Advertisement

Towards Integrating Conversational Agents and Learning Analytics in MOOCs

  • Stavros Demetriadis
  • Anastasios Karakostas
  • Thrasyvoulos Tsiatsos
  • Santi Caballé
  • Yannis Dimitriadis
  • Armin Weinberger
  • Pantelis M. Papadopoulos
  • George Palaigeorgiou
  • Costas Tsimpanis
  • Matthew Hodges
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 17)

Abstract

Higher Education Massive Open Online Courses (MOOCs) introduce a way of transcending formal higher education by realizing technology-enhanced formats of learning and instruction and by granting access to an audience way beyond students enrolled in any one Higher Education Institution. However, although MOOCs have been reported as an efficient and important educational tool, there is a number of issues and problems related to their educational impact. More specifically, there is an important number of drop outs during a course, little participation, and lack of students’ motivation and engagement overall. This may be due to one-size-fits-all instructional approaches and very limited commitment to student-student and teacher-student collaboration. This paper introduces the development agenda of a newly started European project called “colMOOC” that aims to enhance the MOOCs experience by integrating collaborative settings based on Conversational Agents and screening methods based on Learning Analytics, to support both students and teachers during a MOOC course. Conversational pedagogical agents guide and support student dialogue using natural language both in individual and collaborative settings. Integrating this type of conversational agents into MOOCs to trigger peer interaction in discussion groups can considerably increase the engagement and the commitment of online students and, consequently, reduce MOOCs dropout rate. Moreover, Learning Analytics techniques can support teachers’ orchestration and students’ learning during MOOCs by evaluating students’ interaction and participation. The research reported in this paper is currently undertaken within the research project colMOOC funded by the European Commission.

Notes

Acknowledgements

This research was funded by the European Commission through the project “colMOOC: Integrating Conversational Agents and Learning Analytics in MOOCs” (588438-EPP-1-2017-1-EL-EPPKA2-KA).

