series clustering is one option by which disease subpopulations can be identified. Such patient sub-typing is difficult due to information heterogeneity, information sparsity (few longitudinal observations) and complex temporal governing disease dynamics. To deal with these problems, and seeking to gain a robust description of them, we introduce a generative clustering model by way of a mixture of hidden Markov models. Our model deals with non-ergodic temporal dynamics, has variable state cardinality for the mixtures components and initialises the mixture in a more structured way than current methods. With the task of disease progression modelling in mind, we also take a broader perspective on parameter learning in finite mixture models (FFM). In many mixture models, obtaining optimal or near-optimal parameters is difficult with current learning methods, where the most common approach is to employ monotone learning algorithms e.g. the conventional expectation-maximisation algorithm. While effective, the success of any monotone algorithm is crucially dependant on good parameter initialisation. A common approach is to repeat the learning procedure multiple times starting from different points in the parameter space or to employ model specific initialisation schemes e.g. K-means initialisation for Gaussian mixture models. For other types of mixture models the path to good initialisation parameters is often unclear and may require a solution specific not only model, but also the data. To this end, we propose a general heuristic learning algorithm that utilises Boltzmann exploration to assign each observation to a specific base distribution within the mixture model, which we call Boltzmann exploration expectationmaximisation (BEEM). With BEEM, hard assignments allow straight forward parameter learning for each base distribution by conditioning only on its assigned observations. Consequently it can be applied to mixtures of any base distribution where single component parameter learning is tractable. The stochastic learning procedure is able to escape local optima and explores the parameter space, thus mitigates sensitivity to parameter initialisation. We show competitive performance on a number of synthetic benchmark cases as well as on real-world datasets. Finally we employ BEEM for the disease progression sub-typing task and contrast it to a task specific initialisation procedure on synthetic data as well as on a real progression modelling task, where we identify clinical phenotypes in Parkinson’s diseaseI många fall har sjukdomar heterogena effekter på de drabbade. Generellt är det möjligt att utesluta vilken diagnos som ger upphov till ett specifikt symptom, men ofta kan en sjukdom manifestera sig i mer än ett sjukdomsförlopp. Av denna anledning kan det för vissa sjukdomar finnas behov av specialiserad och individanpassad behandling. För att identifiera undertyper av sjukdomsförlopp är klustring via tidsserieanalys en kraftfull metod. Emellertid är sådan analys ofta försvårad av potentiellt ostrukturerad och ogranulär longitudinell uppföljning av patienters ofta komplexa sjukdomsförlopp. För att avhjälpa dessa svårigheter presenterar vi i den här uppsatsen en generativ klustringsmetod baserad på superposition av dolda Markovmodeller som erbjuder en tolkningsbar representation av de identifierade undergrupperna. Vår modell hanterar icke-ergodisk temporal dynamik och tillåter ett variabelt antal dolda tillstånd. Vi presenterar även en uppgiftsspecifik initialiseringsmetod för att hitta sjukdomsförlopp av heterogen försämringshastighet. Med sjukdomsförloppsmodellering i åtanke beaktar vi även andra blandningsmodeller ur ett vidare perspektiv. Gällande blandningsmodeller är det ofta problematiskt att lära sig optimala eller nästan optimala parametrar från den underliggande datan med nuvarande optimeringsmetoder, där det vanligaste tillvägagångssättet är att använda monotona inlärningsalgoritmer, t.ex. den konventionella expectation-maximisation (EM) -algoritmen. EM är en monoton iterativ algoritm som efter varje iteration förbättrar parametrarna, vilket gör den känslig för icke-konvexa optimeringsproblem, vilket är vanligt för blandingsmodeller. Kvaliteten av de resulterande parametrarna är således i högsta grad beroende av hur de initialiseras. En vanligt förekommande metod är därför att starta om parameteroptimering från flera olika punkter parameterrummet eller att använda sig av en modellspecifik initialiseringsprocedur. För exempelvis Gaussiska blandningsmodeller är initialisering med hjälp K-means-algoritmen effektiv. För andra typer av blandningsmodeller är det dock inte lika uppenbart vad som karakteriserar en robust initialiseringsprocess. För