Formazione & insegnamento
ISSN: 2279-7505 | Published: 2024-05-15
This landing page is part of an alternate academic indexing and SEO initiative curated by Pensa MultiMedia and the Executive Editorial Office.
Main Article Landing Page: https://ojs.pensamultimedia.it/index.php/siref/article/view/6976
Full Text HTML (viewer): https://ojs.pensamultimedia.it/index.php/siref/article/view/6976/6186
Full Text PDF (viewer): https://ojs.pensamultimedia.it/index.php/siref/article/view/6976/6088
Full Text HTML (file): https://ojs.pensamultimedia.it/index.php/siref/article/download/6976/6186
Full Text PDF (file): https://ojs.pensamultimedia.it/index.php/siref/article/download/6976/6088
Alternate URL (this mirror): https://formazione-insegnamento.eu/2024-22/1/6976-exploring-higher-education-students-experience-with-ai-
Title: Exploring Higher Education Students' Experience with AI-powered Educational Tools: The Case of an Early Warning System
Abstract: AI-powered educational tools (AIEd) include early warning systems (EWS) to identify at-risk undergraduates, offering personalized assistance. Revealing students' subjective experiences with EWS could contribute to a deeper understanding of what it means to engage with AI in areas of human life, like teaching and learning. Our investigation hence explored students' subjective experiences with EWS, characterizing them according to students' profiles, self-efficacy, prior experience, and perspective on data ethics. The results show that students, largely senior workers with strong academic self-efficacy, had limited experience with this method and minimal expectations. But, using the EWS inspired meaningful reflections. Nonetheless, a comparison between the Computer Science and Economics disciplines demonstrated stronger trust and expectation regarding the system and AI for the former. The study emphasized the importance of helping students' additional experiences and comprehension while embracing AI systems in education to ensure the quality, relevance, and fairness of their educational experience overall.
Keywords: Artificial intelligence; Early Warning System; Higher Education; Student's Experience; Thematic Analysis
Title: Esplorare l'esperienza degli studenti universitari con strumenti educativi basati sull'IA: Il caso di un sistema di allerta precoce
Abstract: Gli strumenti educativi alimentati dall'intelligenza artificiale (AIEd) includono sistemi di allerta precoce (EWS) per identificare gli studenti universitari a rischio, offrendo assistenza personalizzata. Rivelare le esperienze soggettive degli studenti con gli EWS potrebbe contribuire a una comprensione più profonda di cosa significhi interagire con l'IA in aree della vita umana quali l'insegnamento e l'apprendimento. La nostra indagine ha quindi esplorato le esperienze soggettive degli studenti con gli EWS, caratterizzandole secondo i profili degli studenti, l'autoefficacia, l'esperienza pregressa e la prospettiva sull'etica dei dati. I risultati mostrano che gli studenti, per lo più lavoratori senior con forte autoefficacia accademica, avevano esperienze limitate con questo metodo e aspettative minime. Ciononostante, l'utilizzo degli EWS ha ispirato riflessioni significative. Nonostante ciò, un confronto tra le discipline di Informatica ed Economia ha dimostrato una maggiore fiducia e aspettativa riguardo al sistema e all'IA per la prima. Lo studio ha sottolineato l'importanza di aiutare gli studenti a maturare ulteriori esperienze e comprensioni mentre si avvalgono dei sistemi AI nell'educazione per garantire la qualità, la rilevanza e l'equità della loro esperienza educativa complessiva.
