Integration of Adaptive Collaborative Learning Process in a Hybrid Learning Environment

Fatima Zohra Lhafra (1), Otman Abdoun (2)
(1) Abdelmalek Essaadi University, Computer Science Department, Faculty of Science, Tetouan, Morocco
(2) Abdelmalek Essaadi University, Computer Science Department, Faculty of Science, Tetouan, Morocco
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Lhafra, Fatima Zohra, and Otman Abdoun. “Integration of Adaptive Collaborative Learning Process in a Hybrid Learning Environment”. International Journal on Advanced Science, Engineering and Information Technology, vol. 13, no. 2, Apr. 2023, pp. 638-50, doi:10.18517/ijaseit.13.2.16608.
Technology integration has been crucial in the practice of the learning process. The use of technology aims to find effective solutions to traditional learning problems. Despite the enormous efforts adopted, using e-learning systems was optional in many education systems. However, the COVID-19 health crisis has shown the importance of the transition to e-learning to ensure pedagogical continuity. According to several studies that have measured the impact of COVID-19 on education systems and the adopted solutions, blended learning represents an effective solution for combining the advantages of face-to-face and distance learning. But the implementation strategies regarding this mode of learning are still limited. For this purpose, we propose a hybrid learning model based on collaborative work through an intelligent assignment of learner roles. This approach aims to support adaptive learning via a hybrid learning environment. The proposed solution is based mainly on collaborative work as an active learning method, using the Naí¯ve Bayes algorithm and Belbin theory. The usefulness of collaborative work is to keep the learning rhythm between face-to-face and distance learning and to encourage learners' engagement and motivation through this mode of learning. According to Belbin's theory, the results of this work propose an adequate role for each learner. This intelligent assignment leads the learner to live the learning situation and not undergo it.

V. GherheÈ™, C. E. Stoian, M. A. FărcaÈ™iu, and M. Stanici, “E-learning vs. Face-to-face learning: Analyzing students’ preferences and behaviors,” Sustainability (Switzerland), vol. 13, no. 8, 2021, doi: 10.3390/su13084381.

F. Zohra Lhafra and O. Abdoun, “Integration of evolutionary algorithm in an agent-oriented approach for an adaptive e-learning,” International Journal of Electrical and Computer Engineering (IJECE), vol. 13, no. 2, p. 1964, Apr. 2023, doi: 10.11591/ijece.v13i2.pp1964-1978.

F. Z. Lhafra and O. Abdoun, “Design and Implementation of a Serious Game Based on Recommender Systems for the Learning Assessment Process at Primary Education Level,” 2023, pp. 200-210. doi: 10.1007/978-3-031-15191-0_19.

K. Agarwal and J. Chitranshi, “Covid-19 pandemic: impact and opportunities for education in India,” CARDIOMETRY, no. 22, pp. 215-222, May 2022, doi: 10.18137/cardiometry.2022.22.215222.

Suyadi and I. D. Selvi, “Online learning and child abuse: the COVID-19 pandemic impact on work and school from home in Indonesia,” Heliyon, vol. 8, no. 1, p. e08790, Jan. 2022, doi: 10.1016/j.heliyon.2022.e08790.

R. Sabates, E. Carter, and J. M. B. Stern, “Using educational transitions to estimate learning loss due to COVID-19 school closures: The case of Complementary Basic Education in Ghana,” Int J Educ Dev, vol. 82, p. 102377, Apr. 2021, doi: 10.1016/j.ijedudev.2021.102377.

S. Tabatadze and K. Chachkhiani, “COVID-19 and Emergency Remote Teaching in the Country of Georgia: Catalyst for Educational Change and Reforms in Georgia?,” Educ Stud, vol. 57, no. 1, pp. 78-95, Jan. 2021, doi: 10.1080/00131946.2020.1863806.

T. Chen, L. Peng, B. Jing, C. Wu, J. Yang, and G. Cong, “The Impact of the COVID-19 Pandemic on User Experience with Online Education Platforms in China,” Sustainability, vol. 12, no. 18, p. 7329, Sep. 2020, doi: 10.3390/su12187329.

O. Zawacki”Richter, “The current state and impact of Covid”19 on digital higher education in Germany,” Hum Behav Emerg Technol, vol. 3, no. 1, pp. 218-226, Jan. 2021, doi: 10.1002/hbe2.238.

F. Z. LHAFRA and A. OTHMAN, “The impact of COVID-19 on education: Performance Analysis of Tracks and Tools for Distance Education in Schools during the Coronavirus Pandemic in Morocco,” in The 4th International Conference on Networking, Information Systems amp Security., Apr. 2021, pp. 1-8. doi: 10.1145/3454127.3458774.