detta ändamål presenterar vi en generell heuristisk inlärningsalgoritm som använder sig av Boltzmann-utforskning, där varje baskomponent i blandningsmetoden tillskrivs en delmäng av observationerna på ett stokastiskt vis. På så vis kan optimeringsproblemet delas upp i mindre komponenter där varje basmodell kan optimeras separat betingade endast på de tillskrivna observationerna; vi kallar denna metod för Boltzmann exploration expectation-maximisation (BEEM). Följaktligen är metoden tillämpbar för alla blandingsmodeller där parameterinlärning av enskild baskomponent är möjlig. Den stokastiska elementet i inlärningsmetoden tillåter oss att inte fastna i lokala optima och är därför robust mot ofördelaktig parameterinitialisering. Vi visar att metoden presterar väl på ett antal syntetiska samt praktiska klustringsuppgifter. Slutligen tillämpar vi BEEM för den tidigare nämnda sjukdomsdomsförloppsgrupperings-problemet där vi även jämför mot en uppgiftsspecifik initialiseringsmetod. Metoden prövas på syntetiska sjukdomsförlopp samt longitudinell data från en studie av Parkinsons sjukdom
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Wikdahl, Johanna, and Katarina Witting. "Mind the Gap : en fallstudie om framgångsfaktorer och delaktighet vid övergångar mellan skolmiljöer." Thesis, Högskolan Kristianstad, Fakulteten för lärarutbildning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-19686.
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Preschool, preschool class and comprehensive school are separate forms of schooling in Sweden. As a consequence, Swedish children experience two transitions between three school forms in little more than a year’s time. The aim of this qualitative study is to provide a better understanding of successful transitions. The study data is based on semi-structured interviews and the sample consists of both teachers and pupils. The study is based on a sociocultural theory with inspiration from Wenger´s model of learning and Wengers-Trayners theory of situated learning and community of practice. All three frameworks´ focus is on the environments importance for the students learning and learning as a process of participation in communities of practice. Results show that the students see themselves as active participants in their own development and learning but they interpret participation differently. The teachers, on the other hand, agreed upon what constitutes learning as well as the content in the education. But when the pupil transit between settings, the teachers do not exchange knowledge, experiences and information about said content, nor successful working methods. Instead, the focus is put on the pupil as an individual. In order to promote continuity for all pupils experiencing transitions between settings, it is important that there is a shared approach between all professionals involved. Here, a teacher with a Postgraduate Diploma in Special Educational Needs could help align practice and bridge the gap.I Sverige går barn och elever i förskola, förskoleklass och skola. Det innebär att alla barn och elever erfar minst två övergångar mellan olika skolformer inom loppet av drygt ett år. Vid dessa övergångar kan eleverna uppleva en förändring vad gäller förväntningar från vuxna, hur kunskapsinhämtning prioriteras samt hur de ges möjlighet till delaktighet i respektive skolform. Syftet med detta examensarbete är att utifrån olika perspektiv nå kunskap om dessa övergångar samt att synliggöra de framgångsfaktorer som återges. Studien är en fallstudie vilket är en kvalitativ metod. Empirin samlades in under verksamhetsbesök men framförallt genom intervjuer i fokusgrupper. Därefter användes tematisering för att se mönster och likheter. Resultatet visar att eleverna upplever delaktighet, främst utifrån leken. Däremot definierar eleverna delaktighet på olika sätt beroende på vilken skolform de relaterar till. I studiens intervjuer framträder även en enig syn på vad kunskap och lärande är hos personalen men kunskaper, erfarenheter och information om de framgångsfaktorer som gynnat eleverna i lärandet kommuniceras inte i informationsöverföringen. Istället är informationen till stor del baserad på eleverna som individer. Inte heller används elevernas kunskap och erfarenhet i planeringen av övergångar vilket kunde användas till att planera kommande övergångar. Således visar studien på ett behov av att främja samverkan mellan skolformerna. Vår specialpedagogiska implikation är därför att en specialpedagog vars förståelse för de olika skolformerna kan ta vara på elevernas kunskap och fungera som övergångsamordnare. På så vis kan upplevelsen av sammanhang öka och progressionen i elevernas utveckling och lärande stödjas.
