Learning analytics and interactive multimedia experience in enhancing student learning experience: a systemic approach

Learning Analytics (LA) is a feedback loop process that generates data based on learning activities defined by teachers. These data are then stored, adapted, reviewed, and cleaned to derive recommendations to improve the learning experience in an endless cycle. LA has been integrated into user-exper...

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Autores:
Parra-Valencia, Jorge-Andrick
Peláez Ayala, Carlos Alberto
Solano Alegría, Andrés Fernando
López Sotelo, Jesús Alfonso
Ospina Galindez, Johann Alexis
Tipo de recurso:
Part of book
Fecha de publicación:
2023
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
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oai:red.uao.edu.co:10614/15917
Acceso en línea:
https://hdl.handle.net/10614/15917
https://doi.org/10.1007/978-3-031-40635-5_6
https://red.uao.edu.co/
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Drechos reservados - Springer Nature, 2023
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network_name_str RED: Repositorio Educativo Digital UAO
repository_id_str
dc.title.eng.fl_str_mv Learning analytics and interactive multimedia experience in enhancing student learning experience: a systemic approach
title Learning analytics and interactive multimedia experience in enhancing student learning experience: a systemic approach
spellingShingle Learning analytics and interactive multimedia experience in enhancing student learning experience: a systemic approach
title_short Learning analytics and interactive multimedia experience in enhancing student learning experience: a systemic approach
title_full Learning analytics and interactive multimedia experience in enhancing student learning experience: a systemic approach
title_fullStr Learning analytics and interactive multimedia experience in enhancing student learning experience: a systemic approach
title_full_unstemmed Learning analytics and interactive multimedia experience in enhancing student learning experience: a systemic approach
title_sort Learning analytics and interactive multimedia experience in enhancing student learning experience: a systemic approach
dc.creator.fl_str_mv Parra-Valencia, Jorge-Andrick
Peláez Ayala, Carlos Alberto
Solano Alegría, Andrés Fernando
López Sotelo, Jesús Alfonso
Ospina Galindez, Johann Alexis
dc.contributor.author.none.fl_str_mv Parra-Valencia, Jorge-Andrick
Peláez Ayala, Carlos Alberto
Solano Alegría, Andrés Fernando
López Sotelo, Jesús Alfonso
Ospina Galindez, Johann Alexis
description Learning Analytics (LA) is a feedback loop process that generates data based on learning activities defined by teachers. These data are then stored, adapted, reviewed, and cleaned to derive recommendations to improve the learning experience in an endless cycle. LA has been integrated into user-experience-oriented multimedia systems, and the design of Interactive Multimedia Experiences (IME) can include LA to enhance the learning experience and collect data. However, the efficacy of LA in improving students’ learning experiences remains uncertain with mixed findings from various studies. Therefore, further research is required to evaluate the effects of LA tools on student retention. It is also crucial to consider the correlation between multimedia elements and performance, including the quantity and quality of multimedia features and how they interact with learners’ needs and abilities. It is essential to investigate the effectiveness of multimedia elements in engaging users and their impact on learning outcomes. This chapter proposes the identification of critical feedback loops that connect the LA process with IME in multimedia projects. The goal was to develop a dynamic hypothesis explaining how the learning experience relates to user experience and teacher enthusiasm. Researchers and teachers will collaborate to identify reference modes, variables, and feedback loops that connect the Learning Experience with the User Experience. By doing so, we can better understand and improve students’ learning experiences by effectively using LA tools and multimedia elements in IME
publishDate 2023
dc.date.issued.none.fl_str_mv 2023
dc.date.accessioned.none.fl_str_mv 2024-11-22T14:21:23Z
dc.date.available.none.fl_str_mv 2024-11-22T14:21:23Z
dc.type.spa.fl_str_mv Capítulo - Parte de Libro
