Using Ethnographic Methods to Explore How International Business Students Approach Their Academic Assignments and Their Experiences of the Spaces They Use for Studying
Bibliographic record
Abstract
Abstract Objective – Understanding students’ approaches to studying and their experiences of library spaces and other learning spaces are central to developing library spaces, policies, resources and support services that fit with and meet students’ evolving needs. The aim of the research was to explore how international students approach academic assignments and how they experience the spaces they use for studying to determine what constituted enablers or barriers to study. The paper focuses on how the two ethnographic methods of retrospective interviewing and cognitive mapping produce rich qualitative data that puts the students’ lived experience at the centre and allows us a better understanding of where study practices and study spaces fit into their lives. Methods – The study used a qualitative ethnographic approach for data collection which took place in April 2016. We used two innovative interview activities, the retrospective process interview and a cognitive mapping activity, to elicit student practices in relation to how they approach an assignment and which spaces they use for study. We conducted eight interviews with international students in the Business School, produced interview notes with transcribed excerpts, and developed a themed coding frame. Results – The retrospective process interview offered a way of gathering detailed information about the resources students draw on when working on academic assignments, including library provided resources and personal social networks. The cognitive mapping activity enabled us to develop a better understanding of where students go to study and what they find enabling or disruptive about different types of spaces. The combination of the two methods gave students the opportunity to discuss how their study practices changed over time and provided insight into their student journeys, both in how their requirements for and knowledge of spaces, and their use of resources, were evolving. Conclusion – The study shows how ethnographic methods can be used to develop a greater understanding of study practices inside and outside library spaces, how students use and feel about library spaces, and where the library fits into the students’ lives and journey. This can be beneficial for universities and other institutions, and their stakeholders, looking to make significant changes to library buildings and/or campus environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".