Abstract:

Higher education institutions increasingly rely on data to inform teaching quality enhancement. While large volumes of educational data are now available, the ways in which institutions interpret and use these data for meaningful improvement remain uneven. This paper investigates how data-informed decision-making processes are implemented at institutional level and how they influence teaching practices.

Data-informed decision-making is defined as the systematic use of quantitative and qualitative evidence (e.g., student performance data, course evaluations, and learning analytics indicators) to support educational improvement. The study adopts a qualitative multiple-case design conducted during the 2023–2024 academic year across three universities (including one Romanian institution) that have implemented institutional initiatives such as teaching analytics dashboards and evaluation systems.
Cases were selected based on two criteria: the existence of formal data-informed teaching enhancement initiatives and evidence of their use in institutional processes (e.g., quality assurance reports or professional development programs). The sample included 18 participants: 6 institutional leaders, 5 quality assurance professionals, and 7 academic staff engaged with teaching analytics.

Data were collected through semi-structured interviews (45–60 minutes) following a common protocol addressing data use, interpretation practices, and perceived impact on teaching. Interviews were recorded, transcribed, and analyzed using thematic analysis. Coding combined deductive categories (e.g., accountability, reflection, decision-making) with inductive themes emerging from the data.

Findings indicate that data-informed approaches are most effective when embedded in collaborative interpretation practices. For example, in two cases, regular departmental meetings structured around course evaluation data enabled academic staff to identify specific issues such as low student participation in online activities or unclear assessment criteria, leading to targeted adjustments in course design. Participants reported that such dialogic use of data supported reflective teaching and incremental improvement.

Conversely, in contexts where data were used primarily for reporting purposes, participants described limited impact on teaching practices. Overreliance on quantitative indicators (e.g., satisfaction scores) was perceived as reducing complex teaching processes to simplified metrics and, in some cases, discouraging pedagogical experimentation.

The study suggests that the effectiveness of data-informed decision-making depends on how data are integrated into institutional practices. Its contribution lies in demonstrating that data use becomes meaningful when linked to structured dialogue and professional interpretation, rather than treated as a purely technical or evaluative tool.

Keywords:

Data-informed decision-making, teaching quality, institutional governance, educational analytics, higher education management.