Reviewed By: Reviewed by Natalie Enright, Chelsea Maradiaga, Bracha Schefres and Angela Yam
Article synopsis and core research question
In the research article “Examining Factors Predicting Students’ Digital Competence,” Hatlevik, Guðmundsdóttir, and Loi are interested in determining how users process information and to what extent are technological skills acquired. This paper addresses the levels of familiarity and understanding of information and communication technology (ICT) as assessed among Norwegian ninth grade students. Digital competence is defined as “the skills, knowledge, and attitudes that make learners able to use digital media for participation, work, and problem solving, independently and in collaboration with others in a critical, responsible, and creative manner.” (Hatlevik, Guðmundsdóttir, & Loi, 2015, p. 124) Due to the increasing variety of technologies in the daily lives of users around the world, this 2015 study holds an important role in analyzing digital literacy and how people acquire the relevant skillsets.
Three factors are identified as shaping the diverse technological experiences of students following the research results: digital competence, mastery orientation, and family background. Mastery orientation refers to how one’s attitude and actions approach learning or performance-related activities, while family background covers cultural, social, and economic demographic indicators. Eight hypotheses were formulated to frame the relationships between cultural capital, language at home, strategic use of information, academic achievements, and predicted digital competence.
Global efforts toward advocating and promoting digital competence aim to not only make technological tools more accessible for users to meet information needs, but they also encourage the lifelong development of online equity, self-representation, and exchange of information.
Methods used to answer the research question.
For this study a cross-sectional survey was used to analyze the data collected from a survey given to one class of 9th graders chosen by each of the 150 schools contacted to participate. The study was conducted in 2013. Potential participating schools were contacted using mail, e-mail, and by phone. “The final sample for this study was made of 852 students from 38 participating schools. The response rate at the school level was 25.3%” (Hatlevik et al., 2015, p. 127). There was no replacement for schools who did not participate.
The questions for the survey were comprised of themes based on the learning objectives for the completion of 10th grade. The themes included: “five questions about digital responsibility, three questions about digital communication, eight questions about how to retrieve and handle digital information, and ten questions about how to create and process digital information” (Hatlevik et al., 2015, p. 127).
Students were then asked how many books they had at home. The data collected from this part of a self-report questionnaire was used to establish cultural capital. Other answers to the self-report questionnaire were used to determine language integration and mastery orientation for each student. Three questions were asked to measure mastery orientation using Likert-type agree-disagree scale, ranging from Strongly agree to Strongly disagree. Results showed a score of 0.87 suggesting a high level of consistency. “The scale of marks/grades are 1(the lowest mark), 2, 3,4, 5 and 6 (the highest mark)” (Hatlevik et al., 2015, p. 127).
The comparative fit index (CFI) and the Tucker-Lewis fit index (TLI) are the two indices that were used to evaluate the fit of the model with the hypotheses. In order to estimate the misspecification of the model, the root mean square error of approximation (RMSEA) was calculated. (Hatlevik et al., 2015)
Finally, www.gsi.udir.no, a national database, indicated that on an average there were 2.19 (sd 0.71) students at each computer in the schools that participated in the study” (Hatlevik et al., 2015, p. 128).
Findings and conclusions
The study found that 2.5% of students had no books, 10.2% had 1-10 books, 15.9% had 11-50 books, 15.4% had 51-100 books, 22.2% had 101-250 books, 17% had 251-500 books and 16.8% had more than 500 books. For languages spoken at home, studies found that 83.3% of students spoke Norwegian and 16.7% spoke another language other than Norwegian or combined with Norwegian.
The results from the theoretical model were statistically significant, however there were two questions which measured digital competence that had to be removed due to the factor loading falling below 0.20 and a new analysis was run. It showed acceptable results with values of CFI = 0.947, TLI = 0.943, and the RMSEA = 0.024 [LO 90 = 0.020 and HI 90 = 0.027].
An analysis of the theoretical model that was developed with eight hypotheses shows that all hypotheses are supported. From the structural equation modelling (SEM) approach, the study found that “students’ cultural capital and language integration at home is positively related“ (Hatlevik et al., 2015, p. 132) which has a positive forecast to digital competence. Looking at both of the student’s mastery orientation and previous achievements also provides positive outlooks to digital competence.
Limitations of this study includes having a response rate of 25.3% at the school level, schools and students with positive interactions towards technology could be overrepresented, and self-selection bias. However, there were variations between students’ digital competence, therefore it seems that there is a diverse sample of student participation.
This study’s findings shows diversity amongst the students regarding digital competence which is also supported by many national tests involving reading, mathematics, science and information literacy. It is up to the school leaders and teachers to identify the diversity in their students’ digital competence and take action to improve their student’s digital competence. They have to also take note that a student’s family background, previous achievements in school, and mastery orientation are related to their digital competence. Teachers would need to be aware of these factors when they are planning and conducting teaching, and helping students to develop adaptive methods for information use. “Digital skills and competence requires hard work and persistence as does developing other key competences such as reading, writing, or doing calculations.” (Hatlevik et al., 2015, p. 133).
Unanswered questions and an attempt to answer them
The design of the test seems to evaluate a 9th grade students’ digital competence without the intervention of classroom instruction since students are tested on end of the year 10th grade material. If the test comprised of questions that were from the 9th grade curriculum or the test comprised of students who had just finished 10th grade, then the study would evaluate classroom instruction. However, to completely rule out the significance of classroom instruction on digital competence, a control group would need to be studied comprising of students who had just completed 10th grade and had been asked identical questions.
A second question that arises pertains to the use of quantity of books in the home as a measure of cultural capital. Understandably, the authors of the study wished to align their research that used books “in several other international studies” (Hatlevik et al., 2015, p. 127), it is not considered to be sensitive or private information, and books can be counted fairly easily. However, in the 21st century this gauge may becoming less accurate as more people are moving away from print materials and towards digital books. It was not clear if the study included digital content as well. Perhaps a future study should address this issue and include digital as well as print material.
Finally, the study indicates that there is a positive correlation between cultural capital and language integration, factors that can be used as a proxy for student’s family background, as well as a student’s mastery in orientation is a positive prediction of digital competence. As such, the study recommends “more information about how teachers can help students to develop adaptive strategies for information use” (Hatlevik et al., 2015, p. 132). This would suggest that the authors support culturally responsive teaching practices to mitigate factors that contribute to poor digital competence in students. While this may be true, it is important to note that the response rate was 25.3% at the school level. The study did not indicate if some of the schools were culturally diverse, lower academic performing, or were lower socioeconomic schools suggesting a possible poor test sample. The authors addressed this issue in the article noting that “nevertheless, the results from the study give insight into factors predicting digital competence” (Hatlevik et al., 2015, p. 133). Even so, the low response rate begs the question what results from a larger sample that includes all aspects of diversity would look like. It would be worthwhile to address these issues in further studies.
Hatlevik, O. E., Guðmundsdóttir, G. B., Loi, M. (2015). Examining factors predicting
students’ digital competence. Journal of Information Technology Education: Research, 14, 123-137. Retrieved from http://www.jite.org/documents/Vol14/JITEV14ResearchP123-137Hatlevik0873.pdf