Validating students' lifelong learning competencies scale in digital age

Knowledge is changing and there are many changes in science and technology, so citizens must be updated and upgrade their knowledge and skills. Today lifelong learning is based on competence that has attracted the attention of the learning community. Students' lifelong learning competency is important. There are some scales such as attitudes to lifelong learning, lifelong learner characteristics, education lifelong learning and effective lifelong learning for this purpose. But in this context there isn't scale for measureing students lifelong competencies with rergard to digital competencies in Iran. The aim of this study was to examine the factor structure and reliability of Uzunboylu and Hursen (2011) lifelong learning competence scale.

In terms of data collection quantitative study employing a descriptive-correlational research design were used in this study. The research population was all students in University of Birjand (12,000 students). The sample size is based on a valid general rule for factor analysis is 300 subjects. Accordingly, a sample of 300 students from University of Birjand were selected by multi-stage cluster sampling. In this way, from colleges of science, literature, agriculture, engineering, education-psychology and art three college of education and psychology, agriculture and art were selected. After administrating questionnaire data analyzed using SPSS and AMOS software.

Exploratory factor analysis results identified six components for lifelong learning competencies that explained %49.46 variance of lifelong learning competency constructs. Confirmatory factor analysis Results Separated clearly of 42 items lifelong learning in the 6 Factor using the Appropriate fitness indicators. The findings of the model indicate that the fitness indices are desirable. the Chi-square ratio to degrees of freedom was1.72 which is small and indicating the fit of the model with the data, the comparative fit indicate (CFI), the Fit Fitness indicate (GEI), the Adapted Fitness indicate (AGFI) are 0.85, 0.96 and 0.92, respectively, which expresses the good fit of the model with data. The root mean square error (RMSEA) is also 0.051 which is also the appropriate fit condition for the model. In general, these fitness indices indicate a good fit of the model with research data. Cronbach's alpha coefficient of all items was 0/91 and for subscales self-management, learning how to learn, initiative and entrepreneurship, digital competence, acquiring information and decision-making was between 0/66 to 0/85. For correlation between total score and subscales Pearson correlation coefficient was used. The results showed of the correlation between the subscales of the Persian version and the scale of the whole scale in the range of 0.86 to 0.88. Thus, each of the six sub-scales has a high correlation with the total scale scores.

the scale can be used to assess the competency of lifelong learning and ultimately to improve the quality of education in the digital society. As efforts to improve the quality of education are essential in higher education, and expected students to become independent and lifelong learners. As a result, it can be seen from these scale that the do teaching method at universities develop these competencies.