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dc.identifier.citation.eng.fl_str_mv Parra-Valencia, JA., Peláez, C.A., Solano Alegría, A. F., López Sotelo, J.A. y Ospina Galíndez, J. A. (2023). Learning Analytics and Interactive Multimedia Experience in Enhancing Student Learning Experience: A Systemic Approach. In: Qudrat-Ullah, H. (eds) Managing Complex Tasks with Systems Thinking. Understanding Complex Systems. Springer. https://doi.org/10.1007/978-3-031-40635-5_6
dc.identifier.isbn.spa.fl_str_mv 9783031406348
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10614/15917
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-3-031-40635-5_6
dc.identifier.instname.spa.fl_str_mv Universidad Autónoma de Occidente
dc.identifier.reponame.spa.fl_str_mv Respositorio Educativo Digital UAO
dc.identifier.repourl.none.fl_str_mv https://red.uao.edu.co/
dc.identifier.eisbn.spa.fl_str_mv 9783031406355
identifier_str_mv Parra-Valencia, JA., Peláez, C.A., Solano Alegría, A. F., López Sotelo, J.A. y Ospina Galíndez, J. A. (2023). Learning Analytics and Interactive Multimedia Experience in Enhancing Student Learning Experience: A Systemic Approach. In: Qudrat-Ullah, H. (eds) Managing Complex Tasks with Systems Thinking. Understanding Complex Systems. Springer. https://doi.org/10.1007/978-3-031-40635-5_6
9783031406348
Universidad Autónoma de Occidente
Respositorio Educativo Digital UAO
9783031406355
url https://hdl.handle.net/10614/15917
https://doi.org/10.1007/978-3-031-40635-5_6
https://red.uao.edu.co/
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language eng
dc.relation.ispartofseries.eng.fl_str_mv serie Understanding Complex Systems ((UCS))
dc.relation.citationendpage.spa.fl_str_mv 175
dc.relation.citationstartpage.spa.fl_str_mv 151
dc.relation.ispartofbook.eng.fl_str_mv Managing complex tasks with systems thinking
dc.relation.references.none.fl_str_mv Abdelnour-Nocera, J., Oussena, S., & Burns, C. M. (2015). Human work interaction design of the smart university. In Human work interaction design. Work analysis and interaction design methods for pervasive and smart workplaces. https://doi.org/10.1007/978-3-319-27048-7_9 About or with Teachers? A systematic review of learning analytics interventions to support teacher professional development [Scite Report]. (n.d.). Accessed April 2, 2023. https://scite.ai/reports/about-or-with-teachers-a-V0zxbQeW?showReferences=true Berard, C. (2010). Group model building using system dynamics: An analysis of methodological frameworks 8(1). Google Scholar Budiarto, F., & Jazuli, A. (2021). Interactive learning multimedia improving learning motivation elementary school students. In Proceedings of the 1st International Conference on Social Sciences, ICONESS 2021, 19 July 2021, Purwokerto, Central Java, Indon. https://doi.org/10.4108/eai.19-7-2021.2312497 Chan, A. S. C., Botelho, M. G., & Lam, O. L. T. (2021). An exploration of student access to a learning management system—Challenges and recommendations for educators and researchers. European Journal of Dental Education 25(4). https://doi.org/10.1111/eje.12664 Dawson, S., Jovanović, J., Gašević, D., & Pardo, A. (2017). From prediction to impact: Evaluation of a learning analytics retention program. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference. Google Scholar Delobelle, P. (2020). A guide for participatory systems analysis using a group model building approach. In SAGE research methods cases: Medicine and health. SAGE Publications Ltd. Google Scholar Freitas, S., Gibson, D., Plessis, C. D., Halloran, P., Williams, E., Ambrose, M., Dunwell, I., & Arnab, S. (2015). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology, 46(6), 1175–1188. https://doi.org/10.1111/bjet.12212 Hovmand, P. S., & Hovmand, P. S. (2014). Group model building and community-based system dynamics process. Springer. Book Google Scholar Ifenthaler, D., Schumacher, C., & Kuzilek, J. (2023). Investigating students’ use of self-assessments in higher education using learning analytics. Journal of Computer Assisted Learning, 39(1), 255–268. https://doi.org/10.1111/jcal.12744 Article Google Scholar Ifenthaler, D., & Yau, J. Y.