Demystifying Learning Analytics in Personalised Learning

  • Authors

    • Andino Maseleno STMIK Pringsewu
    • Noraisikin Sabani Curtin Universiti Malaysia
    • Miftachul Huda Universiti Teknologi Malaysia
    • Roslee Ahmad Universiti Sains Islam
    • Kamarul Azmi Jasmi Universiti Teknologi Malaysia
    • Bushrah Basiron Universiti Teknologi Malaysia
    https://doi.org/10.14419/ijet.v7i3.9789

    Received date: March 1, 2018

    Accepted date: June 12, 2018

    Published date: June 23, 2018

  • learning analytics, framework, personalised learning.

  • Abstract

    This paper presents learning analytics as a mean to improve students’ learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper’s contribution to knowledge is in considering personalised learning within the context framework of learning analytics.

  • References

    1. . Baran, E. (2013). Connect, Participate and Learn: Transforming Pedagogies in Higher Education, Bulletin of the IEEE Technical Committee on Learning Technology, Vol. 15 No. 1, pp. 9-12.
    2. . EDUCAUSE. (2013), "7 things you should know about connected learning." Retrieved July, 2016, fromhttps://library.educause.edu/resources/2013/5/7-things-you-should-know-about-connected-learning
    3. . Siemens, G. (2004), “Connectivism: A Learning Theory for the Digital Age.” Elearnspace http://www.elearnspace.org/Articles/connectivism.htm
    4. . Sin, K and L. Muthu. (2015), “Application of Big Data in Educa-tion Data Mining and Learning Analytics – A Literature Review”, ICTACT Journal n Soft Computing, Vol. 5 Issue 4, pp. 1035-1049.
    5. . Halim, N.D.A., M.B. Ali, and N. Yahaya. (2011), “Personalized Learning Environment: Accommodating Individual Differences in Online Learning”, International Conference on Social Science and Humanity, IPEDR vol. 5 No. 2, pp. V2-398-V2-400.
    6. . Picciano, A.G. (2014), “Big Data and Learning Analytics in Blend-ed Learning Environments: Benefits and Concerns”, International Journal of Artificial Intelligence and Interactive Multimedia. Vol. 2 No. 7, pp. 35-43.
    7. . Trapp, S. (2006). E. Tomadaki and P. Scott (Eds.): “Innovative Approaches for Learning and Knowledge Sharing,” EC-TEL 2006 Workshops Proceedings, ISSN 1613-0073, pp. 28-35, 2006.
    8. . Leadbeater, C. (2008). We think: Mass innovation, not mass produc-tion. London, UK: Profile.
    9. . Bates, S. (2014). Personalised learning: Implications for curricula, staff and students. Paper presented at the Universitas 21 (U21) Ed-ucational Innovation Conference, Sydney, Australia.
    10. . Bentley, T. and R. Miller. (2004). Personalised learning: creating the ingredients for system and society wide change. (IARTV Occa-sional paper No 87). Melbourne, Australia: Incorporated Association of Registered Teachers of Victoria.
    11. . Creel, M. (2016), “Serious about Personalized Learning? Three Things Teachers and Leaders Do to Personalize Learning For In-structional Leaders and Teachers”, Powerful Practices, The Instruc-tional Leadership Experience.
    12. . Uhler, B.D. and J.E. Hurn. (2013), “Using Learning Analytics to Predict (and Improve) Student Success: A Faculty Perspective”, Journal of Interactive Online Learning, Vol. 12 No. 1, pp. 17-26.
    13. . Khalil, M. and M. Ebner. (2015). “Learning Analytics: Principles and Constraints”. In Proceedings of World Conference on Educa-tional Multimedia, Hypermedia and Telecommunications 2015. pp. 1326-1336. Chesapeake, VA: AACE.
    14. . Ferguson, R. (2012). “Learning analytics: drivers, developments and challenges”. International Journal of Technology Enhanced Learning, Vol. 4 Issue 5/6 pp. 304–317.
    15. . Brown, M. (2011), “Learning Analytics: The Coming Third Wave”, First International Conference on Learning Analytics and Knowledge (LAK11) conference.
    16. . Epstein, S. B. Epstein. (1961). The First Book of Teaching Machines. Danbury, CT: Franklin Watts, Inc.
