Collecting and Analysing Learners Data to Support the Adaptive Engine of OPERA, a Learning System for Mathematics

Marisa Oliveira, Alcinda Barreiras, Graça Marcos, Hermínia Ferreira, Ana Azevedo, Carlos Vaz de Carvalho

2017

Abstract

Learning mathematics has always been (and still is) a major issue. Many students fail to understand the basic concepts and/or are unable to apply them. These students end up moving to other subject areas or simply dropping out. One of the major reasons for this problem is the fact that the educational system is only prepared to apply standardized teaching methods that do not respect or fit the individual characteristics of each student. This paper presents the OPERA learning adaptive system that provides the foundations for further mathematics learning while addressing the diversity of the users/learners. OPERA collects learner interaction data to monitor the learning process in an active and contextualized way and to identify the users’ difficulties and achieved knowledge in each stage. Based on the data analysis, OPERA then reorganizes the sequence of contents and provides the precise information needed to progress which makes learning much more efficient.

References

  1. ADLnet. (2016). The xAPI overview. Obtido de https://www.adlnet.gov/adl-research/performancetracking-analysis/experience-api/
  2. Amrieh, E., Hamtini, T., and Aljarah, I. (2015). Preprocessing and analyzing educational data set using X-API for improving student's performance. IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), (pp. 1-5).
  3. Andrade, A., Gouveia, D., Nogueira, F., Russo, P., Vaz de Carvalho, C. (2015). Games to support problem-based learning. 10th Iberian Conference on Information Systems and Technologies (CISTI), (pp. 1-4). Aveiro, Portugal.
  4. doi: 10.1109/CISTI.2015.7170621
  5. Bakharia, A., Kitto, K., Pardo, A., and Gasevic, D. (2016). Recipe for Success - Lessons Learnt from Using xAPI within the Connected Learning Analytics Toolkit. 6th International Conference on Learning Analytics and Knowledge (LAK 2016). Edinburgh, Scotland.
  6. Berg, A., Scheffel, M., Drachsler, H., Ternier, S., and Specht, M. (2016). Dutch Cooking with xAPI Recipes: The Good, the Bad, and the Consistent. IEEE 16th International Conference on Advanced Learning Technologies (ICALT).
  7. Brusilovsky, P., and Peylo, C. (2003). Adaptive and Intelligent Web-based Educational Systems. International Journal of Artificial Intelligence in Education, 13(2-4), 156-169. https://doi.org/ 10.1109/ICAICT.2010.5612054
  8. De Nies, T., Salliau, F., Verborgh, R., and Van de Walle, R. (2016). TinCan2PROV: Exposing Interoperable Provenance of Learning Processes through Experience API Logs. Linked Learning Workshop.
  9. Folden, R. W. (2012). General perspective in learning management systems. In R. Babo and A. Azevedo (Eds.), Higher Education Institutions and Learning Management Systems: Adoption and Standardization (pp. 1-27). Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-60960-884-2.ch001
  10. Guo, R., Palmer-Brown, D., Lee, S. W., and Cai, F. F. (2014). Intelligent diagnostic feedback for online multiple-choice questions. Artificial Intelligence Review, 42(3), 369-383. https://doi.org/ 10.1007/s10462-013-9419-6
  11. Hamann, M., Saul, C., and Wuttke, H.-D. (2015). PANDA: A platform for open learning analytics. 7th International Conference on Computer Supported Education, CSEDU 2015, 1, 467-473. https://doi.org/10.5220/0005489804670473
  12. Kara, N., and Sevem, N. (2013). Adaptive Learning Systems: Beyond Teaching Machines. Contemporary Educational Technology, 4(2).
  13. Kazanidis, I., and Satratzemi, M., (2009). Adaptivity in ProPer: an adaptive SCORM compliant LMS. Journal of Distance Education Technologies. Vol.7 No. 2, pp. 44-62.
  14. Long, R., Medford, A., Diaz, G., Murphy, J., Ruprecht, C., Kilcullen, T., and Harvey Jr., R. (2016). Evaluating Adaptive Training for Teams using the Experience API. MODSIM World 2016.
  15. Manso-Vázquez, M., Caeiro-Rodríguez, M., and LlamasNistal, M. (2015). xAPI-SRL: Uses of an application profile for self-regulated learning based on the analysis of learning strategies. IEEE Frontiers in Education Conference (FIE), (pp. 1-8).
