Background Data commentary, in-text comments on the visual presentation of data, is acknowledged as a central aspect of academic writing in many engineering disciplines. At the same time, it is a feature that has been shown to be challenging for students. One of the genres in which data commentary plays a significant role in many engineering disciplines is the master’s thesis. Comparatively little research has been done on the process of master thesis supervision, and combining the study of data commentary and master’s thesis supervision is therefore particularly interesting.
Purpose This study explores the challenges of data commentary writing through interviews with master’s students and thesis supervisors of chemical engineering.
Sample and method Master’s students at a Swedish university were invited to participate in a workshop about the writing of data commentary. Nine master’s students and five supervisors were interviewed about what is difficult and important about writing data commentaries in their discipline as well as about decisions made in data commentaries written by the students. The interviews were divided into a semi-structured and a discourse-based part.
Results Our results indicate that data commentary comes with a variety of challenges. Among the most difficult and important aspects are selection of content and clarity. The study also indicates a close connection between data commentary and disciplinary learning in chemical engineering, suggesting that highlighting data commentary in the teaching of master’s thesis writing will be time well spent.
Conclusions In order to make the teaching and learning of data commentary effective in the context investigated, we propose that important measures are: the development of a shared metalanguage among students and supervisors, a genre approach, and collaboration between engineering and communication faculty. 相似文献
Most researchers acknowledge that virtually all structural equation models (SEMs) are approximations due to violating distributional assumptions and structural misspecifications. There is a large literature on the unmet distributional assumptions, but much less on structural misspecifications. In this paper, we examine the robustness to structural misspecification of the model implied instrumental variable, two-stage least square (MIIV-2SLS) estimator of SEMs. We introduce two types of robustness: robust-unchanged and robust-consistent. We develop new robustness analytic conditions for MIIV-2SLS and illustrate these with hypothetical models, simulated data, and an empirical example. Our conditions enable a researcher to know whether, for example, a structural misspecification in the latent variable model influences the MIIV-2SLS estimator for measurement model equations and vice versa. Similarly, we establish robustness conditions for correlated errors. The new robustness conditions provide guidance on the types of structural misspecifications that affect parameter estimates and they assist in diagnosing the source of detected problems with MIIVs. 相似文献