Keywords: Alta formazione; Analisi tematica; Esperienze degli studenti; Intelligenza Artificiale; Sistema di Allerta Precoce
Title: Explorer l'expérience des étudiants en enseignement supérieur avec les outils éducatifs alimentés par l'IA: le cas d'un système d'alerte précoce
Abstract: Les outils éducatifs alimentés par l'IA (AIED) incluent les systèmes d'alerte précoce (EWS) pour identifier les étudiants de premier cycle à risque, offrant une assistance personnalisée.La révélation des expériences subjectives des élèves avec l'EWS pourrait contribuer à une compréhension plus approfondie de ce que signifie s'engager avec l'IA dans des domaines de la vie humaine, comme l'enseignement et l'apprentissage.Notre enquête a donc exploré les expériences subjectives des élèves avec les EW, les caractérisant selon les profils des étudiants, l'auto-efficacité, l'expérience antérieure et la perspective de l'éthique des données.Les résultats montrent que les étudiants, en grande partie des travailleurs supérieurs ayant une forte auto-efficacité académique, avaient une expérience limitée avec cette méthode et des attentes minimales.Mais, en utilisant les réflexions significatives a inspiré des réflexions.Néanmoins, une comparaison entre les disciplines de l'informatique et de l'économie a démontré une confiance et des attentes plus fortes concernant le système et l'IA pour les premiers.L'étude a souligné l'importance d'aider les expériences et la compréhension supplémentaires des étudiants tout en adoptant les systèmes d'IA dans l'éducation pour assurer la qualité, la pertinence et l'équité de leur expérience éducative dans son ensemble. (This version of record did not originally feature translated metadata in this target language; the translation is hereby provided by Google Translation)
Keywords: Intelligence artificielle;Système d'alerte précoce;L'enseignement supérieur;L'expérience de l'étudiant;Analyse thématique
Title: Explorar la experiencia de los estudiantes universitarios con herramientas educativas basadas en IA: El caso de un sistema de alerta temprana
Abstract: Las herramientas educativas impulsadas por la inteligencia artificial (AIEd) incluyen sistemas de alerta temprana (EWS) para identificar a los estudiantes universitarios en riesgo, ofreciendo asistencia personalizada. Revelar las experiencias subjetivas de los estudiantes con los EWS podría contribuir a una comprensión más profunda de lo que significa interactuar con la IA en áreas de la vida humana como la enseñanza y el aprendizaje. Nuestra investigación, por lo tanto, ha explorado las experiencias subjetivas de los estudiantes con los EWS, caracterizándolas según los perfiles de los estudiantes, la autoeficacia, la experiencia previa y la perspectiva sobre la ética de los datos. Los resultados muestran que los estudiantes, en su mayoría trabajadores senior con alta autoeficacia académica, tenían experiencias limitadas con este método y expectativas mínimas. Sin embargo, el uso de los EWS ha inspirado reflexiones significativas. A pesar de ello, una comparación entre las disciplinas de Informática y Economía demostró una mayor confianza y expectativa respecto al sistema y la IA para la primera. El estudio ha subrayado la importancia de ayudar a los estudiantes a desarrollar más experiencias y comprensiones mientras utilizan sistemas de IA en la educación para garantizar la calidad, relevancia y equidad de su experiencia educativa en general.
Keywords: Análisis Temático; Educación Superior; Experiencias de los Estudiantes; Inteligencia Artificial; Sistema de Alerta Temprana
Title: Explorando a experiência de estudantes universitários com ferramentas educacionais baseadas em IA: O caso de um sistema de alerta precoce
Abstract: As ferramentas educacionais alimentadas por inteligência artificial (AIEd) incluem sistemas de alerta precoce (EWS) para identificar estudantes universitários em risco, oferecendo assistência personalizada. Revelar as experiências subjetivas dos estudantes com os EWS poderia contribuir para uma compreensão mais profunda do que significa interagir com a IA em áreas da vida humana como o ensino e a aprendizagem. Portanto, nossa investigação explorou as experiências subjetivas dos estudantes com os EWS, caracterizando-as de acordo com os perfis dos estudantes, autoeficácia, experiência anterior e perspectiva sobre a ética dos dados. Os resultados mostram que os estudantes, em sua maioria trabalhadores seniores com forte autoeficácia acadêmica, tinham experiência limitada com este método e expectativas mínimas. No entanto, o uso dos EWS inspirou reflexões significativas. Apesar disso, uma comparação entre as disciplinas de Informática e Economia demonstrou maior confiança e expectativa em relação ao sistema e à IA para a primeira. O estudo enfatizou a importância de auxiliar os estudantes a desenvolverem mais experiências e compreensões ao utilizar sistemas de IA na educação para garantir a qualidade, relevância e equidade de sua experiência educacional geral.