-K. (2020). Reflections on different learning analytics indicators for supporting study success. International Journal of Learning Analytics and Artificial Intelligence for Education (IJAI), 2(2). https://doi.org/10.3991/ijai.v2i2.15639 Ifenthaler, D., & Yau, J. (2021). Supporting teaching staff through data analytics: A systematic review. In ASCILITE 2021: Back to the Future—ASCILITE ’21 Proceedings ASCILITE 2021 in Armidale. https://doi.org/10.14742/ascilite2021.0105 Larusson, J. A., & Alterman, R. (2009). Wikis to support the “collaborative” part of collaborative learning. International Journal of Computer-Supported Collaborative Learning, 4, 371–402. Google Scholar Lee, W. W., & Owens, D. L. (2004). Multimedia-based instructional design: Computer-based training, web-based training, distance broadcast training, performance-based solutions. Wiley. Google Scholar Li, K. S., Chen, P. G., Lai, T. Y., Lin, C. H., Cheng, C. C., Chen, C. C., ... & Hu, C. (2015, December). Sub-60mV-swing negative-capacitance FinFET without hysteresis. In 2015 IEEE International Electron Devices Meeting (IEDM) (pp. 22–6). IEEE. Google Scholar Mah, D. (2016). Learning analytics and digital badges: Potential impact on student retention in higher education. Technology, Knowledge and Learning, 21, 285–305. Article Google Scholar Martins da Silva, L., Dias, L. P. S., Rigo, S., Barbosa, J. L. V., Leithardt, D. R. F., & Leithardt, V. R. Q. (2021). A literature review on intelligent services applied to distance learning. Education Sciences, 11(11). https://doi.org/10.3390/educsci11110666 Martinez-Maldonado, R., Schneider, B., Charleer, S., Shum, S. B., Klerkx, J., & Duval, E. (2016). Interactive surfaces and learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge—LAK '16. https://doi.org/10.1145/2883851.2883873 Mavrikis, M., Geraniou, E., Santos, S., & Poulovassilis, A. (2019). Intelligent analysis and data visualisation for teacher assistance tools: The case of exploratory learning. British Journal of Educational Technology, 50(6). https://doi.org/10.1111/bjet.12876 McCoy, C., & Shih, P. M. (2016). Teachers as producers of data analytics: A case study of a teacher-focused educational data science program. Journal of Learning Analytics, 3(3). https://doi.org/10.18608/jla.2016.33.10 Melnikova, J., Batuchina, A., Šakytė-Statnickė, G., & Šmitienė, G. (2022). The benefits of learning analytics for education: A study of the experiences of teachers in Norway and Lithuania. Human, Technologies and Quality of Education. https://doi.org/10.22364/htqe.2022.21 Mutimukwe, C., Viberg, O., Oberg, L.-M., & Cerratto-Pargman, T. (2022). Students’ privacy concerns in learning analytics: Model development. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13234 Article Google Scholar Nur’Azizah, H., Rahayu, W., & Cahyana, U. (2018). Development of interactive multimedia learning to improve analytical thinking ability of elementary school student on water cycle material. International Journal of Multidisciplinary and Current Research, 6(4). https://doi.org/10.14741/ijmcr/v.6.4.13 Omar, M. Z., Makhtar, M., Wan Ibrahim, M. H., & Aziz, A. A. (2020). Sentiment analysis of user feedback in e-learning environment. International Journal of Engineering Trends and Technology. https://doi.org/10.14445/22315381/cati2p224 Parlangeli, O., Marchigiani, E., & Bagnara, S. (1999). Multimedia systems in distance education: Effects of usability on learning. Interacting with Computers, 12, 37–49. Article Google Scholar Peláez, C., Solano, A., & Granollers, T. (2021). Proposal to conceive multimedia systems from a value creation perspective and a collaborative work routes approach. Interact. Des. Archit. Available online: http://www.mifav.uniroma2.it/inevent/events/idea2010/doc/49_1.pdf Qudrat-Ullah, H. (2008). Behavior validity of a simulation model for sustainable development. International Journal of Management and Decision Making, 9(2), 129–139. Google Scholar Qudrat-Ullah, H., & Baek Seo, S. (2010). How to do structural validity of a system dynamics type simulation model: The case of an energy policy model. Energy Policy, 38(5), 2216–2224. Google Scholar Ramaswami, G., Susnjak, T., & Mathrani, A. (2022). Supporting students’ academic performance using explainable machine learning with automated prescriptive analytics. Big Data and Cognitive Computing, 6(4). https://doi.org/10.3390/bdcc6040105 Roberts, L. D., Howell, J. A., Seaman, K., & Gibson, D. I. (2016). Student attitudes toward learning analytics in higher education: ‘The fitbit version of the learning world.’ Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2016.01959 Article Google Scholar Rouwette, E. A. J. A., Vennix, J. A. M., & van Mullekom, T. (2002). Group model building effectiveness: A review of assessment studies. System Dynamics Review: the Journal of the System Dynamics Society, 18(1), 5–45. Article Google Scholar Shen, H., Liang, L., Law, N., Hemberg, E., & O’Reilly, U.