Bibliographic record
Abstract
This paper reports on a recent study that investigated students'; experiences of tutor support in a Canadian University online executive MBA programme. Tutor support is seen as intertwined with the interaction between tutor and student, and the tutor';s pedagogical aim to influence the student's participation in learning activities. In the study reported here, online tutor support is defined as any interaction between student and tutor that influences the student';s participation in learning activities. Despite the consensus of the importance of tutor support, current theory and research into tutor support suggests a focus on understanding the phenomenon of online tutor support as an objective reality from the perspective of the tutor and to a much lesser extent the student, the students'; experience of this phenomenon has gone largely unexamined. From the tutor';s perspective, the reviewed literature appears to focus on characterising the roles of online tutors and the online tutors'; activities and interaction strategies. From the student';s perspective, the reviewed literature appears to investigate the extent students'; participation in online learning activities is influenced by the online tutor';s interaction and the students'; perceptions of online tutor support. The context of this study, the Canadian University online executive MBA programme, provides asynchronous text–based interaction between online tutors and students and is delivered almost entirely online. Each course module has three online tutor support interaction situations. 1. Discussion Databases (sometime referred to as bulletin boards or forums): A “reflective question” database where specific questions related to the course material are posted and students reflect, respond, comment or counter-respond on topics or comments from other students. The second database is designed for “case study” discussions. 2. Marking of Assignments and Comments: Students are also assessed on their participation in the activities in each of the two databases mentioned. 3. Individual Support: Through an open “Ask the Tutor” database, through one-to-one e-mails and by telephone if necessary. To understand the under-researched area of the students'; experiences of online tutor support, in the study reported here, the phenomenographic research approach was used, aiming to understand and describe qualitative differences in students'; experiences of online tutor support. Consistent with the phenomenographic approach, students'; experiences of online tutor support were investigated through semi-structured individual interviews. The interviews were conducted by telephone with 31 students. Five distinctive categories of description of the students'; described experience of tutor support were identified: A - Uninvolved, B - Confirming, C -Elaborating, D - Encouraging, and F - Confrontational.These categories of description are logically related to each other from the least to the most dramatic awareness of online tutor support. In Categories A, B and C, the dimensions of the aspect of interaction seem to suggest online tutor support is academic in nature and has an increasing influence on the students'; awareness of tutor support from Category A through to Category C. In Categories D and E, the aspect of interaction suggests online tutor support is more affective in nature and has an increasing influence on the students'; awareness of tutor support from Category D to Category E. The discussion of this study results are compared and contrasted with the reviewed literature and a number of directions for further research and of considerations for educational practice are suggested.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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 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".