Countering Technical Unconsciousness in Journalism Field: Applying Situated Learning Model in Data Journalism Education

The history of journalism is in many ways defined by technological change (Pavlik John, 2000). There is a long history of the fusion of computing power and news reporting. As data driven forms of journalism become more central to the profession, data journalism is now an emerging form of practice in the journalism field.

However, as Coddington (2014) has mentioned, the different forms of practice are not just “synonyms”, actually, there are “significant differences between the forms they take and their implications for changing journalistic practice as a whole” which scholars should pay attention to. In fact, given such a “quantitative turn” in journalism field, the journalistic routines might consequently be reshaped and the required core skills of a journalist might also consequently be redefined, from purely dealing with words towards dealing with all kinds of data, including the numeric. Previously, when it comes to such data, journalists just have to adopt some existed results, and thus the technical process of how the data is dealt with has largely remained in the dark. Most journalists might have known the balance of news sources and the framing of words, but they may not know how the numeric data could also be framed, biased and used just like words.

To counter such technical unconsciousness in journalism field and to better educate future journalists with more qualified data literacy, this study has firstly established a project contemplated in an interdisciplinary setting according to situated learning model. By involving people from diverse academic backgrounds from journalism, computer science, statistics to visual communication, the project aims to enhance learning effectiveness in data literacy with a new education model.

On the other hand, to evaluate whether such a situated multi-disciplinary model could bring a rise in learning effectiveness in data journalism field, this study has then implemented 30 questionnaires together with 15 semi-structured in-depth interviews to test this model. Our finding has proved that such a multi-disciplinary situated learning model has been perceived as more effective and useful than traditional learning models. Students have been significantly more aware of the technical procedures of data journalism and have gained a significant rise in their data literacy.