Authentic Assessment for Early Childhood Intervention: In-Vivo & Virtual Practices for Interdisciplinary Professionals
Bibliographic record
Abstract
Abstract The pandemic has exposed the many glaring difficulties inherent in implementing effective assessment and intervention for young children with neurodevelopmental delays and disabilities in our respective countries, but, especially in the US. The urgency for innovative models of assessment linked to interdisciplinary services and supports in both remote and in-vivo settings became prominent. Yet, the commitment to developmentally-appropriate practice (DAP), assessment linked to intervention, is the hallmark of ECI, whether virtual or in-vivo. However, interdisciplinary professionals have rallied during these challenging times by displaying creativity, compassion, and superb clinical judgment in providing responsive services via both virtual and in-vivo platforms to families and young children with special needs in rural and urban regions. Virtual service delivery has required judicious changes in our professional practices using more responsive and less scripted postures. Our family-centered approaches enabled us to engage with parents as partners in assessment and intervention and to plan and deliver supports that were more tailored. We believe that our “lessons learned” from the pandemic about implementing authentic assessment for early childhood intervention (AA for ECI) among parents and interdisciplinary professionals will make our ongoing partnerships with families and other professionals stronger and more enduring. We hope that this article and the step-by-step model that we have offered will help you in your own professional lives to maintain the outlook that emphasizes the importance of both authentic assessment methods & processes, whether in-vivo or virtual, for undercovering each child’s hidden and true capabilities and needs and by adhering to our enduring commitment to protect children’s inherent human rights. Keywords: authentic, assessment, best practices, virtual, remote, early childhood intervention
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".