Developing and Validating a Digital Competence Self-assessment Scale for University Students

Session Information

ERG SES C 01, ICT and Education

Paper Session

Time:
2015-09-07
11:00-12:30
Room:
397. [Main]
Chair:
Ildiko Hrubos

Contribution

Digital technologies are massively being used in schools and society, and this leads a transforming way of learning and studying not only inside but outside university. Electronic tools and mobile devices, social networking environments and online learning are increasingly becoming popular among university students. Therefore digital learning is becoming a central part of student’s daily life as a form of informal learning, young people are developing significant competences that correspond to important cognitive processes and new learning styles. While emphasis on the value and integration of Digital Literacy has gathered strength in K-12(Szabo, Montgomerie, & Davies, 2002),higher education institutions are confronted with the realization that universally adopted normative testing on admissions-critical academic instruments, have not adopted nor codified to measure Digital Competence for university students. The aim of this study is to develop and validate an assessment scale, to identify the predicting factors for students’ digital competence, and to explore the status of university students’ digital competence.

Digital Competence has been acknowledged as one of the 8 key competences for Lifelong Learning by the European Union(Commission, 2006). Digital Competences are related to many concepts: Digital Literacy, Media Literacy, ICT Literacy, Information literacy, Internet Literacy which are used to identify and analyze students’ ability to achieve with digital technology (Hatlevik & Christophersen, 2013). In this study, a digital competence framework was adapted and developed which emphasizes the co-existence of dimensions characterized both on the technological, cognitive and ethical levels, and also their integration(Calvani, Cartelli, Fini, & Ranieri, 2009). Technological dimension refers to being able to explore and face problems and new technological contexts in a flexible way, including three sub dimensions: a) Visual literacy, b) Trouble shooting, c) understanding technical concepts ; The cognitive dimension refers to being able to read, select, interpret and evaluate data and information taking into account their pertinence and reliability, including three sub dimensions: a) organizing and connecting textual and visual data, b) Organizing structured data, c) information research; The ethical dimension refers to being able to interact with other individuals constructively and with sense of responsibility using available technologies, which also including three sub dimensions: a) staying safe online, b) respect on the net, and c) social and emotional responsibility.

Method

A pilot study had been conducted where a well-structured online survey was designed and administered according to the framework mentioned above, The online survey was randomly sent to students from two universities in Beijing. All 34 items were scored on a 5-point Likert scale. A total of 230 responses was collected and included 65 incomplete responses. 150 valid cases had been analyzed, amongst the valid respondents, 98 were females and 52 were males. The overall alpha coefficient for the digital competence scale is 0.93, and all sub scales’ alpha coefficient greater than 0.74, the results show that the scales of the designed questionnaire are good, Then the CFA(confirmatory factor analysis) was carried out with AMOS 16.0 to validate the Digital Competence Scales, The CFA results show a good data fit (Overall Scale: CMIN/DF=1.371, GFI=0.965, AGFI=0.917, CFI=0.990, RMAEA=0.051 ). In order to examine the difference between female and male students on digital competence, the independent-sample t-test is conducted. The independent-sample t-test was also conducted to compare the students major in Arts and Science. One way ANOVA is conducted to test differences among students with different PC used years. The analysis indicates that there are significant differences among these groups on some sub-scales.

Expected Outcomes

The study confirmed that the three dimensional Digital Competence model was satisfactory. The independent sample t-test shows there are no significant differences between female and male students at VL ( Visual literacy), UTC (understanding technical concepts), OSD ( Organizing structured data), IR ( information research), staying safe online (SSO), RN (respect on the net), SE (social and emotional responsibility). But there are significant differences between female and male students at two sub-scales: TS (Trouble shooting, P <.05), OCTV (organizing and connecting textual and visual data, P < .05). The results also show there is no significant difference between students major in Arts and Science at all sub scales of Digital Competence. The One way ANOVA analysis indicates that there are significant differences among PC used years group at UTC, VL, TS, OSD, IR and no significant differences at SSO, RN, SE and almost no significant differences at OCTV. The Post Hoc Tests by using Scheffe test show students who have PC used years from 13 to 16 years (group 4) and 9 to 12 years (group3) have significant difference with those who have PC used years from 5 to 8 years (group 2) and 0 to 4 years ( group 1) at UTC,VL,TS,OCTV, OSD, IR. The students have longer PC used years (more than 8 years) have better digital competences than those who have less years( less than 5 years) at some sub-scales. In terms of SSO, RN and SE, all students with different PC used years appear to be no significant difference.

References

Reference List Calvani, A., Cartelli, A., Fini, A., & Ranieri, M. (2009). Models and instruments for assessing digital competence at school. Journal of e-Learning and Knowledge Society-English Version, 4(3). Commission, European. (2006). Recommendation of the European Parliament and of the Council of 18 December 2006 on key competences for lifelong learning. Official Journal of the European Union, 394, 10-18. Hatlevik, Ove Edvard, & Christophersen, Knut-Andreas. (2013). Digital competence at the beginning of upper secondary school: Identifying factors explaining digital inclusion. Computers & Education, 63(0), 240-247. doi: http://dx.doi.org/10.1016/j.compedu.2012.11.015 Szabo, Michael, Montgomerie, T Craig, & Davies, JoAnne. (2002). Assessing information and communication technology literacy of education undergraduates: Instrument development. Paper presented at the World Conference on Educational Multimedia, Hypermedia and Telecommunications. Eom, S (2008) Strategies for enhancing the learning outcomes for web-based distance education students further investigation of the relationships between motivation and learning outcomes. AIS SIG-ED IAIM 2008 Conference.

Author Information

Tao He (presenting / submitting)
Vrije Universiteit Brussel (Free University of Brussels)
Educational Sciences
Beijing
Vrije Universiteit Brussel (Free University of Brussels), Belgium
Vrije Universiteit Brussel
Department of Educational Sciences
Brussels
Vrije Universiteit Brussel (Free University of Brussels), Belgium
Vrije Universiteit Brussel (Free University of Brussels), Belgium

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