Students' perspectives on impacts of the PhD process : the PhD as the acquisition of intellectual virtues
Bibliographic record
Abstract
idea of PhD students as knowledge workers or “super-technicians” (Pearson et al., 2009, p. 100), its predominantly descriptive nature also limits its potential in providing deeper insights into students’ experiences during the doctoral education undertaking. This highlights the need for research that conceptualises the different and diverse elements of the doctoral education experience as a complex, interrelated range of activities and further, the need for a framework that recognises how all these elements and processes contribute to enabling the production of a skilled, resourceful and competent PhD graduate. 3.3 Theorising the PhD experience There is also a paucity of research that theorises students’ experiences of the PhD process, although more recent research is contributing to this gap by providing accounts of the doctoral experience that move beyond descriptive accounts. Haggis’s (2002) research with eight PhD students in the UK, for example, is amongst the small number of articles on students’ experiences of the PhD to use a theoretical lens to theorise what students described. Haggis (2002) problematises current approaches and understandings of adult learning that promote a notion of learning as linear, rote and methodological. Haggis (2002) draws on the learning theories of Brockbank and McGill (1998) and Taylor (1987) to show how students’ experiences during the PhD are complex, implicit, contextual and multifactorial. She notes that this complexity means that students’ learning is therefore experienced in a diversity of ways through a diversity of experiences. For example, “frustration . . . blocked some, irritated others and boosted still others. Relationships were central in some cases, but incidental in others” (Haggis, 2002, p. 216). Hopwood (Hopwood, in press-b) draws on data from 22 interviews and three focus groups held with 33 doctoral students from seven institutions to examine students’ experiences of teaching, student journal editing and mentoring during candidature. Hopwood (Hopwood, in press-b) frames his work within a socio-cultural perspective Mowbray Students’ perspectives on impacts of the PhD process 51 that recognises the agency of students in promoting their learning to examine some of the human, environmental and provisional factors that contribute to the students’ formal and informal, personal and professional learning and development in these areas during candidature. Accounts of doctoral students’ experiences during candidature such as those of Haggis (2002), and Hopwood (Hopwood, in press-a; Hopwood, in press-b) illuminate some of the complexities of students’ experiences during candidature that influence their learning and development. In recognising these complexities, their research approaches align with theories of knowledge that recognise learning and knowledge as multi-faceted, multi-factorial and multi-contextual. For example, Aristotle’s notion of the different, interrelated branches of knowledge (discussed in (Aristotle, 2002) describes how different types of experiences enable different, yet interrelated types of knowledge. The learning theories of Bandura (1977) and Vygotsky (1978) have highlighted the fundamental role social interactions play in promoting learning while more recently, McWilliam and Taylor’s (2001), Polanyi’s (1958, 1998, 1967) and Stehr’s (2005) notions of lived and tacit knowledge and contemporary theories of knowledge and learning recognise and uphold the scope and significance of emotions, attitudes, motivations and social processes, content, interactions and relations in the learning processes of individuals (Brockbank & McGill, 1998; Illeris, 2007, 2009). Thus, research that theorises students’ experiences of the PhD, such as Haggis’s (2002) and Hopwood’s (in-press-a; in-press-b), and Parry’s (2007), provides another perspective to understand students’ experiences during the PhD. In using and theorising students’ experiences, such research also shows that people matter in accounts of learning and that what individuals carry with them can add value. Further, it demonstrates how, theoretically, framing students’ experiences of the PhD can extend our understandings of the learning processes that enable different types of knowledge to be produced (Howells & Roberts, 2000; Polanyi, 1958, 1998; Stehr, 2005). In doing so, these studies indicate the need for more research that theorises students’ experiences during candidature, to illuminate other impacts of the PhD process. Mowbray Students’ perspectives on impacts of the PhD process
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".