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  1. Електронний науковий архів Науково-технічної бібліотеки Національного університету "Львівська політехніка"
  2. Електронний архів Науково-технічної бібліотеки
  3. Вісники та науково-технічні збірники, журнали
  4. Econtechmod
  5. Econtechmod. – 2019. – Vol. 8, No. 4

putin IS MURDERER

Please use this identifier to cite or link to this item: https://oldena.lpnu.ua/handle/ntb/46301
Title: Identification and analysis of learning styles of MOOC students
Authors: Verhun, V.
Affiliation: Lviv Polytechnic National University
Bibliographic description (Ukraine): Verhun V. Identification and analysis of learning styles of MOOC students / V. Verhun // Econtechmod : scientific journal. — Lublin, 2019. — Vol 8. — No 4. — P. 17–20.
Bibliographic description (International): Verhun V. Identification and analysis of learning styles of MOOC students / V. Verhun // Econtechmod : scientific journal. — Lublin, 2019. — Vol 8. — No 4. — P. 17–20.
Is part of: Econtechmod : scientific journal, 4 (8), 2019
Issue: 4
Volume: 8
Issue Date: 26-Jun-2019
Place of the edition/event: Lublin
Keywords: MOOC
learning styles
software engineering
Number of pages: 4
Page range: 17-20
Start page: 17
End page: 20
Abstract: t. The paper presents the results of analyzing the impact of learning styles on the success of the MOOC course. The study was based on the Kolb’s learning style questionnaire. The survey was shared among the students of software engineering MOOC course. The results of the survey were statistically analyzed. Compared the influence of different learning styles and their strength to successful completion of the course. Analyzed the strength of different learning styles among the students of different ages and different education. The results of the research show that the learning style has an impact to the course finishing success and should be considered for the effective educational program creation.
URI: https://ena.lpnu.ua/handle/ntb/46301
Copyright owner: © Copyright by Lviv Polytechnic National University 2019
© Copyright by University of Engineering and Economics in Rzeszów 2019
URL for reference material: http://tinyurl.com/cdfvvqy
https://ssrn.com/abstract=2350964
http://dx.doi.org/10.2139/ssrn.2350964
References (Ukraine): 1. Meyer, R. What it’s like to teach a MOOC (and what the heck’s a MOOC?), Retrieved 20th July 2013, available at: http://tinyurl.com/cdfvvqy
2. Christensen, Gayle and Steinmetz, Andrew and Alcorn, Brandon and Bennett, Amy and Woods, Deirdre and Emanuel, Ezekiel, The MOOC Phenomenon: Who Takes Massive Open Online Courses and Why? (November 6, 2013). Available at SSRN: https://ssrn.com/abstract=2350964 or http://dx.doi.org/10.2139/ssrn.2350964
3. Jordan, K. Initial trends in enrolment and completion of massive open online courses Massive Open Online Courses, 2014. International Review of Research in Open and Distance Learning, 15(1).
4. Xing, Wanli, et al. “Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization”. Computers in human behavior 58 (2016): 119–129.
5. Onah, Daniel & Sinclair, Jane & Boyatt, R. (2014). Dropout Rates of Massive Open Online Courses: Behavioural Patterns. 10.13140/RG.2.1.2402.0009.
6. Sharkey, Mike, and Robert Sanders. “A process for predicting MOOC attrition”. Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs. 2014.
7. Chaplot, Devendra Singh, Eunhee Rhim, and Jihie Kim. “Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks”. AIED Workshops. Vol. 53. 2015.
