Table 3.
Findings of the linguistic analysis – self positioning in the two groups following the ML training
| Theme No | Verbalized meaning | ISTs | PSTs |
|---|---|---|---|
| 1 | Acknowledging the importance of trying out the digital tools | 174 (15%) | 89 (13.5%) |
| 2 | Concerns and negativity regarding the use of the digital tools | 122 (11%) | 68 (10%) |
| 3 | The impact of COVID-19 on teaching | 86 (8%) | 56 (8.5%) |
| 4 | “Who am I as an educator?” | 103 (9%) | 31 (5%) |
| 5 | Effects of the program on familiarity with technology | 109 (9.6%) | 67 (10%) |
| 6 | Dissatisfaction with the program (time slot, technology, workload) | 30 (2.5%) | 39 (6%) |
| 7 | Program outcomes and training necessity for the digitalization of teaching | 304 (27%) | 105 (16%) |
| 8 | Need for exposure to digital tools | 27 (2.4%) | 44 (7%) |
| 9 | Challenges faced while implementing the digital tools | 85 (7.3%) | 46 (7%) |
| 10 | Adaptations to digital tools for use in special education | 25 (2%) | 63 (10%) |
| 11 | Acknowledging group support in the process of learning | 25 (2.2%) | 22 (3%) |
| 12 | Feeling motivated following successful engagement with the digitalization | 35 (3%) | 7 (1%) |
| 13 | Feeling discouraged due to lack of support from schools and pedagogical mentors | – | 20 (3%) |
| Total | Total instances of self-positioning (N) | 1,136 | 657 |
| Total | Uses of “I” as a linguistic self-positioning resource | 1,295 | 742 |
| Total | Total words analysed | 35,925 | 30,145 |