Keywords: Análise Temática; Educação Superior; Experiências dos Estudantes; Inteligência Artificial; Sistema de Alerta Precoce
Akhtar, S., Warburton, S., & Xu, W. (2017). The use of an online learning and teaching system for monitoring computer-aided design student participation and predicting student success. International Journal of Technology and Design Education, 27(2). https://doi.org/10.1007/s10798-015-9346-8
Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. ACM International Conference Proceeding Series, 267–270. https://doi.org/10.1145/2330601.2330666
Bañeres, D., Karadeniz, A., Guerrero-Roldán, A.-E., & Elena Rodríguez, M. (2021). A Predictive System for Supporting At-Risk Students' Identification. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1 (Vol. 1288, pp. 891–904). Springer International Publishing. https://doi.org/10.1007/978-3-030-63128-4_67
Bañeres, D., Rodríguez, M. E., Guerrero-Roldán, A. E., & Karadeniz, A. (2020). An Early Warning System to Detect At-Risk Students in Online Higher Education. Applied Sciences, 10(13), 4427. https://doi.org/10.3390/app10134427
Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly: Management Information Systems, 28(2), 229–254. https://doi.org/10.2307/25148634
Bisdas, S., Topriceanu, C. C., Zakrzewska, Z., Irimia, A. V., Shakallis, L., Subhash, J., Casapu, M. M., Leon-Rojas, J., Pinto dos Santos, D., Andrews, D. M., Zeicu, C., Bouhuwaish, A. M., Lestari, A. N., Abu-Ismail, L., Sadiq, A. S., Khamees, A., Mohammed, K. M. G., Williams, E., Omran, A. I., ... Ebrahim, E. H. (2021). Artificial Intelligence in Medicine: A Multinational Multi-Center Survey on the Medical and Dental Students' Perception. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.795284
Bochniarz, K. T., Czerwiński, S. K., Sawicki, A., & Atroszko, P. A. (2022). Attitudes to AI among high school students: Understanding distrust towards humans will not help us understand distrust towards AI. Personality and Individual Differences, 185. https://doi.org/10.1016/j.paid.2021.111299
Bodily, R., & Verbert, K. (2017). Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Transactions on Learning Technologies, 10(4). https://doi.org/10.1109/TLT.2017.2740172
Bogina, V., Hartman, A., Kuflik, T., & Shulner-Tal, A. (2022). Educating Software and AI Stakeholders About Algorithmic Fairness, Accountability, Transparency and Ethics. International Journal of Artificial Intelligence in Education, 32(3). https://doi.org/10.1007/s40593-021-00248-0
Bozkurt, A., Xiao, F., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Stewart, B., Costello, E., Mason, J., Stracke, C., Romero-Hall, E., & Jandric, P. (2023). Speculative Futures on ChatGPT and Generative Artificial Intelligence (AI): A Collective Reflection from the Educational Landscape. Asian Journal of Distance Education, 18, 53–130 [Journal Article, Zenodo]. https://doi.org/10.5281/zenodo.7636568
Braun, V., Clarke, V., Hayfield, N., & Terry, G. (2019). Thematic analysis. Handbook of Research Methods in Health Social Sciences, 843–860. https://doi.org/10.1007/978-981-10-5251-4_103/COVER
Buckingham-Shum, S. J. (2019). Critical data studies, abstraction and learning analytics: Editorial to Selwyn's LAK keynote and invited commentaries. Journal of Learning Analytics, 6(3). https://doi.org/10.18608/jla.2019.63.2
Casey, K., & Azcona, D. (2017). Utilizing student activity patterns to predict performance. International Journal of Educational Technology in Higher Education, 14(1). https://doi.org/10.1186/s41239-017-0044-3
Chen, M., Siu-Yung, M., Chai, C. S., Zheng, C., & Park, M. Y. (2021). A Pilot Study of Students' Behavioral Intention to Use AI for Language Learning in Higher Education. Proceedings - 2021 International Symposium on Educational Technology, ISET 2021. https://doi.org/10.1109/ISET52350.2021.00045
