Abstract
Personalised learning (PL) is learning in which the stage of learning and the instructional approach are optimised for the needs of each learner. The concept of PL allows e-learning design to shift from a ‘one size fits all’ approach to an adaptive and student-centred approach. This paper aims to provide a literature review of PL based on: the PL components used to analyse learner diversity, the PL features offered, the methods used in developing the PL model, the resulting model, the learning theories applied and the impact of PL implementation. Thirty-nine out of 1654 articles published between 2017 and 2021 which were found by Kitchenham method were studied and analysed. The results are derived from synthesized through qualitative synthesis using thematic analysis. The results reveal that most of the articles used knowledge level and learner characteristics to analyse learner diversity. The teaching materials and learning path were the most widely offered PL features in PL model. There is a trend in determining PL features using the knowledge graph method and the use of machine learning classification algorithms to analyse learner diversity. The results also show that PL implementation improves learning outcomes and increases learner’s satisfaction, motivation, and engagement. Research analysing the impact of PL implementation on learning is limited. In addition, only a few studies explicitly referred to learning theory in relation to PL model development. Further research topics are suggested.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Not applicable.
References
Alamri, H. A., Watson, S., & Watson, W. (2021). Learning technology models that support personalization within blended learning environments in higher education. TechTrends, 65(1), 62–78. https://doi.org/10.1007/s11528-020-00530-3
Anantharaman, H., Mubarak, A., & Shobana, B. T. (2019). Modelling an adaptive e-learning system using LSTM and random forest classification. In 2018 IEEE Conference on E-Learning, e-Management and e-Services, IC3e 2018, 29–34. https://doi.org/10.1109/IC3e.2018.8632646
Apoki, U. C. (2021). The design of waspec: A fully personalised moodle system using semantic web technologies. Computers, 10(5), 1–22. https://doi.org/10.3390/computers10050059
Araujo, P., Costa, C., Viana, W., De Lima Veras, N., & Farias, E. J. P. (2020). Automatic personalisation of study guides in flipped classroom: A case study in a distributed systems course. In Proceedings - Frontiers in Education Conference, FIE, October 2020. https://doi.org/10.1109/FIE44824.2020.9274186
Azcona, D., Hsiao, I. H., & Smeaton, A. F. (2019). Personalizing computer science education by leveraging multimodal learning analytics. In Proceedings - Frontiers in Education Conference, FIE, October 2018, 1–9. https://doi.org/10.1109/FIE.2018.8658596
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. https://doi.org/10.1007/s10648-021-09615-8
Bourkoukou, O., & El-Bachari, E. (2018). Toward a hybrid recommender system for e-learning personalization based on data mining techniques. International Journal on Informatics Visualization., 2(4), 271–278. https://doi.org/10.30630/joiv.2.4.158
Braun, V., Clarke, V., Braun, V., & Clarke, V. (2006). Applied qualitative research in psychology. Applied Qualitative Research in Psychology. https://doi.org/10.1057/978-1-137-35913-1
Cagliero, L., Farinetti, L., & Baralis, E. (2019). Recommending personalized summaries of teaching materials. IEEE Access, 7, 22729–22739. https://doi.org/10.1109/ACCESS.2019.2899655
Chen, Y. H., Huang, N. F., Tzeng, J. W., Lee, C. A., Huang, Y. X., & Huang, H. H. (2022). A personalized learning path recommender system with LINE bot in MOOCs Based on LSTM. In 2022 11th International Conference on Educational and Information Technology, ICEIT 2022, 40–45. https://doi.org/10.1109/ICEIT54416.2022.9690754
Chen, S. Y., & Wang, J. H. (2020). Individual differences and personalized learning: A review and appraisal. Universal Access in the Information Society. https://doi.org/10.1007/s10209-020-00753-4
Constantinidou, F., & Baker, S. (2002). Stimulus modality and verbal learning performance in normal aging. Brain and Language, 82(3), 296–311. https://doi.org/10.1016/S0093-934X(02)00018-4
Cook, D. A., Thompson, W. G., Thomas, K. G., & Thomas, M. R. (2009). Lack of interaction between sensing-intuitive learning styles and problem-first versus information-first instruction: A randomized crossover trial. Advances in Health Sciences Education, 14(1), 79–90. https://doi.org/10.1007/s10459-007-9089-8
Costa, N., Junior, C. P., Araujo, R., & Fernandez, M. (2019). Application of AI planning in the context of e-learning. In Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019, 57–59. https://doi.org/10.1109/ICALT.2019.00021
Crosley, J. (2021). What is (exactly) thematic analysis? A plain-language explanation & definition (with examples). Gradcoach. https://gradcoach.com/what-is-thematic-analysis/