-M. (2020). Understanding learner behavior through learning design informed learning analytics. In Proceedings of the Seventh ACM Conference on Learning @ Scale. https://doi.org/10.1145/3386527.3405919 Sterman, J. (2002). System dynamics: Systems thinking and modeling for a complex world. Google Scholar Vanaken, H., & Masand, S. N. (2019). Awareness and collaboration across stakeholder groups important for e-consent achieving value-driven adoption. Therapeutic Innovation & Regulatory Science, 53(6). https://doi.org/10.1177/2168479019861924 Vesin, B., Mangaroska, K., & Giannakos, M. N. (2018). Learning in smart environments: User-centered design and analytics of an adaptive learning system. Smart Learning Environments, 5(1). https://doi.org/10.1186/s40561-018-0071-0 Voinov, A. (2021). Participatory modeling for group decision support. Handbook of Group Decision and Negotiation, 395–411. Google Scholar Wilkinson, K., McNamara, I., Wilson, D., & Riggs, K. (2019). Using learning analytics to evaluate course design and student behavior in an online wine business course. International Journal of Innovation in Science and Mathematics Education, 27(4). https://doi.org/10.30722/IJISME.27.04.008 Yahya, A. A., Sulaiman, A., Mashraqi, A. M., Zaidan, Z. M., & Halawani, H. (2021). Toward a better understanding of academic programs educational objectives: A data analytics-based approach. Applied Sciences, 11(20). https://doi.org/10.3390/app11209623 Yanyan, X. (2021). Analyzing the quality of business English teaching using multimedia data mining. Mobile Information Systems. https://doi.org/10.1155/2021/9912460
dc.rights.eng.fl_str_mv Drechos reservados - Springer Nature, 2023
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spelling Parra-Valencia, Jorge-AndrickPeláez Ayala, Carlos Albertovirtual::5804-1Solano Alegría, Andrés Fernandovirtual::5805-1López Sotelo, Jesús Alfonsovirtual::5806-1Ospina Galindez, Johann Alexisvirtual::5807-12024-11-22T14:21:23Z2024-11-22T14:21:23Z2023Parra-Valencia, JA., Peláez, C.A., Solano Alegría, A. F., López Sotelo, J.A. y Ospina Galíndez, J. A. (2023). Learning Analytics and Interactive Multimedia Experience in Enhancing Student Learning Experience: A Systemic Approach. In: Qudrat-Ullah, H. (eds) Managing Complex Tasks with Systems Thinking. Understanding Complex Systems. Springer. https://doi.org/10.1007/978-3-031-40635-5_69783031406348https://hdl.handle.net/10614/15917https://doi.org/10.1007/978-3-031-40635-5_6Universidad Autónoma de OccidenteRespositorio Educativo Digital UAOhttps://red.uao.edu.co/9783031406355Learning Analytics (LA) is a feedback loop process that generates data based on learning activities defined by teachers. These data are then stored, adapted, reviewed, and cleaned to derive recommendations to improve the learning experience in an endless cycle. LA has been integrated into user-experience-oriented multimedia systems, and the design of Interactive Multimedia Experiences (IME) can include LA to enhance the learning experience and collect data. However, the efficacy of LA in improving students’ learning experiences remains uncertain with mixed findings from various studies. Therefore, further research is required to evaluate the effects of LA tools on student retention. It is also crucial to consider the correlation between multimedia elements and performance, including the quantity and quality of multimedia features and how they interact with learners’ needs and abilities. It is essential to investigate the effectiveness of multimedia elements in engaging users and their impact on learning outcomes. This chapter proposes the identification of critical feedback loops that connect the LA process with IME in multimedia projects. The goal was to develop a dynamic hypothesis explaining how the learning experience relates to user experience and teacher enthusiasm. Researchers and teachers will collaborate to identify reference modes, variables, and feedback loops that connect the Learning Experience with the User Experience. By doing so, we can better understand and improve students’ learning experiences by effectively using LA tools and multimedia elements in IME25 páginasapplication/pdfengSpringer NatureSwitzerlandserie Understanding Complex Systems ((UCS))175151Managing complex tasks with systems thinkingAbdelnour-Nocera, J., Oussena, S., & Burns, C. M. (2015). Human work interaction design of the smart university. In Human work interaction design. Work analysis and interaction design methods for pervasive and smart workplaces. https://doi.org/10.1007/978-3-319-27048-7_9 About or with Teachers? A systematic review of learning analytics interventions to support teacher professional development [Scite Report]. (n.d.). Accessed April 2, 2023. https://scite.ai/reports/about-or-with-teachers-a-V0zxbQeW?showReferences=true Berard, C. (2010). Group model building using system dynamics: An analysis of methodological frameworks 8(1). Google Scholar Budiarto, F., & Jazuli, A. (2021). Interactive learning multimedia improving learning motivation elementary school students. In Proceedings of the 1st International Conference on Social Sciences, ICONESS 2021, 19 July 2021, Purwokerto, Central Java, Indon. https://doi.org/10.4108/eai.19-7-2021.2312497 Chan, A. S. C., Botelho, M. G., & Lam, O. L. T. (2021). An exploration of student access to a learning management system—Challenges and recommendations for educators and researchers. European Journal of Dental Education 25(4). https://doi.org/10.1111/eje.12664 Dawson, S., Jovanović, J., Gašević, D., & Pardo, A. (2017). From prediction to impact: Evaluation of a learning analytics retention program. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference. Google Scholar Delobelle, P. (2020). A guide for participatory systems analysis using a group model building approach. In SAGE research methods cases: Medicine and health. SAGE Publications Ltd. Google Scholar Freitas, S., Gibson, D., Plessis, C. D., Halloran, P., Williams, E., Ambrose, M., Dunwell, I., & Arnab, S. (2015). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology, 46(6), 1175–1188. https://doi.org/10.1111/bjet.12212 Hovmand, P. S., & Hovmand, P. S. (2014). Group model building and community-based system dynamics process. Springer. Book Google Scholar Ifenthaler, D., Schumacher, C., & Kuzilek, J. (2023). Investigating students’ use of self-assessments in higher education using learning analytics. Journal of Computer Assisted Learning, 39(1), 255–268. https://doi.org/10.1111/jcal.12744 Article Google Scholar Ifenthaler, D., & Yau, J. Y.-K. (2020). Reflections on different learning analytics indicators for supporting study success. International Journal of Learning Analytics and Artificial Intelligence for Education (IJAI), 2(2). https://doi.org/10.3991/ijai.v2i2.15639 Ifenthaler, D., & Yau, J. (2021). Supporting teaching staff through data analytics: A systematic review. In ASCILITE 2021: Back to the Future—ASCILITE ’21 Proceedings ASCILITE 2021 in Armidale. https://doi.org/10.14742/ascilite2021.0105 Larusson, J. A., & Alterman, R. (2009). Wikis to support the “collaborative” part of collaborative learning. International Journal of Computer-Supported Collaborative Learning, 4, 371–402. Google Scholar Lee, W. W., & Owens, D. L. (2004). Multimedia-based instructional design: Computer-based training, web-based training, distance broadcast training, performance-based solutions. Wiley. Google Scholar Li, K. S., Chen, P. G., Lai, T. Y., Lin, C. H., Cheng, C. C., Chen, C. C., ... & Hu, C. (2015, December). Sub-60mV-swing negative-capacitance FinFET without hysteresis. In 2015 IEEE International Electron Devices Meeting (IEDM) (pp. 22–6). IEEE. Google Scholar Mah, D. (2016). Learning analytics and digital badges: Potential impact on student retention in higher education. Technology, Knowledge and Learning, 21, 285–305. Article Google Scholar Martins da Silva, L., Dias, L. P. S., Rigo, S., Barbosa, J. L. V., Leithardt, D. R. F., & Leithardt, V. R. Q. (2021). A literature review on intelligent services applied to distance learning. Education Sciences, 11(11). https://doi.org/10.3390/educsci11110666 Martinez-Maldonado, R., Schneider, B., Charleer, S., Shum, S. B., Klerkx, J., & Duval, E. (2016). Interactive surfaces and learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge—LAK '16. https://doi.org/10.1145/2883851.2883873 Mavrikis, M., Geraniou, E., Santos, S., & Poulovassilis, A. (2019). Intelligent analysis and data visualisation for teacher assistance tools: The case of exploratory learning. British Journal of Educational Technology, 50(6). https://doi.org/10.1111/bjet.12876 McCoy, C., & Shih, P. M. (2016). Teachers as producers of data analytics: A case study of a teacher-focused educational data science program. Journal of Learning Analytics, 3(3). https://doi.org/10.18608/jla.2016.33.10 Melnikova, J., Batuchina, A., Šakytė-Statnickė, G., & Šmitienė, G. (2022). The benefits of learning analytics for education: A study of the experiences of teachers in Norway and Lithuania. Human, Technologies and Quality of Education. https://doi.org/10.22364/htqe.2022.21 Mutimukwe, C., Viberg, O., Oberg, L.