Relevant bibliographies by topics / Active learning

Academic literature on the topic 'Active learning'

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Published: 4 June 2021
Last updated: 31 August 2024

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Journal articles on the topic "Active learning"

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Rosenthal, Nadine. "Active Learning/Empowered Learning." Adult Learning 1, no. 5 (February 1990): 16–18. http://dx.doi.org/10.1177/104515959000100508.

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Ravindran, Vinitha. "Active Learning." Indian Journal of Continuing Nursing Education 21, no. 2 (2020): 107. http://dx.doi.org/10.4103/ijcn.ijcn_11_21.

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Wolfe, Kara. "Active Learning." Journal of Teaching in Travel & Tourism 6, no. 1 (July 18, 2006): 77–82. http://dx.doi.org/10.1300/j172v06n01_05.

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Scott, Rachel. "Active learning." 5 to 7 Educator 2005, no. 4 (February 2005): 18–19. http://dx.doi.org/10.12968/ftse.2005.4.4.17898.

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Hurd, Peter D. "Active Learning." Journal of Pharmacy Teaching 7, no. 3-4 (2000): 29–47. http://dx.doi.org/10.1300/j060v07n03_03.

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Bekes, Carolyn. "Active learning*." Critical Care Medicine 35, no. 7 (July 2007): 1785–86. http://dx.doi.org/10.1097/01.ccm.0000269938.64692.a0.

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Senecal, Kristin, and Michael J. Fratantuano. "Active Learning:." College & Undergraduate Libraries 1, no. 2 (December 9, 1994): 139–50. http://dx.doi.org/10.1300/j106v01n02_13.

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Settles, Burr. "Active Learning." Synthesis Lectures on Artificial Intelligence and Machine Learning 6, no. 1 (June 30, 2012): 1–114. http://dx.doi.org/10.2200/s00429ed1v01y201207aim018.

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Curwen, Andrew. "Active learning." Veterinary Record 172, no. 12 (March 23, 2013): i. http://dx.doi.org/10.1136/vr.f1944.

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Comendador, Roselyn M., and Elisa N. Chua PhD. "Active Learning and Contextualized Feedback as Assessment Tools for Science Learning." International Journal of Research Publication and Reviews 5, no. 7 (July 2024): 2191–203. http://dx.doi.org/10.55248/gengpi.5.0724.1806.

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Dissertations / Theses on the topic "Active learning"

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Hsu, Daniel Joseph. "Algorithms for active learning." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3404377.

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Thesis (Ph. D.)--University of California, San Diego, 2010.
Title from first page of PDF file (viewed June 10, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (leaves 97-101).
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Simpson, Colin Gordon. "Exploring Chinese business management students' experience of active learning pedagogies : how much action is possible in active learning classrooms?" Thesis, University of Exeter, 2013. http://hdl.handle.net/10871/14660.

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This phenomenological study explores how certain “innovative” pedagogies were experienced by a group of Chinese students studying Business Management at a mid-ranking UK university. Analysis of the transcripts of interviews (some in Chinese) with 24 students using NVivo shows that whilst most students felt that Active Learning pedagogies effectively supported their learning, for some students the “zone of indeterminacy” in which group projects and simulations were carried out was an uncomfortable space. Salient aspects of these students’ experiences were language, relationships and metacognitive skills, and the discussion explores the way in which these three experiential themes can be conceptualised as interrelated elements of the action (Biesta, 2006) which takes place in Active Learning classrooms. The following recommendations are made: HEIs should attempt to provide students with the advanced skills of negotiation which they will need to use in the flexible, ill-structured environments associated with Active Learning pedagogies; tutors should develop consistent approaches to collaborative assignments focussing on group work processes as well as task completion; the development of metacognitive skills through Active Learning pedagogies should be promoted through the use of explicit reflective elements embedded within the teaching, learning and assessment activities. The concluding discussion proposes that the successful use of Active Learning pedagogies requires a reconceptualisation of the purpose of education and that these pedagogies provide a potential readjustment of the balance between the functions of qualification, socialisation and subjectification (Biesta, 2010).
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Brinker, Klaus. "Active learning with kernel machines." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=974403946.

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Ribeiro, de Mello Carlos Eduardo. "Active Learning : an unbiased approach." Phd thesis, Châtenay-Malabry, Ecole centrale de Paris, 2013. http://tel.archives-ouvertes.fr/tel-01000266.

