Doctoral supervision is often learned informally, shaped by personal doctoral experience rather than explicit pedagogical preparation. This course enables participants to critically examine those formative experiences, identify recurrent patterns in doctoral supervision, and develop a scholarly, system-aware understanding of effective supervisory practice.
Rather than beginning with prescriptive models of supervision, the course adopts an experience-to-theory trajectory that reflects how academic expertise develops at post-doctoral level.
Upon completion of the course, participants will be able to:
1. Analyse doctoral supervision through the lens of their own doctoral experience
2. Identify recurrent strengths and weaknesses in supervisory feedback practices
3. Relate lived supervisory experiences to established research literature
4. Distinguish individual supervisory challenges from systemic issues
5. Articulate analytically grounded principles of effective doctoral supervision
6. Contribute to scholarly dialogue on doctoral supervision and faculty development
Research methods and tools covered or trained in the course:
Data collection techniques covered or trained in the course:
Course is suitable for the following doctoral journey steps:
This course is made of the following tasks:
Task 1: Experiencing Doctoral Supervision: A Personal Analytic Account
Focus: Participants begin by analysing the feedback they themselves received during their doctoral studies.
Learning Activity: Analytic reconstruction of one or more doctoral feedback episodes, examining:
• clarity and ambiguity
• developmental intent
• cognitive and emotional impact
Purpose: To establish each participant’s individual experiential starting point.
Task 2: Comparing Doctoral Feedback Experiences Across Peers
Focus: From individual experience to shared understanding.
Learning Activity: Comparative engagement with anonymised peer reflections to identify:
• similarities and differences
• disciplinary and institutional influences
• implicit assumptions about supervision
Purpose: To recognise that many supervisory experiences are shared rather than unique.
Task 3: Recurrent Problems and Recurrent Good Practices
Focus: Pattern recognition across doctoral supervision experiences.
Learning Activity: Synthesis of:
• recurring feedback problems
• recurring effective practices
• well-intended feedback that failed to achieve its purpose
Purpose: To distinguish isolated experiences from structural patterns in supervision.
Task 4: Doctoral Supervision in the Literature
Task 5: From Recurrent Issues to Mitigation Strategies
Task 6: Supervision as a Systemic Practice
Focus: Doctoral supervision as an institutional and systemic phenomenon.
Learning Activity: Collective exploration of:
• institutional structures
• workload models
• incentive systems
• cultural norms affecting supervision
Purpose: To understand which supervisory challenges are system-produced rather than individual.
Task 7: Writing the Essentials of Effective Supervision
Focus: Scholarly synthesis.
Learning Activity: Development of a short position paper titled:“The Essentials of Effective Doctoral Supervision”. The paper integrates:
• personal doctoral experience
• peer-identified patterns
• supervisory literature
• systemic considerations
Purpose: To enable participants to formulate a structured and analytically grounded understanding of doctoral supervision as a multi-layered academic practice.
Task 8: Comparative Discussion and Best Paper Award
Focus: Collective sense-making and scholarly dialogue.
Learning Activity: Participants:
• discuss and compare submitted papers
• reflect on differing conceptualisations of effective supervision
• participate in a peer vote for the Best Paper Award
Purpose: To reinforce doctoral supervision as an intellectual and scholarly practice.
Research methods and tools covered or trained in the course:
Data collection techniques covered or trained in the course:
Course is suitable for the following doctoral journey steps:
Research methods and tools covered or trained in the course:
Data collection techniques covered or trained in the course:
Course is suitable for the following doctoral journey steps:

Select course edition
Use Brain Coins for on-demand support
Our Advanced Courses come in three formats: as a free self-study with peer support, as a fully coach supported edition or with flexible on-demand feedback whenever you need it. You can select the option that suits your learning needs best.
Brain Coins (BCs) are a currency used at the DoctorateHub to pay for on-demand services in a flexible way. You can purchase BCs at a discount and use them across the system in the combination it suits you best. BCs are valid for 12 months after the first BC has been spent.
Self-paced
Self-study with peer support. We provide the structure, you focus on your thesis research.
Ideal for those who prefer to advance on their own schedule or who are on a budget.
Duration: “As long as it takes you“
Learning at your own pace, in your own time.
No tuition fee
Dedicated coach
Fully supported by a DoctorateHub coach dedicated to the course topic.
Ideal for those who prefer tight guidance to progress through their research and thesis development.
Start “Whenever you want“
Courses are 8 or 12 weeks long.
Detailed information is available in the course detail view.
Tuition fee
1,950.00€
Club Support
Join one of our Clubs to get started.
Ideal for those who have unpredictable agendas, or that just need some orientation.