Cui, Y., & Wang, J. (2020). Research on personalized learning path discovery based on differential evolution algorithm and knowledge graph. Communications in Computer and Information Science. https://doi.org/10.1007/978-981-15-2810-1_51
Cuong, N. D. H., Arch-Int, N., & Arch-Int, S. (2018). FUSE: A fuzzy-semantic framework for personalizing learning recommendations. International Journal of Information Technology and Decision Making, 17(4), 1173–1201. https://doi.org/10.1142/S0219622018500220
Dabarera, C., Renandya, W. A., & Zhang, L. J. (2014). The impact of metacognitive scaffolding and monitoring on reading comprehension. System, 42(1), 462–473. https://doi.org/10.1016/j.system.2013.12.020
Deng, Y., Lu, D., Chung, C. J., Huang, D., & Zeng, Z. (2019). Personalized learning in a virtual hands-on lab platform for computer science education. In Proceedings-Frontiers in Education Conference, FIE, October 2018, 1–8. https://doi.org/10.1109/FIE.2018.8659291
El Guabassi, I., Bousalem, Z., Al Achhab, M., Jellouli, I., & El Mohajir, B. E. (2018). Personalized adaptive content system for context-aware ubiquitous learning. Procedia Computer Science, 127, 444–453. https://doi.org/10.1016/j.procs.2018.01.142
Ertmer, P. A., & Newby, T. J. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43–71. https://doi.org/10.1002/piq
Felder, R. M., & Brent, R. (2005). Understanding student differences. Journal of Engineering Education, 94(1), 57–72.
Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7), 674–681.
Ferguson, D., Droege, C., Guojonsdottir, H., Lester, J., Meyer, G., Ralph, G., Sampson, N., & Williams, J. (2001). Designing personalized learning for every student. Association for Supervision and Curriculum Development.
Furey, W. (2020). The Stubborn Myth of “Learning Styles” – State teacher-license prep materials peddle a debunked theory. Education Next, 20(3), 8-12. https://www.educationnext.org/wp-content/uploads/2022/01/ednext_XX_3_furey.pdf
Garrido, A., Morales, L., & Serina, I. (2016). On the use of case-based planning for e-learning personalization. Expert Systems with Applications, 60, 1–15. https://doi.org/10.1016/j.eswa.2016.04.030
Grivokostopoulou, F., Perikos, I., Paraskevas, M., & Hatzilygeroudis, I. (2019). An ontology-based approach for user modelling and personalization in e-learning systems. In Proceedings-18th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2019, 225–229. https://doi.org/10.1109/ICIS46139.2019.8940269
Gu, P., Ma, J., Chen, W., Deng, L., & Jiang, L. (2017). A personalized learning strategy recommendation approach for programming learning. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10179 LNCS, 267–274. https://doi.org/10.1007/978-3-319-55705-2_21
Han, L., Zhang, Z., & Lei, K. (2018). Intelligent knowledge recommendation algorithm research and practice. ACM International Conference Proceeding Series, 73–78. https://doi.org/10.1145/3210713.3210731
He, H., Zhu, Z., Guo, Q., & Huang, X. (2019). A personalized e-learning services recommendation algorithm based on user learning ability. In Proceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019, 2161–377X, 318–320. https://doi.org/10.1109/ICALT.2019.00099
Hidayat, A. F., Suwawi, D. D. J., & Laksitowening, K. A. (2020). Learning content recommendations on personalized learning environment using collaborative filtering method. In 2020 8th International Conference on Information and Communication Technology, ICoICT 2020. https://doi.org/10.1109/ICoICT49345.2020.9166371
Hoic-Bozic, N., Holenko Dlab, M., & Mornar, V. (2016). Recommender system and Web 2.0 tools to enhance a blended learning model. IEEE Transactions on Education, 59(1), 39–44. https://doi.org/10.1109/TE.2015.2427116
Huang, Y., & Shen, J. (2018). An implicit knowledge oriented algorithm for learning path recommendation. In Proceedings-3rd International Conference on Computational Intelligence and Applications, ICCIA 2018, 36–41. https://doi.org/10.1109/ICCIA.2018.00015
Iatrellis, O., Kameas, A., & Fitsilis, P. (2017). Personalized learning pathways using semantic web rules. ACM International Conference Proceeding Series, Part F1325. https://doi.org/10.1145/3139367.3139404
Ibrahim, T. S., Saleh, A. I., Elgaml, N., & Abdelsalam, M. M. (2020). A fog based recommendation system for promoting the performance of e-learning environments. Computers and Electrical Engineering, 87(July), 106791. https://doi.org/10.1016/j.compeleceng.2020.106791
Ingkavara, T., Panjaburee, P., Srisawasdi, N., & Sajjapanroj, S. (2022). The use of a personalized learning approach to implementing self-regulated online learning. Computers and Education: Artificial Intelligence, 3(June), 100086. https://doi.org/10.1016/j.caeai.2022.100086
Izzudin, M. I., & Judi, H. M. (2022). Personalised learning analytics promoting student’s achievement and enhancing instructor’s intervention in self-regulated meaningful learning. International Journal of Information and Education Technology., 12(11), 1243–1247. https://doi.org/10.18178/ijiet.2022.12.11.1745
