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Journal articles on the topic 'Active learning'

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Published: 4 June 2021
Last updated: 31 August 2024

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Ahrari, Seyedali, Jamilah Othman, Salleh Hassan, Bahaman Abu Samah, and Jeffrey Lawrence D`Silva. "Active Citizenship by Active Learning." Journal of Applied Sciences 14, no. 20 (October 1, 2014): 2450–59. http://dx.doi.org/10.3923/jas.2014.2450.2459.

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12

Annette, John. "`Active learning for active citizenship'." Education, Citizenship and Social Justice 4, no. 2 (June 25, 2009): 149–60. http://dx.doi.org/10.1177/1746197909103934.

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13

Bhoir, Mehera. "Active Learning in Undergraduate Students by Seminars." Indian Journal of Anatomy 6, no. 4 (2017): 487–90. http://dx.doi.org/10.21088/ija.2320.0022.6417.14.

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14

UGURLU, Yucel, and Nobuo EZAKI. "Empowering Tomorrow's Engineers Using Active Learning Projects." IEICE ESS Fundamentals Review 10, no. 4 (2017): 238–45. http://dx.doi.org/10.1587/essfr.10.4_238.

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15

Salem, Ahmed. "DEVELOPING AN ACTIVE LEARNING EXPERIENCE FOR ACTIVE LEARNING COURSES." Journal of Al-Azhar University Engineering Sector 12, no. 45 (October 1, 2017): 1311–20. http://dx.doi.org/10.21608/auej.2017.19139.

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16

Goldberg, Lynette R., Cynthia McCormick Richburg, and Lisa A. Wood. "Active Learning Through Service-Learning." Communication Disorders Quarterly 27, no. 3 (June 2006): 131–45. http://dx.doi.org/10.1177/15257401060270030201.

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17

Stemp-Morlock, Graeme. "Learning more about active learning." Communications of the ACM 52, no. 4 (April 2009): 11–13. http://dx.doi.org/10.1145/1498765.1498771.

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18

Henrickson, Kerry. "Active Learning Software." American Biology Teacher 70, no. 7 (September 2008): 430. http://dx.doi.org/10.1662/0002-7685(2008)70[430:ctr]2.0.co;2.

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19

D’Silva, Icy, J. Christopher Hall, and Yoshinori Mine. "Active Learning Strategies." Journal of the World Universities Forum 2, no. 3 (2009): 67–76. http://dx.doi.org/10.18848/1835-2030/cgp/v02i03/56590.

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20

Wang, Viktor, Leslie Hitch, and Geraldine Torrisi-Steele. "Active Learning Online." International Journal of Online Pedagogy and Course Design 12, no. 1 (January 2022): 1–13. http://dx.doi.org/10.4018/ijopcd.2022010105.

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Preparing graduates for the present and future workforce is an important strategic learning and teaching goal of higher education. Towards realizing this goal, institutions are expending significant effort promoting active learning as an institution-wide teaching approach. Active learning defined as learners deeply participating in the learning process are being increasingly used in face-to-face contexts, but can it be used just as effectively in the online environments now common in higher education? In their 2017 paper, the authors established that active learning online is certainly possible. In this current article the authors assert that not only is active learning online possible, but that it is a necessity to bolster workforce and higher order thinking skills needed in this current century. Importantly, the faculties have a crucial role to play in implementing active learning online, and active learning online permeate the whole of the online learning experience within courses.
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21

Anahideh, Hadis, Abolfazl Asudeh, and Saravanan Thirumuruganathan. "Fair active learning." Expert Systems with Applications 199 (August 2022): 116981. http://dx.doi.org/10.1016/j.eswa.2022.116981.

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22

Russell, Ewan. "Remote active learning." MSOR Connections 20, no. 1 (April 8, 2022): 46–55. http://dx.doi.org/10.21100/msor.v20i1.1308.

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Following the necessary shift to online learning in mathematics due to the COVID-19 pandemic, it was widely reported across the sector that encouraging students to engage in online synchronous sessions was very challenging. In this case study, the design and delivery of synchronous sessions for a large Year 1 mathematics module will be discussed. These synchronous sessions utilised an active learning approach which sought to address the difficulties in online learning through a focus on playfulness and games. This approach created opportunities for peer learning and successfully encouraged student engagement and interaction.
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23

Henrickson, K. "Active Learning Software." American Biology Teacher 70, no. 7 (September 1, 2008): 430. http://dx.doi.org/10.2307/30163317.

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24

Strangfeld, Jennifer A. "Promoting Active Learning." Teaching Sociology 41, no. 2 (March 4, 2013): 199–206. http://dx.doi.org/10.1177/0092055x12472492.

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25

Shen, Pengcheng, Chunguang Li, and Zhaoyang Zhang. "Distributed Active Learning." IEEE Access 4 (2016): 2572–79. http://dx.doi.org/10.1109/access.2016.2572198.

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26

Crocker, John. "Active learning systems." Computers in Entertainment 1, no. 1 (October 2003): 1–5. http://dx.doi.org/10.1145/950566.950587.

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27

Thorne, Deborah, Glenn Ward, Dan Segrist, and Tevian Dray. "Letters: Active Learning." Academe 91, no. 3 (2005): 8. http://dx.doi.org/10.2307/40252773.

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28

Balcan, Maria-Florina, Alina Beygelzimer, and John Langford. "Agnostic active learning." Journal of Computer and System Sciences 75, no. 1 (January 2009): 78–89. http://dx.doi.org/10.1016/j.jcss.2008.07.003.

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29

Nardo, Jocelyn Elizabeth, Natalie C. Chapman, Eli Yuan Shi, Carl Wieman, and Shima Salehi. "Perspectives on Active Learning: Challenges for Equitable Active Learning Implementation." Journal of Chemical Education 99, no. 4 (March 2, 2022): 1691–99. http://dx.doi.org/10.1021/acs.jchemed.1c01233.

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30

Mutiara Hati, G., and R. Afriazi. "Active Learning Training: Shifting the Attention Toward Students’ Active Learning." KnE Social Sciences 3, no. 14 (March 31, 2019): 295. http://dx.doi.org/10.18502/kss.v3i14.4316.

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31

Ilhan, Hamza Osman, and Mehmet Fatih Amasyalı. "Active Learning as a Way of Increasing Accuracy." International Journal of Computer Theory and Engineering 6, no. 6 (December 2014): 460–65. http://dx.doi.org/10.7763/ijcte.2014.v6.910.

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32

Li, Changsheng, Kaihang Mao, Lingyan Liang, Dongchun Ren, Wei Zhang, Ye Yuan, and Guoren Wang. "Unsupervised Active Learning via Subspace Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8332–39. http://dx.doi.org/10.1609/aaai.v35i9.17013.

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Unsupervised active learning has been an active research topic in machine learning community, with the purpose of choosing representative samples to be labelled in an unsupervised manner. Previous works usually take the minimization of data reconstruction loss as the criterion to select representative samples which can better approximate original inputs. However, data are often drawn from low-dimensional subspaces embedded in an arbitrary high-dimensional space in many scenarios, thus it might severely bring in noise if attempting to precisely reconstruct all entries of one observation, leading to a suboptimal solution. In view of this, this paper proposes a novel unsupervised Active Learning model via Subspace Learning, called ALSL. In contrast to previous approaches, ALSL aims to discovery the low-rank structures of data, and then perform sample selection based on learnt low-rank representations. To this end, we devise two different strategies and propose two corresponding formulations to perform unsupervised active learning with and under low-rank sample representations respectively. Since the proposed formulations involve several non-smooth regularization terms, we develop a simple but effective optimization procedure to solve them. Extensive experiments are performed on five publicly available datasets, and experimental results demonstrate the proposed first formulation achieves comparable performance with the state-of-the-arts, while the second formulation significantly outperforms them, achieving a 13\% improvement over the second best baseline at most.
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33

Tuia, Devis, and Jordi Munoz-Mari. "Learning User's Confidence for Active Learning." IEEE Transactions on Geoscience and Remote Sensing 51, no. 2 (February 2013): 872–80. http://dx.doi.org/10.1109/tgrs.2012.2203605.

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34

Wijaya, Hengki, I. Putu Ayub Darmawan, Suzana Claudia Setiana, Helaluddin Helaluddin, and Ivan Th J. Weismann. "Active Reconnecting Learning Strategies to Increase Student Interest and Active Learning." Indonesian Journal of Instructional Media and Model 3, no. 1 (April 21, 2021): 26. http://dx.doi.org/10.32585/ijimm.v3i1.1290.

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The main issue of education in the last few decades is the efforts and solutions in improving the quality of teaching and learning. In this case, the teacher’s steps are to conduct classroom action research, which is a form of a bottom-up approach in improving the quality of education. This action research introduces active reconnecting learning strategies for increasing student interest and learning activities. The CAR design includes three stages, namely the pre-cycle, cycle 1, and Cycle 2. Some of the instruments used in this study are checklist observation sheets, questionnaires, and learning outcomes tests, which are then analyzed using quantitative approaches. The results showed that this strategy was proven to improve student learning outcomes, interests, and activeness. Student learning interest increased from 59.35 to 65.5 in cycle 1 and 80.5 in cycle 2. Likewise, student learning activeness observations increased with the highest percentage on one indicator from 25% (pre-cycle) to 80% and 90% (cycle 1 and 2). Student learning outcomes have also increased from an average grade of 60 in the pre-cycle phase to 74.25 (cycle 1). And it rose again to 80 in cycle 2.
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35

Yee, Sean, and Kimberly Rogers. "Active learning and STEM education: Who is active? Who is learning?" School Science and Mathematics 122, no. 2 (February 2022): 71–73. http://dx.doi.org/10.1111/ssm.12517.

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36

Hao, Guang-Yuan, Hengguan Huang, Haotian Wang, Jie Gao, and Hao Wang. "Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 12286–94. http://dx.doi.org/10.1609/aaai.v38i11.29119.

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Active learning (AL) aims to improve model performance within a fixed labeling budget by choosing the most informative data points to label. Existing AL focuses on the single-domain setting, where all data come from the same domain (e.g., the same dataset). However, many real-world tasks often involve multiple domains. For example, in visual recognition, it is often desirable to train an image classifier that works across different environments (e.g., different backgrounds), where images from each environment constitute one domain. Such a multi-domain AL setting is challenging for prior methods because they (1) ignore the similarity among different domains when assigning labeling budget and (2) fail to handle distribution shift of data across different domains. In this paper, we propose the first general method, dubbed composite active learning (CAL), for multi-domain AL. Our approach explicitly considers the domain-level and instance-level information in the problem; CAL first assigns domain-level budgets according to domain-level importance, which is estimated by optimizing an upper error bound that we develop; with the domain-level budgets, CAL then leverages a certain instance-level query strategy to select samples to label from each domain. Our theoretical analysis shows that our method achieves a better error bound compared to current AL methods. Our empirical results demonstrate that our approach significantly outperforms the state-of-the-art AL methods on both synthetic and real-world multi-domain datasets. Code is available at https://github.com/Wang-ML-Lab/multi-domain-active-learning.
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37

Woodward, Val. "Active Learning for Active Citizenship: An Overview." OR Insight 20, S2 (April 2007): 13–19. http://dx.doi.org/10.1057/ori.2007.9.

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38

Riley, Jennifer, and Kerry Ward. "Active Learning, Cooperative Active Learning, and Passive Learning Methods in an Accounting Information Systems Course." Issues in Accounting Education 32, no. 2 (December 1, 2015): 1–16. http://dx.doi.org/10.2308/iace-51366.