Duration: “As long as it takes you“
Learn at your own pace in a peer environment, with optional Pro and Pro light support subscriptions.
Explore the Clubs
Schedule video-orientation call
Good for: One video-orientation call to discuss a task or assignment and to understand how to take your research further.
1 BC
Get written orientation feedback
Good for: One-time written feedback on a course task assignment of around 2.500 words in length.
1 BC
Get one Research Mentoring month
Good for: One Research Mentoring month with written feedback and video-orientation calls.
12 BC
Jim W.
Thesis stage: Viva & Graduation
I would highly recommend working with DoctorateHub. In the past, I have benefitted from the webinars that DoctorateHub organises periodically. When the time came for me to prepare for the final submission of my thesis as well as my viva voce, I reached out to the team at DoctorateHub to see about a more formal engagement that might allow me to prepare for my upcoming viva. Over the course of eight weeks, I had the opportunity to work with a viva coach who challenged my research through a close examination of my write-up and in a series of mock viva engagements. All of this was done in a tough but collaborative way with my viva coach. As a result, I feel more prepared and grounded going into my viva. My thesis write-up is better now too. For me, the timing was perfect as I was able to go through the entire eight weeks before the final submission date of my thesis write-up. Overall, my viva coach helped me to see things through the eyes of the examiner and what they would likely question in the document and, importantly, in the context of the viva examination. I found the programme well worth the investment of time, effort, and treasure.
Noeleen
Thesis stage: Research execution: Data collection and analysis
The course has been invaluable in:
Despite the difficulty in balancing work, completing the Thesis and the course, at first the 8 weeks could appear to others as taking time away from Thesis development, but actually, it enhanced mine.
I would highly recommend it. Thank you. I do wish you the best with taking the course forward.
Viji
Thesis stage: Research execution: Data collection and analysis
It was a good 8 weeks, though I would have loved to have had this module during my DBA programs theoretical stage ...or even during one of the DBA DDP’s (Doctoral Development Plan).
A key learnings for me was to keep the focus on the problem, and not get diverted towards a solution based approach.
The mirroring technique and the table shared in the module will be an useful tool for candidates to compare and ensure that relevant points have been reflected on and addressed in the respective sections of the thesis.
I see myself using the table for communicating how triangulation has been supportive both at the data collection and analysis stage of the thesis.
I would like to take this opportunity to thank you and the team at the DoctorateHub for your time and patience and appreciate your feedback and comments that have ensured further clarity in my thesis. Wishing everyone the best in their future endeavours.
Bongi
Thesis stage: Research execution: Data collection and analysis
Thank you for your invaluable input on my thesis journey.
I acknowledge the sacrifice in time and effort you made to help me grow as I learn to adopt an academic perspective of my work-based problem.
It was rigorous but worth it.
May your journeys yield great success.
Douglas
Thesis stage: Research execution: Data collection and analysis
The learning on this course has been phenomenal as it has brought to view a number of blind spots in research, in fact they are not even blind spots, they were complete unknowns. The main one relates to what is broken in any given situation, if there is nothing broken then it is unlikely that it worth researching, at least for a DBA. Finding the broken pieces, added to the rigorous challenges from yourself, have highlighted my shift in thinking about problems in general, particularly digging deeper into the problem by asking why of the problem. I moved out of a comfort zone around what I have interpreted as action research. The why RQ allows for the superficial or surface layer problem to be problematised with relevant stakeholders, who in turn, (and as a collective) can penetrate to the deeper domains with a view to establishing the actual causes of such problems. In other words, the problem can be properly 'ventilated and saturated' which I have termed as key to staying on the problem side instead of jumping into the solution without properly understanding the problem. All of which is key learning for me and for participants involved in the exercises on this course.
Once I shifted away from strategy formation (rejected by supervisor) and shifted to observing what was going on in practice, and ended up on a lack of strategic thinking and had to source the literature for what was a new topic for me, I battled timewise to shift gears from strategy formation and strategic thinking literature and would have preferred more time to get immersed in the new literature to get the 'objects' under control through reading. I felt that I rushed it and it became messy. The strategic thinking literature that I have been immersing myself in is highly theoretical and conceptual which worries me as there is very little in terms of experiential. This is likewise with already established RQ's, there are none that I have found, and also, the literature refers to hypothesis testing which petrifies me as there is a fairly big chunk that is quants orientated.
I won't go as far as saying that the above didn't work out well when it actually has, I have learned about how the mirroring and juggling works in practice.
In all, I have learned more about problem identification, RQ's and framing than on all of the earlier coursework in this doctoral program - I am very grateful for your time and efforts, Sir. Much appreciated!
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