اكتشاف المشاعر ومشاركة الطلاب في التعلم عن بعد أثناء الحجر الصحي بسبب كوفيد 19

محتوى المقالة الرئيسي

بنيوسف عبد الاوي
https://orcid.org/0000-0002-5950-0187
احمد رميدة
https://orcid.org/0000-0002-2981-8936
زينب صبري
https://orcid.org/0009-0003-6172-1530
يونس البوزكري الإدريسي
أنيس مؤمن
https://orcid.org/0000-0001-5330-0136

الملخص

يعد التعلم عن بعد أحد أساليب التدريس والتعلم التي تم تبنيها بعد جائحة كوفيد 19. مهمة جذب ومعرفة اهتمام المتعلمين إلى الفصل صعبة على الأساتذة. في هذا البحث، قمنا بإنشاء آلية لتقدير مستويات مشاركة الطلاب ومعرفة احاسيسهم حيث نستخدم البيانات المرئية من مقاطع الفيديو المرسلة من طرف الطلاب المشاركين في دورات التعلم نظرًا لوجود العديد من الطرق لقياس مستويات مشاركة الطلاب. نقوم بمعالجة هذه الفيديوهات لتحديد مقدار مشاركة الطلاب واكتشاف عواطفهم. لقد قمنا بإنشاء نظامنا وتجريبه، الذي مكننا من تقييم انتباه الطلاب وتحديد عواطفهم. توضح النتائج أنه من الممكن قياس المشاركة ومعرفة المشاعر استغلالها في اتخاذ قرارات في صالح نتائج الطلاب وتحسين طرق التعليم والتعلم. يمكن تطبيق هذه التقنية في سيناريوهات أخرى، مثل القيادة الذاتية والأمان مع تعديل بسيط.

تفاصيل المقالة

كيفية الاقتباس
1.
اكتشاف المشاعر ومشاركة الطلاب في التعلم عن بعد أثناء الحجر الصحي بسبب كوفيد 19. Baghdad Sci.J [انترنت]. 1 أبريل، 2024 [وثق 21 مايو، 2024];21(4):1432. موجود في: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8698
القسم
article

كيفية الاقتباس

1.
اكتشاف المشاعر ومشاركة الطلاب في التعلم عن بعد أثناء الحجر الصحي بسبب كوفيد 19. Baghdad Sci.J [انترنت]. 1 أبريل، 2024 [وثق 21 مايو، 2024];21(4):1432. موجود في: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8698

المراجع

Berry LA, Kowal KB. Effect of Role-Play in Online Discussions on Student Engagement and Critical Thinking. Online Learn J 2022; 26(3): 4-21; https://doi.org/10.24059/olj.v26i3.3367.

Elshami W, Taha MH, Abdalla ME, Abuzaid M, Saravanan C, Al Kawas S. Factors that affect student engagement in online learning in health professions education. Nurse Educ Today. 2022 Mar 1; 110: 105261; https://doi.org/10.1016/j.nedt.2021.105261.

Al-Sakkaf A, Omar M, Ahmad M. Social Worked-Examples Technique to Enhance Student Engagement in Program Visualization. Baghdad Sci J . 2019; 16(2): 0453; https://doi.org/10.21123/bsj.2019.16.2(SI).0453.

Alruwais N, Zakariah M. Student-Engagement Detection in Classroom Using Machine Learning Algorithm. Electronics .2023; 12(3): 731; https://doi.org/10.3390/electronics12030731.

Ali MM, Hassan N. Defining concepts of student engagement and factors contributing to their engagement in schools. Creat Educ. 2018 Oct 25; 9(14): 2161-70; https://doi.org/10.4236/ce.2018.914157.

Astin AW. Student involvement: A developmental theory for higher education. J Coll Stud Pers. 1984 Jul 25; 25(4): 297-308; https://psycnet.apa.org/record/1999-01418-006.

Chen Y, Zhou J, Gao Q, Gao J, Zhang W. MDNN: Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning. CMES – Comput Model Eng Sci. 2023 Jul 1; 136(1): 381-401. https://doi.org/10.32604/cmes.2023.023234.

Maluenda-Albornoz J, Berríos-Riquelme J, Infante-Villagrán V, Lobos-Peña K. Perceived Social Support and Engagement in First-Year Students: The Mediating Role of Belonging during COVID-19. Sustainability. 2022 Dec 29; 15(1): 597; https://doi.org/10.3390/su15010597.

Tomaszewski W, Xiang N, Huang Y, Western M, McCourt B, McCarthy I. The Impact of Effective Teaching Practices on Academic Achievement When Mediated by Student Engagement: Evidence from Australian High Schools. Educ Sci. 2022 May 20; 12(5): 358; https://doi.org/10.3390/educsci12050358.

Ladino Nocua AC, Cruz Gonzalez JP, Castiblanco Jimenez IA, Gomez Acevedo JS, Marcolin F, Vezzetti E. Assessment of Cognitive Student Engagement Using Heart Rate Data in Distance Learning during COVID-19. Educ Sci. 2021 Sep 14; 11(9): 540; https://doi.org/10.3390/educsci11090540.