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Koval, Igor. "Learning Multimodal Digital Models of Disease Progression from Longitudinal Data : Methods & Algorithms for the Description, Prediction and Simulation of Alzheimer’s Disease Progression." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX008.
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La thèse s'intéresse à l'apprentissage statistique de modèles digitaux de progression des maladies neurodégénératives, en particulier la maladie d'Alzheimer. Ces modèles ont pour but de reconstruire la dynamique complexe et hétérogène de l'évolution de la structure, des fonctions et des facultés cognitives du cerveau, à un niveau moyenne mais également à l'échelle individuelle. Pour répondre à cet objectif, la thèse considère un modèle génératif à effets mixtes qui, à partir de données longitudinales, c'est à dire des observations répétées pour chaque patient, et éventuellement multimodales, recombine les trajectoires spatiotemporelles individuelles en un scénario moyen de progression de la maladie, estimant conjointement la variabilité de cette progression caractéristique. Cette variabilité est le résultat du non alignement temporel (en terme de vitesse de progression et âge de début de la maladie) et d'une variabilité spatiale qui prend la forme d'une modification de la séquence d'événements qui interviennent durant l'apparition et la progression de la maladie. Les différentes parties de la thèse forment une suite logique, depuis la problématique médicale, en passant par la description du modèle statistique associée, l'application de celui-ci pour la description de l'évolution de la maladie d'Alzheimer, et, enfin, le développement d'outils numériques à destination du corps médical pour tirer pleinement parti des méthodes présentéesThis thesis focuses on the statistical learning of digital models of neurodegenerative disease progression, especially Alzheimer's disease. It aims at reconstructing the complex and heterogeneous dynamic of evolution of the structure, the functions and the cognitive abilities of the brain, at both an average and individual level. To do so, we consider a mixed-effects model that, based on longitudinal data, namely repeated observations per subjects that present multiple modalities, in parallel recombines the individual spatiotemporal trajectories into a group-average scenario of change, and, estimates the variability of this characteristic progression which characterizes the individual trajectories. This variability results from a temporal un-alignment (in term of pace of progression and age at disease onset) along with a spatial variability that takes the form of a modification in the sequence of events that appear during the course of the disease. The different parts of the thesis are ordered in a coherent sequence: from the medical problematic, followed by the statistical model introduced to tackle the aforementioned challenge and its application to the description of the course of Alzheimer's disease, and, finally, numerical tools developed to make the previous model available to the medical community
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Geifman, Nophar, Richard E. Kennedy, Lon S. Schneider, Iain Buchan, and Roberta Diaz Brinton. "Data-driven identification of endophenotypes of Alzheimer’s disease progression: implications for clinical trials and therapeutic interventions." BIOMED CENTRAL LTD, 2018. http://hdl.handle.net/10150/627086.
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Background: Given the complex and progressive nature of Alzheimer's disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype definitions. Methods: Longitudinal patient-level data for 1160 AD patients receiving placebo or no treatment with a follow-up of up to 18 months were extracted from an integrated clinical trials dataset. We used latent class mixed modelling (LCMM) to identify patient subgroups demonstrating distinct patterns of change over time in disease severity, as measured by the Alzheimer's Disease Assessment Scale-cognitive subscale score. The optimal number of subgroups (classes) was selected by the model which had the lowest Bayesian Information Criterion. Other patient-level variables were used to define these subgroups' distinguishing characteristics and to investigate the interactions between patient characteristics and patterns of disease progression. Results: The LCMM resulted in three distinct subgroups of patients, with 10.3% in Class 1, 76.5% in Class 2 and 13.2% in Class 3. While all classes demonstrated some degree of cognitive decline, each demonstrated a different pattern of change in cognitive scores, potentially reflecting different subtypes of AD patients. Class 1 represents rapid decliners with a steep decline in cognition over time, and who tended to be younger and better educated. Class 2 represents slow decliners, while Class 3 represents severely impaired slow decliners: patients with a similar rate of decline to Class 2 but with worse baseline cognitive scores. Class 2 demonstrated a significantly higher proportion of patients with a history of statins use; Class 3 showed lower levels of blood monocytes and serum calcium, and higher blood glucose levels. Conclusions: Our results, 'learned' from clinical data, indicate the existence of at least three subgroups of Alzheimer's patients, each demonstrating a different trajectory of disease progression. This hypothesis-generating approach has detected distinct AD subgroups that may prove to be discrete endophenotypes linked to specific aetiologies. These findings could enable stratification within a clinical trial or study context, which may help identify new targets for intervention and guide better care.
