Recommender systems in education: A literature review and bibliometric analysis
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Abstract
This study aims to provide an overview regarding the use of recommender systems in education through a systematic review and a bibliometric analysis. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was followed and a total of 1,622 related documents from Scopus and WoS are examined from 2001 to 2022. The study goes over the literature and describes personalized learning, artificial intelligence (AI) in education as well as recommender systems and educational recommender systems. Besides descriptive statistics about the document collection, the result analysis involves the citation, sources, authors, affiliations, countries, and document information and categories of the related articles. The thematic evolution of the topic throughout the years is also examined. Based on the results, the recency and significance of recommender systems and their potentials in the educational domain were evident. Their ability to take into account learners' unique traits, experiences, skills, and preferences was highlighted. Recommender systems emerged as a learning tool that can empower learners, improve education quality and learning outcomes, increase learners' motivation, engagement, achievements, and satisfaction, and enable learners to be in charge of their own learning. Finally, recommender systems arose as an effective educational tool that can promote and improve adaptive learning and personalized learning.
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References
- Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749. https://doi.org/10.1109/tkde.2005.99
- Aggarwal, C. C. (2016). Recommender systems (Vol. 1). Cham: Springer International Publishing.
- Amato, F., Moscato, V., Picariello, A., & Piccialli, F. (2019). SOS: A multimedia recommender system for online social networks. Future Generation Computer Systems, 93, 914–923. https://doi.org/10.1016/j.future.2017.04.028
- Aria, M., & Cuccurullo, C. (2017). Bibliometrix : An r-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
- Ashraf, E., Manickam, S., & Karuppayah, S. (2021). A comprehensive review of course recommender systems in e-learnering. Journal of Educators Online, 18(1).
- Athanassopoulos, S., Manoli, P., Gouvi, M., Lavidas, K., & Komis, V. (2023). The use of ChatGPT as a learning tool to improve foreign language writing in a multilingual and multicultural classroom. Advances in Mobile Learning Educational Research, 3(2), 818-824. https://doi.org/10.25082/AMLER.2023.02.009
- Becker, S. A., Cummins, M., Davis, A., Freeman, A., Hall, C. G., & Ananthanarayanan, V. (2017). NMC horizon report: 2017 higher education edition. The New Media Consortium.
- Bernacki, M. L., Greene, M. J., & Lobczowski, N. G. (2021). A systematic review of research on personalized learning: Personalized by whom, to what, how, and for what purpose(s)? Educational Psychology Review, 33(4), 1675–1715. https://doi.org/10.1007/s10648-021-09615-8
- Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132. https://doi.org/10.1016/j.knosys.2013.03.012
- Brown, M., McCormack, M., Reeves, J., Brook, D. C., Grajek, S., Alexander, B., Bali, M., Bulger, S., Dark, S., Engelbert, N., et al. (2020). 2020 educause horizon report teaching and learning edition (pp. 1–58). Educause Horizon Report.
- Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education, 13(2–4), 159–172.
- Brynjolfsson, E., & Mcafee, A. (2017). Artificial intelligence, for real. Harvard Business Review, 1, 1–31.
- Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlstrom, P., Henke, N., & Trench, M. (2017). Artificial intelligence: The next digital frontier?
- Burke, R. (2000). Knowledge-based recommender systems. Encyclopedia of Library and Information Systems, 69(S 32), 175–186.
- Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331–370. https://doi.org/10.1023/a:1021240730564
- Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2017). Artificial intelligence and the `good society': The US, EU, and UK approach. Science and Engineering Ethics. https://doi.org/10.1007/s11948-017-9901-7
- Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16–24. https://doi.org/10.1016/j.procs.2018.08.233
- Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/access.2020.2988510
- Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002
- Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28–47.
