Skip to main content
SpringerLink
Log in

A Survey on Learning Path Recommendation

  • Conference paper
  • First Online:
Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1491))

  • 834 Accesses

Abstract

With the popularization of online learning, a wide range of learning activities have occurred and produced a huge amount of related data. A learning path consists of a set of learning activities that help users achieve particular learning goals. Learning path recommendation is important in smart education applications, which can provide suitable learning resource sequences for large-scale online learners, reduce the impact of information overload on learners, and help learners realize learning goals more quickly. Besides, it is necessary to apply popular technologies such as data mining, machine learning, optimization, knowledge graph and user profile in the domain of learning path recommendation to effectively handle related personalizing learning path parameter problems. So far, a variety of learning path recommendation methods have been proposed, which can be conducted in two ways: 1) single learner-oriented recommendation and 2) grouped learners-oriented recommendation. This paper presents an overview of these methods and analyzes future research directions of learning path recommendation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Yu, J., Luo, G., Xiao, T., Zhong, Q., Wang, Y., Feng, W., et al.: MOOCCube: a large-scale data repository for NLP applications in MOOCs. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3135–3142 (2020)

    Google Scholar 

  2. Lin, C., Yeh, Y., Yu, H., Chang, R.I.: Data mining for providing a personalized learning path in creativity: an application of decision trees. Comput. Educ. 68, 199–210 (2013)

    Article  Google Scholar 

  3. Zhao, X., Cen, L.: Research on personalized learning path recommendation of adaptive learning system oriented to user needs. Chinese J. ICT Educ. 21, 28–31 (2016)

    Google Scholar 

  4. Klasnja-Milicevic, A., Vesin, B., Ivanovic, M.: Social tagging strategy for enhancing e-learning experience. Comput. Educ. 118, 166–181 (2018)

    Google Scholar 

  5. Cheng, Y.: A method of swarm intelligence-based learning path recommendation for online learning. J. Syst. Manag. 20(02), 232–237 (2011)

    Google Scholar 

  6. Zhou, Y., Huang, C., Hu, Q., Zhu, J., Tang, Y.: Personalized learning full-path recommendation model based on LSTM neural networks. Inf. Sci. 444, 135–152 (2018)

    Article  Google Scholar 

  7. Wan, S., Niu, Z.: A learner oriented learning recommendation approach based on mixed concept mapping and immune algorithm. Knowl. Based Syst. 103, 28–40 (2016)

    Article  Google Scholar 

  8. Zhu, H., Tian, F., Wu, K., Shah, N., Chen, Y., Ni, Y., et al.: A multi-constraint learning path recommendation algorithm based on knowledge map. Knowl. Based Syst. 143, 102–114 (2018)

    Article  Google Scholar 

  9. Huang, Z., Zhao, C., Huang, X., Wan, L., Chen, Z.: Research on adaptive learning path recommendation based on situational awareness. Electr. Educ. Res. 36(05), 77–84 (2015)

    Google Scholar 

  10. Yang, J., Zhang, Y., Huang, Z., Liu, H., Huang, X.: Smap: a dynamic learning path recommendation tool that can adapt the Felder-Silverman learning style model. Dist. Educ. China 05, 77–86 (2013)

    Google Scholar 

  11. Durand, G., Belacel, N., LaPlante, F.: Graph theory based model for learning path recommendation. Inf. Sci. 251, 10–21 (2013)

    Article  Google Scholar 

  12. Shi, D., Wang, T., Xing, H., Xu, H.: A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning. Knowl. Based Syst. 195, 105618 (2020)

    Article  Google Scholar 

  13. Xie, H., Zou, D., Wang, F., Wong, T., Rao, Y., Wang, S.: Discover learning path for group users: a profile-based approach. Neurocomputing 254, 59–70 (2017)

