With the advent of the intelligent environment, as novice researchers, graduate students face digital challenges in their research topic selection (RTS). The purpose of this paper is to explore their cognitive processes during data-driven decision making (DDDM) in RTS, thus developing technical and instructional strategies to facilitate their research tasks.
This study developes a theoretical model that considers data-driven RTS as a second-order factor comprising both rational and experiential modes. Additionally, data literacy and visual data presentation were proposed as an antecedent and a consequence of data-driven RTS, respectively. The proposed model was examined by employing structural equation modeling based on a sample of 931 graduate students.
The results indicate that data-driven RTS is a second-order factor that positively affects the level of support of visual data presentation and that data literacy has a positive impact on DDDM in RTS. Furthermore, data literacy indirectly affects the level of support of visual data presentation.
These findings provide support for developers of knowledge discovery systems, data scientists, universities and libraries on the optimization of data visualization and data literacy instruction that conform to students’ cognitive styles to inform RTS.
This paper reveals the cognitive mechanisms underlying the effects of data literacy and data-driven RTS under rational and experiential modes on the level of support of the tabular or graphical presentations. It provides insights into the match between the visualization formats and cognitive modes.
The authors would like to acknowledge reviewers, editors, and all participants for their contribution to the improvement of this study. This study is supported by grants from the National Natural Science Foundation of China under the agreement 71774121 and 91546124.
Li, Q., Wang, P., Sun, Y., Zhang, Y. and Chen, C. (2019), "Data-driven decision making in graduate students’ research topic selection: Cognitive processes and challenging factors", Aslib Journal of Information Management, Vol. 71 No. 5, pp. 657-676. https://doi.org/10.1108/AJIM-01-2019-0019Download as .RIS
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