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在线教育环境中学习共同体研究综述

Summary

This survey, written in Chinese by a Wuhan University team, addresses persistent problems in online education: high dropout, low course completion and weak supervision. The authors argue that learners in MOOCs mostly study alone, and that organising them into learning communities can counter isolation and improve persistence. The survey combines perspectives from education and computer science. It contrasts informal pedagogical definitions with formal computational ones: student grouping (Agrawal et al.) and learning team formation (Liu et al.). Building on these, the authors propose their own formal definition of a dynamic learning community, in which groups are rebuilt as learners' knowledge changes across a sequence of tasks. They then classify construction methods into homogeneous, heterogeneous and hybrid approaches. Finally, they review management mechanisms for keeping communities active, grouped as sharing, collaboration and incentives. Compared with six earlier surveys, this one is the only one to cover formal definitions and management mechanisms together. It gives researchers in educational data mining a structured map of the field and a set of open problems.

Key points

  • Motivating statistics:
    • The 2021 Class Central report (China excluded) counts about 950 universities, 19.4k courses, 1,670 micro-credentials, 70 online degrees and roughly 220 million registered MOOC learners.
    • Cited studies put online dropout at 40–80%, with course retention about 10–20% below classroom teaching.
  • Agrawal et al. grouping model:
    • Each student has an ability score θ in [0, 1], e.g. estimated with item response theory.
    • Within a group, members above the group's mean ability are leaders and those below are followers.
    • Two problems are defined: 1-GROUP, which selects one group of at most k students, and ℓ-GROUP, which partitions all students into equal groups to maximise total gain.
  • Liu et al. team formation: Each learner has a skill-proficiency vector over V skills. Teams must be disjoint and differ in size by at most one learner. Separate objectives are defined for diversity and for gain.
  • Authors' dynamic definition:
    • K roughly equal communities are formed over L consecutive tasks.
    • Within each community, learning preferences should be similar (measured by cosine similarity), while ability gaps should be maximised.
  • Proposed future directions: dynamic community construction, managing multiple communities at once, and precise teaching evaluation of communities.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages · Natural language processing

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.13328/j.cnki.jos.006735

@article{2023,
  title = {{在线教育环境中学习共同体研究综述}},
  author = {张俊涛 and 杨先娣 and 宋伟 and 张雪龙 and 贺宇阳 and 彭智勇},
  journal = {软件学报 (Journal of Software)},
  year = {2023},
  doi = {10.13328/j.cnki.jos.006735},
  url = {https://doi.org/10.13328/j.cnki.jos.006735}
}

References

96 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Bakhshinategh B, Zaiane OR, ElAtia S, Ipperciel D. Educational data mining applications and tasks: A survey of the last 10 years. Education and Information Technologies, 2018, 23(1): 537–553. [doi: 10.1007/s10639-017-9616-z] — [doi:10.1007/s10639-017-9616-z](https://doi.org/10.1007/s10639-017-9616-z) find
  2. Jong BS, Wu LY, Chan TY. Dynamic grouping strategies based on a conceptual graph for cooperative learning. IEEE Trans. on Knowledge and Data Engineering, 2006, 18(6): 738–747. [doi: 10.1109/TKDE.2006.93] — [doi:10.1109/tkde.2006.93](https://doi.org/10.1109/tkde.2006.93) find
  3. Perera D, Kay J, Koprinska I, Yacef K, Zaïane OR. Clustering and sequential pattern mining of online collaborative learning data. IEEE Trans. on Knowledge and Data Engineering, 2009, 21(6): 759–772. [doi: 10.1109/TKDE.2008.138] — [doi:10.1109/tkde.2008.138](https://doi.org/10.1109/tkde.2008.138) find