Gallinat A. Student engagement in the management of accelerated change. Learn Teach. 2018 Mar 1; 11(1): 35–56; https://doi.org/10.3167/latiss.2018.110103.

Coates H. A model of online and general campus‐based student engagement. Assess Eval High Educ. 2007 Apr 1; 32(2): 121–41; https://doi.org/10.1080/02602930600801878.

Altuwairqi K, Jarraya SK, Allinjawi A, Hammami M. A new emotion–based affective model to detect student's engagement. J King Saud Univ Comput. 2021 Jan 1; 33(1): 99-109; https://doi.org/10.1016/j.jksuci.2018.12.008.

Schlechty PC. Engaging Students: The Next Level of Working on the Work. John Wiley & Sons; 2011. 224 p. 2011 Feb 16; https://www.wiley.com/en-gb/Engaging+Students%3A+The+Next+Level+of+Working+on+the+Work-p-9781118015520.

D'Mello S, Graesser A. Dynamics of affective states during complex learning. Learn Instr. 2012 Apr 1; 22(2): 145–57; https://doi.org/10.1016/j.learninstruc.2011.10.001.

Whitehill J, Serpell Z, Lin YC, Foster A, Movellan JR. The faces of engagement: Automatic recognition of student engagementfrom facial expressions. IEEE Trans. Affect Comput. .2014 Apr 10; 5(1): 86-98; https://doi.org/10.1109/TAFFC.2014.2316163.

Jang M, Park C, Yang HS, Kim JH, Cho YJ, Lee DW, et al. Building an automated engagement recognizer based on video analysis. ACM/IEEE Proc Int Conf Hum Robot Interact. 2014 Mar 3 (pp. 182-183). Bielefeld Germany. https://doi.org/10.1145/2559636.2563687

Li J, Ngai G, Leong HV, Chan SC. Multimodal human attention detection for reading from facial expression, eye gaze, and mouse dynamics. ACM SIGAPP Appl Comput Rev. 2016 Nov 4; 16(3): 37-49; https://doi.org/10.1145/3015297.3015301.

Monkaresi H, Bosch N, Calvo RA, D'Mello SK. Automated detection of engagement using video-based estimation of facial expressions and heart rate. IEEE Trans Affect Comput. 2016 Jan 6; 8(1): 15-28; https://doi.org/10.1109/TAFFC.2016.2515084.

Abdellaoui B, Moumen A, El Idrissi YE, Remaida A. Face detection to recognize students' emotion and their engagement: A systematic review. ICECOCS. 2020 Dec 2 (pp. 1-6). IEEE; https://doi.org/10.1109/ICECOCS50124.2020.9314600.

Kumari J, Rajesh R, Pooja KM. Facial expression recognition: A survey. Procedia Comput Sci. 2015 Jan 1; 58: 486-91; https://doi.org/10.1016/j.procs.2015.08.011

Omar MR, Husni H. Teen-Computer Interaction: Building a Conceptual Model with Thoughts-Emotion-Behaviour. Baghdad Sci J. 2019 Jun 20;16(2(SI): 0485; https://doi.org/10.21123/bsj.2019.16.2(SI).0485.

Mellouk W, Handouzi W. Facial emotion recognition using deep learning: review and insights. Procedia Comput Sci. 2020 Jan 1; 175: 689-94 ; https://doi.org/10.1016/j.procs.2020.07.101.

Villar BF, Viñas PF, Turiel JP, Marinero JC, Gordaliza A. Influence on the user's emotional state of the graphic complexity level in virtual therapies based on a robot-assisted neuro-rehabilitation platform. Comput Methods Programs Biomed. 2020 Jul 1; 190: 105359; https://doi.org/10.1016/j.cmpb.2020.105359.

Abdurrasyid A, Indrianto I, Susanti MN. Face detection and global positioning system on a walking aid for blind people. Bull Electr Eng Inform. 2022 Jun 1; 11(3): 1558-67; https://doi.org/10.11591/eei.v11i3.3429.

Henrie CR, Halverson LR, Graham CR. Measuring student engagement in technology-mediated learning: A review. Comput Educ. 2015 Dec 1; 90: 36-53; https://doi.org/10.1016/j.compedu.2015.09.005.

Karimah SN, Hasegawa S. Automatic engagement estimation in smart education/learning settings: a systematic review of engagement definitions, datasets, and methods. Smart Learn Environ. 2022 Dec; 9(1): 1-48; https://doi.org/10.1186/s40561-022-00212-y.

Shao M, Pham-Hung M, Alves SFDR, Snyder M, Eshaghi K, Benhabib B, et al. Long-Term Exercise Assistance: Group and One-on-One Interactions between a Social Robot and Seniors. Robotics. 2023 Jan 6; 12(1): 9; https://doi.org/10.3390/robotics12010009.