- Chiu, T. K. F., & Chai, C. (2020). Sustainable curriculum planning for artificial intelligence education: A Self-Determination theory perspective. Sustainability, 12(14), 5568. https://doi.org/10.3390/su12145568
- Cope, B., Kalantzis, M., & Searsmith, D. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(12), 1229–1245. https://doi.org/10.1080/00131857.2020.1728732
- Dabbagh, N., & Kitsantas, A. (2012). Personal learning environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. The Internet and Higher Education, 15(1), 3–8. https://doi.org/10.1016/j.iheduc.2011.06.002
- Dascalu, M.-I., Bodea, C.-N., Mihailescu, M. N., Tanase, E. A., & Ordoñez de Pablos, P. (2016). Educational recommender systems and their application in lifelong learning. Behaviour & Information Technology, 35(4), 290–297. https://doi.org/10.1080/0144929x.2015.1128977
- De Houwer, J., Barnes-Holmes, D., & Moors, A. (2013). What is learning? On the nature and merits of a functional definition of learning. Psychonomic Bulletin & Review, 20(4), 631–642. https://doi.org/10.3758/s13423-013-0386-3
- Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
- Drachsler, H., Verbert, K., Santos, O. C., & Manouselis, N. (2015). Panorama of recommender systems to support learning. In Recommender systems handbook (pp. 421–451). https://doi.org/10.1007/978-1-4899-7637-6_12
- Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of big data - evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
- Education, U. S. D. of. (2010). Transforming american education: Learning powered by technology. Office of Educational Technology, US Department of Education Washington, DC.
- Education, U. S. D. of. (2016). Future ready learning: Reimagining the role of technology in education. Office of Educational Technology, US Department of Education Washington, DCy.
- Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z
- Garcia-Martinez, S., & Hamou-Lhadj, A. (2013). Educational recommender systems: A Pedagogical-Focused perspective. In Multimedia services in intelligent environments (pp. 113–124). https://doi.org/10.1007/978-3-319-00375-7_8
- George, G., & Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142, 103642. https://doi.org/10.1016/j.compedu.2019.103642
- Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of google scholar, PubMed, and 26 other resources. Research Synthesis Methods, 11(2), 181–217. https://doi.org/10.1002/jrsm.1378
- Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
- Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5–53. https://doi.org/10.1145/963770.963772
- Hinojo-Lucena, F.-J., Aznar-Díaz, I., Cáceres-Reche, M.-P., & Romero-Rodríguez, J.-M. (2019). Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature. Education Sciences, 9(1), 51. https://doi.org/10.3390/educsci9010051
- Holmes, W., Bialik, M., & Fadel, C. (2020). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
- Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial intelligence in education. In Data ethics: Building trust: How digital technologies can serve humanity (pp. 621–653). https://doi.org/10.58863/20.500.12424/4276068
- Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023). Effects of artificial Intelligence-Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104684. https://doi.org/10.1016/j.compedu.2022.104684
- Hung, C.-Y., Sun, J. C.-Y., & Liu, J.-Y. (2019). Effects of flipped classrooms integrated with MOOCs and game-based learning on the learning motivation and outcomes of students from different backgrounds. Interactive Learning Environments, 27(8), 1028–1046. https://doi.org/10.1080/10494820.2018.1481103
- Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
- Ipek Z. H., Gözüm, A. C. I., Papadakis, St., & Kalogiannakis, M. (2023). Educational applications of ChatGPT, an AI system: A systematic review research, Educational Process, 12(3), 26-55. https://doi.org/10.22521/edupij.2023.123.2
- Isinkaye, F. o., Folajimi, Y. o., & Ojokoh, B. a. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261–273. https://doi.org/10.1016/j.eij.2015.06.005
- Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: An introduction. Cambridge University Press.
- Jurayev, T. N. (2023). The use of mobile learning applications in higher education institutes. Advances in Mobile Learning Educational Research, 3(1), 610-620. https://doi.org/10.25082/AMLER.2023.01.010
- Kanakaris, V., Lampropoulos, G., & Siakas, K. (2019). A Survey and a Case-Study Regarding Social Media Security and Privacy on Greek Future IT Professionals. International Journal of Human Capital and Information Technology Professionals (IJHCITP), 10(1), 22–37. https://doi.org/10.4018/IJHCITP.2019010102
- Karakaya, M. Ö., & Aytekin, T. (2018). Effective methods for increasing aggregate diversity in recommender systems. Knowledge and Information Systems, 56(2), 355–372. https://doi.org/10.1007/s10115-017-1135-0
- Karakose, T., Demirkol, M., Aslan, N., Köse, H., & Yirci, R. (2023). A Conversation with ChatGPT about the Impact of the COVID-19 Pandemic on Education: Comparative Review Based on Human–AI Collaboration. International Journal, 12(3), 7-25.
