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Journal articles on the topic 'Learning trends'

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Published: 4 June 2021
Last updated: 19 February 2022

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1

McCourt, Kath. "Learning global trends." Nursing Standard 25, no. 43 (June 29, 2011): 64. http://dx.doi.org/10.7748/ns2011.06.25.43.64.p5721.

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McCourt, Kath. "Learning global trends." Nursing Standard 25, no. 43 (June 29, 2011): 64. http://dx.doi.org/10.7748/ns.25.43.64.s56.

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3

Lee, Kian Yang. "Study on Deep Learning: Applications and Research Trends." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1603–11. http://dx.doi.org/10.5373/jardcs/v12sp7/20202264.

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4

Ksiazek, Kelly, Karen McGlathery, Laura Reynolds, Arthur Schwarzschild, Carissa Wilkerson, Tim Carruthers, Cassie Gurbisz, Joanna L. Woerner, and Laura Murray. "Learning about Coastal Trends." Science Activities: Classroom Projects and Curriculum Ideas 46, no. 3 (September 2009): 27–31. http://dx.doi.org/10.3200/sats.46.3.27-31.

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5

Topping, Keith J. "Trends in Peer Learning." Educational Psychology 25, no. 6 (December 2005): 631–45. http://dx.doi.org/10.1080/01443410500345172.

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6

Kovalenko, T. V. "Current European Trends in the Development of Lifelong Learning." Business Inform 4, no. 495 (2019): 72–77. http://dx.doi.org/10.32983/2222-4459-2019-4-72-77.

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7

Serafín, Čestmír. "Lifelong Learning and Constructivism." Lifelong Learning 1, no. 2 (2011): 7–13. http://dx.doi.org/10.11118/lifele201101027.

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Changes in education, in its concept, has always changed in response to changes in society. The current trend of lifelong learning is therefore closely tied not only to societal trends, but also to education and, thus, with creatively humanist, trends whose goal is personality development and the constructivist creation of new knowledge. The primary aim of educators is the personality development of the learner, who is to be educated and consequently equipped not only with the necessary knowledgeable, but also with cognitive and social competences, and the moral and spiritual values of life.
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8

MAREŠOVÁ, Hana, and Milan KLEMENT. "CURRENT TRENDS IN E-LEARNING DEVELOPMENT." Journal of Technology and Information 4, no. 3 (December 1, 2012): 54–59. http://dx.doi.org/10.5507/jtie.2012.050.

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9

Rajdev, Ankita, and Sunita Ramchandani. "TRADITIONAL LEARNING TO BLENDED LEARNING: TRENDS AND ISSUES." BSSS Journal of Education 10, no. 1 (June 30, 2021): 103–12. http://dx.doi.org/10.51767/je1011.

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As Benjamin Franklin quoted, “Tell me and I forget,Show me and I remember, Involve me and I learn.” This statement is true as it forms the essence of Blended Learning. Blended learning methods leads to continuous understanding and learning. It provides methods of teaching which leads to sharpen the skills and attitude furtherto seek resolutions to problems and to create new knowledge and awareness. This research intends to introduce blended learning to its readers and to analyze the trends and issues specially related to mathematics and nursing field.Theresearch focuses on different components of blended learning with emphasis on flexibility as the core component. This is a secondary research analyzing different journals, research articles, thesis, books etc. Lastly, it will highlight the comparison of blended learning with traditional methods. The results of the analysis were interpreted using descriptive analysis, percentages, and frequencies. This analysis found that various fields were practiced in the flipped classroom approach, and some technology tools were used as the online platform for its practice
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10

Uther, Maria. "Mobile Learning—Trends and Practices." Education Sciences 9, no. 1 (February 5, 2019): 33. http://dx.doi.org/10.3390/educsci9010033.

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Mobile learning has become one of the more influential aspects in the field of educational technology given the ubiquity of modern mobile devices and proliferation of educational applications or ‘apps’ for mobile devices. Within this special issue, there are a range of studies and reviews which cover a breadth of current topics in the field, namely user motivations for using mobile learning, issues in evaluation and domain-specific considerations (e.g., use within language learning or audio-based applications). Together these studies represent the synthesis of a range of methods, approaches and applications that highlight benefits and areas of future growth of mobile technologies.
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11

Little, Bob. "Trends in learning content management." Industrial and Commercial Training 40, no. 5 (July 11, 2008): 261–65. http://dx.doi.org/10.1108/00197850810886504.

