iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S00607-021-00903-3
AwARE: a framework for adaptive recommendation of educational resources | Computing Skip to main content

Advertisement

Springer Nature Link
Log in

AwARE: a framework for adaptive recommendation of educational resources

  • Regular Paper
  • Published:
Computing Aims and scope Submit manuscript

Abstract

Recommender systems appeared in the early 90s to help users deal with cognitive overload brought by the internet. From there to now, such systems have assumed many other roles like help users to explore, improve decision making, or even entertain. The system needs to look to user characteristics to accomplish such new goals. These characteristics help understand what the user task is and how to adapt the recommendation to support such task. Related research has proposed recommender systems in education. These recommender systems help learners to find the educational resources most fit for their needs. In this paper, we present an integration model between recommender and adaptive hypermedia systems. It results in a new process for educational resource recommendation, using a new algorithm of adaptive recommendation. Through a prototype and an online experiment on the educational scenario, we proved that AwARE could improve the recommendation accuracy, interaction with the system, and user satisfaction. Besides the prototype description, the paper presents a protocol to evaluate the proposed approach by both the providers’ and consumers’ point of view.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. The basic assumption of Latent Factor models is that there exist an unknown low-dimensional representation of users and items where user-item affinity can be modeled accurately.

  2. http://www.teachwithmovies.org.

  3. When filling a Likert item, users specify their level of agreement or disagreement on a symmetric agree-disagree scale.

References

  1. Abech M, Costa CA, Barbosa JL, Rigo SJ, Rosa Righi R (2016) A model for learning objects adaptation in light of mobile and context-aware computing. Pers Ubiquitous Comput 20(2):167–184. https://doi.org/10.1007/s00779-016-0902-3

    Article  Google Scholar 

  2. Adomavicius G, Sankaranarayanan R, Sen S, Tuzhilin A (2005) Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans Inf Syst 23(1):103–145. https://doi.org/10.1145/1055709.1055714

    Article  Google Scholar 

  3. Atif Y, Mathew SS, Lakas A (2015) Building a smart campus to support ubiquitous learning. J Ambient Intell Humaniz Comput 6(2):223–238. https://doi.org/10.1007/s12652-014-0226-y

    Article  Google Scholar 

  4. Baltrunas L, Kaminskas M, Ludwig B, Moling O, Ricci F, Aydin A, Lüke KH, Schwaiger, R (2011) InCarMusic: context-aware music recommendations in a car. In: Huemer C, Setzer T (eds) E-commerce and web technologies SE-8, Lecture notes in business information processing, vol 85. Springer, Berlin, pp 89–100. https://doi.org/10.1007/978-3-642-23014-1_8

  5. Baltrunas L, Ludwig B, Ricci F (2011) Matrix factorization techniques for context aware recommendation. In: Proceedings of the fifth ACM conference on Recommender systems, RecSys ’11. ACM, New York, NY, USA, pp 301–304. https://doi.org/10.1145/2043932.2043988

  6. Baltrunas L, Ricci F (2009) Context-based splitting of item ratings in collaborative filtering. In: Proceedings of the third ACM conference on Recommender systems—RecSys ’09, RecSys ’09. ACM Press, New York, New York, USA, p 245. https://doi.org/10.1145/1639714.1639759

  7. Benouaret I, Lenne D (2015) Personalizing the museum experience through context-aware recommendations. In: 2015 IEEE international conference on systems, man, and cybernetics. IEEE, pp 743–748. https://doi.org/10.1109/SMC.2015.139. http://ieeexplore.ieee.org/document/7379271/

  8. Brusilovsky P (2001) Adaptive hypermedia. User Model User Adapt Interact 11(1–2):87–110. https://doi.org/10.1023/A:1011143116306

    Article  MATH  Google Scholar 

  9. Brusilovsky P, Millán E (2007) User models for adaptive hypermedia and adaptive educational systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web methods and strategies of web personalization, chapter 1. Springer, Berlin, pp 3–53

  10. Brusilovsky P, Somyurek S, Guerra J, Hosseini R, Zadorozhny V, Durlach PJ (2016) Open social student modeling for personalized learning. IEEE Trans Emerg Top Comput 4(3):450–461. https://doi.org/10.1109/TETC.2015.2501243

    Article  Google Scholar 

  11. Calero Valdez A, Ziefle M, Verbert K (2016) HCI for recommender systems: the past, the present and the future. In: Proceedings of the 10th ACM conference on recommender systems, RecSys ’16. ACM, New York, NY, USA, pp 123–126. https://doi.org/10.1145/2959100.2959158