    17. . Gallagher, R.P. (2014). “Implementations of technology enhanced personalized learning: exploration of success criteria, concerns, and characteristics”, No. 3628787 EdD, proquest dissertations and the-ses full text; proquest dissertations and theses global database, Pep-perdine University, Ann Arbor, MI, available at: http://search.proquest.com/docview/
    18. . Abdrakhmanova, Zh. K. Aisultanova, Chaltikenova Lyazat and Zh. Satkenova, 2017. Implementation of Modern Computer Science and Information Technologies in Teaching. Journal of Engineering and Applied Sciences, 12: 573-577.
    19. . Jenkins, J.M. and J. W. Keefe, (2002) “Two schools: Two approach-es to personalized learning,” Phi Delta Kappan, vol. 83, no. 6, pp. 449-456.
    20. . Chatti, M.A., M. R. Agustiawan, M. Jarke et al. (2010), “Toward a personal learning environment framework,” International Journal of Virtual and Personal Learning Environments (IJVPLE), vol. 1, no. 4, pp. 66-85.
    21. . US Department of Education (2010), Transforming American Edu-cation: learning powered by technology. Washington DC: Author. Retrieved from https://www.ed.gov/sites/default/files/NETP-2010-final-report.pdf
    22. . Sampson, D., C. Karagiannidis, and Kinshuk. (2002), “Personalised Learning: Educational, Technological and Standardisation Perspec-tive,” Interactive Educational Multimedia, vol. 4, pp. 24-39.
    23. . Kim, C. “The role of affective and motivational factors in designing personalized learning environments,” Educational Technology Re-search and Development, vol. 60, no. 4, pp. 563-584, 2012.
    24. . McDonald, J. and J. Timonen. (2009), “How could pedagogy make a choice between personalized learning systems,” Proceedings of the Authoring of Adaptive and Adaptable Hypermedia.
    25. . Thyagharajan, K. and R. Nayak. (2007), “Adaptive content creation for personalized e-learning using web services,” Journal of Applied Sciences Research, vol. 3, no. 9, pp. 828-836.
    26. . Henning, P.A., F. Heberle, A. Streicher et al. (2014), “Personalized Web Learning: Merging Open Educational Resources into Adaptive Courses for Higher Education,” Personalization Approaches in Learning Environments, pp. 55.
    27. . Yousuf, B., and O. Conlan. (2014), "Enhancing Learner Engage-ment through Personalized Visual Narratives." pp. 89-93.
    28. . Lin, Y.-S., Y.-C. Chang, K.-H. Liew et al. (2015), “Effects of con-cept map extraction and a test-based diagnostic environment on learning achievement and learners' perceptions,” British Journal of Educational Technology, pp. n/a-n/a.
    29. . Mohd, C. K. N. C. K., F. Shahbodin, A. N. C. Pee et al. (2013), “Personalized Learning Environment (PLE) Approach: Preliminary Analysis in Malaysian’s Secondary School,” International Journal of Computer and Information Technology, vol. 2, no. 03, pp. 2279-0764.
    30. . Gamrat, C., H. T. Zimmerman, J. Dudek et al. (2014), “Personalized workplace learning: An exploratory study on digital badging within a teacher professional development program,” British Journal of Ed-ucational Technology, vol. 45, no. 6, pp. 1136-1148.
    31. . Siemens, G., and D. Gasevic. (2012), “Guest Editorial-Learning and Knowledge Analytics,” Educational Technology & Society, vol. 15, no. 3, pp. 1-2.
    32. . Baker, R.S.J.D., and K. Yacef. (2009). The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Ed-ucational Data Mining, Vo. 1 No. 1, pp. 3–17.
    33. . SoLAR (Society for Learning Analytic Research. (2011), “Open Learning Analytics: an integrated & modularized platform. Proposal to design, implement and evaluate an open platform to integrate heterogeneous learning analytics techniques, SoLAR.
    34. . Goldstein, P.J., and Katz, R. N. (2005). Academic analytics: The uses of management information and technology in higher education, ECAR Research Study Volume 8. Retrieved from http://www.educause.edu/ers0508