  4. Agrawal R, Golshan B, Terzi E. Grouping students in educational settings. In: Proc. of the 20th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery, 2014. 1017–1026. [doi: 10.1145/2623330.2623748] — [doi:10.1145/2623330.2623748](https://doi.org/10.1145/2623330.2623748) find
  5. Agrawal R, Nandanwar S, Musti MN. Grouping students for maximizing learning from peers. In: Proc. of the 10th Int ’l Conf. on Educational Data Mining. Wuhan: EDM, 2017. find
  6. Esfandiari M, Wei D, Amer-Yahia S, Roy SB. Optimizing peer learning in online groups with affinities. In: Proc. of the 25th ACM SIGKDD Int ’l Conf. on Knowledge Discovery & Data Mining. Anchorage: Association for Computing Machinery, 2019. 1216 –1226. [doi: 10.1145/3292500.3330945] — [doi:10.1145/3292500.3330945](https://doi.org/10.1145/3292500.3330945) find
  7. Wei D, Koutis I, Roy SB. Peer learning through targeted dynamic groups formation. In: Proc. of the 37th IEEE Int ’l Conf. on Data Engineering. Chania: IEEE, 2021. 121–132. [doi: 10.1109/ICDE51399.2021.00018] — [doi:10.1109/icde51399.2021.00018](https://doi.org/10.1109/icde51399.2021.00018) find
  8. Pappano L. The year of the MOOC. The New York Times, 2012, 2(12): 1–7. find
  9. Zheng QH, Dong B, Qian BY, Tian F, Wei BF, Zhang WZ, Liu J. The state of the art and future tendency of smart education. Journal of Computer Research and Development, 2019, 56(1): 209 –224 (in Chinese with English abstract). [doi: 10.7544/issn1000-1239.2019.20180758] — [doi:10.7544/issn1000-1239.2019.20180758](https://doi.org/10.7544/issn1000-1239.2019.20180758) find
  10. Shah D. A decade of MOOCs: A review of MOOC stats and trends in 2021. 2021. https://www.classcentral.com/report/moocs-stats-and-trends-2021/ ↗
  11. Anderson A, Huttenlocher D, Kleinberg J, Leskovec J. Engaging with massive online courses. In: Proc. of the 23rd Int’l Conf. on World Wide Web. Seoul: Association for Computing Machinery, 2014. 687–698. [doi: 10.1145/2566486.2568042] — [doi:10.1145/2566486.2568042](https://doi.org/10.1145/2566486.2568042) find
  12. Kizilcec RF, Piech C, Schneider E. Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. In: Proc. of the 3rd Int’l Conf. on Learning Analytics and Knowledge. Leuven: Association for Computing Machinery, 2013. 170–179. [doi: 10.1145/2460296.2460330] — [doi:10.1145/2460296.2460330](https://doi.org/10.1145/2460296.2460330) find
  13. Onah DFO, Sinclair J, Boyatt R. Dropout rates of massive open online courses: Behavioural patterns. In: Proc. of the 6th Int’l Conf. on Education and New Learning Technologies. Barcelona: IATED Academy, 2014. 5825–5834. find
  14. He JZ, Bailey J, Rubinstein BIP, Zhang R. Identifying at-risk students in massive open online courses. In: Proc. of the 29th AAAI Conf. on Artificial Intelligence. Austin: AAAI Press, 2015. 1749–1755. find
  15. Herbert M. Staying the course: A study in online student satisfaction and retention. Online Journal of Distance Learning Administration, 2006, 9(4): 300–317. find
  16. Prenkaj B, Velardi P, Stilo G, Distante D, Faralli S. A survey of machine learning approaches for student dropout prediction in online courses. ACM Computing Surveys, 2020, 53(3): 57. [doi: 10.1145/3388792] — [doi:10.1145/3388792](https://doi.org/10.1145/3388792) find
  17. Rovai AP. Building sense of community at a distance. The Int’l Review of Research in Open and Distributed Learning, 2002, 3(1): 1–16. [doi: 10.19173/irrodl.v3i1.79] — [doi:10.19173/irrodl.v3i1.79](https://doi.org/10.19173/irrodl.v3i1.79) find
  18. Stanford-Bowers DE. Persistence in online classes: A study of perceptions among community college stakeholders. Journal of Online Learning and Teaching, 2008, 4(1): 37–50. find
  19. Zhou Q, Mou C, Yang D. Research progress on educational data mining: A survey. Ruan Jian Xue Bao/Journal of Software, 2015, 26(11): 3026−3042 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4887.htm [doi: 10.13328/j.cnki.jos.004887] — [doi:10.13328/j.cnki.jos.004887](https://doi.org/10.13328/j.cnki.jos.004887) ↗
