Covid-19 Döneminde Uzaktan Eğitimde Mentor Gerekliliğinin Makine Öğrenmesi Yaklaşımları ile Belirlenmesi ve Belirleyicilerin Açıklanması
Öz
Covid-19 salgını, dünya genelinde ciddi yaşam kaybına neden olmuş ve Mart 2020'de bu salgın süreci pandemi olarak tanımlanmıştır. Hastalığın yayılımını engelleyebilmek için Covid-19 kısıtlamaları kapsamında eğitim kurumları acilen uzaktan eğitime geçmiştir. Öğrenciler tüm uyaran ve disiplinlerin kendi kontrol ve sorumluluklarına bağlı kaldığı bu süreçte normalden çok daha fazla adaptasyon sorunu yaşamaktadır. Farklı yaşam koşullarında yeterli motivasyonu sağlayamayan öğrenciler online derslere uyum göstermekte büyük oranda zorluk çekmektedir. Söz konusu zorluğu azaltabilmek adına bu makalede, öğrencilerin motivasyon ihtiyaçları doğrultusunda bir mentor tarafından desteğe ihtiyaç duyup duymadıklarını makine öğrenmesi yaklaşımları kullanılarak tespit eden bir çalışma yürütülmüştür. Bu çalışma ile sosyal farkındalık arttırılarak mentor kavramı ile öğrencilere dışsal motivasyon sağlanıp fırsat eşitliği sunulması amaçlanmaktadır. Çalışma kapsamında Ürdün Üniversitesi öğrencilerine yapılan anket veri kümesi olarak kullanılmış, çeşitli makine öğrenmesi algoritmaları kullanılarak deneyler yapılmış ve elde edilen sonuçların karşılaştırmalı analizi yapılmıştır. Analiz sonucu Destek Vektör Makinesi %95 F1 skoru ile bu problem için en yüsek başarıyı üreten sınıflandırıcı olarak tespit edilmiştir. Çözümü modellemek için kullanılan diğer sınıflandırıcılardan da yakın sonuçlar elde edilmiştir. Diğer taraftan Karar Ağaçları algoritmasının açıklanabilirlik yapısı kullanılarak sınıflandırmadaki en etkili belirleyiciler bulunmuştur. Böylece mentor gerekliliğinin tespiti için öğrencilere uzun anketler uygulamanın mümkün olmadığı durumlarda, en verimli sonucu alabilecek belirleyicilerin kullanımı tercih edilebilecektir. Yapılan çalışmada mentor gerekliliğinin tespitinde kullanılabilecek en etkili belirleyici olarak, öğrencilerin “E-öğrenme sistemini kullanmak üretkenliğimi artırıyor.” seçeneğine vermiş oldukları cevap tespit edilmiştir. Çalışma kapsamında belirleyicilerin etkililik hiyererşisi en yüksekten başlayarak ilk üç seviyeye göre ayrıntılandırılmış, uygulamanın olanağına göre tercihe sunulmuştur.
Anahtar Kelimeler
Açıklanabilirlik Covid-19 Makine Öğrenmesi Mentorlük Pandemi
Kaynakça
- Bingöl Schrijer, B . (2020). COVID-19 Salgını Süresince Eğitim Hakkı, Fırsat Eşitliği ve Sınavlara İlişkin Temel Problemler . İstanbul Hukuk Mecmuası , 78 (2) , 837-884 . DOI: 10.26650/mecmua.2020.78.2.0019
- Bostan, S., Erdem, R., Öztürk, Y. E., Kılıç, T., & Yılmaz, A. (2020). The Effect of COVID-19 Pandemic on the Turkish Society. Electronic Journal of General Medicine, 17(6), em237. https://doi.org/10.29333/ejgm/7944
- Atasoy, R., Özden, C., & Kara, D. N. (2020). Covid-19 Pandemi Sürecinde Yapılan E-Ders Uygulamalarının Etkililiğinin Öğrencilerin Perspektifinden Değerlendirilmesi. Electronic Turkish Studies, 15(6).
- Tohidi, H., & Jabbari, M. M. (2012). The effects of motivation in education. Procedia-Social and Behavioral Sciences, 31, 820-824.
