review-article

Review and classification of content recommenders in E-learning environment

Authors:

Renumol Vemballiveli Govinda PillaiAuthors Info & Claims

Volume 34, Issue 9

Pages 7670 - 7685

https://doi.org/10.1016/j.jksuci.2021.06.009

Published: 01 October 2022 Publication History

Abstract

E-learning recommender systems are becoming more popular due to the massive learning materials available online and the changing pedagogy. A content recommender system in the e-learning domain helps the learners by suggesting appropriate learning resources based on their preferences and learning goals. This paper presents a literature review on the recent studies conducted on content recommenders in the e-learning domain. The articles chosen for the review are mainly studies on personalized and adaptive learning systems. For that, we have collected and analyzed a set of journal papers published in this field during the period 2015–2020. Based on the analysis, we have categorized the different recommendation techniques, data inputs, algorithms, similarity measures, and evaluation metrics used in these studies. The paper also highlights the current trends in the recommendation process and the merits and demerits of the selected studies. Thus it provides an insight into the current state-of-the-art.

References

[1]

G. Adomavicius, A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions, IEEE Trans. Knowl. Data Eng. 17 (6) (2005) 734–749.

Digital Library

[2]

G. Adomavicius, A. Tuzhilin, Context-aware recommender systems, in: F. Ricci, L. Rokach, B. Shapira, P.B. Kantor (Eds.), Recommender Systems Handbook, Springer US, Boston, MA, 2011, pp. 217–253,.

[3]

E. Aeiad, F. Meziane, An adaptable and personalised E-learning system applied to computer science Programmes design, Educ. Inf. Technol. 24 (2) (2019) 1485–1509.

[4]

A. Agarwal, M. Chauhan, Similarity measures used in recommender systems: a study, Int. J. Eng. Technol. Sci. Res. 4 (6) (2017) 619–626.

[5]

C.C. Aggarwal, Recommender Systems, Springer International Publishing, Cham, 2016, pp. 225–254.

[6]

A. Al Abri, Y. Jamoussi, Z. AlKhanjari, N. Kraiem, PerLCol: a framework for personalized e-learning with social collaboration support, Int. J. Comp. Digital Syst.ms 9 (03) (2020).

[7]

N.A. Albatayneh, K.I. Ghauth, F.F. Chua, Utilizing learners' negative ratings in semantic content-based recommender system for e-learning forum, J. Educ. Technol. Soc. 21 (1) (2018) 112–125.

[8]

X. Amatriain, A. Jaimes, N. Oliver, J. Pujol, Data mining methods for recommender systems, in: F. Ricci, L. Rokach, B. Shapira, P. Kantor (Eds.), Recommender systems handbook, Springer, Boston, MA, 2010, pp. 39–71.

[9]

M. Anuradha, V. Ganesan, S. Oliver, T. Jayasankar, R. Gopi, Hybrid firefly with differential evolution algorithm for multi agent system using clustering based personalization, J. Ambient Intell. Hum. Comput. (2020) 1–10.

[10]

S. Bag, S. Kumar, A. Awasthi, M.K. Tiwari, A noise correction-based approach to support a recommender system in a highly sparse rating environment, Decis. Support Syst. 118 (2019) 46–57.

[11]

S. Benhamdi, A. Babouri, R. Chiky, Personalized recommender system for e-Learning environment, Educ. Inf. Technol. 22 (4) (2017) 1455–1477.

[12]

S. Bhaskaran, B. Santhi, An efficient personalized trust based hybrid recommendation (tbhr) strategy for e-learning system in cloud computing, Cluster Comp. 22 (S1) (2019) 1137–1149.

[13]

J. Bobadilla, F. Serradilla, A. Hernando, Collaborative filtering adapted to recommender systems of e-learning, Knowl.-Based Syst. 22 (4) (2009) 261–265.

[14]

B. Bouihi, M. Bahaj, Ontology and rule-based recommender system for e-learning applications, Int. J. Emerg. Technol. Learn. (iJET) 14 (15) (2019) 4–13.

[15]

O. Bourkoukou, E. El Bachari, E-learning personalization based on collaborative filtering and learner's preference, J. Eng. Sci. Technol. 11 (11) (2016) 1565–1581.

[16]

O. Bourkoukou, E. El Bachari, M. El Adnani, A personalized e-learning based on recommender system, Int. J. Learn. Teach. 2 (2) (2016) 99–103.