  20. Kulkarni C, Cambre J, Kotturi Y, Bernstein MS, Klemmer SR. Talkabout: Making distance matter with small groups in massive classes. In: Proc. of the 18th ACM Conf. on Computer Supported Cooperative Work & Social Computing. Vancouver: ACM, 2015. 1116–1128. [doi: 10.1145/2675133.2675166] — [doi:10.1145/2675133.2675166](https://doi.org/10.1145/2675133.2675166) find
  21. Liu IF, Chen MC, Sun YS, Wible D, Kuo CH. Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & Education, 2010, 54(2): 600–610. [doi: 10.1016/j.compedu.2009.09.009] — [doi:10.1016/j.compedu.2009.09.009](https://doi.org/10.1016/j.compedu.2009.09.009) find
  22. Hu Y, Li YF. The connotation and related researches reviews of virtual learning communities. Modern Distance Education Research, 2012, (3): 32–42 (in Chinese with English abstract). [doi: 10.3969/j.issn.1009-5195.2012.03.005] — [doi:10.3969/j.issn.1009-5195.2012.03.005](https://doi.org/10.3969/j.issn.1009-5195.2012.03.005) find
  23. Ji H, Zhu YF. Inheritance and innovation: From community to learning community. Distance Education in China, 2019, (10): 74 –79 (in Chinese with English abstract). [doi: 10.13541/j.cnki.chinade.2019.10.007] — [doi:10.13541/j.cnki.chinade.2019.10.007](https://doi.org/10.13541/j.cnki.chinade.2019.10.007) find
  24. Stoll L, Bolam R, McMahon A, Wallace M, Thomas S. Professional learning communities: A review of the literature. Journal of Educational Change, 2006, 7(4): 221–258. [doi: 10.1007/s10833-006-0001-8] — [doi:10.1007/s10833-006-0001-8](https://doi.org/10.1007/s10833-006-0001-8) find
  25. Krouska A, Troussas C, Virvou M. Applying genetic algorithms for student grouping in collaborative learning: A synthetic literature review. Intelligent Decision Technologies, 2019, 13(4): 395–406. [doi: 10.3233/IDT-190184] — [doi:10.3233/idt-190184](https://doi.org/10.3233/idt-190184) find
  26. Costaguta R. Algorithms and machine learning techniques in collaborative group formation. In: Proc. of the 14th Mexican Int’l Conf. on Artificial Intelligence. Morelos: Springer, 2015. 249–258. [doi: 10.1007/978-3-319-27101-9_18] — [doi:10.1007/978-3-319-27101-9_18](https://doi.org/10.1007/978-3-319-27101-9_18) find
  27. Odo C, Masthoff J, Beacham N. Group formation for collaborative learning— A systematic literature review. In: Proc. of the 20th Int’l Conf. on Artificial Intelligence in Education. Chicago: Springer, 2019. 206–212. [doi: 10.1007/978-3-030-23207-8_39] — [doi:10.1007/978-3-030-23207-8_39](https://doi.org/10.1007/978-3-030-23207-8_39) find
  28. Liu YP, Liu Q, Wu RZ, Chen EH, Su Y, Chen ZG, Hu GP. Collaborative learning team formation: A cognitive modeling perspective. In: Proc. of the 21st Int’l Conf. on Database Systems for Advanced Applications. Dallas: Springer, 2016. 383–400. [doi: 10.1007/978-3-319-32049-6_24] — [doi:10.1007/978-3-319-32049-6_24](https://doi.org/10.1007/978-3-319-32049-6_24) find
  29. Gogoulou A, Gouli E, Boas G, Liakou E, Grigoriadou M. Forming homogeneous, heterogeneous and mixed groups of learners. In: Proc. of the 2007 Workshop on Personalisation in E-learning Environments at Individual and Group Level, the 11th Int ’l Conf. on User Modeling. 2007. 33–40. find
  30. Xie T, Zheng QH, Zhang WZ. A behavioral sequence analyzing framework for grouping students in an e-learning system. Knowledge-based Systems, 2016, 111: 36–50. [doi: 10.1016/j.knosys.2016.08.001] — [doi:10.1016/j.knosys.2016.08.001](https://doi.org/10.1016/j.knosys.2016.08.001) find
  31. Du J, Zheng QH, Ding J, Gong ZY. A method for learner grouping based on personality clustering. In: Proc. of the 10th Int’l Conf. on Computer Supported Cooperative Work in Design. Nanjing: IEEE, 2006. 1–6. [doi: 10.1109/CSCWD.2006.253206] — [doi:10.1109/cscwd.2006.253206](https://doi.org/10.1109/cscwd.2006.253206) find
  32. Tian F, Wang SB, Zheng C, Zheng QH. Research on E-learner personality grouping based on fuzzy clustering analysis. In: Proc. of the 12th Int ’l Conf. on Computer Supported Cooperative Work in Design. Xi ’an: IEEE, 2008. 1035 –1040. [doi: 10.1109/CSCWD.2008.4537122] — [doi:10.1109/cscwd.2008.4537122](https://doi.org/10.1109/cscwd.2008.4537122) find
