Bridging Knowledge and Action in the Workplace: An Evaluation on Internship Learning Outcomes of Child Development Associate Degree Program Students
Bibliographic record
Abstract
The efficiency of the education systems fundamentally depends on the quality of teaching and learning in classrooms, workshops, laboratories, and other educational spaces. Perfect teachers, well-designed courses, and proper facilities, provision of necessary resources are required for an excellent education, but not enough. This study aims to evaluate child development associate degree program students in their learning during their summer internships the scope of Raelin’s Work-Based Learning Model. The individual level of this model takes place two types of learning (theory and practice) and four types of individual learning (conceptualization, experimentation, experience, and reflection) that arise from a matrix of two forms of knowledge (explicit and implicit). This research was designed as a case study, one of the qualitative research methods. Depending on the tradition of qualitative research, observation, semi-structured interview, and document review strategies were used to increase the reliability of this study. In the analysis of the qualitative data, the descriptive analysis technique was used to define and interpret the data in line with the predetermined themes. The findings obtained in this study revealed that although the students made various observations and practices during their internships, it has been identified that these studies did not include the learning types in the context of the model.
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 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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".