Thomas C, Sarma KP, Gajula SS, Jayagopi DB. Automatic prediction of presentation style and student engagement from videos. Comput Educ Artif Intell. 2022 Jan 1; 3: 100079; https://doi.org/10.1016/j.caeai.2022.100079.

Remaida A, Moumen A, El Idrissi YE, Abdellaoui B, Harraki Y. The use of personality tests as a pre-employment tool: A comparative study. InSHS Web of Conferences. SHS Web Conf. 2021; 119: 05007; https://doi.org/10.1051/shsconf/202111905007.

Sabri Z, FAKHRI Y, MOUMEN A. The Effects of Gamification on E-learning Education: Systematic Literature Review and Conceptual Model. Statistics. Optim Inf Comput. 2022 Feb 8; 10(1): 75-92; https://doi.org/10.19139/soic-2310-5070-1115.

Al-Fallooji AS, Al-Azawei A. Predicting Users' Personality on Social Media: A Comparative Study of Different Machine Learning Techniques. Karbala Int J Mod Sci. 2022; 8(4): 617-30; https://doi.org/10.33640/2405-609X.3262

Al-Shareeda MA, Manickam S. COVID-19 vehicle based on an efficient mutual authentication scheme for 5G-enabled vehicular fog computing. Int J Environ Res Public Health. 2022 Nov 24; 19(23): 15618; http://dx.doi.org/10.3390/ijerph192315618

Mejjad N, Cherif EK, Rodero A, Krawczyk DA, El Kharraz J, Moumen A, et al. Disposal Behavior of Used Masks during the COVID-19 Pandemic in the Moroccan Community: Potential Environmental Impact. Int J Environ Res Public Health. 2021 Apr 20;18(8):4382; https://doi.org/10.3390/ijerph18084382

Gloster AT, Lamnisos D, Lubenko J, Presti G, Squatrito V, Constantinou M, et al. Impact of COVID-19 pandemic on mental health: An international study. PloS one. 2020 Dec 31; 15(12): e0244809; https://doi.org/10.1371/journal.pone.0244809

Musolino S. Families, Relational Scenarios and Emotions in the Time of the COVID-19 Pandemic. Ital Sociol Rev. 2020 Dec 6; 10(3S): 737; https://doi.org/10.13136/isr.v10i3S.396

Matthewman S, Huppatz K. A sociology of Covid-19. J Sociol. 2020 Dec; 56(4): 675-83; https://doi.org/10.1177/1440783320939416

Mohammed BA, Al-Shareeda MA, Manickam S, Al-Mekhlafi ZG, Alreshidi A, Alazmi M, et al. FC-PA: Fog Computing-based Pseudonym Authentication Scheme in 5G-enabled Vehicular Networks. IEEE Access. 2023 Feb 22; 11: 18571-81. https://doi.org/10.1109/ACCESS.2023.3247222

Al-Mekhlafi ZG, Al-Shareeda MA, Manickam S, Mohammed BA, Qtaish A. Lattice-based lightweight quantum resistant scheme in 5G-enabled vehicular networks. Math. . 2023 Jan 12; 11(2): 399; https://dx.doi.org/10.3390/math11020399

Al-Shareeda MA, Manickam S. Man-in-the-middle attacks in mobile ad hoc networks (MANETs): Analysis and evaluation. Sym. 2022 Jul 27; 14(8): 1543; https://doi.org/10.3390/sym14081543

Singh AK, Srivastava S. Development and Validation of Student Engagement Scale in the Indian Context. Global Bus Rev 2014; 15(3): 505-515; https://doi.org/10.1177/0972150914535137

Gupta S, Kumar P, Tekchandani R. A multimodal facial cues based engagement detection system in e-learning context using deep learning approach. Multimed Tools Appl. 2023 Feb 10: 1-27; https://doi.org/10.1007/s11042-023-14392-3

Awais M, Raza M, Singh N, Bashir K, Manzoor U, Islam SU, et al. LSTM-based emotion detection using physiological signals: IoT framework for healthcare and distance learning in COVID-19. IEEE Internet Things J. 2020 Dec 10; 8(23): 16863-71; https://doi.org/10.1109/JIOT.2020.3044031

Thiruthuvanathan M, Krishnan B, Rangaswamy M. Engagement detection through facial emotional recognition using a shallow residual convolutional neural networks. Int J Intell Eng Syst. 2021 Mar 1; 14(2): 236-47; https://doi.org/10.22266/ijies2021.0430.21

Rodriguez P, Ortigosa A, Carro RM. Detecting and making use of emotions to enhance student motivation in e-learning environments. Int J Contin Eng Educ Life Long Learn. 2014 Jan 1; 24(2): 168-83; https://doi.org/10.1504/IJCEELL.2014.060156

المؤلفات المشابهة

يمكنك أيضاً إبدأ بحثاً متقدماً عن المشابهات لهذا المؤلَّف.