[17]

O. Bourkoukou, E. El Bachari, M. El Adnani, A recommender model in e-learning environment, Arabian J. Sci. Eng. 42 (2) (2017) 607–617.

[18]

R. Burke, Lecture Notes in Computer ScienceThe Adaptive Web, Springer Berlin Heidelberg, Berlin, Heidelberg, 2007, pp. 377–408.

[19]

J. Buder, C. Schwind, Learning with personalized recommender systems: a psychological view, Comput. Hum. Behav. 28 (1) (2012) 207–216.

[20]

W. Chen, Z. Niu, X. Zhao, Y.i. Li, A hybrid recommendation algorithm adapted in e-learning environments, World Wide Web 17 (2) (2014) 271–284.

Digital Library

[21]

B.C.L. Christudas, E. Kirubakaran, P.R.J. Thangaiah, An evolutionary approach for personalization of content delivery in e-learning systems based on learner behavior forcing compatibility of learning materials, Telematics Inform. 35 (3) (2018) 520–533.

[22]

X. Deng, H. Li, F. Huangfu, A trust-aware neural collaborative filtering for elearning recommendation, Educ. Sci.: Theory Practice 18 (5) (2018).

[23]

F.A. Dorça, R.D. Araújo, V.C. De Carvalho, D.T. Resende, R.G. Cattelan, An automatic and dynamic approach for personalized recommendation of learning objects considering students learning styles: an experimental analysis, Inform. Educ. 15 (1) (2016) 45–62.

[24]

H. Drachsler, K. Verbert, O.C. Santos, N. Manouselis, Recommender Systems Handbook, Springer US, Boston, MA, 2015, pp. 421–451.

[25]

P. Dwivedi, K.K. Bharadwaj, e-Learning recommender system for a group of learners based on the unified learner profile approach, Expert Syst. 32 (2) (2015) 264–276.

[26]

P. Dwivedi, V. Kant, K.K. Bharadwaj, Learning path recommendation based on modified variable length genetic algorithm, Educ. Inf. Technol. 23 (2) (2018) 819–836.

[27]

F. Essalmi, L.J.B. Ayed, M. Jemni, Kinshuk, S. Graf, A fully personalization strategy of E-learning scenarios, Comput. Hum. Behav. 26 (4) (2010) 581–591.

[28]

R.M. Felder, L.K. Silverman, Learning and teaching styles in engineering education, Eng. Educ. 78 (7) (1988) 674–681.

[29]

S. Fraihat, Q. Shambour, A framework of semantic recommender system for e-learning, J. Software 10 (3) (2015) 317–330.

[30]

I. Garcia, S. Pajares, L. Sebastia, E. Onaindia, Preference elicitation techniques for group recommender systems, Inf. Sci. 189 (2012) 155–175.

[31]

G. George, A.M. Lal, Review of ontology-based recommender systems in e-learning, Comput. Educ. 142 (2019) 103642.

[32]

T.R. Gruber, A translation approach to portable ontology specifications, Knowledge Acquisit. 5 (2) (1993) 199–220.

[33]

Harrathi, M., Touzani, N., Braham, R., 2017. A hybrid knowlegde-based approach for recommending massive learning activities. In 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) (pp. 49–54). IEEE.

[34]

J. He, W.W. Chu, A Social Network-Based Recommender System (SNRS). In Data mining for Social Network Data, Springer, Boston, MA, 2010, pp. 47–74.

[35]

Z. Huang, X. Lu, H. Duan, Context-aware recommendation using rough set model and collaborative filtering, Artif. Intell. Rev. 35 (1) (2011) 85–99.

Digital Library

[36]

G.J. Hwang, H.Y. Sung, S.C. Chang, X.C. Huang, A fuzzy expert system-based adaptive learning approach to improving students' learning performances by considering affective and cognitive factors, Comp. Educ.: Artificial Intellig. 1 (2020).

[37]

IEEE Learning Technology Standards Committee. (2002). Draft Standard for Learning Objects Metadata LOM. http://Itsc. ieee. org/.

[38]

T.S. Ibrahim, A.I. Saleh, N. Elgaml, M.M. Abdelsalam, A fog based recommendation system for promoting the performance of E-Learning environments, Comput. Electr. Eng. 87 (2020) 106791,.

[39]

H. Imran, M. Belghis-Zadeh, T.-W. Chang, Kinshuk, S. Graf, PLORS: a personalized learning object recommender system, Vietnam J. Comp. Sci. 3 (1) (2016) 3–13.

