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Learning Analytics in Distance and Mobile Learning for Designing Personalised Software

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Machine Learning Paradigms

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 158))

Abstract

Distance Learning, in its synchronous and asynchronous form, has gained an increasing interest over the last decades, both because of the realization of the “digital era” and also due to the reachability and accessibility it offered to human education. Lately, mobile learning has also been gaining a lot of interest due to the widespread popularity of Smartphones. In order to improve human educational interaction with Personal Computers and Smartphones, collecting learning analytics data and utilizing them is considered as a valuable requirement. Distance and mobile learning analytics may improve and assist the entire learning process by providing personalized software solutions. This paper focuses on the collection and the combination of the learning analytics data offered by different modalities in Personal Computers and modern Smartphones. For this combination two different Multi-Criteria Decision making theories are used, namely the Analytical Hierarchy Process and the Simple Additive Weighting model.

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References

  1. Merisitis, JP., Phipps, RA.: What’s the Difference?: Outcomes of Distance Vs. Traditional Classroom-Based Learning, Change: The Magazine of Higher Learning 31(3), 12–14 (1999). https://doi.org/10.1080/0091389909602686

  2. Peraton, H.: Open and Distance Learning in the Developing World. Routledge, Taylor & Francis Group, London and New York (2007)

    Google Scholar 

  3. Moller, L., Foshay, W., Huett, J.: The evolution of distance education: implications for instructional design on the potential of the Web. TechTrends 52(4), 66–70 (2008)

    Article  Google Scholar 

  4. Gartner: Gartner says by 2018, more than 50 percent of users will use a tablet or smartphone first for all online activities. http://www.gartner.com/newsroom/id/2939217 (2014)

  5. Gartner: Gartner forecasts at worldwide device shipments until 2018. http://www.gartner.com/newsroom/id/3560517 (2017)

  6. The Telegraph: Mobile web usage overtakes desktop for first time. http://www.telegraph.co.uk/technology/2016/11/01/mobile-web-usage-overtakes-desktop-for-first-time/ (2016)

  7. Zopounidis, C.: Knowledge-based multi-criteria decision support. Eur. J. Oper. Res. 195, 827–828 (2009)

    Article  Google Scholar 

  8. Saaty, Τ.: The Analytic Hierarchy Process. McGraw-Hill, New York, NY (1980)

    Google Scholar 

  9. Tiwari, N.:. Using the Analytic Hierarchy Process (AHP) to identify Performance Scenarios for Enterprise Application. Comput. Meas. Group, Measure It 4(3) (2006)

    Google Scholar 

  10. Mulubrhan, F., Akmar Mokhtar, A., Muhammad, M.: Comparative analysis between fuzzy and traditional analytical hierarchy process. In: MATEC Web of Conferences 13 (2014)

    Google Scholar 

  11. Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, vol. 186. Springer, Berlin, Heidelberg, New York (1981)

    Google Scholar 

  12. Casey, D.: A journey to legitimacy: the historical development of distance education through technology. TechTrends 52(2), 45–51 (2008)

    Article  Google Scholar 

  13. Hong, J.C., Tai, K.H., Hwang, M.Y., Kuo, Y.C., Chen, J.S.: Internet cognitive failure relevant to users’ satisfaction with content and interface design to reflect continuance intention to use a government e-learning system. Comput. Hum. Behav. 66, 353–362 (2017)

    Article  Google Scholar 

  14. Al-Gahtani, S.S.: Empirical investigation of e-learning acceptance and assimilation: a structural equation model. Appl. Comput. Inf. 12(1), 27–50 (2016)

    MathSciNet  Google Scholar 

  15. Moore, J.L., Dickson-Deane, C., Galyen, K.: e-Learning, online learning, and distance learning environments: are they the same? Internet High. Educ. 14, 129–135 (2010)

    Article  Google Scholar 

  16. Gunawardena, C.N., McIsaac, M.S.: Distance education. In: Jonassen, D.H. (ed.), Handbook of Research for Educational Communications and Technology, 2nd edn., pp. 355–395. Lawrence Erlbaum Associates, Inc., Publishers, Mahwah, New Jersey (2004)

    Google Scholar 

  17. Luo, N., Zhang, M., Qi, D.: Effects of different interactions on students’ sense of community in e-learning environment. Comput. Educ. 115(2017), 153–160 (2017)

    Google Scholar 

  18. Feldmann, B.: Two decades of e-learning in distance teaching from web 1.0 to web 2.0 at the University of Hagen. In: Learning Technology for Education Challenges, Communication in Computer and Information Science, vol. 446, pp. 163–172 (2014)

