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Record W6977686472 · doi:10.7939/r3-sev5-xq22

The Needs of Evaluation in the Field of Early Childhood Development from the Early Learning and Childcare Educators' Perspective

2023· dissertation· en· W6977686472 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2023
Typedissertation
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsEarly childhoodGovernment (linguistics)Field (mathematics)Perspective (graphical)Early childhood educationCapacity buildingSet (abstract data type)

Abstract

fetched live from OpenAlex

Early childhood development (ECD) is an intersectoral and interdisciplinary field as it includes many sectors and programs that serve children from conception to six years of age. This life period is the foundation for human development. Therefore, positive early experiences set the path for adulthood in terms of education, economic stability, and health and wellbeing. In 2015, Canada committed to the United Nations’ 2030 agenda of Sustainable Development Goals (SDGs). The federal government is funding innovative initiatives that raise awareness around SDGs. Particularly, it is giving significant attention to enhancing early childhood experiences in Canada due to its importance in achieving the SDGs. However, there are still areas in this field that call for improvement. Evaluation is one way to identify those areas and learn how this field can be enhanced, but there are gaps in doing and using evaluation in this field. Therefore, it was important to understand the evaluation assets and needs in the ECD field. Understanding evaluation capacity assets and needs requires learning from stakeholders that are involved in this field. The Evaluation Capacity Network (ECN) conducted a study in 2021 to understand those needs and assets in ECD and to learn how it can effectively tailor its support in building the evaluation capacity of organizations and individuals. This thesis research builds on the ECN’s study to learn about the field’s capacity and context, and while this field is intersectoral, the thesis research focused on early learning and childcare (ELCC) as a subsection. ELCC is an important sector as it is where Canadian children spend most of their time when interacting with people other than the family. Among the diverse stakeholders in ELCC, this research focused on ELCC educators in Alberta, Canada. The research used qualitative methodology and drew on two data sources. Secondary data from five focus groups conducted by the ECN with ECD stakeholders in North America were used to reflect the field’s capacity at the organizational and system levels. This was followed by seven semi-structured interviews with ELCC educators in Alberta to reflect their individual capacity needs and assets to engage in evaluation. The findings revealed that educators have a unique evaluation capacity due to their natural evaluation practice with children that is embedded in their day-to-day work and interaction with children. Their natural evaluation practice makes the quality of their evaluation vary from one another based on their experiences. This research suggests that educators need evaluation capacity building at individual, organizational, and system levels. It is significant, however, to consider how educators define evaluation and the contexts in which they work to tailor evaluation capacity building opportunities more meaningfully. Improving educators’ evaluation capacity will ultimately enhance the collection of baseline data about children in the province that is currently lacking and ensure more coherent evaluation in the system to determine if significant funds and initiatives are making change.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.337
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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