Study on the Prospect of Library Resources Utilization Education Based on the Results of Short Thesis Writing Competitions
Bibliographic record
Abstract
In 2004,Taiwan’s Ministry of Education initiated the first Short Thesis Competition for Senior High School Students for the purpose of helping them cultivate reading and researching skills as well as guiding them to make use of library resources and services to do research.Mingdao High School has been practicing a program called “Learning to Learn”, in an attempt to make students, by using various learning strategies, think and learn for themselves so as to adapt themselves to the constantly changing society. To ensure the success of this program, the school library offered courses in short thesis writing, and encouraged students to participate in the island-wide Short Thesis Competitions. To Mingdao High School’s satisfaction, the contestants’ theses were all highly rated.The object of this study is, through an analysis of the results of the short thesis competitions, to understand the efficiency of Mingdao High school’s current education on students’ application of the library’s resources. In addition, we also want to share our experience about how we lead students to write their theses, hoping it will serve as a reference for other senior high schools. The research method this study adopts is called the content-analysis approach. In this paper, the award-winning short theses of the previous years are analyzed, including the types of topics chosen and how reference books are cited. A questionnaire is also used to help us understand how short thesis writing helps students learn.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".