I found this paper to be a challenging read as it required me to learn some new concepts. I still don’t have a full understanding of the topic, but I appreciate the author’s goals and approach. If I’d been reviewing a draft of this paper, I would have requested some additional references to introduce the topic, but I’m sure the vast majority of readers won’t be in the same position.
The aim of the paper is to describe methods of developing and measuring self-efficacy skills (defined by the author as the ability to learn and problem solve on one’s own) for data management; as I mentor and work with students and new data analysts, I really appreciate the work that Wu has done to ensure students have a solid understanding of this often forgotten topic. Wu discusses how areas such as handling different data formats (JSON, XML, and so on), database technologies (NoSQL, SQL), and newer technologies like cloud services, virtual machines, and so on, are all key components that someone dealing with data should understand and be comfortable with.
In the end, Wu’s paper outlines the self-efficacy scale’s effectiveness; confirms that it’s validated through exploratory factor analysis; and demonstrates its usefulness through various analyses. Wu also covers the additional directions that the work will be going in, including investigations into why females have “higher self-efficacy than males on the depth of data management knowledge” (p. 191). I think this paper would be an interesting read for students in computer science and related areas to see what skills they may need, but I think the bigger impact would be for those who teach these students--whether in high school, college, or university--to see where they may want to shift the focus of their coursework.