Fostering success : the question of belongingness at the graduate level
Bibliographic record
Abstract
This mixed methods research study explores the relationship between belongingness and academic success for graduate students from non-English speaking backgrounds (NESB). With Canadian universities diversifying student populations by drastically increasing the number of students being admitted from non-English speaking backgrounds, knowing how best to support NESB learners is of great concern to institutions, administrations, and educators. Researchers from many disciplines, particularly psychology, recognize that belongingness is an essential human need and motivation, yet it is often overlooked in education. Belongingness has been advanced as a powerful means of fostering academic success in higher education, yet in the field of Additional Language Teaching and Learning (ALTL), it is not well understood. This research can inform both educators and those involved with institutional policy enactment in ways to build stronger academic and institutional learning communities for NESB students. In this study, graduate students from both English and non-English speaking backgrounds were surveyed at a research-intensive Western Canadian university in order to better understand perceptions of belongingness, language acquisition, and academic success. NESB participants were then interviewed to gain a deeper understanding of the topic. Data from 36 survey responses and 3 interviews were gathered and interpreted through hermeneutic phenomenological approaches. The results of the study indicate that participants, particularly NESB students, perceived belongingness as an important aspect of their academic success at the graduate level. In particular, they identified that their peer to peer relationships, their relationships between students and faculty, and the classroom and campus environment all played key roles in their perceptions of belongingness. The data suggests that having a greater sense of belongingness would increase students’ feelings of happiness and satisfaction, as well as increase loyalty and allegiance to the university. This research has implications for educators and institutions concerned with inclusive education and best practices for English as a second language (ESL) and English as an additional language (EAL) students. It may also have impacts for other student populations as well, such as Aboriginal students, at-risk students, and even students from traditional or mainstream backgrounds.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".