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Journal articles on the topic 'Learning by discovering'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Zupan, Blaž, Marko Bohanec, Janez Demšar, and Ivan Bratko. "Learning by discovering concept hierarchies." Artificial Intelligence 109, no. 1-2 (1999): 211–42. http://dx.doi.org/10.1016/s0004-3702(99)00008-9.

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2

Weber, James. "Discovering Through Experimental Learning Projects." Proceedings of the International Association for Business and Society 5 (1994): 1183–88. http://dx.doi.org/10.5840/iabsproc19945106.

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3

Talabis, Mark Ryan del Moral. "Honeynet Learning: Discovering Information Security." Information Systems Security 15, no. 6 (2006): 56–63. http://dx.doi.org/10.1080/10658980601052019.

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4

Pataraia, Nino, Anoush Margaryan, Isobel Falconer, Allison Littlejohn, and Jennifer Falconer. "Discovering academics' key learning connections." Journal of Workplace Learning 26, no. 1 (2014): 56–72. http://dx.doi.org/10.1108/jwl-03-2013-0012.

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Purpose – The aim of this exploratory study is to investigate the role of personal networks in supporting academics' professional learning. In particular, the paper examines the composition of academics' networks and the implications of network tendencies for academics' learning about teaching. Design/methodology/approach – The study adopts a mixed-methods approach. Firstly, the composition of academics' networks is examined using social network analysis. Secondly, the role of these networks in academics' learning about teaching is analysed through semi-structured interviews. Findings – Findin

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5

Wang, Lidong, and Cheryl Ann Alexander. "Machine Learning in Big Data." International Journal of Mathematical, Engineering and Management Sciences 1, no. 2 (2016): 52–61. http://dx.doi.org/10.33889/ijmems.2016.1.2-006.

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Machine learning is an artificial intelligence method of discovering knowledge for making intelligent decisions. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in machine learning, main technologies in Big Data, and some applications of machine learning in Big Data. Challenges of machine learning applications in Big Data are discussed. Some new methods and technology progress of machine learning in Big Data are also presented.

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6

Adams, David, H. K. Colebatch, and Christopher K. Walker. "Learning about Learning: Discovering the Work of Policy." Australian Journal of Public Administration 74, no. 2 (2015): 101–11. http://dx.doi.org/10.1111/1467-8500.12119.

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7

Wang, Lidong. "Machine Learning in Big Data." International Journal of Advances in Applied Sciences 4, no. 4 (2015): 117. http://dx.doi.org/10.11591/ijaas.v4.i4.pp117-123.

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<p>Machine learning is an artificial intelligence method of discovering knowledge for making intelligent decisions. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in machine learning, main technologies in Big Data, and some applications of machine learning in Big Data. Challenges of machine learning applications in Big Data are discussed. Some new methods and technology progress of machine learning in Big Data are also presented.</p>

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8

Gershman, Samuel J., Kenneth A. Norman, and Yael Niv. "Discovering latent causes in reinforcement learning." Current Opinion in Behavioral Sciences 5 (October 2015): 43–50. http://dx.doi.org/10.1016/j.cobeha.2015.07.007.

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9

Gelfer, Jeffrey I. "Discovering and learning art with blocks." Day Care & Early Education 17, no. 4 (1990): 21–24. http://dx.doi.org/10.1007/bf01623194.

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10

Kordík, Pavel, Jan Černý, and Tomáš Frýda. "Discovering predictive ensembles for transfer learning and meta-learning." Machine Learning 107, no. 1 (2017): 177–207. http://dx.doi.org/10.1007/s10994-017-5682-0.

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11

Cheeks, Loretta H., Tracy L. Stepien, Dara M. Wald, and Ashraf Gaffar. "Discovering News Frames." International Journal of Multimedia Data Engineering and Management 7, no. 4 (2016): 45–62. http://dx.doi.org/10.4018/ijmdem.2016100103.