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Conroy, Megan Elizabeth MD. "A qualitative study on entrustment decision making in the intensive care unit: about more than the learner." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1618222237764719.
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Yan, Fan. "Mapping Students' Ideas About Chemical Reactions At Different Educational Levels." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556950.
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Understanding chemical reactions is crucial in learning chemistry at all educational levels. Nevertheless, research in science education has revealed that many students struggle to understand chemical processes. Improving teaching and learning about chemical reactions demands that we develop a clearer understanding of student reasoning in this area and of how this reasoning evolves with training in the discipline. Thus, we have carried out a qualitative study using semi-structured interviews as the main data collection tool to explore students reasoning about reaction mechanism and causality. The participants of this study included students at different levels of training in chemistry: general chemistry students (n=22), organic chemistry students (n=16), first year graduate students (n=13) and Ph.D. candidates (n=14). We identified major conceptual modes along critical dimensions of analysis, and illustrated common ways of reasoning using typical cases. Main findings indicate that although significant progress is observed in student reasoning in some areas, major conceptual difficulties seem to persist even at the more advanced educational levels. In addition, our findings suggest that students struggle to integrate important concepts when thinking about mechanism and causality in chemical reactions. The results of our study are relevant to chemistry educators interested in learning progressions, assessment, and conceptual development.
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Oskar, Karlsson. "Skolväsendet vilar på demokratins grund : En undersökning om progression idemokratiundervisningen i grundskolan." Thesis, Högskolan i Halmstad, Akademin för lärande, humaniora och samhälle, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-30477.
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Assarsson, Lotta, and Rebecca Fagerlund. "En studie om hur lärare anser att barns attityder och syn på matematik kan förändras med hjälp av progressivt lärande." Thesis, Malmö högskola, Lärarutbildningen (LUT), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-36479.
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Syftet med vår undersökning är att kvalitativt undersöka huruvida progressivt lärande, enligt lärarna, kan förändra elevers syn på och attityder till matematikämnet till det bättre. Hur kan elever med hjälp av förändrad pedagogik ändra sin syn och attityd till det bättre i matematiken? Metoden vi använt oss av är strukturerade intervjuer och urvalsgruppen är fem lärare vid en skola i södra Sverige. I samband med utvärderingen av resultatet beaktas tidigare studier kring ämnet. Dock finns det inte mycket tidigare forskning just vad beträffar ämnet, om elever med hjälp av förändrad pedagogik kan ändra sin syn på matematiken? Än mindre när det handlar om eleverna i de lägre skolåren. Alla lärare i studien var överens om att elevers motivation till och i matematikämnet ökar med hjälp av progressivt lärande. Dock var man inte lika överens om att en elevs attityd till ämnet kan förändras genom ett progressivt arbetssätt. Slutsatsen av resultatet är att lärarna anser att arbetet med progressivt lärande kan förändra elevernas syn på matematiken.
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Guardado, Erika, and Amanda Sköld. "Formativa bedömningens påverkan på elevers progression i skrivande." Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-39504.
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Syftet med kunskapsöversikten är att ta reda på hur elevernas skrivutveckling gynnas av en formativ bedömning. Metoden har bestått av informationssökning där resultatet av källorna sedan har bearbetats och noggrant granskats samt slutligen sammanställts. Såväl nationella som internationella studier har använts i kunskapsöversikten. Resultatet visar på att elever uppfattar feedback som något positivt som fokuserar på både styrkorna och vad som kan förbättras och att det faktiskt är till hjälp för deras progression. Forskningen tar även upp två olika verktyg i form av elevportfolion samt kamratbedömning som kan vara användbara i undervisningen.
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Ion-Margineanu, Adrian. "Machine learning for classifying abnormal brain tissue progression based on multi-parametric Magnetic Resonance data." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1224/document.
