Abstract
Today’s higher education learning environment expects students to have such digital skills as navigating a learning management system, using word processing or presentation software, and searching for online information. The widespread use of digital technology in teaching and learning has necessitated a need for students to be digitally competent in order to perform well in their studies. However, different types of digital competence may be necessary at different stages of the students’ learning journey, and some digital competences may be more essential to students’ academic performance than others. To identify the digital competences that students perceive to be most important for their learning performance, and to determine students’ perception of their level of performance in these digital competences, this study adopted the Digital Competence Framework for Citizens 2.2 (DigComp) to design a survey questionnaire and collect responses from university students. An importance-performance map analysis (IPMA) was conducted to examine the relative importance of each of the five digital competence areas and the 21 digital competences identified in DigComp, as well as how each performs in relation to the others, in predicting student learning performance. Study findings revealed that the problem solving competence area was perceived to be the most important for student learning performance, followed by information and data literacy, and communication and collaboration. The respondents did not perceive the digital content creation and safety competence areas as important for their learning performance. The study findings also found that the respondents differed in their perception of how well they were performing in these competence areas. Using DigComp as a point of departure, this study makes a novel attempt to determine the relative importance of each of the five digital competence areas and 21 digital competences identified in the framework, as well as how each one compares to the others in performance when it comes to predicting how well students will perform in their learning.
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