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Active Learning arises as an important issue in several supervised learning scenarios where obtaining data is cheap, but labeling is costly. In general, this consists in a query strategy, a greedy heuristic based on some selection criterion, which searches for the potentially most informative observations to be labeled in order to form a training set. A query strategy is therefore a biased sampling procedure since it systematically favors some observations by generating biased training sets, instead of making independent and identically distributed draws. The main hypothesis of this thesis lies in the reduction of the bias inherited from the selection criterion. The general proposal consists in reducing the bias by selecting the minimal training set from which the estimated probability distribution is as close as possible to the underlying distribution of overall observations. For that, a novel general active learning query strategy has been developed using an Information-Theoretic framework. Several experiments have been performed in order to evaluate the performance of the proposed strategy. The obtained results confirm the hypothesis about the bias, showing that the proposal outperforms the baselines in different datasets.
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Zhao, Liyue. "Active Learning with Unreliable Annotations." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5893.

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With the proliferation of social media, gathering data has became cheaper and easier than before. However, this data can not be used for supervised machine learning without labels. Asking experts to annotate sufficient data for training is both expensive and time-consuming. Current techniques provide two solutions to reducing the cost and providing sufficient labels: crowdsourcing and active learning. Crowdsourcing, which outsources tasks to a distributed group of people, can be used to provide a large quantity of labels but controlling the quality of labels is hard. Active learning, which requires experts to annotate a subset of the most informative or uncertain data, is very sensitive to the annotation errors. Though these two techniques can be used independently of one another, by using them in combination they can complement each other's weakness. In this thesis, I investigate the development of active learning Support Vector Machines (SVMs) and expand this model to sequential data. Then I discuss the weakness of combining active learning and crowdsourcing, since the active learning is very sensitive to low quality annotations which are unavoidable for labels collected from crowdsourcing. In this thesis, I propose three possible strategies, incremental relabeling, importance-weighted label prediction and active Bayesian Networks. The incremental relabeling strategy requires workers to devote more annotations to uncertain samples, compared to majority voting which allocates different samples the same number of labels. Importance-weighted label prediction employs an ensemble of classifiers to guide the label requests from a pool of unlabeled training data. An active learning version of Bayesian Networks is used to model the difficulty of samples and the expertise of workers simultaneously to evaluate the relative weight of workers' labels during the active learning process. All three strategies apply different techniques with the same expectation -- identifying the optimal solution for applying an active learning model with mixed label quality to crowdsourced data. However, the active Bayesian Networks model, which is the core element of this thesis, provides additional benefits by estimating the expertise of workers during the training phase. As an example application, I also demonstrate the utility of crowdsourcing for human activity recognition problems.
Ph.D.
Doctorate
Computer Science
Engineering and Computer Science
Computer Science
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Ganti, Mahapatruni Ravi Sastry. "New formulations for active learning." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51801.

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In this thesis, we provide computationally efficient algorithms with provable statistical guarantees, for the problem of active learning, by using ideas from sequential analysis. We provide a generic algorithmic framework for active learning in the pool setting, and instantiate this framework by using ideas from learning with experts, stochastic optimization, and multi-armed bandits. For the problem of learning convex combination of a given set of hypothesis, we provide a stochastic mirror descent based active learning algorithm in the stream setting.
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Huddy, Vyvyan. "Active processing in implicit learning." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390696.

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Williams, Kevin. "Active Learning for drug discovery." Thesis, Aberystwyth University, 2014. http://hdl.handle.net/2160/eaf6e66e-17fe-41a9-ac1d-9939abbb8331.