  33. Zhang K, Cui LZ, Wang HY, Sui Q. An improvement of matrix-based clustering method for grouping learners in e-learning. In: Proc. of the 11th Int’l Conf. on Computer Supported Cooperative Work in Design. Melbourne: IEEE, 2007. 1010–1015. [doi: 10.1109/CSCWD.2007.4281577] — [doi:10.1109/cscwd.2007.4281577](https://doi.org/10.1109/cscwd.2007.4281577) find
  34. Zhang JT, Li B, Song W, Lin NZ, Yang XD, Peng ZY. Learning ability community for personalized knowledge tracing. In: Proc. of the 14th Asia-Pacific Web (APWeb) and Web-age Information Management (WAIM) Joint Int ’l Conf. on Web and Big Data. Tianjin: Springer, 2020. 176–192. [doi: 10.1007/978-3-030-… — [doi:10.1007/978-3-030-60290-1_14](https://doi.org/10.1007/978-3-030-60290-1_14) find
  35. Tian F, Gao B, Cui Q, Chen EH, Liu TY. Learning deep representations for graph clustering. In: Proc. of the 28th AAAI Conf. on Artificial Intelligence. Québec City: AAAI, 2014. 1293–1299. [doi: 10.1609/aaai.v28i1.8916] — [doi:10.1609/aaai.v28i1.8916](https://doi.org/10.1609/aaai.v28i1.8916) find
  36. Wang C, Pan SR, Hu RQ, Long GD, Jiang J, Zhang CQ. Attributed graph clustering: A deep attentional embedding approach. In: Proc. of the 28th Int’l Joint Conf. on Artificial Intelligence. Macao: AAAI Press, 2019. 3670–3676. [doi: 10.24963/ijcai.2019/509] — [doi:10.24963/ijcai.2019/509](https://doi.org/10.24963/ijcai.2019/509) find
  37. Li X, Kao B, Ren ZC, Yin DW. Spectral clustering in heterogeneous information networks. In: Proc. of the 33rd AAAI Conf. on Artificial Intelligence. Honolulu: AAAI, 2019. 4221–4228. [doi: 10.1609/aaai.v33i01.33014221] — [doi:10.1609/aaai.v33i01.33014221](https://doi.org/10.1609/aaai.v33i01.33014221) find
  38. Sadikaj Y, Velaj Y, Behzadi S, Plant C. Spectral clustering of attributed multi-relational graphs. In: Proc. of the 27th ACM SIGKDD Conf. on Knowledge Discovery & Data Mining. Association for Computing Machinery, 2021. 1431–1440. [doi: 10.1145/3447548.3467 381] — [doi:10.1145/3447548.3467](https://doi.org/10.1145/3447548.3467) find
  39. Webb NM. Peer interaction and learning in small groups. Int’l Journal of Educational Research, 1989, 13(1): 21–39. [doi: 10.1016/0883-0355(89)90014-1] — [doi:10.1016/0883-0355(89)90014-1](https://doi.org/10.1016/0883-0355%2889%2990014-1) find
  40. Vaquero LM, Cebrian M. The rich club phenomenon in the classroom. Scientific Reports, 2013, 3(1): 1174. [doi: 10.1038/srep01174] — [doi:10.1038/srep01174](https://doi.org/10.1038/srep01174) find
  41. Kim J. Influence of group size on students’ participation in online discussion forums. Computers & Education, 2013, 62: 123–129. [doi: 10.1016/j.compedu.2012.10.025] — [doi:10.1016/j.compedu.2012.10.025](https://doi.org/10.1016/j.compedu.2012.10.025) find
  42. Moreno J, Ovalle DA, Vicari RM. A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics. Computers & Education, 2012, 58(1): 560–569. [doi: 10.1016/j.compedu.2011.09.011] — [doi:10.1016/j.compedu.2011.09.011](https://doi.org/10.1016/j.compedu.2011.09.011) find
  43. Graf S, Bekele R. Forming heterogeneous groups for intelligent collaborative learning systems with ant colony optimization. In: Proc. of the 8th Int’l Conf. on Intelligent Tutoring Systems. Jhongli: Springer, 2006. 217–226. [doi: 10.1007/11774303_22] — [doi:10.1007/11774303_22](https://doi.org/10.1007/11774303_22) find
  44. Chan TY, Chen CM, Wu YL, Jong BS, Hsia YT, Lin TW. Applying the genetic encoded conceptual graph to grouping learning. Expert Systems with Applications, 2010, 37(6): 4103–4118. [doi: 10.1016/j.eswa.2009.11.014] — [doi:10.1016/j.eswa.2009.11.014](https://doi.org/10.1016/j.eswa.2009.11.014) find
  45. Wang YH, Li YC, Liao HC. Using a genetic algorithm to determine optimal complementary learning clusters for ESL in Taiwan. Expert Systems with Applications, 2011, 38(12): 14832–14837. [doi: 10.1016/j.eswa.2011.05.065] — [doi:10.1016/j.eswa.2011.05.065](https://doi.org/10.1016/j.eswa.2011.05.065) find
  46. Pinninghoff JMA, Contreras AR, Salcedo LP, Contreras AR. Genetic algorithms as a tool for structuring collaborative groups. Natural Computing, 2017, 16(2): 231–239. [doi: 10.1007/s11047-016-9574-1] — [doi:10.1007/s11047-016-9574-1](https://doi.org/10.1007/s11047-016-9574-1) find