Chikobava, M., & Romeike, R. (2021). Towards an Operationalization of AI acceptance among Pre-service Teachers. ACM International Conference Proceeding Series. https://doi.org/10.1145/3481312.3481349
Chocarro, R., Cortiñas, M., & Marcos-Matás, G. (2021). Teachers' attitudes towards chatbots in education: a technology acceptance model approach considering the effect of social language, bot proactiveness, and users' characteristics. Educational Studies. https://doi.org/10.1080/03055698.2020.1850426
Demir, K., & Güraksın, G. E. (2022). Determining middle school students' perceptions of the concept of artificial intelligence: A metaphor analysis. Participatory Educational Research, 9(2). https://doi.org/10.17275/per.22.41.9.2
Dreyfus, H. L. (2007). Why Heideggerian AI failed and how fixing it would require making it more Heideggerian. Artificial Intelligence, 171(18). https://doi.org/10.1016/j.artint.2007.10.012
Elo, S., Kääriäinen, M., Kanste, O., Pölkki, T., Utriainen, K., & Kyngäs, H. (2014). Qualitative Content Analysis: A Focus on Trustworthiness. SAGE Open, 4(1). https://doi.org/10.1177/2158244014522633
Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T., & Vuorikari, R. (2016). Research Evidence on the Use of Learning Analytics: implications for education Policy. https://doi.org/10.2791/955210
Floridi, L. (2023). The Ethics of Artificial Intelligence. Principles, Challenges, and Opportunities. Oxford University Press. https://global.oup.com/academic/product/the-ethics-of-artificial-intelligence-9780198883098?cc = it&lang = en&#
Freitas, R., & Salgado, L. (2020). Educators in the loop: Using scenario simulation as a tool to understand and investigate predictive models of student dropout risk in distance learning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12217 LNCS. https://doi.org/10.1007/978-3-030-50334-5_17
Gado, S., Kempen, R., Lingelbach, K., & Bipp, T. (2022). Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence? Psychology Learning and Teaching, 21(1). https://doi.org/10.1177/14757257211037149
Gallagher, S. (2014). Phenomenology | The Encyclopedia of Human-Computer Interaction, 2nd Ed. https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/phenomenology
Ghotbi, N., Ho, M. T., & Mantello, P. (2022). Attitude of college students towards ethical issues of artificial intelligence in an international university in Japan. AI and Society, 37(1). https://doi.org/10.1007/s00146-021-01168-2
González-Pérez, S., Mateos de Cabo, R., & Sáinz, M. (2020). Girls in STEM: Is It a Female Role-Model Thing? Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.02204
Greenland, S. J., & Moore, C. (2022). Large qualitative sample and thematic analysis to redefine student dropout and retention strategy in open online education. British Journal of Educational Technology, 53(3). https://doi.org/10.1111/bjet.13173
Guggemos, J., Seufert, S., & Sonderegger, S. (2020). Humanoid robots in higher education: Evaluating the acceptance of Pepper in the context of an academic writing course using the UTAUT. British Journal of Educational Technology, 51(5), 1864–1883. https://doi.org/10.1111/bjet.13006
Gutiérrez, F., Seipp, K., Ochoa, X., Chiluiza, K., de Laet, T., & Verbert, K. (2020). LADA: A learning analytics dashboard for academic advising. Computers in Human Behavior, 107. https://doi.org/10.1016/j.chb.2018.12.004
Hayes, S. (2021). Postdigital Positionality. Developing Powerful Inclusive Narratives for Learning, Teaching, Research and Policy in Higher Education. Brill. https://brill.com/view/title/57466?language = en
