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Data practices in quality evaluation and assessment: Two universities at a glance
Author(s) -
Raffaghelli Juliana E.,
Grion Valentina,
Rossi Marina
Publication year - 2023
Publication title -
higher education quarterly
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.976
H-Index - 42
eISSN - 1468-2273
pISSN - 0951-5224
DOI - 10.1111/hequ.12361
Subject(s) - context (archaeology) , corporate governance , multivariate analysis of variance , data collection , higher education , data quality , quality (philosophy) , sample (material) , survey data collection , qualitative property , descriptive statistics , sociology , public relations , political science , computer science , business , social science , marketing , statistics , geography , chemistry , mathematics , archaeology , finance , chromatography , machine learning , metric (unit) , philosophy , epistemology , law
As the debate on data in the society and in education grows the attention on data‐trace as ‘primary material’ for governance, educational quality and innovation falls under the spotlights. In this context, HEIs have been put under pressure to adopt quantitative metrics and evaluation approaches enhancing the massive collection of trace data. Nonetheless, each university overall, and the academics specifically, might respond differently to this context of innovation. The present article aims to explore data practices in two higher education institutions. Two relevant areas for the imaginaries related to data and quantification were explored: (a) evaluation of quality in teaching and learning; (b) data to support assessment. The study is based on a survey distributed to the whole university teaching staff of two institutions. Descriptive and inferential statistics comparing multivariate sample means (MANOVA) were applied to 601 responses collected. The results indicated the prevalence of institutionally consolidated data practices relative to quality teaching evaluation, with fragmentation and isolation in some emerging data practices connected to decision‐making and teaching and learning. Moreover, each of the universities revealed distinct institutional profiles which could be put in connection with the organisational culture. The results are discussed in light of the potential strategies at the institutional level, particularly regarding faculty development as means to build a visible, contextualised data culture.