  12. Colombo-Mendoza LO, Valencia-García R, Rodríguez-González A, Alor-Hernández G, Samper-Zapater JJ (2015) Recommetz: a context-aware knowledge-based mobile recommender system for movie showtimes. Expert Syst Appl 42(3):1202–1222. https://doi.org/10.1016/j.eswa.2014.09.016

    Article  Google Scholar 

  13. De Bra P (2008) Adaptive hypermedia. In: Adelsberger H, Kinshuk, Pawlowski J, Sampson D (eds) Handbook on information technologies for education and training SE-2, International handbooks on information systems. Springer, Berlin, pp 29–46. https://doi.org/10.1007/978-3-540-74155-8_2

  14. Ekstrand MD, Tian M, Kazi MRI, Mehrpouyan H, Kluver D (2018) Exploring author gender in book rating and recommendation. In: Proceedings of the 12th ACM conference on recommender systems, RecSys ’18. ACM, New York, NY, USA, pp 242–250. https://doi.org/10.1145/3240323.3240373

  15. Felder RM, Soloman BA (2005) Index of learning styles questionnaire, vol 70. NC State University. Available online at: http://www.engr.ncsu.edu/learningstyles/ilsweb.html. Accessed 14 May 2010

  16. Gasparini I, Pimenta MS, de Oliveira J, Bouzeghoub A (2010) Combining ontologies and scenarios for context-aware e-learning environments. In: Proceedings of the 28th ACM international conference on design of communication, SIGDOC ’10. ACM, New York, NY, USA, pp 229–236. https://doi.org/10.1145/1878450.1878489

  17. Gianotti RC, Cazella SC, Behar PA (2019) A model for integrating personality traits into an educational recommender system. In: 2019 IEEE 19th international conference on advanced learning technologies (ICALT), vol 2161. IEEE, pp 383–385

  18. Harper FM, Xu F, Kaur H, Condiff K, Chang S, Terveen L (2015) Putting users in control of their recommendations. In: Proceedings of the 9th ACM conference on recommender systems, RecSys ’15. ACM, New York, NY, USA, pp 3–10. https://doi.org/10.1145/2792838.2800179

  19. He X, Zhang H, Kan MY, Chua TS (2016) Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, SIGIR ’16. ACM, New York, NY, USA, pp 549–558. https://doi.org/10.1145/2911451.2911489

  20. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53. https://doi.org/10.1145/963770.963772

    Article  Google Scholar 

  21. IEEE P1484.12.2/D1, L.T.S.C.o.t. Final standard for learning technology—learning object metadata (2002). https://doi.org/10.1109/IEEESTD.2002.94128

  22. Imran H, Belghis-Zadeh M, Chang TW, Graf S et al (2016) Plors: a personalized learning object recommender system. Vietnam J Comput Sci 3(1):3–13

    Article  Google Scholar 

  23. Jannach D, Adomavicius G (2016) Recommendations with a purpose. In: Proceedings of the 10th ACM conference on recommender systems, RecSys ’16. ACM, New York, NY, USA, pp 7–10. https://doi.org/10.1145/2959100.2959186

  24. Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511763113

    Book  Google Scholar 

  25. Kapoor K, Kumar V, Terveen L, Konstan JA, Schrater P (2015) “I like to explore sometimes”: adapting to dynamic user novelty preferences. In: Proceedings of the 9th ACM conference on recommender systems, RecSys ’15. ACM, New York, NY, USA, pp 19–26. https://doi.org/10.1145/2792838.2800172

  26. Koren Y (2009) Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining-KDD ’09, KDD ’09. ACM Press, New York, New York, USA, p 447. https://doi.org/10.1145/1557019.1557072

  27. 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

    Article  Google Scholar 

  28. Lu Y, Dong R, Smyth B (2018) Why I like it: multi-task learning for recommendation and explanation. In: Proceedings of the 12th ACM conference on recommender systems, RecSys ’18. ACM, New York, NY, USA, pp 4–12. https://doi.org/10.1145/3240323.3240365

  29. Lunardi GM (2019) Representing the filter bubble: towards a model to diversification in news. In: Guizzardi G, Gailly F, Suzana Pitangueira Maciel R (eds) Advances in conceptual modeling. Springer International Publishing, Cham, pp 239–246

  30. Machado GM, Maran V, Dornelles LP, Gasparini I, Thom LH, de Oliveira JPM (2018) A systematic mapping on adaptive recommender approaches for ubiquitous environments. Computing 100(2):183–209. https://doi.org/10.1007/s00607-017-0572-7