  47. Zou F, Wang L, Hei XH, Chen DB, Yang DD. Teaching-learning-based optimization with dynamic group strategy for global optimization. Information Sciences, 2014, 273: 112–131. [doi: 10.1016/j.ins.2014.03.038] — [doi:10.1016/j.ins.2014.03.038](https://doi.org/10.1016/j.ins.2014.03.038) find
  48. Shih TK, Gunarathne WKTM, Ochirbat A, Su HM. Grouping peers based on complementary degree and social relationship using genetic algorithm. ACM Trans. on Internet Technology, 2018, 19(1): 2. [doi: 10.1145/3193180] — [doi:10.1145/3193180](https://doi.org/10.1145/3193180) find
  49. Abdel-Basset M, Abdel-Fatah L, Sangaiah AK. Chapter 10 —Metaheuristic algorithms: A comprehensive review. In: Sangaiah AK, Sheng M, Zhang ZY, eds. Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications. London: Academic Press, 2018. 185–231. [doi: 10.1016/B978-… — [doi:10.1016/b978-0-12-813314-9.00010-4](https://doi.org/10.1016/b978-0-12-813314-9.00010-4) find
  50. Chen BY, Hwang GH, Lin TS. Impacts of a dynamic grouping strategy on students’ learning effectiveness and experience value in an item bank-based collaborative practice system. British Journal of Educational Technology, 2020, 51(1): 36–52. [doi: 10.1111/bjet.12794] — [doi:10.1111/bjet.12794](https://doi.org/10.1111/bjet.12794) find
  51. Luo L, Yang Y, Ma Y. Research on online collaborative learning group division based on fuzzy C-means algorithm. Computer Engineering and Applications, 2017, 53(16): 68–73 (in Chinese with English abstract). [doi: 10.3778/j.issn.1002-8331.1612-0327] — [doi:10.3778/j.issn.1002-8331.1612-0327](https://doi.org/10.3778/j.issn.1002-8331.1612-0327) find
  52. Li HJ, Yue L, Zhang PW, Yang L. Online learning group formation method from the perspective of multi-objective optimization. Journal of Chinese Computer Systems, 2022, 43(4): 712–722 (in Chinese with English abstract). [doi: 10.20009/j.cnki.21-1106/TP.2020-0985] — [doi:10.20009/j.cnki.21-1106/tp.2020-0985](https://doi.org/10.20009/j.cnki.21-1106/tp.2020-0985) find
  53. Zheng YQ, Li CR, Liu SY, Lu WG. An improved genetic approach for composing optimal collaborative learning groups. Knowledge-based Systems, 2018, 139: 214–225. [doi: 10.1016/j.knosys.2017.10.022] — [doi:10.1016/j.knosys.2017.10.022](https://doi.org/10.1016/j.knosys.2017.10.022) find
  54. Ullmann MRD, Ferreira DJ, Camilo CG, Caetano SS, De Assis L. Formation of learning groups in cMOOCs using particle swarm optimization. In: Proc.of the 2015 IEEE Congress on Evolutionary Computation. Sendai: IEEE, 2015. 3296–3304. [doi: 10.1109/CEC.2015.7257302] — [doi:10.1109/cec.2015.7257302](https://doi.org/10.1109/cec.2015.7257302) find
  55. Lin YT, Huang YM, Cheng SC. An automatic group composition system for composing collaborative learning groups using enhanced particle swarm optimization. Computers & Education, 2010, 55(4): 1483–1493. [doi: 10.1016/j.compedu.2010.06.014] — [doi:10.1016/j.compedu.2010.06.014](https://doi.org/10.1016/j.compedu.2010.06.014) find
  56. Charband Y, Navimipour NJ. Knowledge sharing mechanisms in the education: A systematic review of the state of the art literature and recommendations for future research. Kybernetes, 2018, 47(7): 1456–1490. [doi: 10.1108/K-06-2017-0227] — [doi:10.1108/k-06-2017-0227](https://doi.org/10.1108/k-06-2017-0227) find
  57. Chang HH, Chuang SS. Social capital and individual motivations on knowledge sharing: Participant involvement as a moderator. Information & Management, 2011, 48(1): 9–18. [doi: 10.1016/j.im.2010.11.001] — [doi:10.1016/j.im.2010.11.001](https://doi.org/10.1016/j.im.2010.11.001) find
  58. Chiu CM, Hsu MH, Wang ETG. Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision Support Systems, 2006, 42(3): 1872–1888. [doi: 10.1016/j.dss.2006.04.001] — [doi:10.1016/j.dss.2006.04.001](https://doi.org/10.1016/j.dss.2006.04.001) find
