The importance of an appropriate student workload for successful higher education (HE) has been widely recognized in research (e.g., Thornby et al. 2023; Lachance et al., 2026). That said, there is no clear conceptual consensus on what constitutes student workload (Deon & Yasinian, 2022; Impola, 2025; Kyndt et al., 2014). In HE policy and administration contexts, as well as in curriculum work, it is often measured by the study time required to achieve learning outcomes, as in academic credit systems such as the European Credit Transfer and Accumulation System (ECTS) (European Commission, 2022; Wagenaar, 2019). Equaling study time with workload has been considered a problematic practice, and recent contributors have stressed more holistic approaches to student workload, emphasizing students' own experience and agency regarding workload (Impola, 2025c; Waage & Geirsdóttir, 2026). Finally, whereas much of the research on student workload has prioritized students’ perspectives, a fairly limited amount of research has recognized the importance of teachers' approaches to student workload determination (SWD) in their curricular and course planning (For exceptions, see Impola, 2025b; Waage & Geirsdóttir, 2026).

The presentation is based on a currently ending PhD study on higher education time practices and a new postdoctoral project on the individual and societal contributors to HE student workload in the Finnish context. While Finnish students appear to be spending less time studying than many of their European counterparts (Hauschildt et al., 2026), their experiences of workload and exhaustion have been on the rise (Parikka et al., 2025). Against this background, the current study aimed to investigate sources of student workload from both teacher and student perspectives, focusing on factors beyond time-based workload estimates.

Research questions specific to teacher and curriculum work were:

1) How are HE teachers determining student workload for their courses,

2) Which factors do they find challenging in this work, and

3) Which factors do they consider important for establishing appropriate workload estimates for their courses?

The corresponding questions concerning the student perspective were

1) What factors did students consider relevant for their workload experience in studies,

2) How did they perceive the appropriateness of the current course workload frameworks, and

3) What kind of differences were there between students regarding these views?

In the current study, these questions were addressed through a series of online surveys administered to a sample of Finnish HE teachers and students. Data was received from 208 teachers and 684 students in Spring 2024. The results, though specific to Finnish contexts, can be valuable for similar settings and studies in other European countries as well, especially given their relevance to the development and application of the ECTS system as the central time-based framework for defining and regulating workload within European HE. The mixed-methods setting that uses qualitative, open-response data and blends it with statistical analysis (See below) is a rare and potentially valuable methodological approach to a topic that has thus far relied heavily on quantitative approaches, with some happy exceptions (e.g., Kyndt et al., 2014), and could thus benefit the work of colleagues working on similar matters in other contexts, as well.

A set of online surveys was conducted among teachers and students in two Finnish higher education institutions, including one research-intensive university and one polytechnic university, in Spring 2024. Teachers’ surveys collected information on relevant professional background variables, including prior educational level, potential teacher-training status, and academic work experience. Besides, the survey included Likert-scale items concerning teachers’ SWD attitudes. In addition to quantitative measures, open-response items enabled teachers to specify their approaches, challenges, and anticipated strengths in SWD. Correspondingly, students’ surveys included relevant background variables, such as age, gender, disciplinary background, degree level, and duration of degree studies. In addition, students reported their weekly time use and workload throughout a 10-week study period, along with their learning outcomes. Open-response items enabled students to freely describe sources of their workload experience and attitudes concerning SWD. While most results relevant to the quantitative data have been reported in previous work (Impola, 2025a; 2025b), the current study utilized the qualitative, open-response data via a mixed-method approach. First, open-response data on SWD practices and students’ workload experiences were coded using a theory-driven content analysis in Lumivero’s NVivo software. The established categories themselves yielded an initial set of results to address questions about teachers’ and students' SWD and workload attitudes in general. However, the formed categories were then coded numerically and combined with the survey's quantitative measures in the same dataset in MS Excel. The data were then transferred to IBM SPSS Statistics for relevant group- and means-difference analyses (Chi-square, t-tests, and Mann-Whitney) to identify which teacher and student characteristics, as well as students’ study patterns, may be relevant to their SWD and workload attitudes. After initial bivariate tests, potential multivariate models (e.g., loglinear modelling, multivariate ANOVAs) may be used to control for confounding effects and to develop hypothetical models for future research.

Statistical analyses are still in progress when this abstract is written, yet some preliminary results may be outlined. From the teachers’ perspective, common challenges in determining appropriate workload concerned students, especially their varying skill levels and background knowledge upon entering courses. Factors related specifically to course design concentrated primarily on criticizing institutional standards and rules, binding workload estimation, and, on the other hand, colleagues’ varying and inconsistent practices in defining workload for their courses. Teachers often built their course workload estimates on some predefined guidelines, blended with their own experience and estimates, and possibly complemented by student feedback. Many comments focused on how workload frameworks could be improved to help students learn better study practices, though there were also pleas for clearer standards and collegial collaboration. Factors that had increased students’ workload were divided into education-related, personal, and time-related categories, and these were often intertwined in the respondent-specific data. As for educational factors, the most prevalent issue increasing students’ workload in the responses was the study format, especially excessive group work or independent study. Other main education-related workload factors were related to teaching (especially appropriate organization and scheduling) and course-specific factors (especially to the amount of work involved). Typical workload factors related to students’ lives included work-life balance, financial problems, family and social relations, and personal health issues. The biggest concerns related to time often involved both the perceived temporal intensity of studies and competing demands from study, work, and other life. Surprisingly, the most frequently cited factors for decreased workload were related to personal life, like being in an appropriate phase of studies, or having supportive social relations, rather than being related directly to studies. The most common criticism of workload determination practices concerned the inconsistency in workload across courses, periods, and study subjects.

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