Quantitative Analysis of Online Courses for Learning the JavaScript Programming Language
Today, the JavaScript programming language ranks first both in the world well-known programming rankings and those in Ukraine in particular. The purpose of the research is quantitative analysis of online courses on learning JavaScript on well-known MOOC platforms. In order to achieve the purpose of our research, we used theoretical and quantitative (descriptive) methods. Also, we analyzed such well-known online platforms as Alison, Codecademy, Udemy, edX, Coursera as of December 2023. The number of online courses on each platform, their cost, an approximate user level, video length, etc. was used as criteria for the analysis. The analyzed MOOC was found to offer 1,795 online courses on learning the JavaScript programming language. Among them, 1,077 courses (60%) are paid, 201 courses (11%) can be taken for free, and 517 courses (29%) require optional payment. Most of the online JavaScript courses (788 courses, 44%) are developed for the Beginner level. Only 37 courses are available for experienced JavaScript programmers, which is 2% of the total number of online JavaScript courses. It was found that Codecademy and Udemy online platforms offer their users additional opportunities to learn JavaScript: programming projects (67 online courses); coding exercises (99 online courses); Practice test (55 online courses). The obtained results made it possible to formulate some proposals for choosing the optimal MOOC which helps to learn JavaScript, considering several selection criteria.
Authors
- Anatolii Zemlianskyi
- Тетяна Іванівна Просянкіна-Жарова
- Віра Колмакова (ORCID: https://orcid.org/0000-0001-5414-0939)
- Supriya Chakraborty (ORCID: https://orcid.org/0000-0001-9301-2649)
- Сергій Шаров (ORCID: https://orcid.org/0000-0001-5732-9980)
Institutions
- Techno India University (IN)
- National Academy of Sciences of Ukraine (UA)
- Dmytro Motornyi Tavria State Agrotechnological University (UA)
- Pavlo Tychyna Uman State Pedagogical University (UA)
Publication Details
- Journal
- International Journal on Advanced Science Engineering and Information Technology
- Published
- 2024-08-11
- DOI
- https://doi.org/10.18517/ijaseit.14.4.19976
- Citations
- 1
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.41