NA Technology contributions to personalized learning
A review of the literature
DOI:
https://doi.org/10.61604/dl.v16i28.352Keywords:
Personalized learning, Educative Technology, Learning MethodologyAbstract
In the educational field, research has been conducted from different perspectives, one of them is personalized learning and the other is the integration of technology in educational processes, both very relevant and with a broad prospective. Therefore, for this research it was proposed to determine the contributions that technology has made possible to personalized learning through a review of the literature in the Scopus, Web of Science and ERIC databases. As the main result, it can be observed that personalized learning is possible and real with the support of technology, considering the advances that have occurred in contemporary society. It is concluded that when technology is used to facilitate personalized learning, it promotes the acquisition of knowledge by students and facilitates effective time management in educational activities, which contributes to the improvement of the learning process.
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Abdelaziz, H. A., & Al-Ali, A. (2020). Promoting personalized learning skills: The impact of collaborative learning (a case study on the general Directorate of residency and foreigners affairs in Dubai). International Journal of Learning, Teaching and Educational Research, 19(2), 163–187. https://doi.org/10.26803/ijlter.19.2.10
Aguilar, S. J. (2018). Learning Analytics: at the Nexus of Big Data, Digital Innovation, and Social Justice in Education. Techtrends, 62(1), 37–45. https://doi.org/10.1007/s11528-017-0226-9
Ahku, V. C., & Panchoo, S. (2019). Implementing Personalised Learning For Mixed Ability Students For Computer Programming In A Learning Environment. 2nd International Conference on Next Generation Computing Applications 2019, NextComp 2019 - Proceedings, 1–8. https://doi.org/10.1109/NEXTCOMP.2019.8883447
Akos, P., Wasik, S. Z., McDonald, A., Soler, M., & Lys, D. (2019). The Challenge and Opportunity of Competency-Based Counselor Education. Counselor Education and Supervision, 58(2), 98–111. https://doi.org/10.1002/ceas.12134
Arnet, T. (2016). Connecting ed & tech: Partnering to drive student outcomes. Clayton Christensen Institute for Disruptive Innovation, July, 1–26. https://files.eric.ed.gov/fulltext/ED568676.pdf
Aslan, A., Bakir, I., & Vis, I. F. A. (2020). A dynamic thompson sampling hyper-heuristic framework for learning activity planning in personalized learning. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2020.03.038
Bagon, S., Gacnik, M., & Starcic, A. I. (2018). Information Communication Technology Use among Students in Inclusive Classrooms. International Journal of Emerging Techologies in Learning, 13(6), 56–72. https://doi.org/10.3991/ijet.v13i06.8051
Bendahmane, M., El Falaki, B., & Benattou, M. (2019). Toward a Personalized learning Path through a Services-Oriented Approach. International Journal of Emerging Techologies in Learning, 14(15), 52–66. https://doi.org/10.3991/ijet.v14i15.10951
Bernacki, M. L., & Walkington, C. (2018). The Role of Situational Interest in Personalized Learning. In Journal of Educational Psychology (Vol. 110, Issue 6, pp. 864–881). https://psycnet.apa.org/record/2018-10599-001
Bian, C.-L., Wang, D.-L., Liu, S.-Y., Lu, W.-G., & Dong, J.-Y. (2019). Adaptive learning path recommendation based on graph theory and an improved immune algorithm. KSII Transactions on Internet and Information Systems, 13(5), 2277–2298. https://doi.org/10.3837/tiis.2019.05.003
Bingham, A. J. (2019). A Look at Personalized Learning: Lessons Learned. In Kappa Delta Pi Record (Vol. 55, Issue 3, pp. 124–129). https://doi.org/https://doi.org/10.1080/00228958.2019.1622383
Buch, B., Christiansen, R. B., Hansen, D., Petersen, A. K., & Sørensen, R. S. (2018). Using the 7Cs Framework for Designing MOOCs in Blended Contexts--New Perspectives and Ideas. In Universal Journal of Educational Research (Vol. 6, Issue 3, pp. 421–429).