- Dede, Y., & Argün, Z. (2004). Öğrencilerin matematiğe yönelik içsel ve dışsal motivasyonlarının belirlenmesi. Eğitim ve Bilim, 29(134).
- Aboagye, E., Yawson, J. A., & Appiah, K. N. (2021). COVID-19 and E-learning: The challenges of students in tertiary institutions. Social Education Research, 1-8.
- Horita, R., Nishio, A., & Yamamoto, M. (2021). The effect of remote learning on the mental health of first year university students in Japan. Psychiatry Research, 295, 113561.
- Cicha, K., Rizun, M., Rutecka, P., & Strzelecki, A. (2021). COVID-19 and Higher Education: First-Year Students’ Expectations toward Distance Learning. Sustainability 2021, 13, 1889.
- Andrews, M., & Wallis, M. (1999). Mentorship in nursing: a literature review. Journal of advanced nursing, 29(1), 201-207.
- Lee, L. M., & Bush, T. (2003). Student mentoring in higher education: Hong Kong Baptist University. Mentoring & Tutoring, 11(3), 263-271.
- Wilkes, Z. (2006). The student-mentor relationship: a review of the literature. Nursing standard, 20(37).
- Garcia-Melgar, A., East, J., & Meyers, N. (2021). Peer assisted academic support: a comparison of mentors’ and mentees’ experiences of a drop-in programme. Journal of Further and Higher Education, 1-14.
- Müller, A. M., Goh, C., Lim, L. Z., & Gao, X. (2021). COVID-19 Emergency eLearning and Beyond: Experiences and Perspectives of University Educators. Educ. Sci. 2021, 11, 19.
- Tibingana-Ahimbisibwe, B., Willis, S., Catherall, S., Butler, F., & Harrison, R. (2020). A systematic review of peer-assisted learning in fully online higher education distance learning programmes. Open Learning: The Journal of Open, Distance and e-Learning, 1-22.
- Fayram, J., Boswood, N., Kan, Q., Motzo, A., & Proudfoot, A. (2018). Investigating the benefits of online peer mentoring for student confidence and motivation. International Journal of mentoring and Coaching in Education.
- Packham, G., & Miller, C. (2000). Peer-assisted student support: a new approach to learning. Journal of Further and Higher Education, 24(1), 55-65.
- Ruane, R. (2012). A study of student interaction in an online learning environment specially crafted for cross-level peer mentoring. Drexel University.
- Dias, S. B., Hadjileontiadou, S. J., Diniz, J., & Hadjileontiadis, L. J. (2020). DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era. Scientific reports, 10(1), 1-17.
- Agarwal, A., Sharma, S., Kumar, V., & Kaur, M. (2021). Effect of E-learning on public health and environment during COVID-19 lockdown. Big Data Mining and Analytics, 4(2), 104-115.
- Ahmed, A. S. A. M. S., & Malik, M. H. (2020, November). Machine Learning for Strategic Decision Making during COVID-19 at Higher Education Institutes. In 2020 International Conference on Decision Aid Sciences and Application (DASA) (pp. 663-668). IEEE.
- Lu, D. N., Le, H. Q., & Vu, T. H. (2020). The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach. Education Sciences, 10(10), 270.
- Feldman, M. D., Arean, P. A., Marshall, S. J., Lovett, M., & O'Sullivan, P. (2010). Does mentoring matter: results from a survey of faculty mentees at a large health sciences university. Medical education online, 15(1), 5063.
- Rodger, S., & Tremblay, P. F. (2003). The Effects of a Peer Mentoring Program on Academic Success among First Year University Students. Canadian Journal of Higher Education, 33(3), 1-17.
- van Esch, C., Luse, W., & Bonner, R. L. (2021). The impact of COVID-19 pandemic concerns and gender on mentor seeking behavior and self-efficacy. Equality, Diversity and Inclusion: An International Journal.
- Xu, Y., Tsao, Y., Shih-Wei, C., Ching-Chang, L., Yen, K. T., & Tsai, H. Y. (2020). Effect of e-learning environmental stimuli on learning engagement in the context of COVID-19. Revista Argentina de Clínica Psicológica, 29(5), 538.
- Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., ... & Chouhan, P. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India. Children and Youth Services Review, 116, 105194.
- Al-Okaily, M., Alqudah, H., Matar, A., Lutfi, A., & Taamneh, A. (2020). Dataset on the Acceptance of e-learning System among Universities Students' under the COVID-19 Pandemic Conditions. Data in brief, 32, 106176.
- Al-Okaily et al. (2020), Mendeley Data Repository, Available: https://bit.ly/3i8KkI5
In Covid-19 Period Determining the Mentor Requirement on Distance Education with Machine Learning Approaches and Explanation of the Determinants
Öz
The Covid-19 outbreak caused serious loss of life worldwide, and this epidemic period was defined as a pandemic in March 2020. In order to prevent the spread of the disease, educational institutions urgently switched to distance education within the scope of Covid-19 restrictions. Students experience much more adaptation problems than usual in this period of time in which all stimuli and disciplines depend on their control and responsibilities. Students who cannot provide sufficient motivation in different life situations have great difficulty in adapting to online courses. In order to reduce this difficulty, a study was conducted in this paper to determine whether students need support from a mentor in line with their motivational needs, using machine learning approaches. This study aims to provide equality of opportunity by increasing social awareness and providing external motivation to students by the concept of the mentor. Within the scope of the study, a questionnaire for the students from The University of Jordan was used as a data set, experiments were carried out using various machine learning algorithms, and the comparative analysis of the results obtained was made. As a result of the analysis, the Support Vector Machine was determined as the classifier that produced the highest success for this problem with a 95% F1 score. Similar results were obtained from other classifiers used to model the solution. On the other hand, by using the explainability structure of the Decision Trees algorithm, the most effective determinants in classification were found. Thus, in cases where it is not possible to apply long questionnaires to students to determine the mentoring requirement, the use of determinants that can get the most efficient result can be preferred. In the study, the most effective determinant that can be used in determining the mentoring requirement is the answer students gave to the option of "Using the e-learning system increases my productivity" was determined. Within the scope of the study, the effectiveness hierarchy of the determinants was detailed until the first three levels, starting from the highest, and presented to the preference according to the possibility of the application.
Anahtar Kelimeler
Covid-19 Explainability Machine Learning Mentorship Pandemic
Kaynakça
- Bingöl Schrijer, B . (2020). COVID-19 Salgını Süresince Eğitim Hakkı, Fırsat Eşitliği ve Sınavlara İlişkin Temel Problemler . İstanbul Hukuk Mecmuası , 78 (2) , 837-884 . DOI: 10.26650/mecmua.2020.78.2.0019
- Bostan, S., Erdem, R., Öztürk, Y. E., Kılıç, T., & Yılmaz, A. (2020). The Effect of COVID-19 Pandemic on the Turkish Society. Electronic Journal of General Medicine, 17(6), em237. https://doi.org/10.29333/ejgm/7944
- Atasoy, R., Özden, C., & Kara, D. N. (2020). Covid-19 Pandemi Sürecinde Yapılan E-Ders Uygulamalarının Etkililiğinin Öğrencilerin Perspektifinden Değerlendirilmesi. Electronic Turkish Studies, 15(6).
- Tohidi, H., & Jabbari, M. M. (2012). The effects of motivation in education. Procedia-Social and Behavioral Sciences, 31, 820-824.
- Dede, Y., & Argün, Z. (2004). Öğrencilerin matematiğe yönelik içsel ve dışsal motivasyonlarının belirlenmesi. Eğitim ve Bilim, 29(134).
- Aboagye, E., Yawson, J. A., & Appiah, K. N. (2021). COVID-19 and E-learning: The challenges of students in tertiary institutions. Social Education Research, 1-8.
- Horita, R., Nishio, A., & Yamamoto, M. (2021). The effect of remote learning on the mental health of first year university students in Japan. Psychiatry Research, 295, 113561.
- Cicha, K., Rizun, M., Rutecka, P., & Strzelecki, A. (2021). COVID-19 and Higher Education: First-Year Students’ Expectations toward Distance Learning. Sustainability 2021, 13, 1889.