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«Machine Learning» est un champ d'étude de l'intelligence artificielle qui se concentre sur des algorithmes capables d'adapter leur paramètres en se basant sur les données observées par l'optimisation d'une fonction objective ou d'une fonction de cout. Cette discipline a soulevé l'intérêt de la communauté de la recherche biomédicale puisqu'elle permet d'améliorer la sensibilité et la spécificité de la détection et du diagnostic de nombreuses pathologies tout en augmentant l'objectivité dans le processus de prise de décision thérapeutique. L'imagerie biomédicale est devenue indispensable en médecine, puisque plusieurs modalités comme l'imagerie par résonance magnétique (IRM), la tomodensitométrie et la tomographie par émission de positron sont de plus en plus utilisées en recherche et en clinique. L'IRM est la technique d'imagerie non-invasive de référence pour l'étude du cerveau humain puisqu'elle permet dans un temps d'acquisition raisonnable d'obtenir à la fois des cartographies structurelles et fonctionnelles avec une résolution spatiale élevée. Cependant, avec l'augmentation du volume et de la complexité des données IRM, il devient de plus en plus long et difficile pour le clinicien d'intégrer toutes les données afin de prendre des décisions précises. Le but de cette thèse est de développer des méthodes de « machine learning » automatisées pour la détection de tissu cérébral anormal, en particulier dans le cas de suivi de glioblastome multiforme (GBM) et de sclérose en plaques (SEP). Les techniques d'IRM conventionnelles (IRMc) actuelles sont très utiles pour détecter les principales caractéristiques des tumeurs cérébrales et les lésions de SEP, telles que leur localisation et leur taille, mais ne sont pas suffisantes pour spécifier le grade ou prédire l'évolution de la maladie. Ainsi, les techniques d'IRM avancées, telles que l'imagerie de perfusion (PWI), de diffusion (DKI) et la spectroscopie par résonance magnétique (SRM), sont nécessaires pour apporter des informations complémentaires sur les variations du flux sanguin, de l'organisation tissulaire et du métabolisme induits par la maladie. Dans une première étude de suivi de patients GBM, seuls les paramètres d'IRM avancés ont été explorés dans un relativement petit sous-groupe de patients. Les paramètres de PWI moyens, mesurés dans les régions d'intérêts (ROI) délimités manuellement, se sont avérés être d'excellents marqueurs, puisqu'ils permettent de prédire l'évolution du GBM en moyenne un mois plus tôt que le clinicien. Dans une seconde étude, réalisée sur un échantillon plus important que la précédente, la SRM a été remplacée par l'IRMc et la quantification de la PWI et du kurtosis de diffusion (DKI) a été réalisée de manière automatique. L'extraction des paramètres d'imagerie a été effectuée sur des segmentations semi-automatiques des tumeurs, réduisant ainsi le temps nécessaire au clinicien pour la délimitation du ROI de la partie de la lésion rehaussée au produit de contraste (CE-ROI). L'application d'un algorithme modifié de «boosting» sur les paramètres extraits des ROIs a montré une grande précision pour le diagnostic du GBM. Dans une troisième, une version modifiée des cartes paramétriques de réponse (PRM) est proposée pour prendre en compte la région d'infiltration de la tumeur, réduisant toujours plus le temps nécessaire pour la délimitation de la tumeur par le clinicien, puisque toutes les images IRM sont recalées sur la première. Deux façons de générer les RPM ont été comparées, l'une basée sur l'IRMc et l'autre basée sur la PWI, ces deux paramètres étant les meilleurs pour la discrimination de l'évolution du GBM, comme le montrent les deux études précédentes. Les résultats de cette étude montrent que l'emploi de PRM basés sur l'IRMc permet d'obtenir des résultats supérieurs à ceux obtenus avec les PRM basés sur la PWI [etc…]Machine learning is a subdiscipline in the field of artificial intelligence, which focuses on algorithms capable of adapting their parameters based on a set of observed data, by optimizing an objective or cost function. Machine learning has been the subject of large interest in the biomedical community because it can improve sensitivity and/or specificity of detection and diagnosis of any disease, while increasing the objectivity of the decision-making process. With the late increase in volume and complexity of medical data being collected, there is a clear need for applying machine learning algorithms in multi-parametric analysis for new detection and diagnostic modalities. Biomedical imaging is becoming indispensable for healthcare, as multiple modalities, such as Magnetic Resonance Imaging (MRI), Computed Tomography, and Positron Emission Tomography, are being increasingly used in both research and clinical settings. The non-invasive standard for brain imaging is MRI, as it can provide structural and functional brain maps with high resolution, all within acceptable scanning