Campbell, L. O., & Cox, T. D. (2018). Digital Video as a Personalized Learning Assignment: A Qualitative Study of Student Authored Video Using the ICSDR Model. In Journal of the Scholarship of Teaching and Learning (Vol. 18, Issue 1, pp. 11–24). https://files.eric.ed.gov/fulltext/EJ1169880.pdf
Carey, M. A. (2012). Book Review: Essentials of Accessible Grounded Theory, by Phyllis Noerager Stern and Caroline Jane Porr; and Grounded Theory: A Practical Guide, by Melanie Birks and Jane Mills. In Qualitative Health Research (Vol. 22, Issue 10, pp. 1444–1445). http://explore.bl.uk/primo_library/libweb/action/display.do?tabs=detailsTab&gathStatTab=true&ct=display&fn=search&doc=ETOCRN351678159&indx=1&recIds=ETOCRN351678159
Chang, J., & Lu, X. (2019). The study on students’ participation in personalized learning under the background of artificial intelligence. Proceedings - 10th International Conference on Information Technology in Medicine and Education, ITME 2019, 555–558. https://doi.org/10.1109/ITME.2019.00131
DeMink-Carthew, J., & Netcoh, S. (2019). Mixed Feelings about Choice: Exploring Variation in Middle School Student Experiences with Making Choices in a Personalized Learning Project. RMLE Online, 42(10), 1–20. https://doi.org/10.1080/19404476.2019.1693480
DeMink-Carthew, J., & Olofson, M. W. (2020). Hands-Joined Learning as a Framework for Personalizing Project-Based Learning in a Middle Grades Classroom: An Exploratory Study. RMLE Online, 43(2), 1–17. https://doi.org/10.1080/19404476.2019.1709776
Ding, L. (2018). Exploration of Key Technologies in a Personalized English Learning System. International Journal of Emerging Techologies in Learning, 13(7), 85–96. https://doi.org/10.3991/ijet.v13i07.8789
Duan, X. (2019). Automatic Generation and Evolution of Personalized Curriculum Based on Genetic Algorithm. International Journal of Emerging Techologies in Learning, 14(12), 15–28. https://doi.org/10.3991/ijet.v14i12.10812
Easley, M. (2017). Personalized Learning Environments and Effective School Library Programs. In Knowledge Quest (Vol. 45, Issue 4, pp. 16–23). 2017.
El Aissaoui, O., El Madani, Y. E. A., Oughdir, L., & El Allioui, Y. (2019). A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments. Education And Information Technologies, 24(3), 1943–1959. https://doi.org/10.1007/s10639-018-9820-5
Evans, M. (2012). A Guide to Personalizing Learning: Suggestions for the Race to the Top-District Competition. An Education White Paper. Innosight Institute, August. https://files.eric.ed.gov/fulltext/ED535179.pdf
Fake, H., & Dabbagh, N. (2020). Personalized Learning Within Online Workforce Learning Environments: Exploring Implementations, Obstacles, Opportunities, and Perspectives of Workforce Leaders. Technology Knowledge and Learning. https://doi.org/10.1007/s10758-020-09441-x
Frank, J. (2020). Against technology-mediated personalized learning: resources from John William Miller and Henry Bugbee to support parental resistance. Ethics and Education, 15(1), 98–112. https://doi.org/10.1080/17449642.2019.1700445
Friend, B., Patrick, S., Schneider, C., & Vander Ark, T. (2017). What’s Possible with Personalized Learning? An Overview of Personalized Learning for Schools, Families & Communities. INACOL, 1–32. https://files.eric.ed.gov/fulltext/ED590502.pdf
Gorbunovs, A., Timsans, Z., Zuga, B., & Zagorskis, V. (2018). Conceptual design of the new generation adaptive learning management system. International Journal of Engineering and Technology(UAE), 7(2), 129–133. https://doi.org/10.14419/ijet.v7i2.28.12894
Hallman, H. L. (2019). Personalized learning through 1:1 technology initiatives: implications for teachers and teaching in neoliberal times. Teaching Education, 30(3), 299–318. https://doi.org/10.1080/10476210.2018.1466874