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in Education: Towards a Community-Wide Framework. International Journal of Artificial Intelligence in Education, 32(3). https://doi.org/10.1007/s40593-021-00239-1
Hu, Y. H., Lo, C. L., & Shih, S. P. (2014). Developing early warning systems to predict students' online learning performance. Computers in Human Behavior, 36, 469–478. https://doi.org/10.1016/j.chb.2014.04.002
Jalalov, D. (2023, March 9th). L'evoluzione dei chatbot da T9-Era e GPT-1 a ChatGPT. (The evolution of chatbots from T9-Era and GPT-1 to ChatGPT.) [Blog]. Metaverse Post. https://mpost.io/it/l%27evoluzione-dei-chatbot-dall%27era-t9-e-gpt-1-a-chatgpt/
Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough: Pitfalls of learning analytics dashboards in the educational practice. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10474 LNCS. https://doi.org/10.1007/978-3-319-66610-5_7
Jivet, I., Scheffel, M., Schmitz, M., Robbers, S., Specht, M., & Drachsler, H. (2020). From students with love: An empirical study on learner goals, self-regulated learning and sense-making of learning analytics in higher education. Internet and Higher Education, 47. https://doi.org/10.1016/j.iheduc.2020.100758
Kabathova, J., & Drlik, M. (2021). Towards predicting student's dropout in university courses using different machine learning techniques. Applied Sciences (Switzerland), 11(7). https://doi.org/10.3390/app11073130
Kerr, A., Barry, M., & Kelleher, J. D. (2020). Expectations of artificial intelligence and the performativity of ethics: Implications for communication governance. Big Data and Society, 7(1). https://doi.org/10.1177/2053951720915939
Kim, J. W., Jo, H. I., & Lee, B. G. (2019). The Study on the Factors Influencing on the Behavioral Intention of Chatbot Service for the Financial Sector: Focusing on the UTAUT Model. Journal of Digital Contents Society, 20(1), 41–50. https://doi.org/10.9728/dcs.2019.20.1.41
Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2020). My Teacher Is a Machine: Understanding Students' Perceptions of AI Teaching Assistants in Online Education. International Journal of Human-Computer Interaction, 36(20). https://doi.org/10.1080/10447318.2020.1801227
Krumm, A. E., Waddington, R. J., Teasley, S. D., & Lonn, S. (2014). A learning management system-based early warning system for academic advising in undergraduate engineering. In J. A. Larusson & B. White (Eds.), Learning Analytics: From Research to Practice (pp. 103–119). https://doi.org/10.1007/978-1-4614-3305-7_6
Liz-Domínguez, M., Caeiro-Rodríguez, M., Llamas-Nistal, M., & Mikic-Fonte, F. A. (2019). Systematic literature review of predictive analysis tools in higher education. Applied Sciences (Switzerland), 9 (24). https://doi.org/10.3390/app9245569
Lund, B., & Wang, T. (2023). Chatting about ChatGPT: How may AI and GPT impact academia and libraries? Library Hi Tech News, 40. https://doi.org/10.1108/LHTN-01-2023-0009
Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60. https://doi.org/10.1016/j.futures.2017.03.006
Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers' AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6
OpenAI. (2022, November 30th). Introducing ChatGPT [Blog]. Openai.Com. https://openai.com/blog/chatgpt
Ortigosa, A., Carro, R. M., Bravo-Agapito, J., Lizcano, D., Alcolea, J. J., & Blanco, Ó. (2019). From Lab to Production: Lessons Learnt and Real-Life Challenges of an Early Student-Dropout Prevention System. IEEE Transactions on Learning Technologies, 12(2), 264–277. https://doi.org/10.1109/TLT.2019.2911608
Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: challenges and opportunities for sustainable development. Working Papers on Education Policy, 7.
