Students’ Perceptions on Using Different Listening Assessment Methods: Audio-Only and Video Media
Bibliographic record
Abstract
The importance and usefulness of incorporating video media elements to teach listening have become part of the general understanding and commonplace in the academia nowadays (Alonso, 2013; Macwan, 2015; Garcia, 2012). Hence, it is of vital importance that students are taught effectively and assessed accordingly on their listening skills. The purpose of this study is to examine students’ perceptions towards audio only method and video media method in listening assessment. The participants for this study were 150 students from four different faculties. Pre and post-test were conducted in collecting the data for this study with the same set of questions with two different assessment methods used. The results indicated that the majority of the participants have positive response towards the use of video media as their listening assessment method as it provides authentic, meaningful, and real-life situation contexts. Video has been used as a tool to cater the needs of 21st century learners as these learners are exposed with a lot of visual materials in their daily life. More video media related assessments should be implemented in the second language (L2) classrooms so that students will be more familiar with the different types of assessments present these days. In light of this notion, curriculum developers should be aware of the advancement in technology and ready to invest in changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".