computer literacy

Ecological momentary assessment of digital literacy: Influence of fluid and crystallized intelligence, domain-specific knowledge, and computer usage

IntelligenceReference. Moehring, A., Schroeders, U., Leichtmann, B., & Wilhelm, O. (2016). Ecological momentary assessment of digital literacy: Influence of fluid and crystallized intelligence, domain-specific knowledge, and computer usage. Intelligence, 59, 170–180. http://dx.doi.org/10.1016/j.intell.2016.10.003

Abstract. The ability to comprehend new information is closely related to the successful acquisition of new knowledge. With the ubiquitous availability of the Internet, the procurement of information online constitutes a key aspect in education, work, and our leisure time. In order to investigate individual differences in digital literacy, test takers were presented with health-related comprehension problems with task-specific time restrictions. Instead of reading a given text, they were instructed to search the Internet for the information required to answer the questions. We investigated the relationship between this newly developed test and fluid and crystallized intelligence, while controlling for computer usage, in two studies with adults (n1 = 120) and vocational high school students (n2 = 171). Structural equation modeling was used to investigate the amount of unique variance explained by each predictor. In both studies, about 85% of the variance in the digital literacy factor could be explained by reasoning and knowledge while computer usage did not add to the variance explained. In Study 2, prior health-related knowledge was included as a predictor instead of general knowledge. While the influence of fluid intelligence remained significant, prior knowledge strongly influenced digital literacy (β=.81). Together both predictor variables explained digital literacy exhaustively. These findings are in line with the view that knowledge is a major determinant of higher-level cognition. Further implications about the influence of the restrictiveness of the testing environment are discussed.

Computer usage questionnaire: Structure, correlates, and gender differences

Computers in Human BehaviorReference. Schroeders, U., & Wilhelm, O. (2011). Computer usage questionnaire: Structure, correlates, and gender differences. Computers in Human Behavior, 27, 899–904. doi: 10.1016/j.chb.2010.11.015

Abstract.Computer usage, computer experience, computer familiarity, and computer anxiety are often discussed as constructs potentially compromising computer-based ability assessment. After presenting and discussing these constructs and associated measures we introduce a brief new questionnaire assessing computer usage. The self-report measure consists of 18 questions asking for the frequency of different computer activities and software usage. Participants were N = 976 high school students who completed the questionnaire and several covariates. Based on theoretical considerations and data driven adjustments a model with a general computer usage factor and three nested content factors (Office, Internet, and Games) is established for a subsample (n = 379) and cross-validated with the remaining sample (n = 597). Weak measurement invariance across gender groups could be established using multi-group confirmatory factor analysis. Differential relations between the questionnaire factors and self-report scales of computer usage, self-concept, and evaluation are reported separately for females and males. It is concluded that computer usage is distinct from other behavior oriented measurement approaches and that it shows a diverging, gender-specific pattern of relations with fluid and crystallized intelligence.