    Article  Google Scholar 

  14. Zhu, H., Ni, Y., Feng, T., Pei, F., Yan, C., Zheng, Q.: A group-oriented recommendation algorithm based on similarities of personal learning generative networks. IEEE Access 6, 42729–42739 (2018)

    Article  Google Scholar 

  15. Jeong, H., Kim, M.: HGGC: A hybrid group recommendation model considering group cohesion. Expert Syst. Appl. 136, 73–82 (2019)

    Article  Google Scholar 

  16. Feng, S., Zhang, H., Wang, L., Liu, L., Xu, Y.: Detecting the latent associations hidden in multi-source information for better group recommendation. Knowl. Based Syst. 171, 56–68 (2019)

    Article  Google Scholar 

  17. Wang, X., Liu, Y., Lu, J., Xiong, F., Zhang, G.: TruGRC: trust-aware group recommendation with virtual coordinators. Futur. Gener. Comput. Syst. 94, 224–236 (2019)

    Article  Google Scholar 

  18. Xiao, Y., Pei, Q., Yao, L., Yu, S., Bai, L., Wang, X.: An enhanced probabilistic fairness-aware group recommendation by incorporating social activeness. J. Netw. Comput. Appl. 156, 102579 (2020)

    Article  Google Scholar 

  19. Qin, D., Zhou, X., Chen, L., Huang, G., Zhang, Y.: Dynamic connection-based social group recommendation. IEEE Trans. Knowl. Data Eng. 32(3), 453–467 (2020)

    Article  Google Scholar 

  20. Xin, W., Okamoto, T.: Utilizing learning process to improve recommender system for group learning support. Neural Comput. Appl. 20(5), 611–621 (2009)

    Google Scholar 

  21. Belacel, N., Durand, G., Laplante, F.: A binary integer programming model for global optimization of learning path discovery. In: Proceedings of the 7th International Conference on Educational Data Mining (2014)

    Google Scholar 

  22. Dharani, B., Geetha, T.: Adaptive learning path generation using colored petri nets based on behavioral aspects. In: 2013 International Conference on Recent Trends in Information Technology (ICRTIT), pp. 459–465. IEEE, India (2013)

    Google Scholar 

  23. Nabizadeh, A., Jorge, A., Leal, J.: Rutico: recommending successful learning paths under time constraints. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 153–158. ACM, New York (2017)

    Google Scholar 

  24. Duval, E., Hodgins, W.: A LOM research agenda. In: The Twelfth International World Wide Web Conference (WWW2003), pp. 1–10. ACM, Budapest, Hungary (2003)

    Google Scholar 

  25. Muhammad, A., Zhou, Q., Beydoun, G., Xu, D., Shen, J.: Learning path adaptation in online learning systems. In: 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 421–426. IEEE, Nanchang, China (2016)

    Google Scholar 

  26. Adorni, G., Koceva, F.: Educational concept maps for personalized learning path generation. In: AI* IA 2016 Advances In Artificial Intelligence, pp. 135–148. Springer, Hobart, TAS, Australia (2016)

    Google Scholar 

  27. Zhan, L., Papaemmanouil, O., Koutrika, G.: Coursenavigator: Interactive learning path exploration. In: Proceedings of the Third International Workshop on Exploratory Search in Databases and the Web, pp. 6–11. ACM, Chengdu, China (2016)

    Google Scholar 

  28. McGaghie, W., Issenberg, S., Barsuk, J., Wayne, D.: A critical review of simulation-based mastery learning with translational outcomes. Med. Educ. 48, 375–385 (2014)

    Article  Google Scholar 

  29. Essalmi, F., Ayed, L., Jemni, M., Graf, S.: A fully personalization strategy of e-learning scenarios. Comput. Hum. Behav. 26, 581–591 (2010)

    Article  Google Scholar 

  30. Klašnja-Milicévic, A., Vesin, B., Ivanovic, M., Budimac, Z.: E-learning personalization based on hybrid recommendation strategy and learning style identification. Comput. Educ. 56, 885–899 (2011)