Hamdan, Z. (2019). A roadmap for prescribed “blend-digit” personalized learning-a collaborated responsive approach to succeeding generations in the info global age. Universal Journal of Educational Research, 7(7), 1495–1508. https://doi.org/10.13189/ujer.2019.070703
Hao, X., & Han, S. (2018). An Algorithm for Generating a Recommended Rule Set Based on Learner’s Browse Interest. International Journal of Emerging Techologies in Learning, 13(11), 102–116. https://doi.org/10.3991/ijet.v13i11.9604
Hnida, M., Idrissi, M. K., & Bennani, S. (2018). Automatic Composition of Instructional Units in Virtual Learning Environments. International Journal of Emerging Techologies in Learning, 13(6), 86–100. https://doi.org/10.3991/ijet.v13i06.8107
Huang, L., Wang, C.-D., Chao, H.-Y., Lai, J.-H., & Yu, P. S. (2019). A Score Prediction Approach for Optional Course Recommendation via Cross-User-Domain Collaborative Filtering. IEEE Access, 7, 19550–19563. https://doi.org/10.1109/ACCESS.2019.2897979
Huggins, E. S., & Kellogg, S. (2020). Technology-enabled personalized learning: A promising practice in need of robust research. School Science and Mathematics, 120(1), 1–3. https://doi.org/10.1111/ssm.12384
INACOL. (2018). State Funding Strategies to Support Education Innovation. Issue Brief. In iNACOL. https://files.eric.ed.gov/fulltext/ED590498.pdf
Jones, W. J., & Rhein, D. L. (2018). Tutorial Schools in Thailand: Perceptions and Motivations of Thai High School Students. FIRE: Forum for International Research in Education, 4(2). https://doi.org/10.18275/fire201704021137
Kallio, J. (2018). Participatory Design of Classrooms: Infrastructuring Education Reform in K-12 Personalized Learning Programs. Journal of Learning Spaces, 7(2), 35–49. https://files.eric.ed.gov/fulltext/EJ1200625.pdf
Kaput, K. (2018). Evidence for Student-Centered Learning. In Education Evolving (pp. 1–28). https://files.eric.ed.gov/fulltext/ED581111.pdf
Karolčík, Š., Zilinskiene, I., Slotkiene, A., & Čipková, E. (2019). Analysis of e-learning environment for geography: Opportunities for personalized active learning. Baltic Journal of Modern Computing, 7(3), 405–418. https://doi.org/10.22364/bjmc.2019.7.3.07
Khosravi, H., Kitto, K., & Williams, J. J. (2019). RiPPLE: A Crowdsourced Adaptive Platform for Recommendation of Learning Activities. In Journal of Learning Analytics (Vol. 6, Issue 3, pp. 91–105). https://doi.org/http://dx.doi.org/10.18608/jla.2019.63.12
Kopeyev, Z., Mubarakov, A., Kultan, J., Aimicheva, G., & Tuyakov, Y. (2020). Using a Personalized Learning Style and Google Classroom Technology to Bridge the Knowledge Gap on Computer Science. International Journal of Emerging Techologies in Learning, 15(2), 218–229. https://doi.org/10.3991/ijet.v15i02.11602
Lee, D., Huh, Y., Lin, C.-Y., & Reigeluth, C. M. (2018). Technology functions for personalized learning in learner-centered schools. ETR&D-Educational Technology Research and Development, 66(5), 1269–1302. https://doi.org/10.1007/s11423-018-9615-9
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. In PLoS Medicine (Vol. 6, Issue 7). https://doi.org/10.1371/journal.pmed.1000100
Liu, Z., Dong, L., & Wu, C. (2020). Research on Personalized Recommendations for Students’ Learning Paths Based on Big Data. International Journal of Emerging Techologies in Learning, 15(8), 40–56. https://doi.org/10.3991/ijet.v15i08.12245
Lotfi, E. (2020). Towards a New Platform Based on Learning Outcomes Analysis for Mobile Serious Games. International Journal of Emerging Techologies in Learning, 15(2), 42–57. https://doi.org/10.3991/ijet.v15i02.11637
Mariño, S. I. (2014). Los sistemas expertos para apoyar la gestión inteligente del conocimiento. Revista Vínculos, 11(1), 101–108. https://doi.org/10.14483/2322939X.8018
Marshman, E. M., DeVore, S., & Singh, C. (2018). Challenge of helping introductory physics students transfer their learning by engaging with a self-paced learning tutorial. Frontiers in ICT, 5(MAR). https://doi.org/10.3389/fict.2018.00003
Maseleno, A., Sabani, N., Huda, M., Ahmad, R., Jasmi, K. A., & Basiron, B. (2018). Demystifying learning analytics in personalised learnin. International Journal of Engineering and Technology(UAE), 7(3), 1124–1129. https://doi.org/10.14419/ijet.v7i3.9789
Monge, V. (2015). La codificación en el método de investigación de la Grounded Theory o Teoría Fundamentada. Innovaciones Educativas, 17(22), 77–84. https://doi.org/10.22458/ie.v17i22.1100
Murad, H., & Yang, L. (2018). Personalized e-learning recommender system using multimedia data. International Journal of Advanced Computer Science and Applications, 9(9), 565–567. https://doi.org/10.14569/ijacsa.2018.090971
Niknam, M., & Thulasiraman, P. (2020). LPR: A bio-inspired intelligent learning path recommendation system based on meaningful learning theory. Education and Information Technologies. https://doi.org/10.1007/s10639-020-10133-3
Pane, J. (2018). Strategies for Implementing Personalized Learning While Evidence and Resources Are Underdeveloped. RAND Corporation, October. https://doi.org/https://doi.org/10.7249/pe314
Patrick, S, Worthen, M., & Frost, D. (2018). State Strategies to Develop Teacher Capacity for Personalized, Competency-Based Learning. INACOL, 4, 1–7. https://files.eric.ed.gov/fulltext/ED588371.pdf
Patrick, Susan, Worthen, M., Truong, N., & Frost, D. (2018). Fit for Purpose: Taking the Long View on Systems Change and Policy to Support Competency Education. CompetencyWorks Final Paper. In iNACOL. https://files.eric.ed.gov/fulltext/ED589745.pdf
Raj, N. S., & Renumol, V. G. (2019). A Rule-Based Approach for Adaptive Content Recommendation in a Personalized Learning Environment: An Experimental Analysis. Proceedings - IEEE 10th International Conference on Technology for Education, T4E 2019, 138–141. https://doi.org/10.1109/T4E.2019.00033
Reigeluth, C. M. (2016). Teoría instruccional y tecnología para el nuevo paradigma de la educación. Revista de Educación a Distancia (RED), 50. https://doi.org/10.6018/red/50/1a
Rice, M. F. (2018). Supporting literacy with accessibility: Virtual school course designers’ planning for students with disabilities. Online Learning Journal, 22(4), 161–179. https://doi.org/10.24059/olj.v22i4.1508
Sáiz-Manzanares, M. C., Marticorena-Sánchez, R., Díez-Pastor, J. F., & García-Osorio, C. I. (2019). Does the use of learning management systems with hypermedia mean improved student learning outcomes? Frontiers in Psychology, 10(FEB). https://doi.org/10.3389/fpsyg.2019.00088
Sancho, Alonso, S. (2018). Miradas retro-prospectivas sobre las Tecnologías Educativas. Educatio Siglo XXI, 36(2), 209–228. https://doi.org/10.6018/j/333051
Sarma, C. (2018). Smart technological learning conceptual model. International Journal of Engineering and Technology(UAE), 7(2), 152–156. https://doi.org/10.14419/ijet.v7i2.28.12900
Schmid, R., & Petko, D. (2019). Does the use of educational technology in personalized learning environments correlate with self-reported digital skills and beliefs of secondary-school students? Computers and Education, 136, 75–86. https://doi.org/10.1016/j.compedu.2019.03.006
Shen, J. (2018). Flipping the classroom for information literacy instruction: Considerations towards personalisation and collaborative learning. Journal of Information Literacy, 12(1), 48–67. https://doi.org/10.11645/12.1.2274
Soffer, T., Kahan, T., & Nachmias, R. (2019). Patterns of Student’s Utilization of Flexibility in Online Academic Courses and Their Relation to Course Achievement. International Review of Research in Open and Distributed Learning, 20(3), 202–220.