Jagadeesan, S., & Subbiah, J. (2020). Real-time personalization and recommendation in Adaptive Learning Management System. Journal of Ambient Intelligence and Humanized Computing, 11(11), 4731–4741. https://doi.org/10.1007/s12652-020-01729-1
Jeevamol, J., & Renumol, V. G. (2021). An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem. Education and Information Technologies, 26(4), 4993–5022. https://doi.org/10.1007/s10639-021-10508-0
Jones, M., & McLean, K. (2018). Personalising learning in teacher education. In Personalising Learning in Teacher Education. https://doi.org/10.1007/978-981-10-7930-6
Joseph, L. (2019). Adaptive e-learning system for slow learners based on Felder-Silverman learning style model. Communications in Computer and Information Science (Vol. 1075). https://doi.org/10.1007/978-981-15-0108-1_3
Keifer, G., & Effenberger, F. (1967). Learning personalized : The evolution of the contemporary classroom Angewandte Chemie International Edition, 6(11), 951–952.
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers and Education, 106, 166–171. https://doi.org/10.1016/j.compedu.2016.12.006
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(TR/SE-0401), 28. https://doi.org/10.1.1.122.3308, 240–243. https://doi.org/10.1145/3328905.3332505
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering version 2.3. Engineering, 45(4ve), 1051. https://doi.org/10.1145/1134285.1134500, 126(5), 589–598. https://doi.org/10.1541/ieejias.126.589
Lagman, A. C., Alcober, G. M. I., Fernando, M. C. G., Goh, M. L. I., Lalata, J. A. P., Ortega, J. H. J. C., Perez, M. R. L., Solomo, M. V. S., & Claour, J. P. (2020). Integration of neural network algorithm in adaptive learning management system. ACM International Conference Proceeding Series. https://doi.org/10.1145/3402597.3402613
Laksitowening, (2020). Personalisasi e-Learing Dinamis pada Sistem Pendidikan Berbasis Standar [Unpublished Disertation]. Universitas Indonesia.
Li, K. C., & Wong, B. T. M. (2021). Features and trends of personalised learning: A review of journal publications from 2001 to 2018. Interactive Learning Environments, 29(2), 182–195. https://doi.org/10.1080/10494820.2020.1811735
Maier, U., & Klotz, C. (2022). Personalized feedback in digital learning environments: Classification framework and literature review. Computers and Education: Artificial Intelligence, 3(March), 100080. https://doi.org/10.1016/j.caeai.2022.100080
Massa, L. J., & Mayer, R. E. (2006). Testing the ATI hypothesis: Should multimedia instruction accommodate verbalizer-visualizer cognitive style? Learning and Individual Differences, 16(4), 321–335. https://doi.org/10.1016/j.lindif.2006.10.001
McLoughlin, C. E. (2013). The pedagogy of personalised learning: exemplars, MOOCS and related learning theories. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications, 2013(1), 266–270. http://www.editlib.org/p/111968/
Mödritscher, F. (2006). e-Learning theories in practice: A comparison of three. Journal of Universal Science and Technology of Learning (JUSTL), 28(1), 3–18. http://www.justl.org/justl_0_0/elearning_theories_in_practice
Muangprathub, J., Boonjing, V., & Chamnongthai, K. (2020). Learning recommendation with formal concept analysis for intelligent tutoring system. Heliyon, 6(10), e05227. https://doi.org/10.1016/j.heliyon.2020.e05227
Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022). AI-based personalized e-learning systems: Issues, challenges, and solutions. IEEE Access, 10(June), 81323–81342. https://doi.org/10.1109/ACCESS.2022.3193938
Nafea, S. M., Siewe, F., & He, Y. (2019). On recommendation of learning objects using Felder-Silverman learning style model. IEEE Access, 7, 163034–163048. https://doi.org/10.1109/ACCESS.2019.2935417
Newton, P. M. (2015). The learning styles myth is thriving in higher education. Frontiers in Psychology, 6(DEC), 1–5. https://doi.org/10.3389/fpsyg.2015.01908
Newton, P. M., & Miah, M. (2017). Evidence-based higher education - Is the learning styles “myth” important? Frontiers in Psychology, 8(MAR), 1–9. https://doi.org/10.3389/fpsyg.2017.00444
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13. https://doi.org/10.1177/1609406917733847
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009). Learning styles concepts and evidence. Psychological Science in the Public Interest, Supplement, 9(3), 105–119. https://doi.org/10.1111/j.1539-6053.2009.01038.x
Perišić, J., Milovanović, M., & Kazi, Z. (2018). A semantic approach to enhance moodle with personalization. Computer Applications in Engineering Education, 26(4), 884–901. https://doi.org/10.1002/cae.21929
Pliakos, K., Joo, S. H., Park, J. Y., Cornillie, F., Vens, C., & Van den Noortgate, W. (2019). Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems. Computers and Education, 137(April), 91–103. https://doi.org/10.1016/j.compedu.2019.04.009
Powell, W., & Kusuma-Powell, O. (2011). How to teach now: Five keys to personalized learning in the global classroom (p.178). Association for Supervision and Curriculum Development (ASCD) publications.