- Karakose, T., Papadakis, S., Tülübaş, T., & Polat, H. (2022). Understanding the intellectual structure and evolution of distributed leadership in schools: A science mapping-based bibliometric analysis. Sustainability, 14(24), 16779.
- Karakose, T., Tülübaş, T., & Papadakis, S. (2023). The Scientific Evolution of Social Justice Leadership in Education: Structural and Longitudinal Analysis of the Existing Knowledge Base, 2003-2022. In Frontiers in Education (Vol. 8, p. 1139648). Frontiers.
- Karakose, T., Tülübaş, T., Papadakis, S., & Yirci, R. (2023). Evaluating the Intellectual Structure of the Knowledge Base on Transformational School Leadership: A Bibliometric and Science Mapping Analysis. Education Sciences, 13(7), 708.
- Katsaris, I., & Vidakis, N. (2021). Adaptive e-learning systems through learning styles: A review of the literature. Advances in Mobile Learning Educational Research, 1(2), 124-145. https://doi.org/10.25082/AMLER.2021.02.007
- Khanal, S. S., Prasad, P. w. c., Alsadoon, A., & Maag, A. (2020). A systematic review: Machine learning based recommendation systems for e-learning. Education and Information Technologies, 25(4), 2635–2664. https://doi.org/10.1007/s10639-019-10063-9
- Klašnja-Milićević, A., Ivanović, M., & Nanopoulos, A. (2015). Recommender systems in e-learning environments: A survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, 44(4), 571–604. https://doi.org/10.1007/s10462-015-9440-z
- Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A survey of recommendation systems: Recommendation models, techniques, and application fields. Electronics, 11(1), 141. https://doi.org/10.3390/electronics11010141
- Konstan, J. A., & Riedl, J. (2012). Recommender systems: From algorithms to user experience. User Modeling and User-Adapted Interaction, 22(1-2), 101–123. https://doi.org/10.1007/s11257-011-9112-x
- Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. https://doi.org/10.1109/mc.2009.263
- Kundu, S. S., Sarkar, D., Jana, P., & Kole, D. K. (2021). Personalization in education using recommendation system: An overview. In Intelligent systems reference library (pp. 85–111). https://doi.org/10.1007/978-981-15-8744-3_5
- Lampropoulos, G. (2023a). Artificial intelligence, big data, and machine learning in industry 4.0. In Encyclopedia of data science and machine learning (pp. 2101–2109). IGI Global. https://doi.org/10.4018/978-1-7998-9220-5.ch125
- Lampropoulos, G. (2023b). Augmented reality and artificial intelligence in education: Toward immersive intelligent tutoring systems. In Augmented reality and artificial intelligence (pp. 137–146). https://doi.org/10.1007/978-3-031-27166-3_8
- Lampropoulos, G. (2023c). Educational benefits of digital game-based learning: K-12 teachers’ perspectives and attitudes. Advances in Mobile Learning Educational Research, 3(2), 805-817. https://doi.org/10.25082/AMLER.2023.02.008
- Lampropoulos, G. (2023d). Educational data mining and learning analytics in the 21st century. In Encyclopedia of data science and machine learning (pp. 1642–1651). https://doi.org/10.4018/978-1-7998-9220-5.ch098
- Lampropoulos, G., Anastasiadis, T., Siakas, K., & Siakas, E. (2022a). The impact of personality traits on social media use and engagement: An overview. International Journal on Social and Education Sciences, 4(1), 34–51. https://doi.org/10.46328/ijonses.264
- Lampropoulos, G., Anastasiadis, T., Siakas, K., & Siakas, E. (2022b). The Impact of Personality Traits on Social Media Use and Engagement: An Overview. International Journal on Social and Education Sciences (IJonSES), 4(1), 34–51. https://doi.org/10.46328/ijonses.264
- Lampropoulos, G., Keramopoulos, E., Diamantaras, K., & Evangelidis, G. (2022c). Augmented reality and gamification in education: A systematic literature review of research, applications, and empirical studies. Applied Sciences, 12(13), 6809. https://doi.org/10.3390/app12136809