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12

Quan-Haase, Anabel. "Trends in online learning communities." ACM SIGGROUP Bulletin 25, no. 1 (January 2005): 2–6. http://dx.doi.org/10.1145/1067699.1067700.

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13

Rushby, Nick. "Editorial: Trends in learning technologies." British Journal of Educational Technology 42, no. 6 (October 20, 2011): 885–88. http://dx.doi.org/10.1111/j.1467-8535.2011.01240.x.

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14

Little, Bob, and Ladislava Knihova. "Modern trends in learning architecture." Industrial and Commercial Training 46, no. 1 (January 28, 2014): 34–38. http://dx.doi.org/10.1108/ict-07-2013-0046.

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Purpose – The purpose of this paper is to examine current practice in blended learning – notably the view of blended learning as a synthesis of different approaches to the delivery of learning in order to create high impact learning. Design/methodology/approach – An exploration of the development of blended learning over the years, especially how it is being affected by developments in learning delivery technology and learning design. Findings – Learning architecture and design ought to be enhanced by the thoughtful use of various new tools, strategies and delivery platforms working in harmony. Only then can the learning process finally become highly attractive for new generations of learners. Research limitations/implications – New trends driving virtual learning prescribe the use of a portfolio of new tools. This, therefore, is producing changes in e-learning architecture, including the need for multi-device learning solutions, along with the need to implement multimedia formats in producing virtual learning solutions. Simultaneously, for educators, there have been changes in learning solutions' design based on the emergence of the self-determined and autonomous learner, as described by the concept of heutagogy. Practical implications – The complexities of creating an appropriate blend along with the most effective learning solutions for each learner – including the use of mobile platforms and social learning – have never been more challenging. Originality/value – Customising learning is in its early stages but, with the advent of new delivery technology and the growth of self-determined learners, it will become unavoidable.
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15

Frolova, N. H. "E-LEARNING: TRENDS AND STEPPING STONES." Historical and social-educational ideas 8, no. 5/2 (January 1, 2016): 186–89. http://dx.doi.org/10.17748/2075-9908-2016-8-5/2-186-189.

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16

Liang, Rose, and Der-Thanq Victor Chen. "Online Learning: Trends, Potential and Challenges." Creative Education 03, no. 08 (2012): 1332–35. http://dx.doi.org/10.4236/ce.2012.38195.

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17

Blake, Robert J. "Current Trends in Online Language Learning." Annual Review of Applied Linguistics 31 (March 2011): 19–35. http://dx.doi.org/10.1017/s026719051100002x.

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Online language learning (OLL) can take place in Web-facilitated, hybrid, or fully virtual classes. These formats are beginning to attract serious attention from the language profession and, in particular, the field of computer-assisted language learning (CALL). This article traces recent studies of online learning and then focuses in on its application to language learning through tutorial CALL, social computing, and games for language learning. I strive to show that tutorial CALL and computer-mediated communication can complement each other in the service of modern language instruction, along with the inclusion of language games. Although assessment studies of OLL remain sparse, the evidence is steadily mounting that shows that these new formats can provide learning environments conducive to successful second language development when properly integrated into the curriculum.
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18

Jordan, M. I., and T. M. Mitchell. "Machine learning: Trends, perspectives, and prospects." Science 349, no. 6245 (July 16, 2015): 255–60. http://dx.doi.org/10.1126/science.aaa8415.

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19

Ewell, Peter T. "National Trends in Assessing Student Learning." Journal of Engineering Education 87, no. 2 (April 1998): 107–13. http://dx.doi.org/10.1002/j.2168-9830.1998.tb00330.x.

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20

Lipton, Zachary C., and Jacob Steinhardt. "Troubling Trends in Machine Learning Scholarship." Queue 17, no. 1 (February 2019): 45–77. http://dx.doi.org/10.1145/3317287.3328534.

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21

Gillette, Yvonne. "AAC Distance Learning Practices and Trends." Perspectives on Augmentative and Alternative Communication 9, no. 1 (March 2000): 2–3. http://dx.doi.org/10.1044/aac9.1.2.

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22

Cambria, Erik, Guang-Bin Huang, Liyanaarachchi Lekamalage Chamara Kasun, Hongming Zhou, Chi Man Vong, Jiarun Lin, Jianping Yin, et al. "Extreme Learning Machines [Trends & Controversies]." IEEE Intelligent Systems 28, no. 6 (November 2013): 30–59. http://dx.doi.org/10.1109/mis.2013.140.

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23

Appa, Sawant Ramesh. "The Emerging Trends in E-Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 5 (May 31, 2021): 1061–62. http://dx.doi.org/10.22214/ijraset.2021.34382.

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24

CANAN GÜNGÖREN, Özlem, Duygu GÜR ERDOĞAN, and Gülden KAYA UYANIK. "Examination of Preservice Teachers’ Lifelong Learning Trends by Online Information Searching Strategies." Malaysian Online Journal of Educational Technology 7, no. 4 (October 1, 2019): 60–80. http://dx.doi.org/10.17220/mojet.2019.04.005.

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25

Yang, Harrison Hao. "New World, New Learning: Trends and Issues of E-Learning." Procedia - Social and Behavioral Sciences 77 (April 2013): 429–42. http://dx.doi.org/10.1016/j.sbspro.2013.03.098.

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26

Kassim, Mohd Hisyamuddin, Fadzidah Abdullah, and Alliyah Nur Zafirah Sanusi. "Mobile Augmented Reality Learning: Design Exploration Toward Student Learning Trends." Advanced Science Letters 24, no. 11 (November 1, 2018): 7890–94. http://dx.doi.org/10.1166/asl.2018.12450.

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27

Ridei, Nataliia, Lesia Viktorivna Viktorova, Mykola Yevgeniyovych Chumak, Oksana Kondur, and Liudmyla Petrivna Kanova. "Innovations in higher education in Ukraine (2011-2021): trend analysis." LAPLAGE EM REVISTA 7, no. 3B (September 27, 2021): 569–81. http://dx.doi.org/10.24115/s2446-6220202173b1598p.569-581.

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Innovation - an important aspect of the functioning of the modern education system. The article analyzes innovative trends in higher education in Ukraine over the past 10 years. Ukrainian innovations in higher education are considered in the context of European and world trends and standards. The analysis and generalization of materials on the subject revealed five educational trends: convergence of academic training in professional practice, application of competence approach in assessing learning outcomes, growth of students' independent work, increase of creative component in learning, readiness to function in conditions of uncertainty. Each trend is analyzed according to the scheme: necessity of innovations, content of trends, specific variants of trend implementation. In recent years, higher education institutions have been increasingly stimulating both students and teachers to innovate.
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28

Anand, Dr C., N. Vasuki, S. Nirmala, N. Naveen, and S. Prabakaran. "Greedy Dynamic Blocking for Rumour Detection on Live Twitter Using Machine Learning." Revista Gestão Inovação e Tecnologias 11, no. 2 (June 5, 2021): 364–73. http://dx.doi.org/10.47059/revistageintec.v11i2.1673.

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We propose a community multi-Trends assessment grouping way to deal with train notion classifiers for numerous tweets at the same time. In our methodology, the assessment data in various tweets is shared to prepare more exact and vigorous estimation classifiers for each Trends when marked information is scant. In particular, we decay the opinion classifier of each Trends into two segments, a worldwide one and a Trends-explicit one. Various customer surveys of subjects are currently accessible on the Internet. Naturally distinguishes the significant parts of themes from online shopper surveys. The significant item angles are recognized dependent on two perceptions. With the point of arranging patterns from the get-go. This would permit to give a separated subset of patterns to end clients. We investigate and explore different avenues regarding a bunch of direct language-autonomous highlights dependent on the social spread of patterns to classify them into the presented typology. Our strategy gives an effective method to precisely arrange moving points without need of outer information, empowering news associations to find breaking news progressively, or to rapidly recognize viral images that may improve promoting choices, among others. The examination of social highlights additionally uncovers designs related with each sort of pattern, for example, tweets about continuous occasions being more limited the same number of were likely sent from cell phones, or images having more retweets starting from a couple of innovators. The worldwide model can catch the overall conclusion information and is shared by different tweets. The Trends-explicit Greedy and Dynamic Blocking Algorithms model can catch the particular assessment articulations in each Trend. Likewise, we remove Trends-explicit feeling information from both marked and unlabeled examples in each Trend and use it to improve the learning of Trends-explicit notion classifiers.
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29