Pichai, S. (2023, February 6th). Google AI updates: Bard and new AI features in Search [Blog]. Google.com https://blog.google/technology/ai/bard-google-ai-search-updates/
Pintrich, P. R. (2000). An Achievement Goal Theory Perspective on Issues in Motivation Terminology, Theory, and Research. Contemporary Educational Psychology, 25(1). https://doi.org/10.1006/ceps.1999.1017
Plak, S., Cornelisz, I., Meeter, M., & van Klaveren, C. (2022). Early warning systems for more effective student counselling in higher education: Evidence from a Dutch field experiment. Higher Education Quarterly, 76(1). https://doi.org/10.1111/hequ.12298
Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1). https://doi.org/10.1186/s41039-017-0062-8
Prinsloo, P. (2019). A social cartography of analytics in education as performative politics. British Journal of Educational Technology, 50(6). https://doi.org/10.1111/bjet.12872
Prinsloo, P., Slade, S., & Khalil, M. (2022). The answer is (not only) technological: Considering student data privacy in learning analytics. British Journal of Educational Technology, 53(4), 876–893. https://doi.org/10.1111/BJET.13216
Qin, F., Li, K., & Yan, J. (2020). Understanding user trust in artificial intelligence-based educational systems: Evidence from China. British Journal of Educational Technology, 51(5), 1693–1710. https://doi.org/10.1111/bjet.12994
Raffaghelli, J. E., Loria-Soriano, E., González, M. E. R., Bañeres, D., & Guerrero-Roldán, A. E. (2022). Extracted and Anonymised Qualitative Data on Students' Acceptance of an Early Warning System [dataset]. Zenodo. https://doi.org/10.5281/ZENODO.6841129
Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A.-E., & Bañeres, D. (2022). Applying the UTAUT model to explain the students' acceptance of an early warning system in Higher Education. Computers & Education, 182, 104468. https://doi.org/10.1016/j.compedu.2022.104468
Rienties, B., Herodotou, C., Olney, T., Schencks, M., & Boroowa, A. (2018). Making sense of learning analytics dashboards: A technology acceptance perspective of 95 teachers. International Review of Research in Open and Distance Learning, 19(5), 187–202. https://doi.org/10.19173/irrodl.v19i5.3493
Scherer, R., & Teo, T. (2019). Editorial to the special section—Technology acceptance models: What we know and what we (still) do not know. In British Journal of Educational Technology (Vol. 50, Issue 5, pp. 2387–2393). Blackwell Publishing Ltd. https://doi.org/10.1111/bjet.12866
Selwyn, N. (2019). What's the problem with learning analytics? Journal of Learning Analytics, 6(3). https://doi.org/10.18608/jla.2019.63.3
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00292-9
Sáinz, M., & Eccles, J. (2012). Self-concept of computer and math ability: Gender implications across time and within ICT studies. Journal of Vocational Behavior, 80(2). https://doi.org/10.1016/j.jvb.2011.08.005
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15. https://doi.org/10.1186/s40561-023-00237-x
Tzimas, D., & Demetriadis, S. (2021). Ethical issues in learning analytics: a review of the field. Educational Technology Research and Development, 69(2). https://doi.org/10.1007/s11423-021-09977-4
Valle, N., Antonenko, P., Dawson, K., & Huggins-Manley, A. C. (2021). Staying on target: A systematic literature review on learner-facing learning analytics dashboards. In British Journal of Educational Technology (Vol. 52, Issue 4). https://doi.org/10.1111/bjet.13089
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425–478. https://doi.org/10.2307/30036540
Xing, W., Chen, X., Stein, J., & Marcinkowski, M. (2016). Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Computers in Human Behavior, 58, 119–129. https://doi.org/10.1016/j.chb.2015.12.007
Xu, J. J., & Babaian, T. (2021). Artificial intelligence in business curriculum: The pedagogy and learning outcomes. International Journal of Management Education, 19(3). https://doi.org/10.1016/j.ijme.2021.100550
Zander, L., Höhne, E., Harms, S., Pfost, M., & Hornsey, M. J. (2020). When Grades Are High but Self-Efficacy Is Low: Unpacking the Confidence Gap Between Girls and Boys in Mathematics. Frontiers in Psychology, 11, 2492. https://doi.org/10.3389/fpsyg.2020.552355
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0
Zhou, Y., Zhao, J., & Zhang, J. (2020). Prediction of learners' dropout in E-learning based on the unusual behaviors. Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1857788
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1). https://doi.org/10.3102/0002831207312909
van Brummelen, J., Tabunshchyk, V., & Heng, T. (2021). Alexa, Can I Program You?': Student Perceptions of Conversational Artificial Intelligence before and after Programming Alexa. Proceedings of Interaction Design and Children, IDC 2021. https://doi.org/10.1145/3459990.3460730