[40]

S. Jagadeesan, J. Subbiah, Real-time personalization and recommendation in adaptive learning management system, J. Ambient Intell. Hum. Comput. 11 (11) (2020) 4731–4741.

[41]

A. Jain, C. Gupta, Fuzzy logic in recommender systems, in: Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, Springer, Cham, 2018, pp. 255–273.

[42]

D. Jannach, M. Zanker, A. Felfernig, G. Friedrich, Recommender Systems: An Introduction, Cambridge University Press, 2010.

Digital Library

[43]

J. Jeevamol, V.G. Renumol, An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem, Educ. Inf. Technol. (2021) 1–30.

[44]

Joy, J., Raj, N.S., Renumol V.G., 2019. An ontology model for content recommendation in personalized learning environment. In Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems (pp. 1–6).ACM.

[45]

Joy, J., Renumol, V.G., 2020, December. Comparison of Generic Similarity Measures in E-learning Content Recommender System in Cold-Start Condition. In: 2020 IEEE Bombay Section Signature Conference (IBSSC) (pp. 175–179). IEEE.

[46]

J. Joy, N.S. Raj, V.G. Renumol, Ontology-based E-learning content recommender system for addressing the pure cold-start problem, ACM J. Data Inf. Qual. 13 (3) (2021) 1–27.

[47]

S.S. Khanal, P.W.C. Prasad, A. Alsadoon, A. Maag, A systematic review: machine learning based recommendation systems for e-learning, Educ. Inf. Technol. 25 (4) (2019) 2635–2664.

[48]

Y. Kilani, B. Alhijawi, A. Alsarhan, Using artificial intelligence techniques in collaborative filtering recommender systems: survey, Int. J. Adv. Intelligence Parad. 11 (3–4) (2018) 378–396.

[49]

S.-C. Kim, K.-J. Sung, C.-S. Park, S.K. Kim, Improvement of collaborative filtering using rating normalization, Multimedia Tools Appl. 75 (9) (2016) 4957–4968.

[50]

A. Klašnja-Milićević, M. Ivanović, A. Nanopoulos, Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions, Artif. Intell. Rev. 44 (4) (2015) 571–604.

[51]

A. Klašnja-Milićević, B. Vesin, M. Ivanović, Social tagging strategy for enhancing e-learning experience, Comput. Educ. 118 (2018) 166–181.

[52]

A. Klašnja-Milićević, M. Ivanović, B. Vesin, Z. Budimac, Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques, Appl. Intelligence 48 (6) (2018) 1519–1535.

[53]

Kolekar, S.V., Pai, R.M., M.M., M.P., 2019. Rule based adaptive user interface for adaptive E-learning system. Educ. Inf. Technol., 24(1), 613-641.

[54]

D. Kouis, K. Kyprianos, P. Ermidou, P. Kaimakis, A. Koulouris, A framework for assessing LMSs e-courses content type compatibility with learning styles dimensions, J. e-Learning Knowledge Soc. 16 (2) (2020) 73–86.

[55]

Kulkarni, P. V., Rai, S., Kale, R., 2020. Recommender System in eLearning: A Survey. In Proceeding of International Conference on Computational Science and Applications (pp. 119-126). Springer, Singapore.

[56]

Masthoff, J., 2011. Group recommender systems: Combining individual models. In: Recommender systems handbook (pp. 677–702). Springer, Boston, MA.

[57]

A.E. Labib, J.H. Canós, M.C. Penadés, On the way to learning style models integration: a Learner's Characteristics Ontology, Comput. Hum. Behav. 73 (2017) 433–445.

[58]

X.N. Lam, T. Vu, T.D. Le, A.D. Duong, Addressing cold-start problem in recommendation systems, in: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, 2008, pp. 208–211.

[59]

B. Lika, K. Kolomvatsos, S. Hadjiefthymiades, Facing the cold start problem in recommender systems, Expert Syst. Appl. 41 (4) (2014) 2065–2073.

Digital Library

[60]

N. Manouselis, H. Drachsler, R. Vuorikari, H. Hummel, R. Koper, Recommender systems in technology enhanced learning, in: F. Ricci, L. Rokach, B. Shapira, P.B. Kantor (Eds.), Recommender Systems Handbook, Springer US, Boston, MA, 2011, pp. 387–415.

[61]

P. Massa, P. Avesani, Trust-aware recommender systems, in: Proceedings of the 2007 ACM conference on Recommender systems, 2007, pp. 17–24.