F.-Z. Hibbi, O. Abdoun, and H. el Khatir, “Coronavirus Pandemic in Morocco: Measuring the Impact of Containment and Improving the Learning Process in Higher Education,” International Journal of Information and Education Technology, vol. 11, no. 1, pp. 30-34, 2021, doi: 10.18178/ijiet.2021.11.1.1485.

I. Amin, A. Yousaf, S. Walia, and M. Bashir, “What Shapes E-Learning Effectiveness among Tourism Education Students? An Empirical Assessment during COVID19,” J Hosp Leis Sport Tour Educ, vol. 30, p. 100337, Jun. 2022, doi: 10.1016/j.jhlste.2021.100337.

S. Haleemunnissa, S. Didel, M. K. Swami, K. Singh, and V. Vyas, “Children and COVID19: Understanding impact on the growth trajectory of an evolving generation,” Child Youth Serv Rev, vol. 120, p. 105754, Jan. 2021, doi: 10.1016/j.childyouth.2020.105754.

M. Meeter, “Primary school mathematics during the COVID-19 pandemic: No evidence of learning gaps in adaptive practicing results,” Trends Neurosci Educ, vol. 25, p. 100163, Dec. 2021, doi: 10.1016/j.tine.2021.100163.

“The 5-Phase Process as a Balancing Act during Times of Disruption: Transitioning to Virtual Teaching at an International JK-5 School”.

A. M. Maatuk, E. K. Elberkawi, S. Aljawarneh, H. Rashaideh, and H. Alharbi, “The COVID-19 pandemic and E-learning: challenges and opportunities from the perspective of students and instructors,” J Comput High Educ, vol. 34, no. 1, pp. 21-38, Apr. 2022, doi: 10.1007/s12528-021-09274-2.

A. Raes, L. Detienne, I. Windey, and F. Depaepe, “A systematic literature review on synchronous hybrid learning: gaps identified,” Learn Environ Res, vol. 23, no. 3, pp. 269-290, Oct. 2020, doi: 10.1007/s10984-019-09303-z.

Lestari et al., “Hybrid learning on problem-solving abiities in physics learning: A literature review,” J Phys Conf Ser, vol. 1796, no. 1, p. 012021, Feb. 2021, doi: 10.1088/1742-6596/1796/1/012021.

B. W. Gao, J. Jiang, and Y. Tang, “The effect of blended learning platform and engagement on students’ satisfaction—— the case from the tourism management teaching,” J Hosp Leis Sport Tour Educ, vol. 27, p. 100272, Nov. 2020, doi: 10.1016/j.jhlste.2020.100272.

R. A. Rasheed, A. Kamsin, and N. A. Abdullah, “Challenges in the online component of blended learning: A systematic review,” Comput Educ, vol. 144, p. 103701, Jan. 2020, doi: 10.1016/j.compedu.2019.103701.

N. R. Alsalhi, Mohd. E. Eltahir, and S. S. Al-Qatawneh, “The effect of blended learning on the achievement of ninth grade students in science and their attitudes towards its use,” Heliyon, vol. 5, no. 9, p. e02424, Sep. 2019, doi: 10.1016/j.heliyon.2019.e02424.

H. Syam, M. Basri, A. Abduh, A. A. Patak, and - Rosmaladewi, “Hybrid e-Learning in Industrial Revolution 4.0 for Indonesia Higher Education,” Int J Adv Sci Eng Inf Technol, vol. 9, no. 4, p. 1183, Aug. 2019, doi: 10.18517/ijaseit.9.4.9411.

F. de Brito Lima, S. L. Lautert, and A. S. Gomes, “Contrasting levels of student engagement in blended and non-blended learning scenarios,” Comput Educ, vol. 172, p. 104241, Oct. 2021, doi: 10.1016/j.compedu.2021.104241.

L. M. Khodeir, “Blended learning methods as an approach to teaching project management to architecture students,” Alexandria Engineering Journal, vol. 57, no. 4, pp. 3899-3905, Dec. 2018, doi: 10.1016/j.aej.2018.10.004.

M. A. Biddle and R. M. Hoover, “Teaching motivational interviewing in a blended learning environment,” Curr Pharm Teach Learn, vol. 12, no. 6, pp. 728-734, Jun. 2020, doi: 10.1016/j.cptl.2020.01.027.