8. Aryal S., Porawagama A. S., Hasith M. G. S., Thoradeniya S. C., Kodagoda N. and Suriyawansa K. “MoocRec: Learning Styles-Oriented MOOC Recommender and Search Engine”, 2019 IEEE Global Engineering Education Conference (EDUCON), Dubai, United Arab Emirates, 2019: 1167–1172.
9. Vysotska, V., & Shakhovska, N. 2018. Information technologies of gamification for training and recruitment. Saarbrücken, Germany: LAP LAMBERT Academic Publishing.
10. Boyko N., Pobereyko P. 2016 Basic concepts of dynamic recurrent neural networks development. ECONTECHMOD. Lublin: Polish Academy of Sciences, Vol. 5. No. 2: 63–68.
11. Shakhovska N., Veres O., Hirnyak M.. 2016. Generalized formal model of Big Data. ECONTECHMOD: Vol. 5. No. 2: 33–38.
12. Lytvyn V. 2013. Design of intelligent decision support systems using ontological approach. ECONTECHMOD. 2 (1): 31–37.
13. Lytvyn V., Semotuyk O. and Moroz O. 2013. Definition of the semantic metrics on the basis of thesaurus of subject area. ECONTECHMOD. Vol. 2. No. 4: 47–52.
14. Melnykova, N.; Marikutsa, U.; Sosnowski, S.. 2016 Specifics personalized approach in the analysis of medical information. Econtechmod. Vol. 5. No. 2:109–116.
References (International): 1. Meyer, R. What it’s like to teach a MOOC (and what the heck’s a MOOC?), Retrieved 20th July 2013, available at: http://tinyurl.com/cdfvvqy
2. Christensen, Gayle and Steinmetz, Andrew and Alcorn, Brandon and Bennett, Amy and Woods, Deirdre and Emanuel, Ezekiel, The MOOC Phenomenon: Who Takes Massive Open Online Courses and Why? (November 6, 2013). Available at SSRN: https://ssrn.com/abstract=2350964 or http://dx.doi.org/10.2139/ssrn.2350964
3. Jordan, K. Initial trends in enrolment and completion of massive open online courses Massive Open Online Courses, 2014. International Review of Research in Open and Distance Learning, 15(1).
4. Xing, Wanli, et al. "Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization". Computers in human behavior 58 (2016): 119–129.
5. Onah, Daniel & Sinclair, Jane & Boyatt, R. (2014). Dropout Rates of Massive Open Online Courses: Behavioural Patterns. 10.13140/RG.2.1.2402.0009.
6. Sharkey, Mike, and Robert Sanders. "A process for predicting MOOC attrition". Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs. 2014.
7. Chaplot, Devendra Singh, Eunhee Rhim, and Jihie Kim. "Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks". AIED Workshops. Vol. 53. 2015.
8. Aryal S., Porawagama A. S., Hasith M. G. S., Thoradeniya S. C., Kodagoda N. and Suriyawansa K. "MoocRec: Learning Styles-Oriented MOOC Recommender and Search Engine", 2019 IEEE Global Engineering Education Conference (EDUCON), Dubai, United Arab Emirates, 2019: 1167–1172.
9. Vysotska, V., & Shakhovska, N. 2018. Information technologies of gamification for training and recruitment. Saarbrücken, Germany: LAP LAMBERT Academic Publishing.
10. Boyko N., Pobereyko P. 2016 Basic concepts of dynamic recurrent neural networks development. ECONTECHMOD. Lublin: Polish Academy of Sciences, Vol. 5. No. 2: 63–68.
11. Shakhovska N., Veres O., Hirnyak M.. 2016. Generalized formal model of Big Data. ECONTECHMOD: Vol. 5. No. 2: 33–38.
12. Lytvyn V. 2013. Design of intelligent decision support systems using ontological approach. ECONTECHMOD. 2 (1): 31–37.
13. Lytvyn V., Semotuyk O. and Moroz O. 2013. Definition of the semantic metrics on the basis of thesaurus of subject area. ECONTECHMOD. Vol. 2. No. 4: 47–52.
14. Melnykova, N.; Marikutsa, U.; Sosnowski, S.. 2016 Specifics personalized approach in the analysis of medical information. Econtechmod. Vol. 5. No. 2:109–116.
Content type: Article
Appears in Collections:Econtechmod. – 2019. – Vol. 8, No. 4

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