Tackling the kanji hurdle: an investigation of kanji order and its role in facilitating the kanji learning process
Bibliographic record
Abstract
This research seeks to determine the role of kanji order from the perspective of Japanese language textbooks, teachers of Japanese, and learners of Japanese. Three central research questions were identified: 1. How are Japanese language textbooks different from each other from the perspective of kanji selection and ordering? 2. What beliefs are held by Japanese teachers at universities in Australia, New Zealand, Canada, the UK and the US, in regard to teaching kanji to non-kanji background students of Japanese? 3. Which kanji clusters do non-kanji background learners perceive as easy or difficult to learn? Three stages of research were conducted to answer my research questions: to answer my research questions 1. A textbook analysis was conducted on four Japanese language textbooks in kanji selected for inclusion in different textbooks and the in different textbooks and the 2. A survey of kanji learning and teaching beliefs was conducted to ascertain how learning and teaching beliefs was conducted to ascertain how teachers perceive the teaching and learning process of kanji and whether they regard the order in which kanji are introduced as an important factor in facilitating the kanji learning process. 3. A survey of Japanese language learners regarding kanji clusters was conducted was conducted to determine whether Japanese language learners perceive different perceive different perceive different kanji clusters, groups of clusters, groups of kanji with a common property, as easier to learn or more more difficult to learn than others. As for the first research research, results showed that the selection of kanji for the four textbooks analysed were unique and significantly different from each other. However, kanji orders found within the analysed textbooks were similar. As for the second research research question, results showed that a large proportion of Japanese language teachers believe that the order in which kanji are introduced plays a role in facilitatingthe kanji learning process but they are unsure as to how to implement those beliefs in their teaching. As for the third research question, participants indicated that they perceived As for the third research question, participants indicated that they perceived As for the third research question, participants indicated that they perceived As for the third research question, participants indicated that they perceived certain kanji clusters as easier than others.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".