    Google Scholar 

  31. Feng, X., Xie, H., Peng, Y., Chen, W., Sun, H.: Groupized learning path discovery based on member profile. In: Luo, X., Cao, Y., Yang, B., Liu, J., Ye, F. (eds.) ICWL 2010. LNCS, vol. 6537, pp. 301–310. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20539-2_32

    Chapter  Google Scholar 

  32. Durand, G., Laplante, F., Kop, R.: A learning design recommendation system based on markov decision processes. In: KDD- 2011: 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2011)

    Google Scholar 

  33. Chi, Y.: Developing curriculum sequencing for managing multiple texts in e-learning system. In Proceedings of international conference on engineering education, pp. 1–8 (2010)

    Google Scholar 

  34. Zhang, Y., Du, Y., Meng, X.: Research on group recommender systems and their applications. Chinese J. Comput. 39(04), 745–764 (2016)

    MathSciNet  Google Scholar 

  35. Iglesias, J., Angelov, P., Ledezma, A., Sanchis, A.: Creating evolving user behavior profiles automatically. IEEE Trans. Knowl. Data Eng. 24, 854–867 (2012)

    Article  Google Scholar 

  36. Xu, J., Xing, T., Schaar, M.: Personalized course sequence recommendations. IEEE Trans. Signal Process. 64, 5340–5352 (2016)

    Article  MathSciNet  Google Scholar 

  37. Garrido, A., Onaindia, E.: Assembling learning objects for personalized learning: an AI planning perspective. IEEE Intell. Syst. 28, 64–73 (2013)

    Article  Google Scholar 

  38. Masthoff, J., Gatt, A.: In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Model. User-Adap. Inter. 16(3/4), 281–319 (2006)

    Article  Google Scholar 

  39. Shang, S., Hui, Y., Hui, P., Cuff, P., Kulkarni, S.: Beyond personalization and anonymity: towards a group-based recommender system. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, pp. 266–273 (2014)

    Google Scholar 

  40. Masthoff, J.: Group Recommender Systems: Combining Individual Models. Recommender Systems Handbook, pp. 677–702. Springer, Boston (2011)

    Google Scholar 

  41. Nabizadeh, A.H., Leal, J.P., Rafsanjani, H.N., Shah, R.R.: Learning path personalization and recommendation methods: a survey of the state-of-the-art. Expert Syst. Appl. 159, 113596 (2020)

    Article  Google Scholar 

  42. Railean, E.A.: Teacher-centered versus learner-centered design of screen. In: User Interface Design of Digital Textbooks. LNET, pp. 59–80. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-2456-6_4

    Chapter  Google Scholar 

Download references

Acknowledgement

This work was supported by the Natural Science Foundation of China (No. 62177014, 6177219), National Key R&D Program of China (No. 2018YFB1702600), and Research Foundation of Hunan Provincial Education Department of China (No. 20B222, 19A174, HNJG-2020-0488).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China

    Fengjun Zhu, Yiping Wen & Qi Fu

  2. Key Laboratory of Knowledge Processing and Networked Manufacturing, Hunan University of Science and Technology, Xiangtan, China

    Fengjun Zhu, Yiping Wen & Qi Fu

Authors
  1. Fengjun Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yiping Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qi Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yiping Wen .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Hunan University of Science and Technology, Xiangtan, China

    Buqing Cao

  4. Tongji University, Shanghai, China

    Hongfei Fan

  5. Guangdong University of Technology, Guangzhou, China

    Dongning Liu

  6. University of Warwick, Coventry, UK

    Bowen Du

  7. University of Shanghai for Science and Technology, Shanghai, China

    Liping Gao

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhu, F., Wen, Y., Fu, Q. (2022). A Survey on Learning Path Recommendation. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1491. Springer, Singapore. https://doi.org/10.1007/978-981-19-4546-5_45

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-4546-5_45

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-4545-8

  • Online ISBN: 978-981-19-4546-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation