- Title
- Extending the technology acceptance model for use of e-learning systems by digital learners
- Creator
- Hanif, Aamer; Jamal, Faheem; Imran, Muhammad
- Date
- 2018
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/181811
- Identifier
- vital:16006
- Identifier
-
https://doi.org/10.1109/ACCESS.2018.2881384
- Identifier
- ISBN:2169-3536 (ISSN)
- Abstract
- Technology-based learning systems enable enhanced student learning in higher-education institutions. This paper evaluates the factors affecting behavioral intention of students toward using e-learning systems in universities to augment classroom learning. Based on the technology acceptance model, this paper proposes six external factors that influence the behavioral intention of students toward use of e-learning. A quantitative approach involving structural equation modeling is adopted, and research data collected from 437 undergraduate students enrolled in three academic programs is used for analysis. Results indicate that subjective norm, perception of external control, system accessibility, enjoyment, and result demonstrability have a significant positive influence on perceived usefulness and on perceived ease of use of the e-learning system. This paper also examines the relevance of some previously used external variables, e.g., self-efficacy, experience, and computer anxiety, for present-world students who have been brought up as digital learners and have higher levels of computer literacy and experience. © 2018 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 6, no. (2018), p. 73395-73404
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2018 IEEE
- Rights
- Open Access
- Subject
- 40 Engineering; 46 Information and Computing Sciences; Behavioral intention; Digital learners; E-learning; Technology-acceptance model; Technology-based learning
- Full Text
- Reviewed
- Funder
- The work of M. Imran was supported by the Deanship of Scientific Research, King Saud University, through the Research Group under Grant RG-1435-051.
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