    Article  MathSciNet  Google Scholar 

  31. Manouselis N, Drachsler H, Vuorikari R, Hummel H, Koper R (2011) Recommender systems in technology enhanced learning. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook SE-12. Springer, New York, pp 387–415. https://doi.org/10.1007/978-0-387-85820-3_12

  32. Moebert T, Zender R, Lucke U (2016) A generalized approach for context-aware adaptation in mobile e-learning settings. Springer International Publishing, Cham, pp 23–53. https://doi.org/10.1007/978-3-319-26518-6_2

  33. Nabizadeh AH, Gonçalves D, Gama S, Jorge J, Rafsanjani HN (2020) Adaptive learning path recommender approach using auxiliary learning objects. Comput Educ 147:103777. https://doi.org/10.1016/j.compedu.2019.103777

    Article  Google Scholar 

  34. Otebolaku AM, Andrade MT (2015) Context-aware media recommendations for smart devices. J Ambient Intell Humaniz Comput 6(1):13–36. https://doi.org/10.1007/s12652-014-0234-y

    Article  Google Scholar 

  35. Pernas AM, Diaz A, Motz R, Oliveira JPMd (2012) Enriching adaptation in e-learning systems through a situation-aware ontology network. Interact Technol Smart Educ 9(2):60–73. https://doi.org/10.1108/17415651211242215

    Article  Google Scholar 

  36. Shani G, Gunawardana A (2011) Evaluating recommendation systems. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, Boston, MA, pp 257–297. https://doi.org/10.1007/978-0-387-85820-3_8

  37. Tahmasebi M, Fotouhi F, Esmaeili M (2019) Hybrid adaptive educational hypermedia recommender accommodating use’s learning style and web page features. J AI Data Min 7, 225–238. https://doi.org/10.22044/jadm.2018.6397.1755

  38. Tintarev N, Sullivan E, Guldin D, Qiu S, Odjik D (2018) Same, same, but different: algorithmic diversification of viewpoints in news. In: Adjunct publication of the 26th conference on user modeling, adaptation and personalization, UMAP ’18. ACM, New York, NY, USA, pp 7–13. https://doi.org/10.1145/3213586.3226203

  39. 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. https://doi.org/10.1109/TLT.2012.11

    Article  Google Scholar 

  40. Vicari RM, Bez MR, Behar PA, Silva JMCd, Ribeiro AM, Gluz JC, Passerino LM, Santos, ER, Primo TT, Rossi LHL (2010) Proposta brasileira de metadados para objetos de aprendizagem baseados em agentes (obaa). RENOTE: revista novas tecnologias na educacao 8(2), 1–10

  41. Wang SL, Wu CY (2011) Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Syst Appl 38(9):10831–10838. https://doi.org/10.1016/j.eswa.2011.02.083

    Article  Google Scholar 

  42. Wilhelm M, Ramanathan A, Bonomo A, Jain S, Chi EH, Gillenwater J(2018) Practical diversified recommendations on youtube with determinantal point processes. In: Proceedings of the 27th ACM international conference on information and knowledge management, CIKM ’18. ACM, New York, NY, USA, pp 2165–2173. https://doi.org/10.1145/3269206.3272018

  43. Wu L, Liu Q, Zhou W, Mao G, Huang J, Huang H (2018) A semantic web-based recommendation framework of educational resources in e-learning. Technol Knowl Learn. https://doi.org/10.1007/s10758-018-9395-7

    Article  Google Scholar 

Download references

Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, Conselho Nacional de Desenvolvimento Científico e Tecnológico-Brasil (CNPq, Edital Universal 2016, grant 400.954/2016-8) and (CNPq, Edital Universal 2018, grant 423.518/2018-6).

Author information

Authors and Affiliations

  1. Institute of Informatics - Federal University of Rio Grande do Sul (UFRGS), Av. Bento Gonçalves, Porto Alegre, 9500, Brazil

    Guilherme Medeiros Machado, Gabriel Machado Lunardi, Leandro Krug Wives & José Palazzo Moreira de Oliveira

  2. Laboratory of Ubiquitous, Mobile and Applied Computing (LUMAC) - Federal University of Santa Maria (UFSM), Av. Julio de Castilhos, 342. Room 4, Cachoeira do Sul, Brazil

    Vinicius Maran

Authors
  1. Guilherme Medeiros Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vinicius Maran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gabriel Machado Lunardi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Leandro Krug Wives
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. José Palazzo Moreira de Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vinicius Maran.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Machado, G.M., Maran, V., Lunardi, G.M. et al. AwARE: a framework for adaptive recommendation of educational resources. Computing 103, 675–705 (2021). https://doi.org/10.1007/s00607-021-00903-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-021-00903-3

Keywords

Mathematics Subject Classification

Search

Navigation