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ABSTRACT We report the results of a study to examine the effectiveness of active versus passive learning methods in the accounting information systems area. Two groups of students completed an assignment under two active learning conditions (individual and cooperative), while a third group covered the same topic in a passive lecture. Our findings indicate support for active learning, measured through student performance on exam questions and student feedback on a questionnaire. However, compared to passive learners, we find significantly improved exam performance only for students who work individually in an active environment. Students in the cooperative active environment posted exam scores that were not statistically different from passive participants' scores. Students in both individual and cooperative active environments reported positive feedback on satisfaction, perceived learning, and effectiveness of the method. We conclude that active learning enhances student outcomes, particularly for those who work individually. Data Availability: Data are available upon request.
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39

Dong, Xinyue, Shilin Gu, Wenzhang Zhuge, Tingjin Luo, and Chenping Hou. "Active label distribution learning." Neurocomputing 436 (May 2021): 12–21. http://dx.doi.org/10.1016/j.neucom.2020.12.128.

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40

Tran, Alasdair, Cheng Soon Ong, and Christian Wolf. "Combining active learning suggestions." PeerJ Computer Science 4 (July 23, 2018): e157. http://dx.doi.org/10.7717/peerj-cs.157.

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We study the problem of combining active learning suggestions to identify informative training examples by empirically comparing methods on benchmark datasets. Many active learning heuristics for classification problems have been proposed to help us pick which instance to annotate next. But what is the optimal heuristic for a particular source of data? Motivated by the success of methods that combine predictors, we combine active learners with bandit algorithms and rank aggregation methods. We demonstrate that a combination of active learners outperforms passive learning in large benchmark datasets and removes the need to pick a particular active learner a priori. We discuss challenges to finding good rewards for bandit approaches and show that rank aggregation performs well.
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41

Aono, Atsuko, Itsuko Dohi, Akiko Nishio, Atsuko Inosaki, Tadayuki Sawada, and Keiko Takahashi. "Gender through active learning." Proceedings of the Annual Convention of the Japanese Psychological Association 79 (September 22, 2015): SS—088—SS—088. http://dx.doi.org/10.4992/pacjpa.79.0_ss-088.

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42

Allen, Eileen E. "Active Learning and Teaching:." Reference Librarian 24, no. 51-52 (July 20, 1995): 89–103. http://dx.doi.org/10.1300/j120v24n51_10.

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43

Ghai, Bhavya, Q. Vera Liao, Yunfeng Zhang, Rachel Bellamy, and Klaus Mueller. "Explainable Active Learning (XAL)." Proceedings of the ACM on Human-Computer Interaction 4, CSCW3 (January 5, 2021): 1–28. http://dx.doi.org/10.1145/3432934.

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44

Mingkun Li and I. K. Sethi. "Confidence-based active learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 8 (August 2006): 1251–61. http://dx.doi.org/10.1109/tpami.2006.156.

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45

Goetze, David. "Hybrid Active Learning Situations." Simulation & Gaming 46, no. 6 (December 2015): 792–816. http://dx.doi.org/10.1177/1046878115620089.

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46

Billett, Sue. "Structuring Active Learning Experiences." Early Years 11, no. 2 (March 1991): 19–21. http://dx.doi.org/10.1080/0957514910110206.

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47

McConnell, Jeffrey J. "Active and cooperative learning." ACM SIGCSE Bulletin 37, no. 2 (June 2005): 27–30. http://dx.doi.org/10.1145/1083431.1083457.

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48

McConnell, Jeffrey J. "Active and cooperative learning." ACM SIGCSE Bulletin 37, no. 4 (December 2005): 34–38. http://dx.doi.org/10.1145/1113847.1113872.

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49

Hovelynck, Johan. "Moving Active Learning Forward." Journal of Experiential Education 26, no. 1 (July 2003): 1–7. http://dx.doi.org/10.1177/105382590302600103.

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50

Boulton-Lewis, Gillian, Laurie Buys, and Jan Lovie-Kitchin. "Learning and Active Aging." Educational Gerontology 32, no. 4 (April 1, 2006): 271–82. http://dx.doi.org/10.1080/03601270500494030.

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