Proposing a Model for Analyzing Effects of Media Literacy Components on Improvement of Social Skills and Moderating Impact of Cultural Capital Based on Structural Equations Model Approach

Shahsavari, Ali; Dastjerdi, Reza Ebrahimzadeh

doi:10.5901/mjss.2016.v7n3s3p376

Cited by 1 publication

(1 citation statement)

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“…The scores obtained by the pre-service teachers from the factors in the MLLDS are ranked from high to low as having knowledge (x̄=4.06), being able to judge-see implicit messages (x̄=3.68), and being able to analyze-react (x̄=3.86). In the study performed by Shahsavari and Dastjerdi (2016), a relationship between the skills of individuals to judge, criticize and analyze the implicit messages of the media and social skills was revealed. The fact that the level in the related factors of ML is above the average can also be associated with the social skills of pre-service teachers.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

The Role of Media Literacy in Online Information Searching Strategies

Tatar

Şahin

Doğan

2022

DER

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Along with the spread of Web 2.0 technologies, individuals' habits such as learning, socializing, and getting information have changed rapidly. A lot of information, the accuracy of which cannot be trusted, is available in the web, and it becomes difficult to choose useful, relevant, and accurate information. This pollution is also present in the media. The abilities to choose messages in the media, to look at these messages critically, and to produce your own messages are considered among the 21st-century skills. These reasons bring media literacy (ML) and online information searching strategies (OISS) to the agenda. The processes of ML and OISS have interrelated features. Therefore, it is important and necessary to examine these concepts together. Based on this necessity, the aim of the study was to determine the role of ML in OISS. To this end, the data were collected from 1809 pre-service teachers using the OISS inventory and the ML level determination scale. Data were analyzed by descriptive statistics, MANOVA, and multiple regression analysis. According to the results, pre-service teachers' ML and OISS levels were above the moderate level. ML and OISS vary significantly according to the type of websites. In conclusion, ML was revealed as a predictor variable that could explain OISS at a rate of 33.2%.

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Section: Conclusion and Discussionmentioning

confidence: 99%