  59. Shi CC, Hu P, Fan WG, Qiu LF. How learning effects influence knowledge contribution in online Q&A community? A social cognitive perspective. Decision Support Systems, 2021, 149: 113610. [doi: 10.1016/j.dss.2021.113610] — [doi:10.1016/j.dss.2021.113610](https://doi.org/10.1016/j.dss.2021.113610) find
  60. Yan ZJ, Wang TM, Chen Y, Zhang H. Knowledge sharing in online health communities: A social exchange theory perspective. Information & Management, 2016, 53(5): 643–653. [doi: 10.1016/j.im.2016.02.001] — [doi:10.1016/j.im.2016.02.001](https://doi.org/10.1016/j.im.2016.02.001) find
  61. Chen CJ, Hung SW. To give or to receive? Factors influencing members’ knowledge sharing and community promotion in professional virtual communities. Information & Management, 2010, 47(4): 226–236. [doi: 10.1016/j.im.2010.03.001] — [doi:10.1016/j.im.2010.03.001](https://doi.org/10.1016/j.im.2010.03.001) find
  62. Zhang YJ, Du YL, Meng XW. Research on group recommender systems and their applications. Chinese Journal of Computers, 2016, 39(4): 745–764 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2016.00745] — [doi:10.11897/sp.j.1016.2016.00745](https://doi.org/10.11897/sp.j.1016.2016.00745) find
  63. Chen YL, Cheng LC, Chuang CN. A group recommendation system with consideration of interactions among group members. Expert Systems with Applications, 2008, 34(3): 2082–2090. [doi: 10.1016/j.eswa.2007.02.008] — [doi:10.1016/j.eswa.2007.02.008](https://doi.org/10.1016/j.eswa.2007.02.008) find
  64. Seo YD, Kim YG, Lee E, Seol KS, Baik DK. An enhanced aggregation method considering deviations for a group recommendation. Expert Systems with Applications, 2018, 93: 299–312. [doi: 10.1016/j.eswa.2017.10.027] — [doi:10.1016/j.eswa.2017.10.027](https://doi.org/10.1016/j.eswa.2017.10.027) find
  65. Baltrunas L, Makcinskas T, Ricci F. Group recommendations with rank aggregation and collaborative filtering. In: Proc. of the 4th ACM Conf. on Recommender Systems. Barcelona: Association for Computing Machinery, 2010. 119–126. [doi: 10.1145/1864708.1864733] — [doi:10.1145/1864708.1864733](https://doi.org/10.1145/1864708.1864733) find
  66. Amer-Yahia S, Roy SB, Chawlat A, Das G, Yu C. Group recommendation: Semantics and efficiency. Proc. of the VLDB Endowment, 2009, 2(1): 754–765. [doi: 10.14778/1687627.1687713] — [doi:10.14778/1687627.1687713](https://doi.org/10.14778/1687627.1687713) find
  67. Yuan Q, Cong G, Lin CY. COM: A generative model for group recommendation. In: Proc. of the 20th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. Long Beach: Association for Computing Machinery, 2014. 163 –172. [doi: 10.1145/2623330.2623616] — [doi:10.1145/2623330.2623616](https://doi.org/10.1145/2623330.2623616) find
  68. Cao D, He XN, Miao LH, An YH, Yang C, Hong RC. Attentive group recommendation. In: Proc. of the 41st Int’l ACM SIGIR Conf. on Research & Development in Information Retrieval. Ann Arbor: Association for Computing Machinery, 2018. 645–654. [doi: 10.1145/3209978.3209998] — [doi:10.1145/3209978.3209998](https://doi.org/10.1145/3209978.3209998) find
  69. Tran LV, Pham TAN, Tay Y, Liu YD, Cong C, Li XL. Interact and decide: Medley of sub-attention networks for effective group recommendation. In: Proc. of the 42nd Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. Paris: Association for Computing Machinery, 2019. 255–264. [doi… — [doi:10.1145/3331184.3331251](https://doi.org/10.1145/3331184.3331251) find
  70. Yin HZ, Wang QY, Zheng K, Li ZX, Yang JL, Zhou XF. Social influence-based group representation learning for group recommendation. In: Proc. of the 35th IEEE Int’l Conf. on Data Engineering. Macao: IEEE, 2019. 566–577. [doi: 10.1109/ICDE.2019.00057] — [doi:10.1109/icde.2019.00057](https://doi.org/10.1109/icde.2019.00057) find
  71. Guo L, Yin HZ, Wang QY, Cui B, Huang Z, Cui LZ. Group recommendation with latent voting mechanism. In: Proc. of the 36th IEEE Int’l Conf. on Data Engineering. Dallas: IEEE, 2020. 121–132. [doi: 10.1109/ICDE48307.2020.00018] — [doi:10.1109/icde48307.2020.00018](https://doi.org/10.1109/icde48307.2020.00018) find