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This thesis describes work conducted to enable Robot Scientist Eve to autonomously evaluate drug-like chemicals during high throughput experiments. Eve tests libraries of chemical compounds against yeast-based targets expressing parasite and host (human) proteins (i.e. DHFR, NMT & PGK); the parasites included in this study are responsible for an array of neglected tropical diseases. The raw data for yeast growth curves from an initial screen were evaluated, and decision tree rules were constructed to describe the relative activity and toxicity of compounds. These rules were verified, and versions were subsequently developed for application to routine mass and confirmation screens. Consequently, many potential lead drug-like candidates have been identified in the Maybridge Hitfinder library; several compounds from an approved drug library (the Johns Hopkins Clinical Compound Library) have also been confirmed as exhibiting activity against these yeast-based targets. Further in vivo study of some JHCCL compounds is in progress using extracted parasite proteins; preliminary results indicate the potential for repositioning Triclosan and Tnp-470 as having anti-malarial behaviour based on their interaction with Plasmodium sp. DHFR proteins. In the second phase of the programme, a prototype Active Learning strategy was applied (active k-optimisation) to partial mass screen data as a seed; this allowed Eve to select compounds by assessing and predicting quantitative structure activity relationships (QSAR) between seed and unknown compounds. Simulations of learning and testing QSAR cycles showed that Eve would be able to select active compounds more efficiently under such a regime. Other strategies have been developed that further improve selection efficiency for active compounds, and also promote the ability to find rare category compounds. An econometric model has been developed to demonstrate the potential beneficial impact of Active Learning strategies on the execution costs for such screens.
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Mohamad, Saad. "Active learning for data streams." Thesis, Bournemouth University, 2017. http://eprints.bournemouth.ac.uk/29901/.

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With the exponential growth of data amount and sources, access to large collections of data has become easier and cheaper. However, data is generally unlabelled and labels are often difficult, expensive, and time consuming to obtain. Two learning paradigms have been used by machine learning community to diminish the need for labels in training data: semi-supervised learning (SSL) and active learning (AL). AL is a reliable way to efficiently building up training sets with minimal supervision. By querying the class (label) of the most interesting samples based upon previously seen data and some selection criteria, AL can produce a nearly optimal hypothesis, while requiring the minimum possible quantity of labelled data. SSL, on the other hand, takes the advantage of both labelled and unlabelled data to address the challenge of learning from a small number of labelled samples and large amount of unlabelled data. In this thesis, we borrow the concept of SSL by allowing AL algorithms to make use of redundant unlabelled data so that both labelled and unlabelled data are used in their querying criteria. Another common tradition within the AL community is to assume that data samples are already gathered in a pool and AL has the luxury to exhaustively search in that pool for the samples worth labelling. In this thesis, we go beyond that by applying AL to data streams. In a stream, data may grow infinitely making its storage prior to processing impractical. Due to its dynamic nature, the underlying distribution of the data stream may change over time resulting in the so-called concept drift or possibly emergence and fading of classes, known as concept evolution. Another challenge associated with AL, in general, is the sampling bias where the sampled training set does not reflect on the underlying data distribution. In presence of concept drift, sampling bias is more likely to occur as the training set needs to represent the underlying distribution of the evolving data. Given these challenges, the research questions that the thesis addresses are: can AL improve learning given that data comes in streams? Is it possible to harness AL to handle changes in streams (i.e., concept drift and concept evolution by querying selected samples)? How can sampling bias be attenuated, while maintaining AL advantages? Finally, applying AL for sequential data steams (like time series) requires new approaches especially in the presence of concept drift and concept evolution. Hence, the question is how to handle concept drift and concept evolution in sequential data online and can AL be useful in such case? In this thesis, we develop a set of stream-based AL algorithms to answer these questions in line with the aforementioned challenges. The core idea of these algorithms is to query samples that give the largest reduction of an expected loss function that measures the learning performance. Two types of AL are proposed: decision theory based AL whose losses involve the prediction error and information theory based AL whose losses involve the model parameters. Although, our work focuses on classification problems, AL algorithms for other problems such as regression and parameter estimation can be derived from the proposed AL algorithms. Several experiments have been performed in order to evaluate the performance of the proposed algorithms. The obtained results show that our algorithms outperform other state-of-the-art algorithms.
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Lima, Vinicius Gomes de. "Peer effects in active learning." reponame:Repositório Institucional do FGV, 2017. http://hdl.handle.net/10438/18273.

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This paper investigates peer effects in higher education in an environment of active learning that gives great importance for students’ interaction through group work. Our empirical strategy uses exogenous variation in group composition to estimate peer effects in different exercises. We find no evidence of peer effects in a basic linear-in-means specification considering all assigned peers. However, we find positive and statistically significant impact of peers coming from student’s same high school. We also find no evidence of peer effects with a model that takes into account student and peers’ position in the ability distribution.
Este trabalho investiga efeito de pares no ensino superior em um ambiente de active learning que dá grande importância à interação dos estudantes através do trabalho em grupo. A estratégia empírica utiliza variação exógena na composição dos grupos para estimar o efeito dos pares em diferentes exercícios. Não encontramos evidência de efeito de pares numa especificação linear-in-means básica considerando todos os pares do grupo atribuído ao aluno. Entretanto, encontramos efeito positivo e estatisticamente significante de pares que frequentaram a mesma escola de ensino médio. Não encontramos evidência de efeito de pares em um modelos que procura considerar a posição dos alunos na distribuição de habilidade.
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Books on the topic "Active learning"

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Boulton-Lewis, Gillian, and Maureen Tam, eds. Active Ageing, Active Learning. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-2111-1.