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The Internet is a major source of online news content. Current efforts to evaluate online news content including text, storyline, and sources is limited by the use of small-scale manual techniques that are time consuming and dependent on human judgments. This article explores the use of machine learning algorithms and mathematical techniques for Internet-scale data mining and semantic discovery of news content that will enable researchers to mine, analyze, and visualize large-scale datasets. This research has the potential to inform the integration and application of data mining to address rea

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12

Abdul Halim, Norhajawati, Kamisah Ariffin, and Norizul Azida Darus. "Discovering Students’ Strategies in Learning English Online." Asian Journal of University Education 17, no. 1 (2021): 261. http://dx.doi.org/10.24191/ajue.v17i1.12695.

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Online learning poses challenges that students might never have encountered in a face-to-face learning environment. In learning English, students may confront more challenges as they need both cognitive and metacognitive skills in dealing with the dynamic lessons involving interaction, online exercises, and audio, video and text downloads. The challenges faced in online learning have led students to employ learning strategies to help them learn more efficiently and effectively. This paper examined students’ strategy use in learning English online and the correlation of the strategies with thei

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13

Malamud, Ofer. "Discovering One's Talent: Learning from Academic Specialization." ILR Review 64, no. 2 (2011): 375–405. http://dx.doi.org/10.1177/001979391106400209.

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14

Lau, Tatiana, Hillard T. Pouncy, Samuel J. Gershman, and Mina Cikara. "Discovering social groups via latent structure learning." Journal of Experimental Psychology: General 147, no. 12 (2018): 1881–91. http://dx.doi.org/10.1037/xge0000470.

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15

Francesca, Grippa, De Maggio Marco, Corallo Angelo, and Passiante Giuseppina. "Discovering The Hidden Dynamics Of Learning Communities." Journal of Information Technology Case and Application Research 12, no. 3 (2010): 34–55. http://dx.doi.org/10.1080/15228053.2010.10856188.

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16

Schmidhuber, Jürgen, and Daniel Prelinger. "Discovering Predictable Classifications." Neural Computation 5, no. 4 (1993): 625–35. http://dx.doi.org/10.1162/neco.1993.5.4.625.

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Prediction problems are among the most common learning problems for neural networks (e.g., in the context of time series prediction, control, etc.). With many such problems, however, perfect prediction is inherently impossible. For such cases we present novel unsupervised systems that learn to classify patterns such that the classifications are predictable while still being as specific as possible. The approach can be related to the IMAX method of Becker and Hinton (1989) and Zemel and Hinton (1991). Experiments include a binary stereo task proposed by Becker and Hinton, which can be solved mo

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17

Kurz, Terri L., and Jorge Garcia. "Discovering Mexico through math." Teaching Children Mathematics 18, no. 8 (2012): 472–73. http://dx.doi.org/10.5951/teacchilmath.18.8.0472.

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Keeping in mind how rich and diverse Mexican culture is, this month's regular department collection of four short activities each for grade bands K-2, 3–4, and 5–6 connects the geography, history, and culture of Mexico to discovering and exploring mathematics. These activities can be integrated with support from other historical, cultural, and geographical learning contexts.

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18

Plaut, David C., and James L. McClelland. "Stipulating versus discovering representations." Behavioral and Brain Sciences 23, no. 4 (2000): 489–91. http://dx.doi.org/10.1017/s0140525x00473358.

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Page's proposal to stipulate representations in which individual units correspond to meaningful entities is too unconstrained to support effective theorizing. An approach combining general computational principles with domain-specific assumptions, in which learning is used to discover representations that are effective in solving tasks, provides more insight into why cognitive and neural systems are organized the way they are.

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19

Basu, Abhijit, and Joan Middendorf. "Discovering New Knowledge Through Collaborative Learning in Groups." Journal of Geological Education 43, no. 4 (1995): 317–21. http://dx.doi.org/10.5408/0022-1368-43.4.317.

