Identification and analysis of learning styles of MOOC students

Simple item page
dc.citation.epage20
dc.citation.issue4
dc.citation.spage17
dc.citation.volume8
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorVerhun, V.
dc.coverage.placenameLublin
dc.date.accessioned2020-02-28T09:27:44Z
dc.date.available2020-02-28T09:27:44Z
dc.date.created2019-06-26
dc.date.issued2019-06-26
dc.description.abstractt. 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.
dc.format.extent17-20
dc.format.pages4
dc.identifier.citationVerhun V. Identification and analysis of learning styles of MOOC students / V. Verhun // Econtechmod : scientific journal. — Lublin, 2019. — Vol 8. — No 4. — P. 17–20.
dc.identifier.citationenVerhun V. Identification and analysis of learning styles of MOOC students / V. Verhun // Econtechmod : scientific journal. — Lublin, 2019. — Vol 8. — No 4. — P. 17–20.
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/46301
dc.language.isoen
dc.relation.ispartofEcontechmod : scientific journal, 4 (8), 2019
dc.relation.references1. 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
dc.relation.references2. 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
dc.relation.references3. 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).
dc.relation.references4. 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.
dc.relation.references5. Onah, Daniel & Sinclair, Jane & Boyatt, R. (2014). Dropout Rates of Massive Open Online Courses: Behavioural Patterns. 10.13140/RG.2.1.2402.0009.
dc.relation.references6. 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.
dc.relation.references7. Chaplot, Devendra Singh, Eunhee Rhim, and Jihie Kim. “Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks”. AIED Workshops. Vol. 53. 2015.
dc.relation.references8. 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.
dc.relation.references9. Vysotska, V., & Shakhovska, N. 2018. Information technologies of gamification for training and recruitment. Saarbrücken, Germany: LAP LAMBERT Academic Publishing.
dc.relation.references10. 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.
dc.relation.references11. Shakhovska N., Veres O., Hirnyak M.. 2016. Generalized formal model of Big Data. ECONTECHMOD: Vol. 5. No. 2: 33–38.
dc.relation.references12. Lytvyn V. 2013. Design of intelligent decision support systems using ontological approach. ECONTECHMOD. 2 (1): 31–37.
dc.relation.references13. 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.
dc.relation.references14. Melnykova, N.; Marikutsa, U.; Sosnowski, S.. 2016 Specifics personalized approach in the analysis of medical information. Econtechmod. Vol. 5. No. 2:109–116.
dc.relation.referencesen1. 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
dc.relation.referencesen2. 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
dc.relation.referencesen3. 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).
dc.relation.referencesen4. 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.
dc.relation.referencesen5. Onah, Daniel & Sinclair, Jane & Boyatt, R. (2014). Dropout Rates of Massive Open Online Courses: Behavioural Patterns. 10.13140/RG.2.1.2402.0009.
dc.relation.referencesen6. 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.
dc.relation.referencesen7. Chaplot, Devendra Singh, Eunhee Rhim, and Jihie Kim. "Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks". AIED Workshops. Vol. 53. 2015.
dc.relation.referencesen8. 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.
dc.relation.referencesen9. Vysotska, V., & Shakhovska, N. 2018. Information technologies of gamification for training and recruitment. Saarbrücken, Germany: LAP LAMBERT Academic Publishing.
dc.relation.referencesen10. 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.
dc.relation.referencesen11. Shakhovska N., Veres O., Hirnyak M.. 2016. Generalized formal model of Big Data. ECONTECHMOD: Vol. 5. No. 2: 33–38.
dc.relation.referencesen12. Lytvyn V. 2013. Design of intelligent decision support systems using ontological approach. ECONTECHMOD. 2 (1): 31–37.
dc.relation.referencesen13. 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.
dc.relation.referencesen14. Melnykova, N.; Marikutsa, U.; Sosnowski, S.. 2016 Specifics personalized approach in the analysis of medical information. Econtechmod. Vol. 5. No. 2:109–116.
dc.relation.urihttp://tinyurl.com/cdfvvqy
dc.relation.urihttps://ssrn.com/abstract=2350964
dc.relation.urihttp://dx.doi.org/10.2139/ssrn.2350964
dc.rights.holder© Copyright by Lviv Polytechnic National University 2019
dc.rights.holder© Copyright by University of Engineering and Economics in Rzeszów 2019
dc.subjectMOOC
dc.subjectlearning styles
dc.subjectsoftware engineering
dc.titleIdentification and analysis of learning styles of MOOC students
dc.typeArticle

Files

Original bundle

Now showing 1 - 2 of 2
Thumbnail Image
Name:
2019v8n4_Verhun_V-Identification_and_analysis_17-20.pdf
Size:
87.52 KB
Format:
Adobe Portable Document Format
Download
Thumbnail Image
Name:
2019v8n4_Verhun_V-Identification_and_analysis_17-20__COVER.png
Size:
475.18 KB
Format:
Portable Network Graphics
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.93 KB
Format:
Plain Text
Description:
Download

Collections

Econtechmod. – 2019. – Vol. 8, No. 4