Teaching and Learning arts in school: Perspectives of teachers and students in China
Bibliographic record
Abstract
Teaching and Learning arts in school: Perspectives of teachers and students in China\nThe discipline-based art education (DBAE) was implemented in schools as art can improve learners’ expressiveness and elaboration, creative ability (Burton, Horowitz, & Abeles, 2000), critical thinking (Geahigan, 1997; Lampert, 2006), and their learning in other subjects (Barby & Catterall, 1994). Although the Chinese education system has undergone continuous reform, discipline-based art education (DBAE) does not match the 1) speed of economic development, 2) the blueprint of constructing the quality-oriented educational system, as well as 3) the learning needs of students (Niu, 2005). Additionally, the prosperous art education market on the school outside mirrors the dissatisfaction of students and parents for the art education provided by the school curriculum (Li, 2018).\nThis proposed qualitative case study aims to explore the learning experience of students enrolled in elementary schools in China to understand their perspectives and expectations of DBAE. The theoretical framework of this study is the environment and development of creativity.Eglinton (2003) proposes a theoretical model where art-making, encounters with art, and aesthetic experiences are integrated and equally weighted. Based on this model, The DBAE is important because schools can provide art aesthetic teaching, art-making experience, and an active learning atmosphere with students.\nThis study focuses on the following three research questions:\n1) How do students and their parents perceive their learning in art classes in school?\n2) What factors affect their evaluation of the DBAE?\n3) What expectations do they have for discipline-based art education?\nThe participants in this study are elementary students who enrolled in elementary schools in Tianjin, China. All of these participants have experienced art instruction in school. Some of them have taken extra-curriculum art tutoring. Based on their experiences in terms of the time arrangement, course content, teaching pedagogy, and evaluation methods, a qualitative study for analyzing the deficiencies of DBAE will be conducted. The researcher will 1) survey participants to gather their demographic background and 2) interview them to obtain more in-depth information regarding their arts learning experience in school, including instructions they got, challenges they experienced, and expectations they have.\nIn Canada, the DBAE is also threatened because of the rising impact of neoliberalism. According to Statistic Canada, only 46% of elementary schools reported that they have a music teacher, either full time or part-time in 2018. Only 16% of elementary schools with grades 7 or 8 reports having a visual arts teacher, and just 8% of schools have access to a specialist drama teacher (Arts education, 2018). The role of education is to provide an equal educational opportunity for students no matter where they live and what economic background they have. When students are not free to learn what they want, and teachers cannot carry out new pedagogies, education is not what it should be. With the economic globalization nowadays, there is a need for educators and to communicate and share educational experiences across a range of cultures and countries. It is my hope that this poster presentation will benefit the audience and provide a stepping stone for my future comparative research between Canada and China within this field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".