    35. . Huda, M., Jasmi, K. A., Embong, W. H., Safar, J., Mohamad, A. M., Mohamed, A. K., Muhamad, N. H., Alas, Y., & Rahman, S. K. (2017a). Nurturing Compassion-Based Empathy: Innovative Ap-proach in Higher Education. In M. Badea, & M. Suditu (Eds.), Violence Prevention and Safety Promotion in Higher Educa-tion Settings (pp. 154-173). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2960-6.ch009
    36. . Huda, M., Jasmi, K. A., Alas, Y., Qodriah, S. L., Dacholfany, M. I., & Jamsari, E. A. (2017b). Empowering Civic Responsibility: In-sights From Service Learning. In S. Burton (Ed.), Engaged Scholar-ship and Civic Responsibility in Higher Education(pp. 144-165). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-3649-9.ch007
    37. . Long, P., and G. Siemens. (2011), “Penetrating the fog: analytics in learning and education”, Educause Review Online, Vol. 46 No. 5, pp. 31-40.
    38. . Elias, T. (2011). Learning analytics: Definitions, processes and po-tential. Retrieved from http://learninganalytics.net/LearningAnalyticsDefinitionsProcessesPotential.pdf
    39. . Barneveld, A.V., K.E. Arnold, and J.P. Campbell. (2012). Analytics in Higher Education: Establishing a Common Language (ELI Paper 1: January 2012). Boulder, CO: EDUCAUSE Learning Initiative. Retrieved from http://net.educause.edu/ir/library/pdf/ELI3026.pdf
    40. . Huda, M., Sabani, N., Shahrill, M., Jasmi, K. A., Basiron, B., & Mustari, M. I. (2017c). Empowering Learning Culture as Student Identity Construction in Higher Education. In A. Shahriar, & G. Syed (Eds.), Student Culture and Identity in Higher Education (pp. 160-179). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-2551-6.ch010
    41. . Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, Vol. 18 No. 6, pp. 683-695, doi: 10.1080/13562517.2013.827653.
    42. . Long, P., and G. Siemens. (2011), “Penetrating the fog: analytics in learning and education”, Educause Review Online, Vol. 46 No. 5, pp. 31-40.
    43. . Retalis, S., A. Papasalouros, Y. Psaromilogkos, S. Siscos, and T. Kargidis. (2006), “Towards networked learning analytics-A concept and a tool,” in Proceedings of the 5th International. Conference Networked Learning, Lancaster University, United Kingdom, 2006, pp. 1-8.
    44. . Becker, B. (2013), “Learning analytics: Insights into the natural learning behavior of our students”, Behavioral & Social Sciences Li-brarian, Vol. 32 No. 1, pp. 63-67, doi:10.1080/01639269.2013.751804.
    45. . Huda, M., Jasmi, K. A., Mustari, M. I., Basiron, B., Mohamed, A. K., Embong, W., ... & Safar, J. (2017d). Innovative E-Therapy Ser-vice in Higher Education: Mobile Application Design. International Journal of Interactive Mobile Technologies, 11(4), 83-94.
    46. . Huda, M., Haron, Z., Ripin, M. N., Hehsan, A., & Yaacob, A. B. C. (2017e). Exploring Innovative Learning Environment (ILE): Big Data Era. International Journal of Applied Engineering Re-search, 12(17), 6678-6685.
    47. . Huda, M., Shahrill, M., Maseleno, A., Jasmi, K. A., Mustari, I., & and Basiron, B. (2017f). Exploring Adaptive Teaching Competen-cies in Big Data Era. International Journal of Emerging Technolo-gies in Learning, 12(3), 68-83.
    48. . Weber, C., C. Biswell, and W. Behrens. (2014). Exploring critical issues in gifted education: A case studies approach. Waco, TX: Pru-frock Press.
    49. . BC’s Education Plan. (2015), “Focus on Learning”, British Colum-bia Ministry of Education.
    50. . Huda, M., Jasmi, K. A., Basiran, B., Mustari, M. I. B., & Sabani, A. N. (2017g). Traditional Wisdom on Sustainable Learning: An In-sightful View From Al-Zarnuji's Ta 'lim al-Muta 'allim. SAGE Open, 7(1), 1-8.
    51. . Bartle, E. (2015), “Personalised Learning: an overview”, A discus-sion paper prepared for Professor Joanne Wright, Deputy Vice Chancellor (Academic) for the Vice-Chancellor’s Retreat, March 23rd & 24th.