[62]

Mobasher, B., 2007. Data mining for web personalization. In The adaptive web (pp. 90–135). Springer, Berlin, Heidelberg.

[63]

D.F. Murad, Y. Heryadi, S.M. Isa, W. Budiharto, Personalization of study material based on predicted final grades using multi-criteria user-collaborative filtering recommender system, Educ. Inf. Technol. 25 (6) (2020) 5655–5668.

[64]

A.H. Nabizadeh, D. Gonçalves, S. Gama, J. Jorge, H.N. Rafsanjani, Adaptive learning path recommender approach using auxiliary learning objects, Comput. Educ. 147 (2020) 103777,.

Digital Library

[65]

Nafea, S., Siewe, F., He, Y., 2018. ULEARN: Personalized Course Learning Objects Based on Hybrid Recommendation Approach.

[66]

S.M. Nafea, F. Siewe, Y. He, On recommendation of learning objects using felder-silverman learning style model, IEEE Access 7 (2019) 163034–163048.

[67]

S. Natarajan, S. Vairavasundaram, S. Natarajan, A.H. Gandomi, Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data, Expert Syst. Appl. 149 (2020) 113248,.

[68]

Nihad, E.G., Mohamed, K., El Mokhtar, E.N., 2020. Designing and modeling of a multi-agent adaptive learning system (MAALS) using incremental hybrid case-based reasoning (IHCBR). Int. J. Electr. Comp. Eng. (2088–8708), 10(3).

[69]

S. Ouf, M. Abd Ellatif, S.E. Salama, Y. Helmy, A proposed paradigm for smart learning environment based on semantic web, Comput. Hum. Behav. 72 (2017) 796–818.

Digital Library

[70]

Pazzani, M.J., Billsus, D., 2007. Content-based recommendation systems. In The adaptive web (pp. 325–341). Springer, Berlin, Heidelberg.

[71]

S. Pariserum Perumal, G. Sannasi, K. Arputharaj, An intelligent fuzzy rule-based e-learning recommendation system for dynamic user interests, J. Supercomp. 75 (8) (2019) 5145–5160.

[72]

P.R. Polsani, Use and abuse of reusable learning objects, J. Digital Inf. 3 (4) (2003) 164.

[73]

M.M. Rahman, N.A. Abdullah, A personalized group-based recommendation approach for Web search in E-learning, IEEE Access 6 (2018) 34166–34178.

[74]

F. Ricci, L. Rokach, B. Shapira, Introduction to recommender systems handbook, in: F. Ricci, L. Rokach, B. Shapira, P.B. Kantor (Eds.), Recommender Systems Handbook, Springer US, Boston, MA, 2011, pp. 1–35.

[75]

M. Riyahi, M.K. Sohrabi, Providing effective recommendations in discussion groups using a new hybrid recommender system based on implicit ratings and semantic similarity, Electron. Commer. Res. Appl. 40 (2020) 100938,.

Digital Library

[76]

L. Safoury, A. Salah, Exploiting user demographic attributes for solving cold-start problem in recommender system, Lecture Notes Software Eng. 1 (3) (2013) 303–307.

[77]

B. Saleena, S.K. Srivatsa, Using concept similarity in cross ontology for adaptive e-Learning systems, J. King Saud Univ.-Comp. Inf. Sci. 27 (1) (2015) 1–12.

[78]

S. Sarwar, Z.U. Qayyum, R. García-Castro, M. Safyan, R.F. Munir, Ontology based E-learning framework: a personalized, adaptive and context aware model, Multimedia Tools Appl. 78 (24) (2019) 34745–34771.

[79]

A. Segal, K. Gal, G. Shani, B. Shapira, A difficulty ranking approach to personalization in E-learning, Int. J. Hum Comput Stud. 130 (2019) 261–272.

[80]

Senthilnayaki, B., Venkatalakshmi, K., Kannan, A., 2015. An ontology based framework for intelligent web based e-learning. Int. J. Intelligent Inf. Technol. (IJIIT), 11(2), 23–39.

[81]

D Shi, T Wang, H Xing, H Xu, A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning, Knowledge-Based Systems 195 (2020) 103777.

[82]

L.H. Son, Dealing with the new user cold-start problem in recommender systems: a comparative review, Inf Syst. 58 (2016) 87–104.