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20

Janež, Tadej, Jure Žabkar, Martin Možina, and Ivan Bratko. "Learning Faster by Discovering and Exploiting Object Similarities." International Journal of Advanced Robotic Systems 10, no. 3 (2013): 176. http://dx.doi.org/10.5772/54659.

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21

Aycan, Kıvanç. "Discovering Learning Style with Active Music Education Practices." Cypriot Journal of Educational Sciences 13, no. 4 (2018): 562–76. http://dx.doi.org/10.18844/cjes.v13i4.3483.

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This study was carried out to investigate the relation between active music education methods and learning style (LS) preferences of Erciyes University, Faculty of Fine Arts, music department students. The aim of the research is to discover primarily the LSs of participants. A test used with the participants that consisting of three categories and 20 questions, and identified with visual, auditory, kinesthetic, tactile LSs that they used online, were applied. A structured interview form has been implemented to understand the test data in depth. The results imply participants indicated that if

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22

Milbich, Timo, Omair Ghori, Ferran Diego, and Björn Ommer. "Unsupervised representation learning by discovering reliable image relations." Pattern Recognition 102 (June 2020): 107107. http://dx.doi.org/10.1016/j.patcog.2019.107107.

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23

Elman, Jeffrey L., and David Zipser. "Discovering acoustic features with a connectionist learning algorithm." Journal of the Acoustical Society of America 80, S1 (1986): S17. http://dx.doi.org/10.1121/1.2023681.

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24

Taber, Keith S. "Discovering students' interests opens doors to their learning." Physics Education 39, no. 5 (2004): 378–79. http://dx.doi.org/10.1088/0031-9120/39/5/f01.

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25

Javeed, Farooq, Ansar Siddique, Akhtar Munir, Basit Shehzad, and Muhammad I. U. Lali. "Discovering software developer's coding expertise through deep learning." IET Software 14, no. 3 (2020): 213–20. http://dx.doi.org/10.1049/iet-sen.2019.0290.

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26

Phillips, Carolyn L., and Gregory A. Voth. "Discovering crystals using shape matching and machine learning." Soft Matter 9, no. 35 (2013): 8552. http://dx.doi.org/10.1039/c3sm51449h.

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27

Harris, Richard, and Lorraine Foreman-Peck. "Learning to teach history writing: discovering what works1." Educational Action Research 9, no. 1 (2001): 97–109. http://dx.doi.org/10.1080/09650790100200141.

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28

Wu, W., and J. Chen. "Learning by discovering problem solving heuristics through experience." IEEE Transactions on Knowledge and Data Engineering 3, no. 4 (1991): 415–20. http://dx.doi.org/10.1109/69.109103.

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29

Goldin‐Meadow, Susan. "Discovering the Biases Children Bring to Language Learning." Child Development Perspectives 14, no. 4 (2020): 195–201. http://dx.doi.org/10.1111/cdep.12379.

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30

Agathangelou, Pantelis, Ioannis Katakis, Ioannis Koutoulakis, Fotis Kokkoras, and Dimitrios Gunopulos. "Learning patterns for discovering domain-oriented opinion words." Knowledge and Information Systems 55, no. 1 (2017): 45–77. http://dx.doi.org/10.1007/s10115-017-1072-y.

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31

Ao, Xiang, Ping Luo, Chengkai Li, Fuzhen Zhuang, and Qing He. "Discovering and learning sensational episodes of news events." Information Systems 78 (November 2018): 68–80. http://dx.doi.org/10.1016/j.is.2018.05.003.

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32

Erickson, Bradley J. "Machine Learning: Discovering the Future of Medical Imaging." Journal of Digital Imaging 30, no. 4 (2017): 391. http://dx.doi.org/10.1007/s10278-017-9994-1.

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33

Barmparis, G. D., and G. P. Tsironis. "Discovering nonlinear resonances through physics-informed machine learning." Journal of the Optical Society of America B 38, no. 9 (2021): C120. http://dx.doi.org/10.1364/josab.430206.

