“It Makes the Whole Learning Experience Better”: Student Feedback on the Use of the Interactive Whiteboard in Learning Chinese at Tertiary Level
Bibliographic record
Abstract
The widespread use of interactive whiteboards (IWB) in primary and secondary schools has been well documented, yet there is to date only limited attention to use in tertiary institutions. Macquarie University has installed this technology in many of its teaching spaces in the past few years. This paper reports a case study undertaken in the university’s undergraduate Chinese beginner course, which began to use IWB learning activities in 2009.Our study was undertaken to obtain students’ perceptions of the IWB pedagogy in Chinese language acquisition in general and in particular, of the effectiveness of IWB in the retention of Chinese characters. To many students whose first language is non-logographic, the recognition and retention of characters are the most difficult tasks in learning Chinese. Our findings indicate that the IWB’s affordance to create a variety of visual activities has impacted, most saliently, the retention of characters and syntactical elements. Students also report that the IWB has enhanced the learning experience, reflected in increased motivation and engagement through interaction with this technology. The tertiary students reveal particular learning priorities, in appreciating interaction, intellectual demand and participation, as components of effective learning. The feedback process itself proved to be useful in facilitating critical awareness in both teacher and students, of teaching strategies and learning respectively.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".