times. However, with the increase of MRI data volume and complexity, it is becoming more time consuming and difficult for clinicians to integrate all data and make accurate decisions. The aim of this thesis is to develop machine learning methods for automated preprocessing and diagnosis of abnormal brain tissues, in particular for the followup of glioblastoma multiforme (GBM) and multiple sclerosis (MS). Current conventional MRI (cMRI) techniques are very useful in detecting the main features of brain tumours and MS lesions, such as size and location, but are insufficient in specifying the grade or evolution of the disease. Therefore, the acquisition of advanced MRI, such as perfusion weighted imaging (PWI), diffusion kurtosis imaging (DKI), and magnetic resonance spectroscopic imaging (MRSI), is necessary to provide complementary information such as blood flow, tissue organisation, and metabolism, induced by pathological changes. In the GBM experiments our aim is to discriminate and predict the evolution of patients treated with standard radiochemotherapy and immunotherapy based on conventional and advanced MRI data. In the MS experiments our aim is to discriminate between healthy subjects and MS patients, as well as between different MS forms, based only on clinical and MRSI data. As a first experiment in GBM follow-up, only advanced MRI parameters were explored on a relatively small subset of patients. Average PWI parameters computed on manually delineated regions of interest (ROI) were found to be perfect biomarkers for predicting GBM evolution one month prior to the clinicians. In a second experiment in GBM follow-up of a larger subset of patients, MRSI was replaced by cMRI, while PWI and DKI parameter quantification was automated. Feature extraction was done on semi-manual tumour delineations, thereby reducing the time put by the clinician for manual delineating the contrast enhancing (CE) ROI. Learning a modified boosting algorithm on features extracted from semi-manual ROIs was shown to provide very high accuracy results for GBM diagnosis. In a third experiment in GBM follow-up of an extended subset of patients, a modified version of parametric response maps (PRM) was proposed to take into account the most likely infiltration area of the tumour, reducing even further the time a clinician would have to put for manual delineating the tumour, because all subsequent MRI scans were registered to the first one. Two types of computing PRM were compared, one based on cMRI and one based on PWI, as features extracted with these two modalities were the best in discriminating the GBM evolution, according to results from the previous two experiments. Results obtained within this last GBM analysis showed that using PRM based on cMRI is clearly superior to using PRM based on PWI [etc…]
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Bobeczko, Daniel S. Jr. "A Phenomenological Study of Kindergarten Teacher Perceptions of Standardized Testing and its Influence on Curriculum, Instruction, and Assessment." Cleveland State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=csu1449343351.
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Ahmed, Siraj. "Prediction of Rate of Disease Progression in Parkinson’s Disease Patients Based on RNA-Sequence Using Deep Learning." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41411.
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The advent of recent high throughput sequencing technologies resulted in an unexplored big data of genomics and transcriptomics that might help to answer various research questions in Parkinson’s disease(PD) progression. While the literature has revealed various predictive models that use longitudinal clinical data for disease progression, there is no predictive model based on RNA-Sequence data of PD patients. This study investigates how to predict the PD Progression for a patient’s next medical visit by capturing longitudinal temporal patterns in the RNA-Seq data. Data provided by Parkinson Progression Marker Initiative (PPMI) includes 423 PD patients with a variable number of visits for a period of 4 years. We propose a predictive model based on a Recurrent Neural Network (RNN) with dense connections. The results show that the proposed architecture is able to predict PD progression from high dimensional RNA-seq data with a Root Mean Square Error (RMSE) of 6.0 and rank-order correlation of (r=0.83, p<0.0001) between the predicted and actual disease status of PD. We show empirical evidence that the addition of dense connections and batch normalization into RNN layers boosts its training and generalization capability.
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Bergdahl, Nina. "Co-Creative Learning - using IT to Visualise Progression (CIP) : En studie av former för systematiskt kvalitetsarbete på klassrumsnivå." Thesis, Mittuniversitetet, Avdelningen för kvalitetsteknik, maskinteknik och matematik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-29035.
