Progress-Oriented Workshops for Doctoral Well-being: Evidence From a Two-Country Design-Based Research

Abstract

Aim/Purpose: This paper explores an intervention approach (in the form of workshops) focusing on doctoral progress, to address the problems of low emotional well-being experienced by many doctoral candidates. Background: Doctoral education suffers from two severe overlapping problems: high dropout rates and widespread low emotional well-being (e.g., depression or anxiety symptoms). Yet, there are few interventional approaches specifically designed to address them in the doctoral student population. Among structural, psychosocial, and demographic factors influencing these problems, the self-perception of progress has emerged recently as a crucial motivational factor in doctoral persistence. Methodology: This paper reports on an iterative design-based research study of workshop interventions to foster such perception of progress in doctoral students’ everyday practice. We gathered mixed data over four iterations, with a total of 82 doctoral students from multiple disciplines in Spain and Estonia. Contribution: An approach to preventive interventions that combines research-backed education about mental health and productivity, peer sharing and discussion of experiences, and indicators of progress, as well as self-tracking, analysis, and reflection upon everyday evidence of their own progress. The paper provides initial evidence of the effectiveness of the proposed interventions, across two institutions in two different countries. Further, our data confirms emergent research on the relationships among progress, emotional well-being, and dropout ideation in two new contexts. Finally, the paper also distills design knowledge about doctoral interventions that focus on progress, relevant for doctoral trainers, institutions, and researchers. Findings: Our quantitative and qualitative results confirm previous findings on the relationships among progress, burnout, and dropout ideation. Our iterative evaluation of the workshops also revealed a large positive effect in students’ positive psychological capital after the workshops (Cohen’s d=0.83). Our quantitative and qualitative analyses also started teasing out individual factors in the variance of these benefits. Recommendations for Practitioners: Intervention design guidelines for doctoral trainers include: focusing on actionable productivity and mental health practices, the use of activities targeting perception biases and taboos, or the use of active practices and real (anonymous) data from the participants to make progress visible and encourage reflection. Recommendation for Researchers: The construct of progress, its components, and its relationships with both emotional well-being and doctoral dropout need to be more deeply studied, using multiple methods of data collection, especially from more frequent, ecologically valid data sources (e.g., diaries). Impact on Society: The proposed interventions (and focusing on doctoral progress more generally) hold promise to address the current emotional well-being and dropout challenges facing hundreds of thousands of doctoral students worldwide, ultimately helping increase the research and innovation potential of society as a whole. Future Research: More rigorous evaluative studies of the proposed approach need to be conducted, with larger samples and in other countries/contexts. Aside from the proposed one-shot training events, complementary longitudinal interventions focusing on supporting everyday progress and reflection throughout the doctoral process should be trialed.

Treatment for graduate students: Blunting the emotional toll of postgraduate education.

“…This study evaluates the efficacy of a novel mental health intervention adapted specifically for this population: ML-GS. This efficacy trial of a peer-led intervention for graduate students is part of a new wave of studies examining mental health options for this vulnerable population (Bernstein et al, 2021, 2022; Prieto et al, 2022). Additionally, this study tests an adaptation of an existing mental health intervention (Votta & Deldin, 2022).…”

A holistic model of resources to enhance the doctoral student's well-being

“…This program nurtures the personal growth, intrinsic motivation and professional socialization skills of the scholars, which aids in the timely completion of the Ph.D. program (Kumar and Coe, 2017). The structure of the mentoring program serves to address the challenges of doctoral education, including academic article and thesis writing, socio-emotional support (at the individual level), work–family balance (at the microsystem level), skill development, building connections with professionals in their fields, engagement with culturally diverse nation (at the macrosystem level) and future career path (Cleary et al ., 2023; Prieto et al ., 2022). Indeed, mentoring programs help to reduce the feeling of isolation by allowing students to interact with experienced instructors outside of the classroom (Patterson and Davis, 2015; Rasheem et al ., 2018).…”

Single-case learning analytics: Feasibility of a human-centred analytics approach to support doctoral education

“…Perceiving that one progresses in the doctoral dissertation has been recently found as a critical motivational factor affecting both emotional well-being and dropouts of doctoral studies [Devos et al, 2017, Milicev et al, 2021. Our research team has started piloting training interventions [Prieto et al, 2022] and technological support to support doctoral students in perceiving and understanding their progress. Our initial SCLA4DE prototype technology took the form of a self-tracking and diary platform.…”

“…The use of productivity techniques (cf. [Prieto et al, 2022] to understand their importance for doctoral progress) and the feeling of time scarcity (LackingTime code) already hint at potentially beneficial interventions, like doctoral workshops on time management and productivity. Once detected as important, these variables can also be the target of direct measurement by the student in later iterations (see step #5 in Figure 1).…”