Raj, N. S., & Renumol, V. G. (2021). A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020. Journal of Computers in Education. https://doi.org/10.1007/s40692-021-00199-4
Rodriguez, A. E., & Martinell, A.R. (2019). Recommender system in higher education: A preliminary study of state of the art. In Proceedings - 14th Latin American Conference on Learning Technologies, LACLO 2019, 231–236. https://doi.org/10.1109/LACLO49268.2019.00047
Rogowsky, B. A., Calhoun, B. M., & Tallal, P. (2015). Matching learning style to instructional method: Effects on comprehension. Journal of Educational Psychology, 107(1), 64–78. https://doi.org/10.1037/a0037478
Saleh, M., & Salama, R. M. (2018). Recommendations for building adaptive cognition-based e-learning. International Journal of Advanced Computer Science and Applications, 9(8), 385–393. https://doi.org/10.14569/ijacsa.2018.090850
Schrader, B. (2020). What’s the difference between an ontology and a knowledge graph ? Enterprise Knowledge, p(1–6).
Sferianto, (2014). Pendekatan Tipe Belajar Triple-Factor dalam Proses e-Learning sebagai Basis Rekomendasi dan Personalisasi Pembelajaran [Unpublished Disertation]. Universitas Indonesia.
Shang, Y. (2022). Development of college english personalized learning support system based on data mining technology. 114–117. https://doi.org/10.1109/iccbe56101.2022.9888202
Shemshack, A., & Spector, J. M. (2020). A systematic literature review of personalized learning terms. Smart Learning Environments. https://doi.org/10.1186/s40561-020-00140-9
Shemshack, A., & Spector, J. M. (2021). A comprehensive analysis of personalized learning components. Journal of Computers in Education, 8(4), 485–503. https://doi.org/10.1007/s40692-021-00188-7
Shi, D., Wang, T., Xing, H., & Xu, H. (2020). A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning. Knowledge-Based Systems, 195, 105618. https://doi.org/10.1016/j.knosys.2020.105618
Shou, Z., Lu, X., Wu, Z., Yuan, H., Zhang, H., & Lai, J. (2020). On learning path planning algorithm based on collaborative analysis of learning behavior. IEEE Access, 8, 119863–119879. https://doi.org/10.1109/ACCESS.2020.3005793
Sihombing, J. H., Laksitowening, & Darwiyanto, E. (2020). Personalized e-learning content based on felder-silverman learning style model. In 2020 8th International Conference on Information and Communication Technology, ICoICT 2020,1–6. https://doi.org/10.1109/ICoICT49345.2020.9166452
Su, J. M. (2020). A rule-based self-regulated learning assistance scheme to facilitate personalized learning with adaptive scaffoldings: A case study for learning computer software. Computer Applications in Engineering Education, 28(3), 536–555. https://doi.org/10.1002/cae.22222
Supic, H. (2018). Case-based reasoning model for personalized learning path recommendation in example-based learning activities. In Proceedings-2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE 2018, 148–153. https://doi.org/10.1109/WETICE.2018.00040
Sweta, S., & Lal, K. (2017). Personalized adaptive learner model in e-learning system using FCM and fuzzy inference system. International Journal of Fuzzy Systems, 19(4), 1249–1260. https://doi.org/10.1007/s40815-017-0309-y
Syed, T. A., & Nair, S. S. K. (2018). Personalized recommendation system for advanced learning management systems. ACM International Conference Proceeding Series. https://doi.org/10.1145/3268891.3268899
Tlili, A., Denden, M., Essalmi, F., Jemni, M., Kinshuk, Chen, N. S., & Huang, R. (2019). Does providing a personalized educational game based on personality matter? A case study. IEEE Access, 7, 119566–119575. https://doi.org/10.1109/ACCESS.2019.2936384
Troussas, C., Krouska, A., & Sgouropoulou, C. (2020). Collaboration and fuzzy-modeled personalization for mobile game-based learning in higher education. Computers and Education, 144(2019), 103698. https://doi.org/10.1016/j.compedu.2019.103698