    Google Scholar 

  19. Garcia, R., Falkner, K., Vivian, R.: Systematic literature review: self-regulated learning strategies using e-learning tools for computer science. Comput. Educ. 123, 150–163 (2018)

    Article  Google Scholar 

  20. Alepis, E., Virvou, M.: Object-Oriented User Interfaces for Personalized Mobile Learning. Intelligent Systems Reference Library, vol. 64. Springer, Berlin (2014)

    Google Scholar 

  21. Virvou, M., Alepis, E.: User modeling in mobile learning environments for learners with special needs. In: Tsihrintzis, G., Virvou, M., Jain, L. (eds.): Multimedia Services in Intelligent Environments. Smart Innovation, Systems and Technologies, vol. 25. Springer, Heidelberg (2013)

    Google Scholar 

  22. Nie, L., Zhang, L., Wang, M., Hong, R., Farseev, A., Chua, T.-S.: Learning user attributes via mobile social multimedia analytics. ACM Trans. Intell. Syst. Technol. 8(3), Article 36 (2017)

    Google Scholar 

  23. Ravì, D., Wong, C., Lo, B., Yang, G.Z.: A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE J. Biomed. Heal. Inform. 21(1), 56–64 (2017)

    Article  Google Scholar 

  24. Alsheikh, M.A., Niyato, D., Lin, S., Tan, H.P., Han, Z.: Mobile big data analytics using deep learning and apache spark. IEEE Netw. 30(3), 22–29 (2016)

    Google Scholar 

  25. Tabuenca, B., Kalz, M., Drachsler, H., Specht, M.: Time will tell: the role of mobile learning analytics in self-regulated learning. Comput. Educ. 89, 53–74 (2015)

    Article  Google Scholar 

  26. Kuik, T., Mumblat, Y., Dim, E.: Enabling mobile user modeling: infrastructure for personalization in ubiquitous computing. In: 2nd ACM International Conference on Mobile Software Engineering and Systems, MOBILESoft 2015, Florence, Italy, pp. 48–51, 16–17 May 2015

    Google Scholar 

  27. Avouris, N.M., Yiannoutsou, N.: A review of mobile location-based games for learning across physical and virtual spaces. J. UCS 18(15), 2120–2142 (2012)

    Google Scholar 

  28. Fidas, C.A., Avouris, N.M.: Personalization of mobile applications in cultural heritage environments. In: 6th International Conference on Information, Intelligence, Systems and Applications, IISA 2015, Corfu, Greece, pp. 1–6, 6–8 July 2015

    Google Scholar 

  29. Kirci, P., Unal, P.: Personalization of mobile health applications for remote health monitoring. In: Proceedings of the International Workshop on Personalization in Persuasive Technology Co-located with the 11th International Conference on Persuasive Technology (PT 2016), Salzburg, Austria, April, pp. 120–125, 5 Apr 2016

    Google Scholar 

  30. Wecker, A.J., Kuik, T., Stock, O.: Dynamic personalization based on mobile behavior: From personality to personalization: a blueprint. In: Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct, MobileHCI ‘16, ACM, New York, NY, USA, pp. 978–983(2016)

    Google Scholar 

  31. Zare, S.: Personalization in mobile learning for people with special needs. In: Stephanidis, C. (eds.) Universal Access in Human-Computer Interaction. Applications and Services. UAHCI 2011. Lecture Notes in Computer Science, vol. 6768. Springer, Berlin, Heidelberg (2011)

    Google Scholar 

  32. Qoussini, A.E.M., Jusoh, Y.Y.B.: A review on personalization and agents technology in mobile learning. In: 2014 International Conference on Intelligent Environments, Shanghai, pp. 260–264 (2014)

    Google Scholar 

  33. Badidi, E.: A Cloud-based framework for personalized mobile learning provisioning using learning objects metadata adaptation. In: Proceedings of the 8th International Conference on Computer Supported Education, CSEDU, vol. 1, pp. 368–375 (2016). ISBN 978-989-758-179-3

    Google Scholar 

  34. Sandberg, J., Maris, M., Hoogendoorn, P.: The added value of a gaming context and intelligent adaptation for a mobile learning application for vocabulary learning. Comput. Educ. 76(2014), 119–130 (2014)

    Article  Google Scholar 

  35. Politou, E., Alepis, E., Patsakis, C.: Forgetting personal data and revoking consent under the GDPR: challenges and proposed solutions. J. Cybersecur. 1–20 (2018)