    52. . Chatti, M.A., A.L. Dyckhoff, U. Schroeder, and H. Thüs. (2012), “A Reference Model for Learning Analytics”, International Journal of Technology Enhanced Learning, Vol. 4 Issue 5/6, pp. 318-331
    53. . Suthers, D., and K. Verbert. (2013), “Learning analytics as a “mid-dle space” “, Proceedings of the Third International Conference on Learning Analytics and Knowledge, 1-4, New York, USA: ACM.
    54. . Pistilli, M.D., K.E. Arnold, and M. Bethune. (2012). Signals: Using Academic Analytics to Promote Student Success. EDUCAUSE Review Online. Retrieved from http://www.educause.edu/ero/article/signals-usingacademic-analytics-promote-student-success
    55. . Ali, L., M. Hatala, D. Gasevic, and J. Jovanovic. (2012). A qualita-tive evaluation of evolution of a learning analytics tool. Computers & Education. 58 (1), pp. 470-489.
    56. . Tabaa, Y., and A. Medouri. (2013), “LASyM: A Learning Analytics System for MOOCs”, International Journal of Advanced Computer Science and Applications, Vol. 4 No. 5, pp. 113-119.
    57. . Dawson, S., D. Gašević, G. Siemens, and S. Joksimovic. (2014). Current State and Future Trends: A Citation Network Analysis of the Learning Analytics Field. In Proceedings of the Fourth Interna-tional Conference on Learning Analytics and Knowledge (pp. 231–240). New York, NY, USA: ACM. doi:10.1145/2567574.2567585
    58. . Othman, R., Shahrill, M., Mundia, L., Tan, A., & Huda, M. (2016). Investigating the Relationship Between the Student’s Ability and Learning Preferences: Evidence from Year 7 Mathematics Stu-dents. The New Educational Review, 44(2), 125-138.
    59. . IMS Global Learning Consortium. (2015), “Annual Report 2015: Better Learning from Better Learning Technology”. Retrieved from http://www.imsglobal.org/sites/default/files/2015annualReport.pdf.
    60. . IMS Global Learning Consortium. (2016). “Transitioning to Inte-grated Digital Curriculum Using IMS Standards”. Retrieved from: https://www.imsglobal.org/sites/default/files/K12transition2016.pdf,
    61. . Abel, R., M. Brown, and J. Suess. (2013), “A New Architecture for Learning,” Educause Review, vol. 48, no. 5, pp. 88.
    62. . IMS Global Learning Consortium. (2013), “Learning Measurement for Analytics Whitepaper”. Retrieved from https://www.imsglobal.org/sites/default/files/caliper/IMSLearningAnalyticsWP.pdf
    63. . CAPSS (Connecticut Association of Public School Superintendents). (2014), “A Look To The Future: Personalized Learning in Connecti-cut, White Paper on Personalized Learning”, 2014.
    64. . Huda, M., Maseleno, A., Jasmi, K. A., Mustari, I., & Basiron, B. (2017k). Strengthening Interaction from Direct to Virtual Basis: Insights from Ethical and Professional Empowerment. International Journal of Applied Engineering Research, 12(17), 6901-6909.
    65. . Huda, M., Haron, Z., Ripin, M. N., Hehsan, A., & Yaacob, A. B. C. (2017l). Exploring Innovative Learning Environment (ILE): Big Data Era. International Journal of Applied Engineering Research, 12(17), 6678-6685.
    66. . Maseleno, A., Huda, M., Siregar, M., Ahmad, R., Hehsan, A., Haron, Z., Ripin, M.N., Ihwani, S.S., and Jasmi, K.A. (2017). Combining the Previous Measure of Evidence to Educational Entrance Examination. Journal of Artificial Intelligence 10(3), 85-90.
    67. . Maseleno, A., Pardimin, Huda, M., Ramlan, Hehsan, A., Yusof, Y.M., Haron, Z., Ripin, M.N., Nor, N.H.M., and Junaidi, J. (2018a). Mathematical Theory of Evidence to Subject Expertise Diagnostic. ICIC Express Letters, 12 (4), 369 DOI: 10.24507/icicel.12.04.369
    68. . Maseleno, A., Huda, M., Jasmi, K.A., Basiron, B., Mustari, I., Don, A.G., and Ahmad, R. (2018b). Hau-Kashyap approach for student’s level of expertise. Egyptian Informatics Journal, doi.org/10.1016/j.eij.2018.04.001.
    69. . Huda, M., Teh, K.S.M., Nor, N.H.M., and Nor, M.B.M. (2018a). Transmitting Leadership Based Civic Responsibility: Insights from Service Learning. International Journal of Ethics and Systems, 34(1), 20-31.