References

  1. 1.
    Lankshear, C., Knobel, M. (eds.): Digital Literacies: Concepts, Policies and Practices. Peter Lang, New York (2008). ISBN 9781433101687Google Scholar
  2. 2.
    Siemens, G.: Massive open online courses: innovation in education. In: Open Educational Resources: Innovation, Research and Practice, p. 5 (2013)Google Scholar
  3. 3.
    Tegos, S., Demetriadis, S.N.: Leveraging conversational agents and concept maps to scaffold students’ productive talk. In: Proceedings of 6th International Conference on Intelligent Networking and Collaborative Systems (INCoS 2014), Salerno, Italy, pp. 176–183 (2014)Google Scholar
  4. 4.
    Bassi, R., Daradoumis, T., Xhafa, F., Caballé, S., Sula, A.: Software agents in large scale open e-learning: a critical component for the future of massive online courses (MOOCs). In: Proceedings of the Sixth IEEE International Conference on Intelligent Networking and Collaborative Systems, pp. 184–188. IEEE Computer Society (2014)Google Scholar
  5. 5.
    Daradoumis, T., Bassi, R., Xhafa, F., Caballé, S.: A review on massive e-learning (MOOC) design, delivery and assessment. In: Proceedings of the Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 208–213. IEEE Computer Society (2013)Google Scholar
  6. 6.
    Barak, M., Watted, A., Haick, H.: Motivation to learn in massive open online courses: examining aspects of language and social engagement. Comput. Educ. 94, 49–60 (2016)CrossRefGoogle Scholar
  7. 7.
    Capuano, N., Caballé, S.: Towards adaptive peer assessment for MOOCs. In: Proceedings of the 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 64–69. IEEE Computer Society (2015)Google Scholar
  8. 8.
    Schuwer, R., Jaurena, I.G., Aydin, C.H., Costello, E., Dalsgaard, C., Brown, M., Teixeira, A.: Opportunities and threats of the MOOC movement for higher education: the European perspective. Int. Rev. Res. Open Distrib. Learn. 16(6), 20–38 (2015)CrossRefGoogle Scholar
  9. 9.
    Miguel, J., Caballé, S., Prieto, J.: Providing information security to MOOC: towards effective student authentication. In: Proceedings of the Fifth IEEE International Conference on Intelligent Networking and Collaborative Systems, pp. 289–292. IEEE Computer Society (2013)Google Scholar
  10. 10.
    Capuano, N., Caballé, S., Miguel, J.: Improving peer grading reliability with graph mining techniques. Int. J. Emerg. Technol. Learn. 11(7), 24–33 (2016)CrossRefGoogle Scholar
  11. 11.
    Tegos, S., Demetriadis, S.: Conversational agents improve peer learning through building on prior knowledge. Educ. Technol. Soc. 20(1), 99–111 (2017)Google Scholar
  12. 12.
    Gañán, D., Caballé, S., Clarisó, R., Conesa, J., Bañeres, D.: ICT-FLAG: a web-based e-assessment platform featuring learning analytics and gamification. J. Web Inform. Syst. 13(1), 25–54 (2017)CrossRefGoogle Scholar
  13. 13.
    Tegos, S., Demetriadis, S., Karakostas, A.: Promoting academically productive talk with conversational agent interventions in collaborative learning settings. Comput. Educ. 87, 309–325 (2015)CrossRefGoogle Scholar
  14. 14.
    Karakostas, A., Demetriadis, S.: Adaptive vs. fixed domain support in the context of scripted collaborative learning. Educ. Technol. Soc. 17(1), 206–217 (2014)Google Scholar
  15. 15.
    Tegos, S., Demetriadis, S., Tsiatsos, T.: A configurable conversational agent to trigger students’ productive dialogue: a pilot study in the CALL domain. Int. J. Artif. Intell. Educ. 24(1), 62–91 (2013).  https://doi.org/10.1007/s40593-013-0007-3 CrossRefGoogle Scholar
  16. 16.
    Kumar, R., Rose, C.P.: Architecture for building conversational agents that support collaborative learning. IEEE Trans. Learn. Technol. 4(1), 21–34 (2011)CrossRefGoogle Scholar
  17. 17.
    Michaels, S., O’Connor, M.C., Hall, M.W., Resnick, L.B.: Accountable talk sourcebook: for classroom that works. University of Pittsburgh, Institute for Learning (2010). https://www.ortingschools.org/cms/lib/WA01919463/Centricity/domain/326/purpose/research/accountable%20sourcebook.pdf. Accessed 11 Jan 2018
  18. 18.
    Noroozi, O., Weinberger, A., Biemans, H.J.A., Mulder, M., Chizari, M.: Facilitating argumentative knowledge construction through a transactive discussion script in CSCL. Comput. Educ. 61(2), 59–76 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Stavros Demetriadis
    • 1
  • Anastasios Karakostas
    • 2
  • Thrasyvoulos Tsiatsos
    • 1
  • Santi Caballé
    • 3
  • Yannis Dimitriadis
    • 4
  • Armin Weinberger
    • 5
  • Pantelis M. Papadopoulos
    • 6
  • George Palaigeorgiou
    • 7
  • Costas Tsimpanis
    • 8
  • Matthew Hodges
    • 9
  1. 1.Aristotle University of ThessalonikiThessalonikiGreece
  2. 2.Centre for Research and Technology HellasThermiGreece
  3. 3.Universitat Oberta de CatalunyaBarcelonaSpain
  4. 4.Universidad de ValladolidValladolidSpain
  5. 5.University of SaarlandSaarbrückenGermany
  6. 6.Aarhus UniversityAarhusDenmark
  7. 7.LearnworldsLondonUK
  8. 8.Greek Universities NetworkAthensGreece
  9. 9.Telefónica‎MadridSpain

Personalised recommendations