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34

Widmer, Gerhard. "Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries." Artificial Intelligence 146, no. 2 (2003): 129–48. http://dx.doi.org/10.1016/s0004-3702(03)00016-x.

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35

Wang, Wenbo, Lu Chen, Ming Tan, Shaojun Wang, and Amit P. Sheth. "Discovering Fine-grained Sentiment in Suicide Notes." Biomedical Informatics Insights 5s1 (January 2012): BII.S8963. http://dx.doi.org/10.4137/bii.s8963.

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This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the basel

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36

Morales Martinez, A. G., I. Flament, F. Liu, et al. "Discovering knee osteoarthritis bone shape features using deep learning." Osteoarthritis and Cartilage 27 (April 2019): S386—S387. http://dx.doi.org/10.1016/j.joca.2019.02.386.

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37

Somodevilla, Maria, David Limon Cantu, Ivo Pineda, ConcepciÃ³n PÃ©rez de Celis, and Darnes VilariÃ±o. "Discovering Mexican Birth Rate Patterns Using Machine Learning Techniques." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 1 (2015): 6444–52. http://dx.doi.org/10.24297/ijct.v15i1.1708.

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In this research, we attempt to discover patterns that describe and predict the birth rate in Mexico by using data mining techniques based on relevant demographic and economic information about Mexico. More than twelve million births data obtained from the General Directorate of Health Information in the period 2008-2013 were analyzed. The acquired knowledge allows us to say that in Mexico the birth rate is affected by the social welfare, education and marginality at county level. Due to the diversity of the population and the large number of socioeconomic factors involved in Mexican society,

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38

Hvidsten, T. R. "Discovering regulatory binding-site modules using rule-based learning." Genome Research 15, no. 6 (2005): 856–66. http://dx.doi.org/10.1101/gr.3760605.

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39

Sifain, Andrew E., Nicholas Lubbers, Benjamin T. Nebgen, et al. "Discovering a Transferable Charge Assignment Model Using Machine Learning." Journal of Physical Chemistry Letters 9, no. 16 (2018): 4495–501. http://dx.doi.org/10.1021/acs.jpclett.8b01939.

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40

Freitag, Maria Salete Batista, Altair Camargo, Jéssica Borges de Carvalho, and Ana Maria Falcão de Aragão. "DISCOVERING AUTOSCOPY AS A MEANS TOWARDS REFLECTION AND LEARNING." Revista Alcance 25, no. 3(Set/Dez) (2019): 402. http://dx.doi.org/10.14210/alcance.v25n3(set/dez).p402-413.

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O caso aborda a experiência de formação de pesquisadores por meio da autoscopia, um dispositivo metodológico de natureza qualitativa com abordagem reflexiva. A autoscopia é uma alternativa ao uso de entrevistas em pesquisas qualitativas. Essa fase de pesquisa integra dois momentos: videogravação e sessões reflexivas. Objetiva-se nesse caso fomentar discussões sobre uma experiência vivencial e refletir acerca da viabilidade da aplicação da autoscopia. Conta a história do treinamento prático de pesquisadores, ao destacar os momentos de aprendizado durante o experimento que foi realizado com pesq

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41

Li, Long, Xiu Le, Ling Wang, Qiong Gu, Huihao Zhou, and Jun Xu. "Discovering new DNA gyrase inhibitors using machine learning approaches." RSC Advances 5, no. 128 (2015): 105600–105608. http://dx.doi.org/10.1039/c5ra22568j.

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42

Luming Zhang, Rongrong Ji, Yingjie Xia, Ying Zhang, and Xuelong Li. "Learning a Probabilistic Topology Discovering Model for Scene Categorization." IEEE Transactions on Neural Networks and Learning Systems 26, no. 8 (2015): 1622–34. http://dx.doi.org/10.1109/tnnls.2014.2347398.

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43

MacIntosh, Judith, Emma MacKay, Monique Mallet-Boucher, and Nancy Wiggins. "Discovering Co-learning With Students in Distance Education Sites." Nurse Educator 27, no. 4 (2002): 182–86. http://dx.doi.org/10.1097/00006223-200207000-00012.

