The emergence of moving learning activities online, due to Covid-19 pandemic preventive measures and fast growing technology, forced many educational institutions to design and prepare their distance learning systems on time, without adopting the best practices for an effective and holistic digital transformation. This fact highlights the need of reconsidering the models in online learning and redesigning the learning processes to meet students e-learning needs and receive a more qualitative educational experience. This study intends to show which are the perspectives of students on the next generation learning platform, post-covid, and how this can contribute to a more effective learning in HE. To serve this purpose, an internet-based survey was designed. The questionnaire was distributed via the DSV iLearn platform of the program to all participants-users of the platform, and the method of data analysis chosen was Quantitative analysis. The results of this study showed an overall satisfaction with the platform capabilities and a satisfactory level of students’ skills in the use of ICT technologies and a digital learning platform. Some important conclusions about dependencies of students’ characteristics that affect their engagement with an elearning platform also arose, while areas of improvement were identified regarding the feeling of safety using a digital platform, alternative assessment techniques, new platform technological features and interaction with peers and instructors. Under this study limitations and the potential of a future and more extensive research, the aim of these results is their incorporation in the design, implementation and optimization processes of the next generation learning platform, contributing to the Higher Education e-learning systems performance and responsiveness flexibility, taking also into consideration qualitative factors related to students’ characteristics and needs.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:su-225174 |
| Date | January 2023 |
| Creators | Maofi, Maria |
| Publisher | Stockholms universitet, Institutionen för data- och systemvetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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