  72. Deng ZY, Li CY, Liu SJ, Ali W, Shao J. Knowledge-aware group representation learning for group recommendation. In: Proc. of the 37th IEEE Int’l Conf. on Data Engineering. Chania: IEEE, 2021. 1571–1582. [doi: 10.1109/ICDE51399.2021.00139] — [doi:10.1109/icde51399.2021.00139](https://doi.org/10.1109/icde51399.2021.00139) find
  73. Rodríguez P, Giraldo M, Tabares V, Duque N, Ovalle D. Recommendation system of educational resources for a student group. In: Proc. of the 2016 Int’l Conf. on Practical Applications of Agents and Multi-agent Systems. Sevilla: Springer, 2016. 419–427. [doi: 10.1007/978-3-319-39387-2_35] — [doi:10.1007/978-3-319-39387-2_35](https://doi.org/10.1007/978-3-319-39387-2_35) find
  74. Papamitsiou Z, Economides AA. Motivating students in collaborative activities with game-theoretic group recommendations. IEEE Trans. on Learning Technologies, 2020, 13(2): 374–386. [doi: 10.1109/TLT.2018.2869582] — [doi:10.1109/tlt.2018.2869582](https://doi.org/10.1109/tlt.2018.2869582) find
  75. Johnson DW, Johnson RT, Smith KA. Cooperative learning: Improving university instruction by basing practice on validated theory. Journal on Excellence in College Teaching, 2014, 25(3–4): 85–118. find
  76. Wang CC, Hung JC, Yang CY, Chang HP. A question answering system approach for collaborative learning. In: Proc. of the 10th Int’l Conf. on Computer Supported Cooperative Work in Design. Nanjing: IEEE, 2006. 1–5. [doi: 10.1109/CSCWD.2006.253045] — [doi:10.1109/cscwd.2006.253045](https://doi.org/10.1109/cscwd.2006.253045) find
  77. Agarwal A, Sachdeva N, Yadav RK, Udandarao V, Mittal V, Gupta A, Mathur A. EDUQA: Educational domain question answering system using conceptual network mapping. In: Proc. of the 2019 IEEE Int’l Conf. on Acoustics, Speech and Signal Processing. Brighton: IEEE, 2019. 8137–8141. [doi: 10.1109/ICASSP.20… — [doi:10.1109/icassp.2019.8683538](https://doi.org/10.1109/icassp.2019.8683538) find
  78. Li BC, King I. Routing questions to appropriate answerers in community question answering services. In: Proc. of the 19th ACM Int’l Conf. on Information and Knowledge Management. Toronto: Association for Computing Machinery, 2010. 1585–1588. [doi: 10.1145/1871437.1871678] — [doi:10.1145/1871437.1871678](https://doi.org/10.1145/1871437.1871678) find
  79. Zhou TC, Lyu MR, King I. A classification-based approach to question routing in community question answering. In: Proc. of the 21st Int’l Conf. on World Wide Web. Lyon: Association for Computing Machinery, 2012. 783–790. [doi: 10.1145/2187980.2188201] — [doi:10.1145/2187980.2188201](https://doi.org/10.1145/2187980.2188201) find
  80. Li ZY, Jiang JY, Sun YZ, Wang W. Personalized question routing via heterogeneous network embedding. In: Proc. of the 33rd AAAI Conf. on Artificial Intelligence. Honolulu: IEEE, 2019. 192–199. [doi: 10.1609/aaai.v33i01.3301192] — [doi:10.1609/aaai.v33i01.3301192](https://doi.org/10.1609/aaai.v33i01.3301192) find
  81. Cheng X, Zhu SG, Su S, Chen G. A multi-objective optimization approach for question routing in community question answering services. IEEE Trans. on Knowledge and Data Engineering, 2017, 29(9): 1779–1792. [doi: 10.1109/TKDE.2017.2696008] — [doi:10.1109/tkde.2017.2696008](https://doi.org/10.1109/tkde.2017.2696008) find
  82. Zhou YH, Cong G, Cui B, Jensen CS, Yao JJ. Routing questions to the right users in online communities. In: Proc. of the 25th IEEE Int’l Conf. on Data Engineering. Shanghai: IEEE, 2009. 700–711. [doi: 10.1109/ICDE.2009.44] — [doi:10.1109/icde.2009.44](https://doi.org/10.1109/icde.2009.44) find
  83. Macina J, Srba I, Williams JJ, Bielikova M. Educational question routing in online student communities. In: Proc. of the 11th ACM Conf. on Recommender Systems. Como: Association for Computing Machinery, 2017. 47–55. [doi: 10.1145/3109859.3109886] — [doi:10.1145/3109859.3109886](https://doi.org/10.1145/3109859.3109886) find
  84. Bezerra JM, Hirata C, Randall D. A conceptual framework to define incentive mechanisms for virtual communities. Journal of Universal Computer Science, 2015, 21(9): 1107–1135. find
