Academic Service Learning as Pedagogy: An Approach to Preparing Preservice Teachers for Urban Classrooms
Bibliographic record
Abstract
ABSTRACT ACADEMIC SERVICE-LEARNING AS PEDAGOGY: AN APPROACH TO PREPARING PRESERVICE TEACHERS FOR URBAN CLASSROOMS Teacher education struggles with multifaceted and increasingly complex issues surrounding preparing majority (White) teachers to work effectively with minority (non-White) students, families and communities. Novice teachers entering the workforce need to be culturally responsive. What are the benefits to students, community, and university when academic service learning (AS-L) is a course component? How does AS-L impact the personal intellectual growth of preservice teachers? This phenomenological qualitative study examined dispositions of 177 preservice teachers engaged in literacy and multicultural education courses with AS-L components. A nesting design was selected for this study situating preservice teachers in the center, surrounded by university teacher education coursework, which in turn, is surrounded by a larger circle encompassing local K-12 public schools and the community at large, where all are located. Data were collected over the course of five consecutive semesters, using four different data sources, with written reflections the primary source. Ethnographic techniques of participant observation, informal and formal interviewing were also used to collect data. Artifacts and field notes resulting from observations and interview transcripts were considered when triangulating reflection data, comparing evidence from different sources and using multiple perceptions to clarify meaning. Using different data sources permitted examination of the same phenomena through different lens. Data were analyzed using open coding, an inductive content analysis, and the constant comparative method, both systematic yet dynamic approaches. Comparing different data sources allowed for the comparison of views, situations, actions, and experiences of different individuals. Data analysis led to four significant categories: displacement, transformation, acceptance, and moving from negative, judgmental attitudes to positive, non-judgmental attitudes. This investigation suggested that AS-L components improved and strengthened teacher education courses in terms of adequately preparing preservice teachers to teach successfully in urban environments. This study resulted in preservice teachers whose dispositions and appreciation of diversity and culturally responsive teaching increased.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".