Creating active mathematics learning opportunities for students in China : an action research project in one Chinese high school
Bibliographic record
Abstract
In order to keep pace with the world, China has introduced eight different National Curriculum Reforms (NCR) in basic education since 1949. In the eighth NCR, long-term success of students became the most important and fundamental objective of the reform. More importantly, with respect to mathematics learning, the most recent reform refers not only the acquisition of knowledge, but also refers to the experience of problem solving, communication, and cooperation. Amongst the various factors that may have an impact on mathematics teaching and learning, active learning stands out as having the potential to fulfill students’ long-term development. In the eighth NCR, active learning means learning through doing, learning through experiencing, learning through practicing, and engagement (Li, 2008). However, transitioning from policy design to policy implementation is not easy. In mathematics education, teachers still have to face many challenges in improving their own teaching and advancing their own professional learning. Considering the context of China, the aim of this research is therefore to explore how high school mathematics teachers in China create active learning opportunities for their students with participation in an action research project. This action research study was conducted at Renhe High School and involved 3 volunteer Grade 10 mathematics teachers and their students (128 students in total). This thesis reports the strategies that teachers have tried during the study. According to three participating teachers’ experiences, the main strategies that teachers have tried include: introducing activity into class; small group cooperative learning; peer teaching; problem-based learning; and technology-enhanced learning. Consequently, by participating in the research, teachers’ experiences revealed various understandings of their own pedagogy, which were influenced or changed during the research. Further, the results of the study show that: (1) Introducing action research approaches could help teachers better understand and utilize “The Standards” to create active learning opportunities for students; (2) Collaboration among teachers enable teachers to examine their classroom instructions, revise current practices and develop new strategies; (3) A supportive school culture can help to overcome the pressure from NCEE (National Collage Entrance Examination), which often represents an overwhelming influence of classroom practice.
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".