Experiences of female students completing a full-time Aboriginal program by computer-mediated communication
Bibliographic record
Abstract
This naturalistic inquiry examines the experiences of female Aboriginal students who \nwere completing a full-time college program by computer-mediated communication (CMC). \nThe college program was designed for the preparation of Aboriginal Teacher Assistants. The \nbackground literature examines the work of previous scholars on listening to women?s \nvoices, connected learning, appropriate technology for Aboriginal learners and the use of \ncomputer-mediated communication in meeting learners? needs. Eight students volunteered to \nparticipate in the study. Two were lost through attrition. \nData were collected through two sets of face-to-face interviews, one set of \ntelephone interviews, field notes and observation of online messages posted by the \nparticipants. Participant profiles were created from the participants? own words, and the data \nwere analysed for emergent themes. Three themes were identified. These included \ndemographics, prior educational experiences and learning preferences. \nThe analyses of the interrelationships resulted in the identification of barriers to \npositive post secondary educational learning experiences and to the participants? concepts of \nthemselves as learners. The participants? experiences in the Aboriginal Teacher Assistant \nProgram were then examined in relationship to these barriers. \nThe participants? successfully completed this full-time college program and reported \npositive experiences in doing so. Through the analyses of their experiences, factors that led \nto the participants? success were identified. It was determined that for these women to be \nsuccessful, not only must education must be community-based, flexible, and holistic but also \nfoster and nurture relationships between and among students and instructor. These factors \nwere supported by the CMC method o f delivery. \nImplications include providing (1) appropriate technologies, (2) multiple ways of \nconnecting and interacting and (3) face-to-face components when delivering Aboriginal \nprograms to women at a distance. It also is important that we identify the characteristics of \nteachers who are respectful to Aboriginal values and who are successful in creating \ninterpersonal connectedness through computer-mediated communicative alternatives.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| 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".