In order to leverage the advantages of digitalization, many Higher Education Institutions have initiated transformation towards data-informed models, and attention has been paid to competency-based data literacy among a selective group of administrative team. However, often underrepresented in the discussion is the importance of a data-literate administration as a whole, as further data efforts are built upon seemingly non-data related work. Besides, the importance of a data-literate mindset underlying the specific knowledge and skills is also underestimated. In order to fill the gap, this paper proposes a training framework targeting both issues and aiming for a data-literate mindset in the entire higher education administration. Based on MIT’s definition of data literacy, the data-literate mindset is defined as the awareness and curiosity of the importance and implications of “the ability to read, work with, analyze, and argue with data” (Bhargava & D’Ignazio, 2015). Using Backward Design Model (Wiggins & McTighe, 2005), a list of training outcomes is identified as results, by integrating typical data efforts at HEIs with Bloom's taxonomy of cognitive development (Bloom & Krathwohl, 1956). A set of assessments is accordingly developed, through which the learning outcomes are measured, and at the same time the institutional priorities are addressed. Last, the relevance of certain active learning strategies (Yee, 2019) is analyzed, aligning with both the training outcomes and the assessment plans. The paper also discusses the implications of this framework for HEIs at different stages of data maturity.
Ji Hu, New York University Shanghai, China
Xu Chu Meng, New York University Shanghai, China
Stream: Educational policy
This paper is part of the SEACE2020 Conference Proceedings (View)
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