Assessing the Impact of Gesture Instruction on Hearing Adult University Students Learning American Sign Language as a Foreign Language
Bibliographic record
Abstract
American Sign Language (ASL) is a popular language of study for post-secondary students.For many of these students, the classroom is the only face-to-face contact they have with the language, fluent signers, and the signing community.Current teaching approaches instruct students in the widely accepted signs documented in dictionaries, but in real-world social settings signers also draw on meaningful gestures.Consequently, students may encounter sign language outside of the classroom that is different from the prescribed uses demonstrated and practiced in class.In this qualitative study, classroom research is combined with an exploratory research design and a mixed-methods approach to quantitizing data.Gesture is positioned as a key part of the early learning process for beginner, hearing adult university ASL students.The study was informed by theories of gesture (Kendon, 2004), noticing (Schmidt, 1990(Schmidt, , 2001;;Swain, 1985Swain, , 1993)), comprehensible input (Krashen, 1981(Krashen, , 1985)), comprehensible output (Swain, 1995;Skehan, 1998) and interaction (Long, 1980; 1996).The study investigated whether direct, explicit instruction on gesture: 1) increased the number of communicative gestures produced by students; and 2) resulted in students who could better articulate the uses, functions, and placements of gesture in ASL.During a 12-week course, two existing ASL classes at the same level and taught by the same teacher were assigned to either an explicit or implicit instruction condition and assessed for comparability in their pre-existing gesture use in ASL.Whereas the explicit instruction group received, at fixed intervals, a sequence of five videos that focused on the uses of gesture in ASL, the implicit instruction group received a sequence of five videos that reviewed course content.Total intervention time was 30 minutes for each group.Findings suggest that students who were given explicit iii instruction about gesture in ASL have a deeper understanding of the role that gesture plays in sign language and use significantly more gestures in their own signed discourse, potentially enhancing their ability to communicate effectively in ASL.Implications and future directions for research are discussed.Dr. Janna Fox, my supervisor, thank you for your guidance and advice throughout this research journey.Thank you for being patient with me and pushing me in equal measure.To my first
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".