M. Yigzaw, Y. Tebekaw, Y.-M. Kim, A. Kols, F. Ayalew, and G. Eyassu, “Comparing the effectiveness of a blended learning approach with a conventional learning approach for basic emergency obstetric and newborn care training in Ethiopia,” Midwifery, vol. 78, pp. 42-49, Nov. 2019, doi: 10.1016/j.midw.2019.07.014.

L. Alabdulkarim, “University health sciences students rating for a blended learning course framework,” Saudi J Biol Sci, vol. 28, no. 9, pp. 5379-5385, Sep. 2021, doi: 10.1016/j.sjbs.2021.05.059.

A. Fekry, G. A. Dafoulas, and M. Ismail, “The Relation between Student Behaviours in Group Presentations and their Teamwork Modalities Using Belbin and MBTI Analysis,” Procedia Comput Sci, vol. 164, pp. 292-300, 2019, doi: 10.1016/j.procs.2019.12.186.

J. Moreno, J. D. Sí¡nchez, and A. F. Pineda, “A hybrid approach for composing groups in collaborative learning contexts,” Heliyon, vol. 7, no. 6, p. e07249, Jun. 2021, doi: 10.1016/j.heliyon.2021.e07249.

E. Haataja, M. Dindar, J. Malmberg, and S. Jí¤rvelí¤, “Individuals in a group: Metacognitive and regulatory predictors of learning achievement in collaborative learning,” Learn Individ Differ, vol. 96, p. 102146, May 2022, doi: 10.1016/j.lindif.2022.102146.

J. A. D. Meza, M. L. C. Castro, and R. V. J. Vivas, “The diagram as a mediator in collaborative learning: A conceptual review,” Learn Cult Soc Interact, vol. 35, p. 100634, Aug. 2022, doi: 10.1016/j.lcsi.2022.100634.

M. Markowski, C. Yearley, and H. Bower, “Collaborative Learning in Practice (CLiP) in a London maternity ward-a qualitative pilot study,” Midwifery, vol. 111, p. 103360, Aug. 2022, doi: 10.1016/j.midw.2022.103360.

J. Sun et al., “Children’s engagement during collaborative learning and direct instruction through the lens of participant structure,” Contemp Educ Psychol, vol. 69, p. 102061, Apr. 2022, doi: 10.1016/j.cedpsych.2022.102061.

J.-M. Flores-Parra, M. Castafon-Puga, R. D. Evans, R. Rosales-Cisneros, and C. Gaxiola-Pacheco, “Towards Team Formation Using Belbin Role Types and a Social Networks Analysis Approach,” in 2018 IEEE Technology and Engineering Management Conference (TEMSCON), Jun. 2018, pp. 1-6. doi: 10.1109/TEMSCON.2018.8488386.

R. A. Aguilar Vera, J. C. Dí­az Mendoza, M. A. Muñoz-Mata, and J. P. Ucí¡n Pech, “Influence of Belbin’s Roles in the Quality of the Software Requirements Specification Development by Student Teams,” 2020, pp. 91-101. doi: 10.1007/978-3-030-33547-2_8.

“The Nine Belbin Team Roles,” Belbin.com, 2022. [Online]. Available: https://www.belbin.com/about/belbin-team-roles. .

G. Bonaccorso, Machine Learning Algorithms. Packt Publishing Ltd, 2018.

S. F. Hussain and M. Haris, “A k-means based co-clustering (kCC) algorithm for sparse, high dimensional data,” Expert Syst Appl, vol. 118, pp. 20-34, Mar. 2019, doi: 10.1016/j.eswa.2018.09.006.

T. Xie, R. Liu, and Z. Wei, “Improvement of the Fast Clustering Algorithm Improved by K -Means in the Big Data,” Applied Mathematics and Nonlinear Sciences, vol. 5, no. 1, pp. 1-10, Jan. 2020, doi: 10.2478/amns.2020.1.00001.

G. Shobha and S. Rangaswamy, “Machine Learning,” 2018, pp. 197-228. doi: 10.1016/bs.host.2018.07.004.

H. Parveen and S. Pandey, “Sentiment analysis on Twitter Data-set using Naive Bayes algorithm,” in 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 2016, pp. 416-419. doi: 10.1109/ICATCCT.2016.7912034.

J. Wu and C. Hicks, “Breast Cancer Type Classification Using Machine Learning,” J Pers Med, vol. 11, no. 2, p. 61, Jan. 2021, doi: 10.3390/jpm11020061.

M. Fatima and M. Pasha, “Survey of Machine Learning Algorithms for Disease Diagnostic,” Journal of Intelligent Learning Systems and Applications, vol. 09, no. 01, pp. 1-16, 2017, doi: 10.4236/jilsa.2017.91001.

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