    70. . Huda, M., Maseleno, A., Muhamad, N.H.N., Jasmi, K.A., Ahmad, A., Mustari, M.I., Basiron, B. (2018b). Big Data Emerging Technology: Insights into Innovative Environment for Online Learning Resources. International Journal of Emerging Technologies in Learning 13(1), 23-36. doi:10.3991/ijet.v13i01.6990
    71. . Huda, M., Maseleno, A., Teh, K.S.M., Don, A.G., Basiron, B., Jasmi, K.A., Mustari, M.I., Nasir, B.M., and Ahmad, R. (2018c). Understanding Modern Learning Environment (MLE) in Big Data Era. International Journal of Emerging Technologies in Learning. 13(5), 71-85. doi: 10.3991/ijet.v13i05.8042
    72. . Rosli, M.R.B., Salamon, H.B., and Huda, M. (2018). Distribution Management of Zakat Fund: Recommended Proposal for Asnaf Riqab in Malaysia. International Journal of Civil Engineering and Technology 9(3), pp. 56–64.
    73. . Aminin, S., Huda, M., Ninsiana, W., and Dacholfany, M.I. (2018). Sustaining civic-based moral values: Insights from language learning and literature. International Journal of Civil Engineering and Technology. 9(4), 157-174.
    74. . Huda, M., Jasmi, K. A., Mustari, M. I., Basiron, B., Mohamed, A. K., Embong, W., ... & Safar, J. (2017g). Innovative E-Therapy Service in Higher Education: Mobile Application Design. International Journal of Interactive Mobile Technologies, 11(4), 83-94.
    75. . Huda, M., Siregar, M., Ramlan, Rahman, S.K.A., Mat Teh, K.S., Said, H., Jamsari, E.A., Yacub, J., Dacholfany, M.I., & Ninsiana, W. (2017j). From Live Interaction to Virtual Interaction: An Exposure on the Moral Engagement in the Digital Era. Journal of Theoretical and Applied Information Technology, 95(19), 4964-4972.
    76. . Huda, M. (2018). Empowering Application Strategy in the Technology Adoption: Insights from Professional and Ethical Engagement. Journal of Science and Technology Policy Management. doi.org/10.1108/JSTPM-09-2017-0044.
    77. . Huda, M., & Teh, K. S. M. (2018). Empowering Professional and Ethical Competence on Reflective Teaching Practice in Digital Era. In Dikilitas, K., Mede, E., Atay D. (Eds). Mentorship Strategies in Teacher Education (pp. 136-152). Hershey, PA: IGI Global. doi: 10.4018/978-1-5225-4050-2.ch007
    78. . Huda., M. & Sabani, N. (2018). Empowering Muslim Children’s Spirituality in Malay Archipelago: Integration between National Philosophical Foundations and Tawakkul (Trust in God). International Journal of Children's Spirituality, 23(1), 81-94.
    79. . Moksin, A. I., Shahrill, M., Anshari, M., Huda, M., & Tengah, K. A. (2018). The Learning of Integration in Calculus Using the Autograph Technology. Advanced Science Letters, 24(1), 550-552.
    80. . Huda, M., Anshari, M., Almunawar, M. N., Shahrill, M., Tan, A., Jaidin, J. H., ... & Masri, M. (2016a). Innovative Teaching in Higher Education: The Big Data Approach. The Turkish Online Journal of Educational Technology, 15(Special issue), 1210-1216.
    81. . Huda, M., Yusuf, J. B., Jasmi, K. A., & Nasir, G. A. (2016b). Understanding Comprehensive Learning Requirements in the Light of al-Zarnūjī’s Taʻlīm al-Mutaʻallim. Sage Open, 6(4), 1-14.
    82. . Huda, M., Yusuf, J. B., Jasmi, K. A., & Zakaria, G. N. (2016c). Al-Zarnūjī's Concept of Knowledge ('ilm). SAGE Open, 6(3), 1-13.
    83. . Huda, M., Jasmi, K. A., Mohamed, A. K., Wan Embong, W. H., & and Safar, J. (2016d). Philosophical Investigation of Al-Zarnuji's Ta'lim al-Muta'allim: Strengthening Ethical Engagement into Teaching and Learning. Social Science, 11(22), 5516-551.

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    Maseleno, A., Sabani, N., Huda, M., Ahmad, R., Azmi Jasmi, K., & Basiron, B. (2018). Demystifying Learning Analytics in Personalised Learning. International Journal of Engineering and Technology, 7(3), 1124-1129. https://doi.org/10.14419/ijet.v7i3.9789