- Lampropoulos, G., Keramopoulos, E., Diamantaras, K., & Evangelidis, G. (2023). Integrating augmented reality, gamification, and serious games in computer science education. Education Sciences, 13(6), 618. https://doi.org/10.3390/educsci13060618
- Lee, D., Huh, Y., Lin, C.-Y., & Reigeluth, C. M. (2018). Technology functions for personalized learning in learner-centered schools. Educational Technology Research and Development, 66(5), 1269–1302. https://doi.org/10.1007/s11423-018-9615-9
- Li, D., & Du, Y. (2017). Artificial intelligence with uncertainty. CRC press. https://doi.org/10.1201/9781315366951
- Lin, C. F., Yeh, Y., Hung, Y. H., & Chang, R. I. (2013). Data mining for providing a personalized learning path in creativity: An application of decision trees. Computers & Education, 68, 199–210. https://doi.org/10.1016/j.compedu.2013.05.009
- Lin, J., Pu, H., Li, Y., & Lian, J. (2018). Intelligent recommendation system for course selection in smart education. Procedia Computer Science, 129, 449–453. https://doi.org/10.1016/j.procs.2018.03.023
- Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12–32. https://doi.org/10.1016/j.dss.2015.03.008
- Lü, L., Medo, M., Yeung, C. H., Zhang, Y.-C., Zhang, Z.-K., & Zhou, T. (2012). Recommender systems. Physics Reports, 519(1), 1–49. https://doi.org/10.1016/j.physrep.2012.02.006
- Lynn, N. d., & Emanuel, A. w. r. (2021). A review on recommender systems for course selection in higher education. IOP Conference Series: Materials Science and Engineering, 1098(3), 032039. https://doi.org/10.1088/1757-899x/1098/3/032039
- 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
- Maphosa, V., & Maphosa, M. (2023). Fifteen years of recommender systems research in higher education: Current trends and future direction. Applied Artificial Intelligence, 37(1). https://doi.org/10.1080/08839514.2023.2175106
- McArthur, D., Lewis, M., & Bishary, M. (2005). The roles of artificial intelligence in education: Current progress and future prospects. Journal of Educational Technology, 1(4), 42–80.
- McLoughlin, C., & Lee, M. J. (2007). Listen and learn: A systematic review of the evidence that podcasting supports learning in higher education. In EdMedia+ innovate learning (pp. 1669–1677). Association for the Advancement of Computing in Education (AACE).
- McLoughlin, C., & Lee, M. J. W. (2010). Personalised and self regulated learning in the web 2.0 era: International exemplars of innovative pedagogy using social software. Australasian Journal of Educational Technology, 26(1). https://doi.org/10.14742/ajet.1100
- Melville, P., & Sindhwani, V. (2011). Recommender systems. In Encyclopedia of machine learning (pp. 829–838). https://doi.org/10.1007/978-0-387-30164-8_705
- Mongeon, P., & Paul-Hus, A. (2015). The journal coverage of web of science and scopus: A comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5
- Mu, R. (2018). A survey of recommender systems based on deep learning. IEEE Access, 6, 69009–69022. https://doi.org/10.1109/access.2018.2880197
- Nascimento, P. D., Barreto, R., Primo, T., Gusmão, T., & Oliveira, E. (2017). Recomendação de objetos de aprendizagem baseada em modelos de estilos de aprendizagem: Uma revisão sistemática da literatura. Anais Do XXVIII Simpósio Brasileiro de Informática Na Educação (SBIE 2017). https://doi.org/10.5753/cbie.sbie.2017.213
- Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. https://doi.org/10.1016/j.caeai.2021.100020
- Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. International journal of surgery, 88, 105906. https://doi.org/10.1016/j.ijsu.2021.105906
- Pan, C., & Li, W. (2010). Research paper recommendation with topic analysis. 2010 International Conference on Computer Design and Applications. https://doi.org/10.1109/iccda.2010.5541170
- Papadakis, S., Kiv, A. E., Kravtsov, H. M., Osadchyi, V. V., Marienko, M. V., Pinchuk, O. P., ... & Semerikov, S. O. (2023). Revolutionizing education: using computer simulation and cloud-based smart technology to facilitate successful open learning. In CEUR Workshop Proceedings (Vol. 3358, pp. 1-18).
