Table 1
Demographic Characteristics of Respondents (N = 31).
| DATA | CATEGORIES | FACULTY, % |
|---|---|---|
| Gender | Male | 6 |
| Female | 94 | |
| Subject Taught | Biology | 51,5 |
| Physics | 22,5 | |
| Chemistry | 26 | |
| Educational Stage | Lower Secondary (grades 5–7) | 55 |
| Upper Secondary (grades 8–12) | 32 | |
| Lower and Upper Secondary | 13 | |
| School Size | Small | 13 |
| Medium | 22,5 | |
| Large | 64,5 | |
| Settlement Type | Provincial City | 65 |
| Town | 16 | |
| Village | 19 | |
| Less than 5 years | 19,4 | |
| Between 6 and 10 years | 16,1 | |
| Years of service | Between 11 and 20 years | 9,7 |
| Between 21 and 30 years | 29 | |
| More than 30 years | 19,4 | |
| Not specified | 6,4 |
Figure 1
RAT Model Category and Coding.
Figure 2
Additional Categories and Coding.
Table 2
Coding Frequency in RAT Model Categories Within All Types of Documents (Interviews, Questionnaires, and Observations).
| CATEGORY | CODE | COUNT | CODES, % |
|---|---|---|---|
| Belief level codes (interview) | Replacement (belief), Rb | 23 | 2,7 |
| Belief level codes (interview) | Amplification (belief), Ab | 150 | 17,5 |
| Belief level codes (interview) | Transformation (belief), Tb | 56 | 6,5 |
| Planning level codes (questionnaire) | Replacement (planned), Rp | 15 | 1,8 |
| Planning level codes (questionnaire) | Amplification (planned), Ap | 19 | 2,2 |
| Planning level codes (questionnaire) | Transformation (planned), Tp | 8 | 0,9 |
| Action level codes (classroom observation) | Replacement (action), Ra | 33 | 3,9 |
| Action level codes (classroom observation) | Amplification (action), Aa | 49 | 5,7 |
| Action level codes (classroom observation) | Transformation (action), Ta | 21 | 2,5 |
Figure 3
Distribution of RAT Model Codes Across All Document Types Reported in Cases.
Figure 4
Comparison of RAT Category Use Among Subject Groups.
Table 3
Coding Co-occurrence Within Case. Similarity Index: Jaccard Coefficient (Occurrence).
Figure 5
Coding Co-occurrence. Link Analysis.
Table 4
Hierarchical Agglomerative Clustering Results Based on Jaccard Similarity Between RAT Model Codes in QDA Miner Project.
| NODE | GROUP 1 | GROUP 2 | SIMILARITY |
|---|---|---|---|
| 1 | Amplification (action) | Amplification (belief) | 0,774 |
| 2 | Node 1 | Transformation (belief) | 0,581 |
| 3 | Amplification (planned) | Replacement (action) | 0,522 |
| 4 | Node 2 | Node 3 | 0,491 |
| 5 | Node 4 | Transformation (action) | 0,428 |
| 6 | Replacement (planned) | Transformation (planned) | 0,400 |
| 7 | Node 5 | Replacement (belief) | 0,393 |
| 8 | Node 7 | Node 6 | 0,304 |
Table 5
Case Similarity Table Across the Entire Sample of Science Teachers (N = 31) Based on Interviews and RAT Model Categories.
Table 6
The Agglomeration Results from the Interview-Based Case Similarity Analysis.
| NODE | GROUP 1 | GROUP 2 | SIMILARITY |
|---|---|---|---|
| 1 | 5 | 6 | 1 |
| 2 | 29 | Node 1 | 1 |
| 3 | 28 | Node 2 | 1 |
| 4 | 26 | Node 3 | 1 |
| 5 | 23 | 4 | 1 |
| 6 | 22 | Node 4 | 1 |
| 7 | 21 | 25 | 1 |
| 8 | 20 | Node 5 | 1 |
| 9 | Node 8 | Node 6 | 1 |
| 10 | 19 | Node 8 | 1 |
| 11 | 18 | Node 7 | 1 |
| 12 | 17 | Node 10 | 1 |
| 13 | 16 | Node 11 | 1 |
| 14 | 13 | Node 12 | 1 |
| 15 | 12 | Node 14 | 1 |
| 16 | 11 | Node 11 | 1 |
| 17 | 10 | Node 13 | 1 |
| 18 | Node 17 | Node 17 | 1 |
| 19 | Node 16 | Node 15 | 0,75 |
| 20 | Node 19 | Node 19 | 0,568 |
| 21 | Node 20 | Node 12 | 0,5 |
| 22 | Node 19 | Node 21 | 0,5 |
| 23 | 31 | Node 22 | 0,5 |
| 24 | 30 | Node 23 | 0,5 |
| 25 | Node 24 | Node 24 | 0,5 |
| 26 | 27 | Node 25 | 0,5 |
| 27 | 24 | Node 26 | 0,5 |
| 28 | 15 | Node 27 | 0,5 |
| 29 | 14 | Node 28 | 0,5 |
| 30 | Node 29 | Node 29 | 0,5 |
Table 7
Cross-tab Analysis of RAT Model and Other Codes Against School Size.