  85. Tian CQ, Yang BJ, Zhong JD, Liu XJ. Trust-based incentive mechanism to motivate cooperation in hybrid P2P networks. Computer Networks, 2014, 73: 244–255. [doi: 10.1016/j.comnet.2014.08.010] — [doi:10.1016/j.comnet.2014.08.010](https://doi.org/10.1016/j.comnet.2014.08.010) find
  86. Wu NM, Zhou XW, Sun MX. Incentive mechanisms and impacts of negotiation power and information availability in multi-relay cooperative wireless networks. IEEE Trans. on Wireless Communications, 2019, 18(7): 3752–3765. [doi: 10.1109/TWC.2019.2918538] — [doi:10.1109/twc.2019.2918538](https://doi.org/10.1109/twc.2019.2918538) find
  87. Hou M, Han J, Zhang JS, Liu J. Research on incentive mechanism in network education platform. In: Proc. of the 12th Int’l Conf. on Natural Computation, Fuzzy Systems and Knowledge Discovery. Changsha: IEEE, 2016. 2288 –2293. [doi: 10.1109/FSKD.2016.7603 538] — [doi:10.1109/fskd.2016.7603](https://doi.org/10.1109/fskd.2016.7603) find
  88. Hummel HGK, Burgos D, Tattersall C, Brouns F, Kurvers H, Koper R. Encouraging contributions in learning networks using incentive mechanisms. Journal of Computer Assisted Learning, 2005, 21(5): 355–365. [doi: 10.1111/j.1365-2729.2005.00140.x] — [doi:10.1111/j.1365-2729.2005.00140.x](https://doi.org/10.1111/j.1365-2729.2005.00140.x) find
  89. Cheng R, Vassileva J. Design and evaluation of an adaptive incentive mechanism for sustained educational online communities. User Modeling and User-adapted Interaction, 2006, 16(3): 321–348. [doi: 10.1007/s11257-006-9013-6] — [doi:10.1007/s11257-006-9013-6](https://doi.org/10.1007/s11257-006-9013-6) find
  90. Jin FS, Niu ZD, Zhang QX, Lang HY, Qin K. A user reputation model for DLDE learning 2.0 community. In: Proc. of the 11th Int’l Conf. on Asian Digital Libraries. Bali: Springer, 2008. 61–70. [doi: 10.1007/978-3-540-89533-6_7] — [doi:10.1007/978-3-540-89533-6_7](https://doi.org/10.1007/978-3-540-89533-6_7) find
  91. Chang CH, Chen CM, Zhao RH. An effective group incentive mechanism in a collaborative problem-based learning system for enhancing positive peer interaction and learning performance. In: Proc. of the 7th Int’l Congress on Advanced Applied Informatics. Yonago: IEEE, 2018. 170–175. [doi: 10.1109/IIAI-A… — [doi:10.1109/iiai-aai.2018.00041](https://doi.org/10.1109/iiai-aai.2018.00041) find
  92. Shi C, Hu YX, Zhang ZM, Shao L, Jiang FJ. User feedback and ranking in-a-loop: Towards self-adaptive dialogue systems. In: Proc. of the 44th Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval. Association for Computing Machinery, 2021. 2046–2050. [doi: 10.1145/3404835.3463079… — [doi:10.1145/3404835.3463079](https://doi.org/10.1145/3404835.3463079) find
  93. Cai YZ, Chakravarthy S. Expertise ranking of users in QA community. In: Proc. of the 18th Int ’l Conf. on Database Systems for Advanced Applications. Wuhan: Springer, 2013. 25–40. [doi: 10.1007/978-3-642-37487-6_5] — [doi:10.1007/978-3-642-37487-6_5](https://doi.org/10.1007/978-3-642-37487-6_5) find
  94. Omari A, Carmel D, Rokhlenko O, Szpektor I. Novelty based ranking of human answers for community questions. In: Proc. of the 39th Int ’l ACM SIGIR Conf. on Research and Development in Information Retrieval. Pisa: Association for Computing Machinery, 2016. 215–224. [doi: 10.1145/2911451.2911506] — [doi:10.1145/2911451.2911506](https://doi.org/10.1145/2911451.2911506) find
  95. Narang K, Krishnan A, Wang JT, Yang CQ, Sundaram H, Sutter C. Ranking user-generated content via multi-relational graph convolution. In: Proc. of the 44th Int ’l ACM SIGIR Conf. on Research and Development in Information Retrieval. Association for Computing Machinery, 2021. 470–480. [doi: 10.1145/34… — [doi:10.1145/3404835.3462857](https://doi.org/10.1145/3404835.3462857) find
  96. Backstrom L, Huttenlocher D, Kleinberg J, Lan XY. Group formation in large social networks: Membership, growth, and evolution. In: Proc. of the 12th ACM SIGKDD Int ’l Conf. on Knowledge Discovery and Data Mining. Philadelphia: Association for Computing Machinery, 2006. 44–54. [doi: 10.1145/1150402.1… — [doi:10.1145/1150402.1150412](https://doi.org/10.1145/1150402.1150412) find

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