Sun, M., & Yang, S. (2016). Personalization of learning paths in online communities of creators. Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016, 513–516. https://files.eric.ed.gov/fulltext/ED592696.pdf
Surr, W., Zeiser, K. L., Briggs, O., & Kendziora, K. (2018). Learning With Others a Study Exploring the Relationship Between Collaboration, Personalization, and Equity. American Institutes for Research (AIR), October, 1–9. https://files.eric.ed.gov/fulltext/ED592101.pdf
Tang, X., Chen, Y., Li, X., Liu, J., & Ying, Z. (2019). A reinforcement learning approach to personalized learning recommendation systems. British Journal of Mathematical and Statistical Psychology, 72(1), 108–135. https://doi.org/10.1111/bmsp.12144
Tang, Y., & Hare, R. (2019). Adaptive Narrative Game for Personalized Learning. Proceedings - IEEE International Conference on Service Operations and Logistics, and Informatics 2019, SOLI 2019, 175–180. https://doi.org/10.1109/SOLI48380.2019.8955069
Tisdell, C. C. (2018). Pedagogical alternatives for triple integrals: moving towards more inclusive and personalized learning. International Journal of Mathematical Education in Science and Technology, 49(5), 792–801. https://doi.org/10.1080/0020739X.2017.1408150
Tucker, W., & Long, D. (2018). Advancing Personalized Learning through Effective Use and Protection of Student Data. Education Leaders Report. Volume 4, No. 2. National Association of State Boards of Education, 4(2), 1–16. https://files.eric.ed.gov/fulltext/ED582105.pdf
Twyman, J., & Redding, S. (2015). Personal Competencies / Personalized Learning Reflection on Instruction : A Peer-to-Peer Learning and Observation Tool. Council of Chief State School Officers, January, 1–12. https://files.eric.ed.gov/fulltext/ED558120.pdf
UNESCO. (2017). Aprendizaje personalizado, ¿importa? https://unesdoc.unesco.org/ark:/48223/pf0000250057_spa
Uruñuela, P. (2018). La metodología del aprendizaje - servicio : aprender mejorando el mundo / Pedro María Uruñuela Nájera. http://search.ebscohost.com/login.aspx?direct=true&db=cat06493a&AN=sab.000270269&site=eds-live
Valeeva, N. G., Pavlova, E. B., & Zakirova, Y. L. (2019). M-learning in Teaching ESP: Case Study of Ecology Students. European Journal of Contemporary Education, 8(4), 920–930. https://doi.org/10.13187/ejced.2019.4.920
Wongwatkit, C., Panjaburee, P., Srisawasdi, N., & Seprum, P. (2020). Moderating effects of gender differences on the relationships between perceived learning support, intention to use, and learning performance in a personalized e-learning. Journal of Computers in Education. https://doi.org/10.1007/s40692-020-00154-9
Xiang, X., & Liu, Y. (2019). Exploring and enhancing spatial thinking skills: Learning differences of university students within a web-based GIS mapping environment. British Journal of Educational Technology, 50(4, SI), 1865–1881. https://doi.org/10.1111/bjet.12677
Zhao, X. (2018). Mobile English Teaching System Based on Adaptive Algorithm. International Journal of Emerging Techologies in Learning, 13(8), 64–77. https://doi.org/10.3991/ijet.v13i08.9057
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