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Syftet med studien är att dels att förstå hur systematiskt kvalitetsarbete kan se ut på klassrumsnivå och dels vad det innebär att tillämpa det utifrån elevperspektiv, och dels att jämföra hur elevernas måluppfyllelse utvecklats i jämförelse med en kontrollgrupp. Resultatet av detta har fört med sig att examensarbetet bidrar med ett föreslag på hur kvalitetsarbete kan ske på klassrumsnivå; här kallat Co Creative Learning - using IT to Visualise Progression (CIP). Det här är en aktionsforskningsstudie. Den har genomförts med enkäter, fokusgrupper och en tidsserie; i vilken 65 elevers arbeten inom tre skilda områden bedömdes. För att förstärka reliabilitet och validitet valdes en kontrollgrupp, från samma upptagningsområde, som även de haft en legitimerad lärare med motsvarande antal yrkesverksamma år och som även de uttalat arbetat med formativ bedömning och synliggjorda mål. Två skoluppgifter var identiska och därmed kunde de två gruppernas resultat jämföras. För att minimera inverkan av en bedömande lärares närvaro på utvärderingen, samlades eleverna inte till fokusgrupper förrän efter att deras betyg var satta och kommunicerade. Då reflekterade de kring arbetet med ständiga förbättringar, kvalitet, involvering, målförståelse, upplevd progression och IT-användning. Innan denna avslutande diskussion hade de insamlade datamängder analyserades separat. Resultaten visade effekt på både elevupplevelse och progression. De största skillnaderna låg i elevens upplevelse, där förändring påverkas i första hand elevernas engagemang; i vilken utsträckning eleverna var nöjda studenter med sin egen utveckling. I tidsserien, var skillnaderna inte lika markanta som i elevupplevelsen. Den studerade gruppen började på ett något lägre snittbetyg, och avslutade på ett något högre, än kontrollgruppen. Det var främst pojkar som stod för utvecklingen. Detta kan bero på att flickorna redan hade mycket höga genomsnittliga betyg (vilket innebär att det inte finns utrymme för progression). Jämfört med kontrollgruppen, kvarstår de positiva skillnaderna med pojkarna i den studerade gruppen. Slutsatserna är därför att arbetet gynnat progression, men att den största förtjänsten med arbetssättet återfinns i hur eleverna upplever undervisningen.The purpose of the study is partly to understand how systematic quality management can be implemented at the classroom level and partly what it means to apply it from a student perspective, and to compare student progression with that of a control group. As a result of this, this study contributes with a method on how quality management can be implemented at the classroom level. This working method is named Co-Creative Learning - using IT to Visualise Progression. This is an action research study that encompassed questionnaires, focus groups and a time series. Three assignments from 65 students were assessed in the study, out of which two were compared to those from the control group. To enhance reliability and validity the selected control group were from the same catchment area, had qualified teacher with corresponding number of working years who also worked with formative assessment and visualising criteria. Two assignments were identical for both groups, and thus, the two groups' results could be compared. To minimise the effects of the presence of a teacher during evaluation, the students were not brought together to form focus groups, until after the grades were communicated. They then reflected on continuous improvement efforts, quality, inclusion, understanding objectives, perceived progression and the use of IT. Before the concluding discussion the collected data sets were analysed separately. The results showed the difference in both student experience and progression. The main differences lay in the student experience: where change primarily affected the students’ involvement; the extent to which the students were satisfied students with their own development. In the time series, the differences were not as high as in the student experience. The studied group started at a slightly lower grade, and completed at a slightly higher grade, than the control group. It was mainly the boys who accounted for the progression. One reason for could be that the girls already held high average grades (leaving little room for progression). Compared to the control group, the differences with the boys in the studied group remains. The conclusions drawn are thus that the method enhances progression, but that the main benefits are harvested from the enhanced educational experience of the students.
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Holgersson, Sara, and Anna Emriksson. "Uppgiftsformuleringars språkliga svårighetsnivå : En jämförande analys av två läroböcker i svenska för årskurs 3 och 4." Thesis, Linnéuniversitetet, Institutionen för svenska språket (SV), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-60659.