| CODE | SMALL-SIZED SCHOOL, % | MIDDLE-SIZED SCHOOL, % | LARGE-SIZED SCHOOL, % |
|---|---|---|---|
| Rb | 57,1 | 28,6 | 14,3 |
| Ab | 64,5 | 22,6 | 12,9 |
| Tb | 70,0 | 30,0 | 0,0 |
| Rp | 64,3 | 21,4 | 14,3 |
| Ap | 44,4 | 38,9 | 16,7 |
| Tp | 57,1 | 42,9 | 0,0 |
| Ra | 47,1 | 35,3 | 17,6 |
| Aa | 58,3 | 29,2 | 12,5 |
| Ta | 64,3 | 28,6 | 7,1 |
| Technological infrastructure | 65,5 | 20,7 | 13,8 |
| Institutional support | 74,1 | 18,5 | 7,4 |
| University training | 75,0 | 18,8 | 6,3 |
| CPD of teachers | 60,7 | 25,0 | 14,3 |
| Self-learning and peer exchange | 67,9 | 21,4 | 10,7 |
Table 8
Cross-tab Analysis of RAT Model and Other Codes Against Settlement Type.
| CODE | PROVINCIAL CITY, % | TOWN, % | VILLAGE, % |
|---|---|---|---|
| Rb | 71,4 | 7,1 | 21,4 |
| Ab | 67,7 | 12,9 | 19,4 |
| Tb | 65,0 | 15,0 | 20,0 |
| Rp | 64,3 | 14,3 | 21,4 |
| Ap | 61,1 | 16,7 | 22,2 |
| Tp | 57,1 | 28,6 | 14,3 |
| Ra | 58,8 | 17,6 | 23,5 |
| Aa | 66,7 | 12,5 | 20,8 |
| Ta | 64,3 | 7,1 | 28,6 |
| Technological infrastructure | 69,0 | 13,8 | 17,2 |
| Institutional support | 66,7 | 14,8 | 18,5 |
| University training | 75,0 | 18,8 | 6,3 |
| CPD courses | 64,3 | 14,3 | 21,4 |
| Self-learning and peer exchange | 64,3 | 14,3 | 21,4 |
Table 9
Cross-tab Analysis of the RAT Model and Other Codes Against Years of Service.
| CODE | YEARS OF SERVICE | |||
|---|---|---|---|---|
| <5 | 6–10 | 11–20 | >20 | |
| Rb | 7,1% | 21,3% | 28,4% | 42,6% |
| Ab | 16,0% | 16,0% | 32,0% | 35,2% |
| Tb | 10,0% | 15,0% | 25,0% | 50,0% |
| Rp | 14,2% | 21,3% | 42,6% | 21,3% |
| Ap | 16,8% | 5,6% | 44,8% | 33,6% |
| Tp | 42,9% | 14,3% | 42,9% | 0,0% |
| Ra | 23,6% | 5,9% | 41,3% | 29,5% |
| Aa | 12,6% | 16,8% | 33,6% | 37,8% |
| Ta | 7,1% | 7,1% | 56,8% | 28,4% |
| Technological infrastructure | 13,6% | 17,0% | 34,0% | 34,0% |
| Institutional support | 18,5% | 18,5% | 29,6% | 33,3% |
| University training | 18,9% | 6,3% | 37,8% | 37,8% |
| PD courses | 18,0% | 14,4% | 36,0% | 32,4% |
| Self-learning and peer exchange | 14,4% | 18,0% | 32,4% | 36,0% |