Toto, Giusi Antonia, and Pierpaolo Limone. "CONTEMPORARY TRENDS IN STUDIES ON MOBILE LEARNING OF FOREIGN LANGUAGES: A META-ANALYSIS." International Journal of Engineering Education 1, no. 2 (December 15, 2019): 85–90. http://dx.doi.org/10.14710/ijee.1.2.85-90.

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Previous research has provided a review of the literature on the state of research on mobile learning, but the use of m-learning and MALL to formal and informal learning of foreign languages needs further study. The need to review the language learning assisted by mobile devices emerges from a globalized context, in which pedagogy must question itself to plan and design research drawings in which mobile learning assumes a fundamental importance in teaching practice aimed above all at new generations of students.This study adopts a meta-analysis approach to systematically review the literature, thus providing a more complete analysis and synthesis of 83 studies from 2009 to 2018. Previous studies focus on the effectiveness and design of the mobile learning system.The search for databases and citations was performed by limiting the keywords referring to m-learning and to MALL. Compared to existing reviews, the results confirm revealing a skill-based approach to language learning with an emphasis on vocabulary, learning factors that influence students' perceptions and availability to use new cellular technologies.
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30

Driesen, Annelies, Koen Verbeke, Steven Simoens, and Gert Laekeman. "International Trends in Lifelong Learning for Pharmacists." American Journal of Pharmaceutical Education 71, no. 3 (September 2007): 52. http://dx.doi.org/10.5688/aj710352.

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31

SHINDO, TOSHIHIKO. "Current Trends in Learning and Instructional Psychology :." Annual Report of Educational Psychology in Japan 53 (2014): 57–69. http://dx.doi.org/10.5926/arepj.53.57.

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32

Danilov, Andrew V., Inna D. Nekrasova, and Nnamdi Anyameluhor. "Digitalization Trends in Education and Blended Learning." International Journal of Criminology and Sociology 9 (November 11, 2020): 1062–66. http://dx.doi.org/10.6000/1929-4409.2020.09.121.

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33

Yaroshenko, Tetiana Oleksandrivna. "Distance learning in higher education: current trends." Engineering and Educational Technologies 7, no. 4 (December 30, 2019): 8–21. http://dx.doi.org/10.30929/2307-9770.2019.07.04.01.

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34

Song, Jieun, Young-Yun Cho, Eun Jung Park, and Boyoung Jung. "Research Trends of Organizational Learning in Korea." Journal of Corporate Education and Talent Research 22, no. 2 (June 30, 2020): 121–47. http://dx.doi.org/10.46260/kslp.22.2.5.

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35

Cohen, Gil. "Forecasting Bitcoin Trends Using Algorithmic Learning Systems." Entropy 22, no. 8 (July 30, 2020): 838. http://dx.doi.org/10.3390/e22080838.

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This research has examined the ability of two forecasting methods to forecast Bitcoin’s price trends. The research is based on Bitcoin—USA dollar prices from the beginning of 2012 until the end of March 2020. Such a long period of time that includes volatile periods with strong up and downtrends introduces challenges to any forecasting system. We use particle swarm optimization to find the best forecasting combinations of setups. Results show that Bitcoin’s price changes do not follow the “Random Walk” efficient market hypothesis and that both Darvas Box and Linear Regression techniques can help traders to predict the bitcoin’s price trends. We also find that both methodologies work better predicting an uptrend than a downtrend. The best setup for the Darvas Box strategy is six days of formation. A Darvas box uptrend signal was found efficient predicting four sequential daily returns while a downtrend signal faded after two days on average. The best setup for the Linear Regression model is 42 days with 1 standard deviation.
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36

Kim, Jongguk. "An Analysis of Flipped Learning Research Trends." Korean Association For Learner-Centered Curriculum And Instruction 18, no. 15 (August 10, 2018): 113–30. http://dx.doi.org/10.22251/jlcci.2018.18.15.113.