-M., & Cerratto-Pargman, T. (2022). Students’ privacy concerns in learning analytics: Model development. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13234 Article Google Scholar Nur’Azizah, H., Rahayu, W., & Cahyana, U. (2018). Development of interactive multimedia learning to improve analytical thinking ability of elementary school student on water cycle material. International Journal of Multidisciplinary and Current Research, 6(4). https://doi.org/10.14741/ijmcr/v.6.4.13 Omar, M. Z., Makhtar, M., Wan Ibrahim, M. H., & Aziz, A. A. (2020). Sentiment analysis of user feedback in e-learning environment. International Journal of Engineering Trends and Technology. https://doi.org/10.14445/22315381/cati2p224 Parlangeli, O., Marchigiani, E., & Bagnara, S. (1999). Multimedia systems in distance education: Effects of usability on learning. Interacting with Computers, 12, 37–49. Article Google Scholar Peláez, C., Solano, A., & Granollers, T. (2021). Proposal to conceive multimedia systems from a value creation perspective and a collaborative work routes approach. Interact. Des. Archit. Available online: http://www.mifav.uniroma2.it/inevent/events/idea2010/doc/49_1.pdf Qudrat-Ullah, H. (2008). Behavior validity of a simulation model for sustainable development. International Journal of Management and Decision Making, 9(2), 129–139. Google Scholar Qudrat-Ullah, H., & Baek Seo, S. (2010). How to do structural validity of a system dynamics type simulation model: The case of an energy policy model. Energy Policy, 38(5), 2216–2224. Google Scholar Ramaswami, G., Susnjak, T., & Mathrani, A. (2022). Supporting students’ academic performance using explainable machine learning with automated prescriptive analytics. Big Data and Cognitive Computing, 6(4). https://doi.org/10.3390/bdcc6040105 Roberts, L. D., Howell, J. A., Seaman, K., & Gibson, D. I. (2016). Student attitudes toward learning analytics in higher education: ‘The fitbit version of the learning world.’ Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2016.01959 Article Google Scholar Rouwette, E. A. J. A., Vennix, J. A. M., & van Mullekom, T. (2002). Group model building effectiveness: A review of assessment studies. System Dynamics Review: the Journal of the System Dynamics Society, 18(1), 5–45. Article Google Scholar Shen, H., Liang, L., Law, N., Hemberg, E., & O’Reilly, U.-M. (2020). Understanding learner behavior through learning design informed learning analytics. In Proceedings of the Seventh ACM Conference on Learning @ Scale. https://doi.org/10.1145/3386527.3405919 Sterman, J. (2002). System dynamics: Systems thinking and modeling for a complex world. Google Scholar Vanaken, H., & Masand, S. N. (2019). Awareness and collaboration across stakeholder groups important for e-consent achieving value-driven adoption. Therapeutic Innovation & Regulatory Science, 53(6). https://doi.org/10.1177/2168479019861924 Vesin, B., Mangaroska, K., & Giannakos, M. N. (2018). Learning in smart environments: User-centered design and analytics of an adaptive learning system. Smart Learning Environments, 5(1). https://doi.org/10.1186/s40561-018-0071-0 Voinov, A. (2021). Participatory modeling for group decision support. Handbook of Group Decision and Negotiation, 395–411. Google Scholar Wilkinson, K., McNamara, I., Wilson, D., & Riggs, K. (2019). Using learning analytics to evaluate course design and student behavior in an online wine business course. International Journal of Innovation in Science and Mathematics Education, 27(4). https://doi.org/10.30722/IJISME.27.04.008 Yahya, A. A., Sulaiman, A., Mashraqi, A. M., Zaidan, Z. M., & Halawani, H. (2021). Toward a better understanding of academic programs educational objectives: A data analytics-based approach. Applied Sciences, 11(20). https://doi.org/10.3390/app11209623 Yanyan, X. (2021). Analyzing the quality of business English teaching using multimedia data mining. 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