- Papadakis, S., Zourmpakis, A. I., & Kalogiannakis, M. (2023). Analyzing the Impact of a Gamification Approach on Primary Students’ Motivation and Learning in Science Education. In Learning in the Age of Digital and Green Transition: Proceedings of the 25th International Conference on Interactive Collaborative Learning (ICL2022), Volume 1 (pp. 701-711). Cham: Springer International Publishing.
- Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications, 39(11), 10059–10072. https://doi.org/10.1016/j.eswa.2012.02.038
- Pathak, B., Garfinkel, R., Gopal, R. D., Venkatesan, R., & Yin, F. (2010). Empirical analysis of the impact of recommender systems on sales. Journal of Management Information Systems, 27(2), 159–188. https://doi.org/10.2753/mis0742-1222270205
- Pavlidis, G. (2019). Recommender systems, cultural heritage applications, and the way forward. Journal of Cultural Heritage, 35, 183–196. https://doi.org/10.1016/j.culher.2018.06.003
- Pazzani, M. J., & Billsus, D. (2007). Content-Based recommendation systems. In The adaptive web (pp. 325–341). https://doi.org/10.1007/978-3-540-72079-9_10
- Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development.
- Raj, N. S., & Renumol, V. g. (2022). A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020. Journal of Computers in Education, 9(1), 113–148. https://doi.org/10.1007/s40692-021-00199-4
- Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58. https://doi.org/10.1145/245108.245121
- Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1–35). https://doi.org/10.1007/978-0-387-85820-3_1
- Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender systems: Introduction and challenges. In Recommender systems handbook (pp. 1–34). https://doi.org/10.1007/978-1-4899-7637-6_1
- Rivera, A. C., Tapia-Leon, M., & Lujan-Mora, S. (2018). Recommendation systems in education: A systematic mapping study. In Proceedings of the international conference on information technology & systems (ICITS 2018) (pp. 937–947). https://doi.org/10.1007/978-3-319-73450-7_89
- Roetzel, P. G. (2019). Information overload in the information age: A review of the literature from business administration, business psychology, and related disciplines with a bibliometric approach and framework development. Business Research, 12(2), 479–522. https://doi.org/10.1007/s40685-018-0069-z
- Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
- Rubin, N. (2010). Creating a user-centric learning environment with campus pack personal learning spaces. PLS Webinar, Learning Objects Community.
- Russell, S. J. (2010). Artificial intelligence a modern approach. Pearson Education, Inc.
- Sampson, D., Karagiannidis, C., & Kinshuk. (2002). Personalised learning: Educational, technological and standarisation perspective. Digital Education Review, 4, 24–39.
- Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web (pp. 291–324). https://doi.org/10.1007/978-3-540-72079-9_9
- Shani, G., & Gunawardana, A. (2011). Evaluating recommendation systems. In Recommender systems handbook (pp. 257–297). https://doi.org/10.1007/978-0-387-85820-3_8
- Shemshack, A., & Spector, J. M. (2020). A systematic literature review of personalized learning terms. Smart Learning Environments, 7(1). https://doi.org/10.1186/s40561-020-00140-9
- Silva, F. L. da, Slodkowski, B. K., Silva, K. K. A. da, & Cazella, S. C. (2023). A systematic literature review on educational recommender systems for teaching and learning: Research trends, limitations and opportunities. Education and Information Technologies, 28(3), 3289–3328. https://doi.org/10.1007/s10639-022-11341-9
- Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., Hirschberg, J., Kalyanakrishnan, S., Kamar, E., Kraus, S., Leyton-Brown, K., Parkes, D., Press, W., Saxenian, A., Shah, J., Tambe, M., & Teller, A. (2016). Artificial intelligence and life in 2030: The one hundred year study on artificial intelligence. https://doi.org/10.48550/ARXIV.2211.06318
- Su, X., Khoshgoftaar, T. M., Zhu, X., & Greiner, R. (2008). Imputation-boosted collaborative filtering using machine learning classifiers. Proceedings of the 2008 ACM Symposium on Applied Computing. https://doi.org/10.1145/1363686.1363903
- Tang, K.-Y., Chang, C.-Y., & Hwang, G.-J. (2021). Trends in artificial intelligence-supported e-learning: A systematic review and co-citation network analysis (1998-2019). Interactive Learning Environments, 1–19. https://doi.org/10.1080/10494820.2021.1875001
- Tarus, J. K., Niu, Z., & Mustafa, G. (2018). Knowledge-based recommendation: A review of ontology-based recommender systems for e-learning. Artificial Intelligence Review, 50(1), 21–48. https://doi.org/10.1007/s10462-017-9539-5
- Truong, H. M. (2016). Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in Human Behavior, 55, 1185–1193. https://doi.org/10.1016/j.chb.2015.02.014
- Urdaneta-Ponte, M. C., Mendez-Zorrilla, A., & Oleagordia-Ruiz, I. (2021). Recommendation systems for education: Systematic review. Electronics, 10(14), 1611. https://doi.org/10.3390/electronics10141611
- Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., & Duval, E. (2012). Context-aware recommender systems for learning: A survey and future challenges. IEEE Transactions on Learning Technologies, 5(4), 318–335. https://doi.org/10.1109/TLT.2012.11
- Wakil, K., Bakhtyar, R., Ali, K., & Alaadin, K. (2015). Improving web movie recommender system based on emotions. International Journal of Advanced Computer Science and Applications, 6(2). https://doi.org/10.14569/ijacsa.2015.060232
- Watters, A. (2023). Teaching machines: The history of personalized learning. MIT Press.
- Wilson, S., Liber, O., Johnson, M., Beauvoir, P., Sharples, P., & Milligan, C. (2007). Personal learning environments: Challenging the dominant design of educational systems. Journal of E-Learning and Knowledge Society, 3(2), 27–38.
- Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599. https://doi.org/10.1016/j.compedu.2019.103599
- Xiong, Y., Li, H., Kornhaber, M. L., Suen, H. K., Pursel, B., & Goins, D. D. (2015). Examining the relations among student motivation, engagement, and retention in a MOOC: A structural equation modeling approach. Global Education Review, 2(3), 23–33.
- Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 1–18. https://doi.org/10.1155/2021/8812542
- Zhang, J., Yu, Q., Zheng, F., Long, C., Lu, Z., & Duan, Z. (2016). Comparing keywords plus of WOS and author keywords: A case study of patient adherence research. Journal of the Association for Information Science and Technology, 67(4), 967–972. https://doi.org/10.1002/asi.23437
- Zhang, L., Basham, J. D., & Yang, S. (2020). Understanding the implementation of personalized learning: A research synthesis. Educational Research Review, 31, 100339. https://doi.org/10.1016/j.edurev.2020.100339
- Zhong, J., Xie, H., & Wang, F. L. (2019). The research trends in recommender systems for e-learning. Asian Association of Open Universities Journal, 14(1), 12–27. https://doi.org/10.1108/aaouj-03-2019-0015
- Zhu, J., & Liu, W. (2020). A tale of two databases: The use of web of science and scopus in academic papers. Scientometrics, 123(1), 321–335. https://doi.org/10.1007/s11192-020-03387-8
- Zhu, Z., & He, B. (2012). Smart education: New frontier of educational informatization. E-Education Research, 12, 1–13.
- Zhu, Z.-T., Yu, M.-H., & Riezebos, P. (2016). A research framework of smart education. Smart Learning Environments, 3(1). https://doi.org/10.1186/s40561-016-0026-2
- Zimmerman, B. J. (2000). Attaining Self-Regulation. In Handbook of Self-Regulation: Theory, research, and applications (pp. 13–39). Academic Press. https://doi.org/10.1016/b978-012109890-2/50031-7
- Zourmpakis, A. I., Kalogiannakis, M., & Papadakis, S. (2023a). A Review of the Literature for Designing and Developing a Framework for Adaptive Gamification in Physics Education. The International Handbook of Physics Education Research: Teaching Physics, edited by Mehmet Fatih Taşar and Paula R. L. Heron (AIP Publishing, Melville, New York, 2023), Chapter 5, pp. 5-1–5-26.
- Zourmpakis, A. I., Kalogiannakis, M., & Papadakis, S. (2023b). Adaptive Gamification in Science Education: An Analysis of the Impact of implementation and Adapted game Elements on Students' Motivation. Computers, 12(7), 143.
- Zourmpakis, A. I., Papadakis, S., & Kalogiannakis, M. (2022). Education of preschool and elementary teachers on the use of adaptive gamification in science education. International Journal of Technology Enhanced Learning, 14(1), 1-16.