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37

Huang, Gao, Guang-Bin Huang, Shiji Song, and Keyou You. "Trends in extreme learning machines: A review." Neural Networks 61 (January 2015): 32–48. http://dx.doi.org/10.1016/j.neunet.2014.10.001.

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38

J., Nived, Pruthvirajsinh S., Dhaval B., Tejasvi G., and Rutvij H. "On Machine Learning Trends for Android Applications." International Journal of Computer Applications 179, no. 31 (April 17, 2018): 29–31. http://dx.doi.org/10.5120/ijca2018916616.

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39

Pankaskie, Marvin, and Johanna Sullivan. "On-Line Learning: Trends in Continuing Education." Journal of the American Pharmaceutical Association (1996) 38, no. 3 (May 1998): 382. http://dx.doi.org/10.1016/s1086-5802(16)30342-4.

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40

Dringus, L. P., and Timothy Ellis. "Distance Learning Technologies: Issues, Trends and Opportunities." Internet and Higher Education 2, no. 2-3 (March 1999): 185–88. http://dx.doi.org/10.1016/s1096-7516(00)00019-1.

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41

Li, Fan, Jianxin Yang, Jianmei Wang, Shuangshou Li, and Li Zheng. "Integration of digitization trends in learning factories." Procedia Manufacturing 31 (2019): 343–48. http://dx.doi.org/10.1016/j.promfg.2019.03.054.

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42

Leedham, Maria. "New Trends in Corpora and Language Learning." System 40, no. 1 (March 2012): 162–65. http://dx.doi.org/10.1016/j.system.2011.12.005.

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43

Sarica, Gulcin Nagehan, and Nadire Cavus. "New trends in 21st Century English learning." Procedia - Social and Behavioral Sciences 1, no. 1 (2009): 439–45. http://dx.doi.org/10.1016/j.sbspro.2009.01.079.

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44

Pluta, William J., Boyd F. Richards, and Andrew Mutnick. "PBL and Beyond: Trends in Collaborative Learning." Teaching and Learning in Medicine 25, sup1 (January 2013): S9—S16. http://dx.doi.org/10.1080/10401334.2013.842917.

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45

Pang, Ming Fai, and Ulla Runesson. "The Learning study: recent trends and developments." International Journal for Lesson and Learning Studies 8, no. 3 (July 8, 2019): 162–69. http://dx.doi.org/10.1108/ijlls-07-2019-093.

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46

Hedberg, John G., and Lim Cher Ping. "Charting Trends for E-Learning in Asia." Media Asia 31, no. 2 (January 2004): 82–88. http://dx.doi.org/10.1080/01296612.2004.11726739.

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47

Baumann, Felix W., André Sekulla, Michael Hassler, Benjamin Himpel, and Markus Pfeil. "Trends of machine learning in additive manufacturing." International Journal of Rapid Manufacturing 7, no. 4 (2018): 310. http://dx.doi.org/10.1504/ijrapidm.2018.095788.

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48

Baumann, Felix W., André Sekulla, Michael Hassler, Benjamin Himpel, and Markus Pfeil. "Trends of machine learning in additive manufacturing." International Journal of Rapid Manufacturing 7, no. 4 (2018): 310. http://dx.doi.org/10.1504/ijrapidm.2018.10016883.

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49

Fenwick, Tara. "Workplace learning: Emerging trends and new perspectives." New Directions for Adult and Continuing Education 2008, no. 119 (June 2008): 17–26. http://dx.doi.org/10.1002/ace.302.

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50

Inman, Patricia L., and Sally Vernon. "Assessing Workplace Learning: New Trends and Possibilities." New Directions for Adult and Continuing Education 1997, no. 75 (1997): 75–85. http://dx.doi.org/10.1002/ace.7507.

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