Abstract
Self-efficacy in the use of ICT is a key predictive variable of success for students in higher education. This study intends to explore a range of variables (gender, age, previous experience, training and attitudes towards ICT) related to the use of ICT by mature students, as well as analysing the relationship between these variables and perceived self-efficacy. For this purpose, a survey was administered to 382 students who were preparing to enter the university of Seville through one of the specific routes reserved for students over 25, 40 and 45 years of age. The data were analysed through descriptive procedures and multiple regression analysis. Factor analysis revealed the existence of two dimensions: self-efficacy in information processing software, and self-efficacy with the Internet, both considered as dependant variables. The results showed that these two dimensions of self-efficacy are associated respectively with the use of information processing software and the Internet. older student groups tend to feel less competent in the use of ICT, especially in relation to Internet self-efficacy. Training appears to be a relevant precursor of a student’s competence in using basic information processing software, but not for using the Internet. For mature students, competence in the use of the Internet seems to be an attitudinal issue, so a positive attitude towards ICT facilitates developing their own confidence with using the Internet. The paper concludes by stressing the need for higher education institutions to be aware of and pay special attention to the “digital divide”, and to the peculiarities that older university students may present.
Translated title of the contribution | Self-efficacy in the use of ict amongst mature students |
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Original language | Spanish |
Pages (from-to) | 19-40 |
Number of pages | 22 |
Journal | Educacion XX1 |
Volume | 25 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2022 |
Keywords
- Adult students
- Higher education
- Inclusion
- Information technology
- Multiple regression analysis
- University