    Google Scholar 

  36. Casino, F., Domingo-Ferrer, J., Patsakis, C., Puig, D., Solanas, A.: A k-anonymous approach to privacy preserving collaborative filtering. J. Comput. Syst. Sci. 81(6), 1000–1011 (2015)

    Article  Google Scholar 

  37. Karray, F., Alemzadeh, M., Saleh, J.A., Arab, M.N.: Human-computer interaction: overview on state of the art. Int. J. Smart Sens. Intell. Syst. 1(1) (2008)

    Google Scholar 

  38. Palanque, P., Paterno, F.: Interactive Systems. Design, Specification, and Verification. Springer Science & Business Media, p. 43 (2001). ISBN 9783540416630

    Google Scholar 

  39. Zahálka, J., Rudinac, S., Worring, M.: Interactive multimodal learning for venue recommendation. IEEE Trans. Multimed. 17(12), 2235–2244 (2015)

    Article  Google Scholar 

  40. Shin, H., Lee, M., Kim, E.Y.: Personalized digital TV content recommendation with integration of user behavior profiling and multimodal content rating. IEEE Trans. Consum. Electron. 55(3), 1417–1423 (2009)

    Article  Google Scholar 

  41. Virvou, M., Tsihrintzis, G.A., Alepis, E., Stathopoulou, I.O., Kabassi, K.: Emotion recognition: empirical studies towards the combination of audio-lingual and visual-facial modalities through multi-attribute decision making. Int. J. Artif. Intell. Tools 21(02) (2012)

    Google Scholar 

  42. Alepis, E., Virvou, M., Drakoulis, S.: Human smartphone interaction: Exploring smartphone senses. In: IISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications, Chania, pp. 44–48 (2014)

    Google Scholar 

  43. Tsai, T.-C., Peng, C.-T.: A Smartphone assisted learning system with wireless sensors. In: Communications in Computer and Information Science, CCIS, vol. 216, Issue PART 3, pp. 557–561 (2011)

    Google Scholar 

  44. Mehta, D.D., Zañartu, M., Feng, S.W., Cheyne, H.A.I., Hillman, R.E.: Mobile voice health monitoring using a wearable accelerometer sensor and a smartphone platform. IEEE Trans. Biomed. Eng. 59(12 PART2), pp. 3090–3096, Article number 6257444 (2012)

    Google Scholar 

  45. Prudêncio, J., Aguiar, A., Lucani, D.: Physical Activity Recognition from Smartphone Embedded Sensors. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS, vol. 7887, pp. 863–872 (2013)

    Google Scholar 

  46. Zhang, L., Liu, J., Jiang, H., Guan, Y.: SensTrack: energy-efficient location tracking with smartphone sensors. IEEE Sens. J. 13(10), 3775–3784, Article number 6563204 (2013)

    Google Scholar 

  47. Ahmetovic, D.: Smartphone-assisted mobility in urban environments for visually impaired users through computer vision and sensor fusion. In: Proceedings—IEEE International Conference on Mobile Data Management, vol. 2, pp. 15–18, Article number 6569055 (2013)

    Google Scholar 

  48. Sirah, S., Mikhailov, L., Keane, J.A.: PriEsT: an interactive decision support tool to estimate priorities from pair-wise comparsion judgements. Int Trans Oper Res 22(2), 203–382 (2015)

    Google Scholar 

  49. Kabassi, K., Virvou, M.: Combining decision making theories with a cognitive theory for intelligent help: a comparison. IEEE Trans. Hum. Mach. Syst. 45(2), 176–186 (2015)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Ionian University, Zakynthos, Greece

    Katerina Kabassi

  2. University of Piraeus, Piraeus, Greece

    Efthimios Alepis

Authors
  1. Katerina Kabassi
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  2. Efthimios Alepis
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Corresponding author

Correspondence to Katerina Kabassi .

Editor information

Editors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    Efthimios Alepis

  3. University of Piraeus, Pireas, Greece

    George A. Tsihrintzis

  4. Faculty of Science, Technology and Mathematics, University of Canberra, Canberra, SA, Australia

    Lakhmi C. Jain

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Kabassi, K., Alepis, E. (2020). Learning Analytics in Distance and Mobile Learning for Designing Personalised Software. In: Virvou, M., Alepis, E., Tsihrintzis, G., Jain, L. (eds) Machine Learning Paradigms. Intelligent Systems Reference Library, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-13743-4_10

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