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Settles, Burr. Active Learning. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01560-1.

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Mabrouk, Patricia Ann, ed. Active Learning. Washington, DC: American Chemical Society, 2007. http://dx.doi.org/10.1021/bk-2007-0970.

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Society for Teaching and Learning in Higher Education., ed. Active learning. Halifax, N.S: Society for Teaching and Learning in Higher Education, 1999.

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Matsushita, Kayo, ed. Deep Active Learning. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5660-4.

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Chang-Tik, Chan, Gillian Kidman, and Meng Yew Tee, eds. Collaborative Active Learning. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4383-6.

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Harmin, Merrill. Inspiring Active Learning. Alexandria: ASCD, 2009.

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George, Brown. Assessing active learning. Sheffield: CVCP Universities' Staff Development and Training Unit, 1992.

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Norma, Iredale, and Durham University Business School. Enterprise and Industry Education Unit., eds. Active environmental learning. Durham: Enterprise and Industry Education Unit, Durham University Business School, 1993.

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Henderson, Penny. Promoting active learning. Cambridge [England]: National Extension College, 1989.

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Book chapters on the topic "Active learning"

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Tam, Maureen. "Active Ageing, Active Learning: Elder Learning in Hong Kong." In Active Ageing, Active Learning, 163–74. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2111-1_10.

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Balcan, Maria-Florina, and Ruth Urner. "Active Learning." In Encyclopedia of Algorithms, 1–6. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_769-1.

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Gogus, Aytac. "Active Learning." In Encyclopedia of the Sciences of Learning, 77–80. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_489.

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Hakkani-Tür, Dilek, and Giuseppe Riccardi. "Active Learning." In Spoken Language Understanding, 195–224. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119992691.ch8.

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Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Active Learning." In Encyclopedia of Machine Learning, 10–14. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_6.

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Ramon, Jan. "Active Learning." In Encyclopedia of Systems Biology, 6–8. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_605.

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Ichikawa, Viveka. "Active Learning." In Japan’s School Curriculum for the 2020s, 45–66. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2076-9_4.

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Chandola, Varun, Arindam Banerjee, and Vipin Kumar. "Active Learning." In Encyclopedia of Machine Learning and Data Mining, 42–56. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_912.

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Cohn, David. "Active Learning." In Encyclopedia of Machine Learning and Data Mining, 9–14. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_916.

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Mosalam, Khalid M., and Yuqing Gao. "Active Learning." In Artificial Intelligence in Vision-Based Structural Health Monitoring, 307–22. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52407-3_11.

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Conference papers on the topic "Active learning"

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Renelle, Amy, Stephanie Budgett, and Rhys Jones. "Active Online; Not Actively Learning." In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.khesv.

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Imagine, a keen university tutor, excited to help revamp the statistics tutorials for a first-year course into one focused on student-led learning… only to need to rapidly adjust to online teaching thanks to Covid-19. I have been involved in small-class tutorials for two first-year university courses (150 and 2000 students each), with a few modes of learning implemented – in-person, compulsory and non-compulsory tutorials, asynchronous peer discussion videos and tutor videos, as well as live online tutorials. Covid-19 highlighted difficulties implementing active learning in an online environment. With reduced attendance, blank screens, and silence in response to questions, motivating students to actively learn proved difficult. While the adoption of online teaching required rapid adjustments, we can now make improvements to help maintain quality teaching and social connection within our classes. It is important we spend time investigating how to better encourage active learning, with empirical evidence needed to guide future practices.
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Kudyshev, Zhaxylyk A., Alexander V. Kildishev, Vladimir M. Shalaev, and Alexandra Boltasseva. "Deep learning assisted photonics." In Active Photonic Platforms XII, edited by Ganapathi S. Subramania and Stavroula Foteinopoulou. SPIE, 2020. http://dx.doi.org/10.1117/12.2567198.

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Pimentel, Charles, Paulo Ceotto, Isaac D'Césares, and Felipe Laranja. "Active Learning." In FL2019: FabLearn 2019. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3311890.3311899.