[83]

Srivastav, G., Kant, S., 2019, October. Review on e-Learning Environment Development and context aware recommendation systems using Deep Learning. In 2019 3rd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE) (pp. 615–621). IEEE.

[84]

J.K. Tarus, Z. Niu, A. Yousif, A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining, Future Gener. Comp. Syst. 72 (2017) 37–48.

[85]

J.K. Tarus, Z. Niu, D. Kalui, A hybrid recommender system for e-learning based on context awareness and sequential pattern mining, Soft. Comput. 22 (8) (2018) 2449–2461.

Digital Library

[86]

J.K. Tarus, Z. Niu, G. Mustafa, Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning, Artif. Intell. Rev. 50 (1) (2018) 21–48.

[87]

V. Vanitha, P. Krishnan, A modified ant colony algorithm for personalized learning path construction, J. Intell. Fuzzy Syst. 37 (5) (2019) 6785–6800.

[88]

M. Venkatesh, S. Sathyalakshmi, Smart learning using personalised recommendations in web-based learning systems using artificial bee colony algorithm to improve learning performance, Electr. Govern., Int. J. 16 (1–2) (2020) 101–117.

[89]

B. Vesin, Z. Budimac, Applying recommender systems and adaptive hypermedia for e-learning personalization, Comput Inf. 32 (3) (2013) 629–659.

[90]

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

[91]

S. Wan, Z. Niu, An e-learning recommendation approach based on the self-organization of learning resource, Knowl.-Based Syst. 160 (2018) 71–87.

[92]

S. Wan, Z. Niu, A hybrid e-learning recommendation approach based on learners' influence propagation, IEEE Trans. Knowl. Data Eng. 32 (5) (2019) 827–840.

[93]

J. Xiao, M. Wang, B. Jiang, J. Li, A personalized recommendation system with combinational algorithm for online learning, J. Ambient Intell. Hum. Comput. 9 (3) (2018) 667–677.

[94]

S.-Y. Yang, Developing an ontology-supported information integration and recommendation system for scholars, Expert Syst. Appl. 37 (10) (2010) 7065–7079.

[95]

X. Zhao, Z. Niu, W. Chen, C. Shi, K. Niu, D. Liu, A hybrid approach of topic model and matrix factorization based on two-step recommendation framework, J. Intelligent Inf. Syst. 44 (3) (2015) 335–353.

[96]

H. Zhang, T. Huang, Z. Lv, S. Liu, H. Yang, MOOCRC: a highly accurate resource recommendation model for use in MOOC environments, Mobile Netw. Appl. 24 (1) (2019) 34–46.

[97]

Zhong, J., Xie, H., Wang, F.L., 2019. The research trends in recommender systems for e-learning. Asian Assoc. Open Univ J.

Cited By

Nadimpalli VHauser FBittner DGrabinger LStaufer SMottok J(2023)Systematic Literature Review for the Use of AI Based Techniques in Adaptive Learning Management SystemsProceedings of the 5th European Conference on Software Engineering Education10.1145/3593663.3593681(83-92)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3593663.3593681
Swaminathan BPalani SVairavasundaram S(2023)Feature fusion based deep neural collaborative filtering model for fertilizer predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119441216:COnline publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119441
Zhao XLong SHu D(2022)Analysis and Construction of the User Characteristic Model in the Adaptive Learning System for Personalized LearningComputational Intelligence and Neuroscience10.1155/2022/55031532022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5503153

Index Terms

Review and classification of content recommenders in E-learning environment
1. Applied computing
  1. Education
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image Journal of King Saud University - Computer and Information Sciences

Journal of King Saud University - Computer and Information Sciences Volume 34, Issue 9

Oct 2022

1335 pages

ISSN:1319-1578

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Published: 01 October 2022

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Cited By

Nadimpalli VHauser FBittner DGrabinger LStaufer SMottok J(2023)Systematic Literature Review for the Use of AI Based Techniques in Adaptive Learning Management SystemsProceedings of the 5th European Conference on Software Engineering Education10.1145/3593663.3593681(83-92)Online publication date: 19-Jun-2023
https://dl.acm.org/doi/10.1145/3593663.3593681
Swaminathan BPalani SVairavasundaram S(2023)Feature fusion based deep neural collaborative filtering model for fertilizer predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119441216:COnline publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119441
Zhao XLong SHu D(2022)Analysis and Construction of the User Characteristic Model in the Adaptive Learning System for Personalized LearningComputational Intelligence and Neuroscience10.1155/2022/55031532022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5503153

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