Troussas, C., Krouska, A., & Sgouropoulou, C. (2021). Improving learner-computer interaction through intelligent learning material delivery using instructional design modeling. Entropy, 23(6), 1–11. https://doi.org/10.3390/e23060668
Vanitha, V., & Krishnan, P. (2019). A modified ant colony algorithm for personalized learning path construction. Journal of Intelligent and Fuzzy Systems, 37(5), 6785–6800. https://doi.org/10.3233/JIFS-190349
Wang, F., Zhang, L., Chen, X., Wang, Z., & Xu, X. (2021a). A personalized self-learning system based on knowledge graph and differential evolution algorithm. Concurrency Computation. https://doi.org/10.1002/cpe.6190
Wang, H., & Fu, W. (2021). Personalized learning resource recommendation method based on dynamic collaborative filtering. Mobile Networks and Applications, 26(1), 473–487. https://doi.org/10.1007/s11036-020-01673-6
Wang, S., Xu, Y., Li, Q., & Chen, Y. (2021b). Learning path planning algorithm based on learner behavior analysis. ACM International Conference Proceeding Series. https://doi.org/10.1145/3451400.3451405
Wei, Q., & Yao, X. (2022). Personalized Recommendation of Learning Resources Based on Knowledge Graph. 46–50. https://doi.org/10.1109/iceit54416.2022.9690758
Western Governors University. (2020, July 21). What is humanistic learning theory in education? https://www.wgu.edu/blog/what-humanistic-learning-theory-education2007.html
Wolf, M. A., Wolf, E., Partoyan, E., Schneiderman, M., & Seltz, J. (2010). System [re]design for personalized learning: A report from the 2010 symposium. Software & Information Industry Association.
Xiaoqiong, Y., Guoqing, Y., & Zeng, Z. (2013). Personalized teaching model based on Moodle platform. Lecture Notes in Electrical Engineering, 216 LNEE(VOL. 1), 27–35. https://doi.org/10.1007/978-1-4471-4856-2_4
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 and Education, 140(June), 103599. https://doi.org/10.1016/j.compedu.2019.103599
Yang, L., Yu, Y., & Wei, Y. (2022). Data-driven artificial intelligence recommendation mechanism in online learning resources. International Journal of Crowd Science, 6(3), 150–157. https://doi.org/10.26599/IJCS.2022.9100020
Yonezawa, S., McClure, L., & Jones, M. (2012). Personalization in schools. Education Digest, 28. 1–36.
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. https://doi.org/10.1186/s41239-019-0171-0
Zhen, Y., Zheng, L., & Chen, P. (2021). Constructing knowledge graphs for online collaborative programming. IEEE Access, 9, 117969–117980. https://doi.org/10.1109/access.2021.3106324
Zhong, L., Wei, Y., Yao, H., Deng, W., Wang, Z., & Tong, M. (2020). Review of deep learning-based personalized learning recommendation. ACM International Conference Proceeding Series. https://doi.org/10.1145/3377571.3377587
Zhu, H., Tian, F., Wu, K., Shah, N., Chen, Y., Ni, Y., Zhang, X., Chao, K. M., & Zheng, Q. (2018). A multi-constraint learning path recommendation algorithm based on knowledge map. Knowledge-Based Systems, 143, 102–114. https://doi.org/10.1016/j.knosys.2017.12.011
Acknowledgements
Authors sincerely thank to Lembaga Pengelolaan Dana Pendidikan (LPDP), Republic of Indonesia for supporting this research through the Indonesian Education Scholarship Program.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
RIF contributed to collecting, analysing the data and wrote the initial manuscript. KJ supervised the research by directing, analysing, and reviewing the manuscript. HBS contributed to reviewing the methodology and the manuscript. Each of the named authors made a significant contribution to the underlying research. The final manuscript was read and approved by all authors.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Fariani, R.I., Junus, K. & Santoso, H.B. A Systematic Literature Review on Personalised Learning in the Higher Education Context. Tech Know Learn 28, 449–476 (2023). https://doi.org/10.1007/s10758-022-09628-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10758-022-09628-4