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Tan, James, Y. S., Zengguang Cheng, Johannes Feldmann, Xuan Li, Nathan Youngblood, Utku E. Ali, C. David Wright, Wolfram H. P. Pernice, and Harish Bhaskaran. "Associative learning on phase change photonics." In Active Photonic Platforms XIII, edited by Ganapathi S. Subramania and Stavroula Foteinopoulou. SPIE, 2021. http://dx.doi.org/10.1117/12.2593248.

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Boltasseva, Alexandra, Vladimir M. Shalaev, and Blake Wilson. "Machine learning for photonics." In Active Photonic Platforms (APP) 2022, edited by Ganapathi S. Subramania and Stavroula Foteinopoulou. SPIE, 2022. http://dx.doi.org/10.1117/12.2633108.

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Hazra, Rishi, Parag Dutta, Shubham Gupta, Mohammed Abdul Qaathir, and Ambedkar Dukkipati. "Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation." In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.naacl-main.159.

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Yoo, Donggeun, and In So Kweon. "Learning Loss for Active Learning." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00018.

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Orsborn, Seth, and Ryan Hutcheson. "Cued Active Learning: An Initial Study." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34234.

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It has become common knowledge that effective teaching requires more than just the rote dissemination of knowledge. By using active learning, teachers involve the students in the learning process. As the students subjectively handle the class material, their comprehension and retention improves. In the classroom, teachers commonly prompt actively learning through a verbal cue such as, “We are now going to break into groups.” This forces the student to switch from a receptive mental state to an active mental state. We theorize that this verbal, short duration transition from lecture to active learning, especially in large classroom settings, is not sufficient to make this transition quickly and thus limits how active students are in the active learning session. In this paper we present a technique and exploratory study results for cueing active learning through a representative icon in a visual lecture presentation. This cue enables the students to mentally prepare themselves for actively learning during a more passive part of the lecture. The results of our exploratory study demonstrate that the cued active learning did not conclusively correlate with average student performance, but that it did show a decrease in the standard deviation of performance, thereby demonstrating an improvement in the comprehension of the students that were more likely to perform lower than average. The results of this study will be used to conduct a more formal study including direct measurement of lecture participation by students.
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Epshteyn, Arkady, Adam Vogel, and Gerald DeJong. "Active reinforcement learning." In the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390194.

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Hoi, Steven C. H., and Rong Jin. "Active kernel learning." In the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390207.

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Reports on the topic "Active learning"

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Cohn, David A., Zoubin Ghahramani, and Michael I. Jordan. Active Learning with Statistical Models. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada295617.

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Haack, Jeffrey. Scale Bridging through Active Learning. Office of Scientific and Technical Information (OSTI), January 2021. http://dx.doi.org/10.2172/1756779.

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Kemp, Emily, Jonathan Compton, and Darrien McKenzie. Active Learning for Language Modeling. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1890039.

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Wong, L. Y. Active Conceptual Modeling of Learning Workshop. Fort Belvoir, VA: Defense Technical Information Center, May 2006. http://dx.doi.org/10.21236/ada465650.

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Cook, Steve, and Duncan Watson. Crosswords and the ‘Active Learning’ Quest. The Economics Network, February 2023. http://dx.doi.org/10.53593/n3585a.

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Tumer, Kagan. Coordinating Learning Agents for Active Information Collection. Fort Belvoir, VA: Defense Technical Information Center, June 2011. http://dx.doi.org/10.21236/ada563864.

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KulKarni, S. R., S. K. Mitter, and J. N. Tsitsiklis. Active Learning Using Arbitrary Binary Valued Queries. Fort Belvoir, VA: Defense Technical Information Center, October 1990. http://dx.doi.org/10.21236/ada459598.

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8

Thompson, Michael, and Cynthia Irvine. Active Learning with the CyberCIEGE Video Game. Fort Belvoir, VA: Defense Technical Information Center, August 2011. http://dx.doi.org/10.21236/ada547670.

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Wang, Fulton, and Ali Pinar. Developing an Active Learning algorithm for learning Bayesian classifiers under the Multiple Instance Learning scenario. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1821545.

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Jamieson, Kevin, and Warren L. Davis, IV. Active Learning in the Era of Big Data. Office of Scientific and Technical Information (OSTI), October 2015. http://dx.doi.org/10.2172/1225849.

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