- Andrews, M., & Wallis, M. (1999). Mentorship in nursing: a literature review. Journal of advanced nursing, 29(1), 201-207.
- Lee, L. M., & Bush, T. (2003). Student mentoring in higher education: Hong Kong Baptist University. Mentoring & Tutoring, 11(3), 263-271.
- Wilkes, Z. (2006). The student-mentor relationship: a review of the literature. Nursing standard, 20(37).
- Garcia-Melgar, A., East, J., & Meyers, N. (2021). Peer assisted academic support: a comparison of mentors’ and mentees’ experiences of a drop-in programme. Journal of Further and Higher Education, 1-14.
- Müller, A. M., Goh, C., Lim, L. Z., & Gao, X. (2021). COVID-19 Emergency eLearning and Beyond: Experiences and Perspectives of University Educators. Educ. Sci. 2021, 11, 19.
- Tibingana-Ahimbisibwe, B., Willis, S., Catherall, S., Butler, F., & Harrison, R. (2020). A systematic review of peer-assisted learning in fully online higher education distance learning programmes. Open Learning: The Journal of Open, Distance and e-Learning, 1-22.
- Fayram, J., Boswood, N., Kan, Q., Motzo, A., & Proudfoot, A. (2018). Investigating the benefits of online peer mentoring for student confidence and motivation. International Journal of mentoring and Coaching in Education.
- Packham, G., & Miller, C. (2000). Peer-assisted student support: a new approach to learning. Journal of Further and Higher Education, 24(1), 55-65.
- Ruane, R. (2012). A study of student interaction in an online learning environment specially crafted for cross-level peer mentoring. Drexel University.
- Dias, S. B., Hadjileontiadou, S. J., Diniz, J., & Hadjileontiadis, L. J. (2020). DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era. Scientific reports, 10(1), 1-17.
- Agarwal, A., Sharma, S., Kumar, V., & Kaur, M. (2021). Effect of E-learning on public health and environment during COVID-19 lockdown. Big Data Mining and Analytics, 4(2), 104-115.
- Ahmed, A. S. A. M. S., & Malik, M. H. (2020, November). Machine Learning for Strategic Decision Making during COVID-19 at Higher Education Institutes. In 2020 International Conference on Decision Aid Sciences and Application (DASA) (pp. 663-668). IEEE.
- Lu, D. N., Le, H. Q., & Vu, T. H. (2020). The Factors Affecting Acceptance of E-Learning: A Machine Learning Algorithm Approach. Education Sciences, 10(10), 270.
- Feldman, M. D., Arean, P. A., Marshall, S. J., Lovett, M., & O'Sullivan, P. (2010). Does mentoring matter: results from a survey of faculty mentees at a large health sciences university. Medical education online, 15(1), 5063.
- Rodger, S., & Tremblay, P. F. (2003). The Effects of a Peer Mentoring Program on Academic Success among First Year University Students. Canadian Journal of Higher Education, 33(3), 1-17.
- van Esch, C., Luse, W., & Bonner, R. L. (2021). The impact of COVID-19 pandemic concerns and gender on mentor seeking behavior and self-efficacy. Equality, Diversity and Inclusion: An International Journal.
- Xu, Y., Tsao, Y., Shih-Wei, C., Ching-Chang, L., Yen, K. T., & Tsai, H. Y. (2020). Effect of e-learning environmental stimuli on learning engagement in the context of COVID-19. Revista Argentina de Clínica Psicológica, 29(5), 538.
- Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., ... & Chouhan, P. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India. Children and Youth Services Review, 116, 105194.
- Al-Okaily, M., Alqudah, H., Matar, A., Lutfi, A., & Taamneh, A. (2020). Dataset on the Acceptance of e-learning System among Universities Students' under the COVID-19 Pandemic Conditions. Data in brief, 32, 106176.
- Al-Okaily et al. (2020), Mendeley Data Repository, Available: https://bit.ly/3i8KkI5
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Temmuz 2021 |
| Yayımlandığı Sayı | Yıl 2021 Sayı: 26 - Ejosat Özel Sayı 2021 (HORA) |