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44

Wu, Lianwei, Yuan Rao, Ambreen Nazir, and Haolin Jin. "Discovering differential features: Adversarial learning for information credibility evaluation." Information Sciences 516 (April 2020): 453–73. http://dx.doi.org/10.1016/j.ins.2019.12.040.

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45

Schlesinger, Eric. "Why learning is not a cycle: 1 ‐ discovering pattern." Industrial and Commercial Training 28, no. 2 (1996): 30–35. http://dx.doi.org/10.1108/00197859610109266.

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46

Bartlett, Robin L. "Discovering Diversity in Introductory Economics." Journal of Economic Perspectives 10, no. 2 (1996): 141–53. http://dx.doi.org/10.1257/jep.10.2.141.

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Instructors can begin the process of integrating race and gender issues into introductory economics by reexamining their courses with a new lens of diversity. The content of introductory economics can be expanded by ‘adding and stirring’ race and gender data from standard statistical sources or from the students themselves. This paper offers some ‘add-and-stir’ macro- and microeconomic examples. To discover the appropriate mix of these examples and how to present them, this paper also offers ways of getting to know who your students are and how better to teach them with their diverse interests

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47

Althaus, Catherine, Peter Bridgman, and Glyn Davis. "Learning about Learning: Discovering the Work of Policy by Adams, Colebatch, and Walker." Australian Journal of Public Administration 74, no. 2 (2015): 112–13. http://dx.doi.org/10.1111/1467-8500.12145.

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48

Hamzah, Almed, Ahmad Fathan Hidayatullah, and Andhika Giri Persada. "Discovering Trends of Mobile Learning Research Using Topic Modelling Approach." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 09 (2020): 4. http://dx.doi.org/10.3991/ijim.v14i09.11069.

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This paper reports a map of identified topics from mobile learning research. Mobile learning is an emerging paradigm in an educational context as its adoption in an educational institution is growing rapidly. The students are already using and familiar with it. The publications from the last ten years were examined. Two approaches were employed to identify themes, i.e. word cloud and Latent Dirichlet Allocation. The result shows that mobile learning research is shifting from the development into optimization paradigm. This research is beneficial for mobile learning literature to inform the res

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49

Dudley, Rachel, Meredith Rowe, Valentine Hacquard, and Jeffrey Lidz. "Discovering the factivity of "know"." Semantics and Linguistic Theory 27 (December 9, 2017): 600. http://dx.doi.org/10.3765/salt.v27i0.4185.

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How do children discover which linguistic expressions are associated with presuppositions? Do they take a direct strategy of tracking whether linguistic expressions are associated with particular speaker presuppositions? This strategy may fail children who are trying to learn about the presuppositions of so-called 'soft' presupposition triggers, which can be readily used even when the relevant would-be presupposed content is not part of the common ground. We present a corpus study with the soft trigger "know" and the related, but non-presuppositional "think". We find that a direct learning str

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50

Wespi, Claudia, and Markus Steiner. "„Wirtschaft entdecken“ – eine computergestützte Lernumgebung mit Lernpotenzial für Lernende und Lehrpersonen." Haushalt in Bildung & Forschung 8, no. 4-2019 (2019): 77–89. http://dx.doi.org/10.3224/hibifo.v8i4.05.

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Im Fach Wirtschaft – Arbeit – Haushalt (WAH) sollen Lernende die wechselseitige Abhängigkeit von Haushalten und Unternehmen der Wirtschaft erkennen. Die Lernumgebung „Wirtschaft entdecken“ unterstützt Lernende und Lehrpersonen darin, die Perspektive eines Unternehmens einzunehmen. Das Konzept, in dessen Zentrum eine computerbasierte Simulation mit fünf Levels steht, umfasst die drei Phasen „Heranführung“, „Planspiel“ und „Transfer“.

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