An early warning system to identify and intervene online dropout learners

• Autores: David Bañeres , M. Elena Rodríguez , Ana Elena Guerrero Roldán, Pau Cortadas Guasch
• Localización: International Journal of Educational Technology in Higher Education, ISSN 2365-9440, Nº. 20, 2023
• Idioma: inglés
• DOI: 10.1186/s41239-022-00371-5
• Enlaces
  • Texto completo (pdf)
• Dialnet Métricas: 2 Citas
• Resumen

  • Dropout is one of the major problems online higher education faces. Early identifca‑ tion of the dropout risk level and an intervention mechanism to revert the potential risk have been proved as the key answers to solving the challenge. Predictive modeling has been extensively studied on course dropout. However, intervention practices are scarce, sometimes mixed with mechanisms focused on course failure, and commonly focused on limited interventions driven mainly by teachers’ experience. This work con‑ tributes with a novel approach for identifying course dropout based on a dynamic time interval and intervening, focusing on avoiding dropout at the assessable activity level.

    Moreover, the system can recommend the best interval for a course and assessable activity based on artifcial intelligence techniques to help teachers in this challenging task. The system has been tested on a fully online frst-year course with 581 partici‑ pants from 957 enrolled learners of diferent degrees from the Faculty of Economics and Business at the Universitat Oberta de Catalunya. Results confrm that interventions aimed at goal setting on the ongoing assessable activity signifcantly reduce dropout issues and increase engagement within the course. Additionally, the work explores the diferences between identifcation mechanisms for course dropout and failure aiming to distinguish them as diferent problems that learners may face.

