Developing Critical Thinking Skills Through i-Think Maps: An Action Research
Bibliographic record
Abstract
This paper aims to provide insights into the use of i-Think maps in developing the understanding of critical thinking skill among ESL student teachers. The i-Think map programme was introduced by the Ministry of Education in 2012 which aims to produce innovative learners including those in language learning field. Employing Kemmis and Mac Taggart’s (2000) model of action research, eight different types of i-Think maps were introduced to three trainee teachers for four months in order to obtain feedback concerning their learning experience and understanding of critical thinking skill. Throughout the four-month period, the participants were asked to opt for suitable i-Think maps to summarise the content of the lectures delivered in their teaching session. The participants were trainee teachers at a Teacher Training Institute in Malaysia and they were purposively sampled as they had been diagnosed to have problems in applying critical thinking for coaching. Analysis of the data which were generated from document analysis and semi-structured interviews with the student teachers, showed that the participants were interested in using i-Think maps and they perceived the maps as a useful tool in improving their understanding of critical thinking. This paper contributes to the existing literature on critical thinking skills in ELT classroom by highlighting the importance of using i-Think maps as a teaching aid in enhancing the critical thinking ambience among the students.
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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".