RT info:eu-repo/semantics/article
T1 Disentangling doctoral well-being support in progress-focused workshops: Combining qualitative and quantitative data in single-case learning analytics
A1 Dimitriadis, Yannis
A1 Prieto Santos, Luis Pablo
A1 Jovanovic, Jelena
A1 Odriozola González, Paula
A1 Rodríguez Triana, María Jesús
A1 Díaz Chavarría, Henry Benjamín
A1 Dimitriadis, Yannis
K1 Doctoral education
K1 Learning analytics
K1 Well-being
K1 Idiographic methods
K1 Mixed methods
K1 58 Pedagogía
AB Doctoral education (DE) suffers from widespread well-being issues. Recent evidence from short-term trainingactions shows potential to address them, but also large variability. Further, DE practitioners face challenges inunderstanding whether (and for whom) such interventions work, due to small sample sizes, short interventiondurations, and the inherent uniqueness of each dissertation. This methodological paper proposes a novel,practice-oriented, and idiographic approach to such understanding, supported by learning analytics of quanti-tative and qualitative data. To illustrate this approach, we apply it to two datasets from six authentic doctoralworkshops (N = 105 doctoral students), showcasing how it can provide individualized practice-oriented insightsto doctoral students and help trainers better understand their interventions, while coping with typical limitationsof data from doctoral training. These findings exemplify how the triangulation of simple, interpretable analyticsmodels of mixed longitudinal data can improve students, practitioners’, and researchers’ understanding, re-design, and personalization of such training actions.Educational relevance and implications statement: Collecting data about the context and process of a doctoraltraining action can help practitioners and students understand who benefits more (or less) from such training.The individualized analysis of such data, obtained with even very simple technologies, can also help studentsunderstand their processes and contexts, to better address progress and well-being issues. The use of student-authored short narratives (e.g., diaries), along with longitudinal quantitative data, plays an important role inthese personalized analyses, and the promise of automated qualitative coding makes this approach increasinglyfeasible
PB Elsevier
SN 1041-6080
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/76150
UL https://uvadoc.uva.es/handle/10324/76150
LA eng
NO Learning and Individual Differences, 2025, vol. 121, p. 102705
NO Producción Científica
DS UVaDOC
RD 05-jul-2025