• Referencias bibliográficas
  • Bağrıacık Yılmaz, A., & Karataş, S. (2022). Why do open and distance education students drop out? Views from various stakeholders. International...
  • Bakar, A., Shah, K., & Qingyu, X. (2020). The effect of communication barriers on distance learners achievements. Revista Argentina De...
  • Bandura, A. (1997). Self‑efficacy: The exercise of control. Choice Reviews Online. https://doi.org/10.5860/choice.35‑ 1826
  • Baneres, D., Rodríguez, M. E., Guerrero‑Roldán, A. E., & Karadeniz, A. (2020). An early warning system to detect at‑risk students in online...
  • Bartimote‑Aufflick, K., Bridgeman, A., Walker, R., Sharma, M., & Smith, L. (2016). The study, evaluation, and improvement of university...
  • Borrella, I., Caballero‑Caballero, S., & Ponce‑Cueto, E. (2019). Predict and intervene: Addressing the dropout problem in a MOOC‑based...
  • Borrella, I., Caballero‑Caballero, S., & Ponce‑Cueto, E. (2022). Taking action to reduce dropout in MOOCs: Tested interventions. Computers...
  • Boudjehem, R., & Lafifi, Y. (2021). A new approach to identify dropout learners based on their performance‑based behavior. Journal of...
  • Broadbent, J., & Poon, W. L. (2015). Self‑regulated learning strategies & academic achievement in online higher education learning...
  • Burgos, C., Campanario, M. L., de la Peña, D., Lara, J. A., Lizcano, D., & Martínez, M. A. (2018). Data mining for modeling students’...
  • Chen, Y., Chen, Q., Zhao, M., Boyer, S., Veeramachaneni, K., & Qu, H. (2017). DropoutSeer: Visualizing learning patterns in Massive Open...
  • Dalipi, F., Imran, A. S., & Kastrati, Z. (2018). MOOC dropout prediction using machine learning techniques: Review and research challenges....
  • Darkenwald, G. G., & Gavin, W. J. (1987). Dropout as a function of discrepancies between expectations and actual experiences of the classroom...
  • de Barba, P. G., Malekian, D., Oliveira, E. A., Bailey, J., Ryan, T., & Kennedy, G. (2020). The importance and meaning of session behaviour...
  • Douglas, K. A., Merzdorf, H. E., Hicks, N. M., Sarfraz, M. I., & Bermel, P. (2020). Challenges to assessing motivation in MOOC learners:...
  • Dourado, R. A., Rodrigues, R. L., Ferreira, N., Mello, R. F., Gomes, A. S., & Verbert, K. (2021). A teacher‑facing learning analytics...
  • Elliot, A. J., & Fryer, J. W. (2008). The goal construct in psychology. Handbook of Motivation Science, 18, 235–250.
  • El‑Sabagh, H. A. (2021). Adaptive e‑learning environment based on learning styles and its impact on development students’ engagement. International...
  • Eriksson, T., Adawi, T., & Stöhr, C. (2017). “Time is the bottleneck”: a qualitative study exploring why learners drop out of MOOCs. Journal...
  • Figueroa‑Cañas, J., & Sancho‑Vinuesa, T. (2021). Changing the recent past to reduce ongoing dropout: An early learning analytics intervention...
  • Goel, Y., & Goyal, R. (2020). On the effectiveness of self‑training in MOOC dropout prediction. Open Computer Science, 10(1), 246–258....
  • Grau‑Valldosera, J., & Minguillón, J. (2014). Rethinking dropout in online higher education: The case of the universitat oberta de catalunya....
  • Greenland, S. J., & Moore, C. (2022). Large qualitative sample and thematic analysis to redefine student dropout and retention strategy...
  • Guerrero‑Roldán, A. E., Rodríguez‑González, M. E., Bañeres, D., Elasri‑Ejjaberi, A., & Cortadas, P. (2021). Experiences in the use of...
  • Hart, C. (2012). Factors associated with student persistence in an online program of study: A review of the literature. Journal of Interactive...
  • Henry, M. (2018). The online student experience: An exploration of first‑year university students’ expectations, experiences and outcomes...
  • Hew, K. F. (2015). Towards a model of engaging online students: Lessons from MOOCs and four policy documents. International Journal of Information...
  • Hone, K. S., & el Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers and Education. https://doi.org/10.1016/j.compedu.2016.03.016
  • Itani, A., Brisson, L., & Garlatti, S. (2018). Understanding Learner’s Drop‑Out in MOOCs. Lecture Notes in Computer Science (Including...
  • Jivet, I., Scheffel, M., Schmitz, M., Robbers, S., Specht, M., & Drachsler, H. (2020). From students with love: An empirical study on...
  • Kizilcec, R. F., Pérez‑Sanagustín, M., & Maldonado, J. J. (2016). Recommending self‑regulated learning strategies does not improve performance...
  • Kuechler, W., & Vaishnavi, V. (2012). A framework for theory development in design science research: Multiple perspectives. Journal of...
  • Kurtz, G., Kopolovich, O., Segev, E., Sahar‑Inbar, L., Gal, L., & Hammer, R. (2022). Impact of an instructor’s personalized email intervention...
  • Latham, G. P., & Locke, E. A. (2007). New developments in and directions for goal‑setting research. European Psychologist. https://doi.org/10.1027/1016‑...
  • Lee, Y., & Choi, J. (2011). A review of online course dropout research: Implications for practice and future research. In Educational...
  • Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35‑year odyssey. American...
  • Minguillón, J., Conesa, J., Rodríguez, M. E., & Santanach, F. (2018). Learning analytics in practice: Providing E‑learning researchers...
  • Moreno‑Marcos, P. M., Alario‑Hoyos, C., Munoz‑Merino, P. J., & Kloos, C. D. (2019). Prediction in MOOCs: A review and future research...
  • Mubarak, A. A., Cao, H., & Zhang, W. (2020). Prediction of students’ early dropout based on their interaction logs in online learning...
  • NeCamp, T., Gardner, J., & Brooks, C. (2019). Beyond A/B testing: Sequential randomization for developing interventions in scaled digital...
  • Oates, B. J. (2006). Researching information systems and computing. In Inorganic chemistry (Vol. 37). SAGE Publications Ltd. http://www.pubmedcentral.nih.gov/...
  • Park, J. H., & Choi, H. J. (2009). Factors influencing adult learners’ decision to drop out or persist in online learning. Educational...
  • Rodríguez, M. E., Guerrero‑Roldán, A. E., Baneres, D., & Karadeniz, A. (2019). Towards an intervention mechanism for supporting learners...
  • Rodríguez, M. E., Guerrero‑Roldán, A. E., Baneres, D., & Karadeniz, A. (2022). An Intelligent Nudging System to Guide Online Learners....
  • Ross, E., & McNealy, K. (2020). Creating connection between virtual learners. https://econometricainc.com/wp‑ content/ uploads/2020/10/Polling_Blue‑...
  • Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020). Linking learning behavior analytics and learning science...
  • Simpson, C., Baker, K., & Mellinger, G. (1980). Conventional failures and unconventional dropouts: comparing different types of university...
  • Stephen, J. S., Rockinson‑Szapkiw, A. J., & Dubay, C. (2020). Persistence model of non‑traditional online learners: Selfefficacy, self‑regulation,...
  • Stone, C., & O’Shea, S. (2019). Older, online and first: Recommendations for retention and success. Australasian Journal of Educational...
  • Tang, J. K. T., Xie, H., & Wong, T. L. (2015). A big data framework for early identification of dropout students in MOOC. Communications...
  • Teusner, R., Hille, T., & Staubitz, T. (2018). Effects of automated interventions in programming assignments: Evidence from a field experiment....
  • Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Nudge: Improving Decisions about...
  • Thalhammer, V., Hoffmann, S., von Hippel, A., & Schmidt‑Hertha, B. (2022). Dropout in adult education as a phenomenon of fit—An integrative...
  • Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125....
  • Veletsianos, G., Kimmons, R., Larsen, R., & Rogers, J. (2021). Temporal flexibility, gender, and online learning completion. Distance...
  • Whitehill, J., Mohan, K., Seaton, D., Rosen, Y., & Tingley, D. (2017). MOOC dropout prediction: How to measure accuracy? L@S 2017—Proceedings...
  • Woodley, A., & Simpson, O. (2013). Student Dropout: the elephant in the room. In O. Zawacki‑Richter & T. Anderson (Eds.), Online distance...
  • Xavier, M., & Meneses, J. (2020). A Literature Review on the Definitions of Dropout in Online Higher Education. EDEN Conference Proceedings,...
  • Xavier, M., & Meneses, J. (2022). Persistence and time challenges in an open online university: A case study of the experiences of first‑year...
  • Xenos, M., Pierrakeas, C., & Pintelas, P. (2002). A survey on student dropout rates and dropout causes concerning the students in the...
  • Yair, G., Rotem, N., & Shustak, E. (2020). The riddle of the existential dropout: Lessons from an institutional study of student attrition....
  • Zawacki‑Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications...
  • Zimmerman, B. J. (1990). Self‑regulated learning and academic achievement: An overview. Educational Psychologist. https://doi.org/10.1207/s15326985ep2501_2