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The aim of the study was to compare the wording of assignments in two textbooks in Swedish with the focus on their level of linguistic difficulty. The textbooks are intended for grade 3 and grade 4 respectively. The comparison considers factors that affect the difficulty of a text, and the study is based on the following analytical categories: parts of speech, word length, compound words, sentence length, noun quota, and readability index. The results of the study indicate that the assignment descriptions in grade 4 are at a textually higher level of difficulty in five of six analytical categories, since the assignment descriptions in grade 3 can be perceived as more colloquial, with a lower readability index and a lower density of information than the textbook for grade 4. However, the analysis of compound words shows that the assignment descriptions for grade 3 contain a larger proportion of compounds; unlike the other factors, this does not suggest a textual progression from grade 3 to grade 4. Despite this discrepant result, one can observe a linguistic progression between the assignment descriptions in the textbooks based on the other categories, and from the factors that have been studied it may be assumed that the assignment descriptions in the textbook for grade 4 are at a higher level of linguistic difficulty.Studiens syfte är att jämföra uppgiftsformuleringar i två läroböcker i svenska med fokus på deras språkliga svårighetsnivå. Läroböckerna är avsedda för årskurs 3 respektive årskurs 4. Jämförelsen sker utifrån faktorer som påverkar en texts svårighetsgrad och de analyskategorier som studien baseras på är ordklasser, ordlängd, sammansatta ord, meningslängd, nominalkvot och läsbarhetsindex. Studiens resultat tyder på att uppgiftsformuleringarna i årskurs 4 har en textuellt högre svårighetsnivå i fem av sex analyskategorier eftersom uppgiftsformuleringarna i årskurs 3 kan upplevas mer talspråkliga, har ett lägre läsbarhetsindex samt har en lägre informationstäthet jämfört med läroboken för årskurs 4. Dock visar analysen av sammansatta ord att uppgiftsformuleringarna för årskurs 3 innehåller en större andel sammansatta ord, vilket till skillnad från de övriga faktorerna inte talar för en textuell progression från årskurs 3 till årskurs 4. Trots det avvikande resultatet kan en språklig progression mellan uppgiftsformuleringarna i läroböckerna konstateras utifrån de övriga kategorierna och det kan, utifrån de faktorer som undersökts, antas att uppgiftsformuleringarna i läroboken för årskurs 4 har en högre språklig svårighetsnivå.
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Ball, Malcolm John. "Trade union education : a qualitative study of its contribution to participation and progression in adult learning." Thesis, Leeds Beckett University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327310.
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Nagy, Caroline. "Fler bråk i matematikundervisningen : En aktionsforskningsstudie där lärare lär om progression." Licentiate thesis, Högskolan i Halmstad, Lärande, Profession och Samhällsutveckling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-36057.
Full textAbstract:
Few studies have a focus on progression in teaching and learning mathematics. An assumption for this study was that progression in teaching between school stages was important. The approach of the study was based on action research. Four teachers from preschool to 9th grade (age 1-16) were invited to a temporary team, a community of practice. The overall aim of the study was to develop knowledge about teaching fractions when teachers used students’ understandings as a point of departure for their action plans. A second aim was to illuminate what influences progression in their teaching. The team of teachers used the four phases of action research: plan, act, observe and reflect, during their learning processes. The teachers’ learning sessions were videotaped and transcribed and this provided the main data that formed the basis of the results. Wenger’s dimensions of social learning were used as an analytical tool: joint enterprise, mutual engagement and shared repertoire. Four themes that described teachers’ negotiation of qualities in mathematics instruction were identified: interpreting students’ understandings, basing instruction on students’ understandings, visualizing fractions and ensuring students’ understanding. When teachers, regardless of what stage was involved, reified similar instructions, it did not benefit students’ learning opportunities. In order to improve progression in teaching fractions, it was important that teachers succeeded in identifying students’ understandings and that the team negotiated different qualities in their community of practice. The shared repertoire (the pre-tests and the video recordings) formed the core of negotiating progression based on students’ understandings. The team showed a mutual engagement, with students’ learning as their joint enterprise. An implication of the study is that teachers from different educational stages can negotiate progression and improve it.