Abstract
We used BPMN diagrams to identify indicators that can assist teachers in their intervention actions to support students' self-regulation and co-regulation in an asynchronous e-learning context. The use of BPMN modeling, by making explicit the tasks and procedures implicit in the intervention of the e-learning teacher, also exposed which data were available for developing decision-support indicators, as well as the relevant moments for carrying out interventions. Such indicators can help e-learning teachers focus their interventions to support self-regulation and co-regulation of learning, as well as enabling the creation of live data dashboards to support decision-making for those interventions, thus this process can contribute to devise better instruments for teacher intervention in support of self-regulation and co-regulation of student learning.
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References
OMG - Object Management Group: Business Process Model and Notation (BPMN), Version 2.0 (2011)
Morais, C., Pedrosa, D., Fontes, M.M., Cravino, J., Morgado, L.: Detailing an e-Learning course on software engineering and architecture using BPMN. In: Queirós, R., Portela, F., Pinto, M., Simões, A. (eds.) First International Computer Programming Education Conference (ICPEC 2020), pp. 17:1–17:8. Schloss Dagstuhl–Leibniz-Zentrum für Informatik, Dagstuhl, Germany (2020)
Caeiro-Rodriguez, M.: Making teaching and learning visible: how can learning designs be represented? In: Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality, pp. 265–274. ACM, León Spain (2019)
Savić, G., Segedinac, M., Milenković, D., Hrin, T., Segedinac, M.: A model-driven approach to e-course management. Aust. J. Educ. Technol. (2017). https://doi.org/10.14742/ajet.3124
van Es, R., Koper, R.: Testing the pedagogical expressiveness of IMS LD. Educ. Technol. Soc. 9, 229–249 (2006)
Zimmerman, B.J.: From cognitive modeling to self-regulation: a social cognitive career path. Educ. Psychol. 48, 135–147 (2013). https://doi.org/10.1080/00461520.2013.794676
Panadero, E.: A review of self-regulated learning: six models and four directions for research. Front. Psychol. 8, 422 (2017). https://doi.org/10.3389/fpsyg.2017.00422
Panadero, E., Järvelä, S.: Socially shared regulation of learning: a review. Eur. Psychol. 20, 190–203 (2015). https://doi.org/10.1027/1016-9040/a000226
Harley, J.M., Taub, M., Bouchet, F., Azevedo, R.: A framework to understand the nature of co-regulated learning in human-pedagogical agent interactions. In: SRL@ET (2012)
Bowers, J., Kumar, P.: Students’ perceptions of teaching and social presence: a comparative analysis of face-to-face and online learning environments. Int. J. Web-Based Learn. Teach. Technol. 10, 27–44 (2015). https://doi.org/10.4018/ijwltt.2015010103
Dabbagh, N., Kitsantas, A.: Personal learning environments, social media, and self-regulated learning: a natural formula for connecting formal and informal learning. Internet High. Educ. 15, 3–8 (2012). https://doi.org/10.1016/j.iheduc.2011.06.002
Broadbent, J.: Comparing online and blended learner’s self-regulated learning strategies and academic performance. Internet High. Educ. 33, 24–32 (2017). https://doi.org/10.1016/j.iheduc.2017.01.004
Broadbent, J., Poon, W.L.: Self-regulated learning strategies & academic achievement in online higher education learning environments: a systematic review. Internet High. Educ. 27, 1–3 (2015). https://doi.org/10.1016/j.iheduc.2015.04.007
Pedrosa, D., et al.: Challenges implementing the simprogramming approach in online software engineering education for promoting self and co-regulation of learning. In: 2020 6th International Conference of the Immersive Learning Research Network (iLRN), pp. 236–242. IEEE, San Luis Obispo (2020)
Kebritchi, M., Lipschuetz, A., Santiague, L.: Issues and challenges for teaching successful online courses in higher education: a literature review. J. Educ. Technol. Syst. 46, 4–29 (2017). https://doi.org/10.1177/0047239516661713
Sharp, L.A., Sharp, J.H.: Enhancing student success in online learning experiences through the use of self-regulation strategies. J. Excell. Coll. Teach. 27, 57–75 (2016)
Pérez-Álvarez, R., Maldonado-Mahauad, J., Pérez-Sanagustín, M.: Tools to support self-regulated learning in online environments: literature review. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds.) EC-TEL 2018. LNCS, vol. 11082, pp. 16–30. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98572-5_2