  16. Medina-Medina, N., and García-Cabrera, L. (2016). A taxonomy for user models in adaptive systems: special considerations for learning environments. The Knowledge Engineering Review, 31(2), 124-141. https://doi.org/10.1017/S0269888916000035
  17. Michalewicz, Z., Schmidt, M., Michalewicz, M., and Chiriac, C. (2007). Adaptive Business Intelligence. Berlin: Springer-Verlag. https://doi.org/10.1007/978-3- 540-32929-9
  18. Murphy, J., Hannigan, F., Hruska, M., and Diaz, G. (2016). Leveraging Interoperable Data to Improve Training Effectiveness Using the Experience API (XAPI). Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience, pp. 46-54.
  19. Oxman, S., and Wong, W. (2014). Adaptive Learning Systems. DV X Innovations DeVry Education Group.
  20. Paramythis, A., and Loidl-Reisinger, S. (2004). Adaptive learning environments and e-learning standards. Electronic Journal of Elearning, 2(1), 181-194. https://doi.org/10.1.1.131.288
  21. Peres, P., Mesquita, A., and Pimenta, P. (2015). Guia prático do e-learning: casos práticos nas organizações. Porto, Portugal: Vida Económica.
  22. Poonam, A., and Bhowmick, P. (2016). Architecture for User Experience Tracking and Analytics in National Digital Library (NDL). IEEE Eighth International Conference on Technology for Education (T4E), (pp. 176-179).
  23. Rustici Software. (September de 2016). Benefits of SCORM. Obtido de http://scorm.com/scormexplained/business-of-scorm/benefits-of-scorm/
  24. Rustici Software. (2016). The Enterprise Learning Ecosystem. Obtido de https://experienceapi.com/ ecosystem/
  25. Sharda, R., Delen, D., and Turban, E. (2014). Business Intelligence: a Managerial Perspective on Analytics - Third Edition. Upper Saddle River, NJ: Pearson Prentice Hall.
  26. Traore, M. (2016). Implementation of the xAPI specification (Experience API) within the SOFAD author environment. Obtido de https://www. researchgate.net/publication/272790214_Implementati on_of_the_xAPI_specification_Experience_API_withi n_the_SOFAD_author_environments.pdf
  27. Tsai, H.-L., Lee, C.-J., HSU, W.-H. L., and Chang, Y.-H. (2012). An adaptive e-learning system based on intelligent agents. In Proceeding of the 11th International Conference on Applied Computer and Applied Computer Science (pp. 139-142).
  28. Turban, E., Sharda, R., Aroson, J. E., and Liang, T. (2007). Decision support and business intelligence system. Upper Sadle River. NJ. Retrieved from http://www.britannica.com/EBchecked/topic/287895/i nformation-system/218061/Decision-support-systemsand-business-intelligence
  29. Vaz de Carvalho, C., Escudeiro, P., Caeiro Rodriguez, M., and Llamas Nistal, M. (2016). Sustainability strategies for open educational resources and repositories. Latin American Conference on Learning Objects and Technology (LACLO). San Carlos, Costa Rica: IEEE.
  30. Vidal, J., Rabelo, T., and Lama, M. (2015). Semantic Description of the Experience API Specification. IEEE 15th International Conference on Advanced Learning Technologies, (pp. 268-269).
  31. Wilson, C., and Scott, B. (2017). Adaptive systems in education: a review and conceptual unification. The International Journal of Information and Learning Technology Article Information, 34(1), 2-19.
Download


Paper Citation


in Harvard Style

Oliveira M., Barreiras A., Marcos G., Ferreira H., Azevedo A. and Vaz de Carvalho C. (2017). Collecting and Analysing Learners Data to Support the Adaptive Engine of OPERA, a Learning System for Mathematics . In Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: A2E, ISBN 978-989-758-239-4, pages 631-638. DOI: 10.5220/0006389806310638


in Bibtex Style

@conference{a2e17,
author={Marisa Oliveira and Alcinda Barreiras and Graça Marcos and Hermínia Ferreira and Ana Azevedo and Carlos Vaz de Carvalho},
title={Collecting and Analysing Learners Data to Support the Adaptive Engine of OPERA, a Learning System for Mathematics},
booktitle={Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: A2E,},
year={2017},
pages={631-638},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006389806310638},
isbn={978-989-758-239-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: A2E,
TI - Collecting and Analysing Learners Data to Support the Adaptive Engine of OPERA, a Learning System for Mathematics
SN - 978-989-758-239-4
AU - Oliveira M.
AU - Barreiras A.
AU - Marcos G.
AU - Ferreira H.
AU - Azevedo A.
AU - Vaz de Carvalho C.
PY - 2017
SP - 631
EP - 638
DO - 10.5220/0006389806310638