Personal learning environments: A Big Data perspective
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Nov 16, 2016
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Abstract
Problem Statement: Traditional instructional learning based platforms (e.g., Learning Management Systems) and master – apprentice model is not sufficient for the companies and their human capital anymore. Employees should lead their vocational competence in a connected, fast changing world. It is a great challenge for enterprises and people to find relevant learning resources in an unstructured, scattered, distributed, and overwhelmingly large amount of information ocean and un-learn / learn continuously in this environment.Purpose of Study: Personal Learning Environments enhanced with Big Data analysis opportunities and emerging technologies seem a solution for the aforementioned problem. This study aims to propose a preliminary Personal Learning Environment Architecture leveraging Big Data possibilities.Methods: Systematic literature review method is used to review the literature reviews including the research of Big Data in education and personal learning environments.Findings and Results: Based on the literature knowledge captured, a preliminary personal learning environment architecture is synthesized and proposed.Conclusions and Recommendations: Proposed preliminary architecture seems to address basic usecases in the literature. However a detailed data gathering should be conducted in a large enterprise using more sophisticated technics such as field surveys, descriptive analytics and case studies. Although the architecture is promising for the personal learning environments, it needs systematic validation with more data both technologically and andragogically.
Keywords: big data in education, personal learning environment, personal learning assistant, learning mentor, learning analytics.
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[2] Khan, S. The one world schoolhouse: Education reimagined, Twelve, 2012.
[3] Harmelen, M. V. Personal Learning Environments. Sixth Int. Conf. Adv. Learn. Technol, 2006, pp. 1–2.
[4] Wilson, S., Johnson M., and Sharples, P. Personal Learning Environments : challenging the dominant design of educational systems, 2007.
[5] Dabbagh, N. and Kitsantas, A. Personal Learning Environments , social media , and self-regulated learning : A natural formula for connecting formal and informal learning, Internet High. Educ., 2011.
[6] Devices, M., Environments, P.L. and Services, W. The impact of a mobile Personal Learning Environment in different educational contexts. Univers. Access Inf. Soc., 2015, 14, pp. 375–387.
[7] Ndongfack, M.N. Design and Development of a Personal Learning Environment for Corporate Self-Regulated Learning. J. Comput. Commun, 2016, pp. 1–9.
[8] Gu, N. Implementing a Mobile App as a Personal Learning Environment for Workplace Learners, Mob. Learn. Des. Theor. Appl., 2016, pp. 285–300.
[9] Gartner, Gartner Identifies the Top 10 Strategic Technology Trends for 2016, 2016. Available from: http://www.gartner.com/newsroom/id/3143521.
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[12] Philip C. C. L. and Zhang, C.-Y. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Inf. Sci. (Ny), 2014, 275, pp. 314–347.
[13] Chen, M., Mao, S. and Liu, Y. Big data: A survey. Mob. Networks Appl. 2014, 19, pp. 171–209.
[14] Yin, S. and Kaynak, O. Big Data for Modern Industry : Challenges and Trends. Proc. IEEE, 2015, 103, pp. 143–146.
[15] Chaffey, D. Global social media research summary 2016 Our compilation of the latest social media statistics of consumer adoption and usage, 2016.
[16] Bagriyanik, S. and Karahoca, A. Big data in software engineering : A systematic literature review. Glob. J. Inf. Technol., 2016, 6, pp. 107–116.
[17] Bagriyanik, S. and Karahoca, D. System analyst expectations from requirements engineering tools : A human computer interaction perspective. Glob. J. Comput. Sci., 2014, 4, pp. 7–16.
[18] Bagriyanik, S. and Karahoca, A. Automated COSMIC Function Point measurement using a requirements engineering ontology. Inf. Softw. Technol., 2016, 72, pp. 189–203.
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[20] Khabsa, M. and Giles, C.L. The Number of Scholarly Documents on the Public Web. 2014.
[21] Tay, Aa. 8 surprising things I learnt about Google Scholar. 2014.
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[23] Avella, J.T., Kebritchi, M., Nunn, S. and Kanai, T. Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. Online Learn. J., 2016, 20.
[24] Baker, R.S.J.D. and Yacef, K. The State of Educational Data Mining in 2009 : A Review and Future Visions. J. Educ. Data Mining., 2009, 1, pp. 3–16.
[25] Liew, B.T. and Kang, M. Personal Learning Environment for Education: A Review and Future Directions. ICWL 2011/2012 Work., 2012, pp. 30–38.
[26] Meenakumari, J. and Kudari, J. M. Learning Analytics and its challenges in Education Sector a Survey. Int. J. Comput. Appl., 2015, pp. 6–10.
[27] Anoopkumar, M. A Comprehensive Survey on Educational Data Mining and Use of Data Mining Techniques for Improving Teaching and Predicting Student Performance. n.d.
[28] Ihantola, P., Butler, M., Edwards, S.H., Tech, V., Korhonen, A., Petersen, A., Rivers, K., Rubio, M.Á., Sheard, J., Spacco, J., Szabo, C. and Toll, D. Educational Data Mining and Learning Analytics in Programming : Literature Review and Case Studies. ITiCSE WGR’16, 2015, pp. 41–63.
[29] Cooper, A.R. Learning Analytics Interoperability - a survey of current literature and candidate standards, 2014.
[30] Corbi, A. and Burgos, D. Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API and LIME model Case Study, Int. J. Interact. Multimed. Artif. Intell., 2014, 2 (44).
[31] Papamitsiou, Z. and Economides, A.A. Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Educ. Technol. Soc., 2014, 17, pp. 49–64.
[32] Sachin, R. B. and Vijay, M. S. A Survey and Future Vision of Data mining in Educational Field. IEEE, 2012, pp. 96–100.
[33] O’donnell, E., Sharp, M., Mulwa, C. and Wade, V.P. Web-Mediated Education and Training Environments: A Review of Personalised Interactive eLearning. ePedagogy Online Learn. New Dev. Web Mediat. Hum. Comput. Interact., 2013: pp. 188–207.
[34] Jindal, R. and Borah, M.D. A Survey on Educational Data Mining and Research Trends. Int. J. Database Manag. Syst., 2013, 5, pp. 53–73.
[35] Romero, C. and Ventura, S. Educational Data Mining : A Review of the State of the Art. IEEE Trans. Syst. Man. Cybern., 2010, 40, pp. 601–618.
[36] Peña-ayala, A. Educational data mining : A survey and a data mining-based analysis of recent works. Expert Syst. Appl., 2014, 41, pp. 1432–1462.
[37] K. Sukhija, M. Jindal, N. Aggarwal, The Recent State of Educational Data Mining : A Survey and Future Visions, in: 2015 IEEE 3rd Int. Conf. onMOOCs, Innov. Technol. Educ., 2015: pp. 354–359.
[38] S. R., V. Vaidhehi, N.E. Iyer, Survey of Learning Analytics based on Purpose and Techniques for Improving Student Performance, Int. J. Comput. Appl. 111 (2015) 22–26.
[39] Beuth, I.B., Pontydysgu, G.A. and Citilab, R.T. Understanding Personal Learning Environments: Literature review and synthesis through the A ctivity Theory lens, PLE Conf. 2011, 2011.
[40] Kitchenham, B., Brereton, O.P., Budgen, D., Turner, M., Bailey, J. and Linkman, S. Systematic literature reviews in software engineering – A systematic literature review. Inf. Softw. Technol., 2009, 51, pp. 7–15.