Viberg, O., Khalil, M., Baars, M.: Self-regulated learning and learning analytics in online learning environments: a review of empirical research. In: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. pp. 524–533. Association for Computing Machinery, New York (2020)
Winne, P.: Learning analytics for self-regulated learning. In: Lang, C., Siemens, G., Wise, A.F., Gaševic, D. (eds.) The Handbook of Learning Analytics. pp. 241–249. Society for Learning Analytics Research (SoLAR), Alberta (2017)
Kim, D., Yoon, M., Jo, I.-H., Branch, R.M.: Learning analytics to support self-regulated learning in asynchronous online courses: a case study at a women’s university in South Korea. Comput. Educ. 127, 233–251 (2018). https://doi.org/10.1016/j.compedu.2018.08.023
Hassani, A., Ghanouchi, S.A.: Modeling of a collaborative learning process in the context of MOOCs. In: 2016 Third International Conference on Systems of Collaboration (SysCo), pp. 1–6. IEEE, Casablanca (2016)
Hammad, R., Odeh, M., Khan, Z.: Towards a generalised e-learning business process model. In: BUSTECH 2017 : The Seventh International Conference on Business Intelligence and Technology, pp. 20–28. IARIA, Athens (2017)
Subramanian, V.: Towards business process management based workplace e-learning. In: 2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT), pp. 555–557. IEEE, Austin (2016)
Pereira, A., Mendes, A.Q., Morgado, L., Amante, L., Bidarra, J.: Universidade Aberta’s pedagogical model for distance education: a university for the future. Universidade Aberta, Lisbon, Portugal (2008)
Guia Informativo - Ensino Aprendizagem - Orientações Metodológicas. https://www2.uab.pt/guiainformativo/detailmenu.php?content=24
Web Service API Functions. https://docs.moodle.org/dev/Web_service_API_functions
Prieto, L.P., Asensio-Pérez, J.I., Muñoz-Cristóbal, J.A., Jorrín-Abellán, I.M., Dimitriadis, Y., Gómez-Sánchez, E.: Supporting orchestration of CSCL scenarios in web-based distributed learning environments. Comput. Educ. 73, 9–25 (2014). https://doi.org/10.1016/j.compedu.2013.12.008
Grann, J., Bushway, D.: Competency map: visualizing student learning to promote student success. In: Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, pp. 168–172. Association for Computing Machinery, New York (2014)
Kim, J., Jo, I.-H., Park, Y.: Effects of learning analytics dashboard: analyzing the relations among dashboard utilization, satisfaction, and learning achievement. Asia Pac. Educ. Rev. 17(1), 13–24 (2015). https://doi.org/10.1007/s12564-015-9403-8
Gašević, D., Dawson, S., Rogers, T., Gasevic, D.: Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. Internet High. Educ. 28, 68–84 (2016). https://doi.org/10.1016/j.iheduc.2015.10.002
Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., Kirschner, P.A.: Linking learning behavior analytics and learning science concepts: designing a learning analytics dashboard for feedback to support learning regulation. Comput. Hum. Behav. 107, 105512 (2020). https://doi.org/10.1016/j.chb.2018.05.004
Acknowledgements
This work is co-financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization - COMPETE 2020 and the Lisboa 2020 under the PORTUGAL 2020 Partnership Agreement, and through the Portuguese National Innovation Agency (ANI) as a part of project CHIC POCI-01–0247-FEDER-024498. And also by national funds through the FCT – Fundação para a Ciência e a Tecnologia, I.P., as part of project UID/CED/00194/2019, SCReLProg.
Daniela Pedrosa wishes to thank Fundação para a Ciência e Tecnologia (FCT) and CIDTFF (UID/CED/00194/2019) - Universidade de Aveiro, Portugal, for Stimulus of Scientific Employment – CEECIND/00986/2017 Individual Support 2017.
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Morais, C., Pedrosa, D., Rocio, V., Cravino, J., Morgado, L. (2021). Using BPMN to Identify Indicators for Teacher Intervention in Support of Self-regulation and Co-regulation of Learning in Asynchronous e-learning. In: Reis, A., Barroso, J., Lopes, J.B., Mikropoulos, T., Fan, CW. (eds) Technology and Innovation in Learning, Teaching and Education. TECH-EDU 2020. Communications in Computer and Information Science, vol 1384. Springer, Cham. https://doi.org/10.1007/978-3-030-73988-1_16
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