Teaching Special Education during the COVID-19 Pandemic: An Exploratory Study
Bibliographic record
Abstract
With the onset of the pandemic, there has been a massive shift and transformation in the way knowledge is disseminated. There has been a rise in distance/online learning, this includes teachers teaching remotely using various digital platforms. With almost two years of virtual learning, this has led to a demand for both teachers and students to upskill and equip themselves with technological capability. Effectiveness of e-learning has emerged to be an area of research, particularly in this context of the pandemic. This study explores the effectiveness of this alternate method of teaching on students with learning disabilities through the lens of parents and special tutors from renowned special schools in South Bangalore using interviews and focus group discussions. A pre-designed Likert scale questionnaire was also administered to 170 students with learning disabilities in South Bangalore to record the academic challenges they faced during their online classes. Results from the interactions with both parents and special tutors reveal there is a need to revisit learning and teaching strategies and improve access to new media educational technologies to help maximise uptake and success of the e-learning approach in the context of special education. Parents believe that the learner needs of children with disabilities during the remote learning scenario were not taken into consideration, special tutors shared their experience and limitations some which included difficulty in handling online classes, lack of financial support to invest in relevant technological platforms. The findings from this exploratory study highlight both the need for improving pedagogy in the remote learning contexts along with tutors requiring professional development workshops to conduct effective online classes that include incorporating various digital tools that meets the educational needs of children with learning disabilities. The study serves as an eye opener for special tutors, local education authorities and school administrators to revisit lesson plans, online teaching approaches, explore and invest in potential digital tools that could meet learner needs and improve the learning curve during the pandemic.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".