Preparing Teachers for Assessment within Diverse Classrooms: An Analysis of Teacher Candidates' Conceptualizations.
Bibliographic record
Abstract
Under the current standards-based framework of public education in the US, and in other parts of the world, teachers are required to purposefully use assessments to individualize instruction for students with diverse learning needs (Darling-Hammond & Bransford, 2005; Ryan & Feller, 2009). This standards-based framework was initiated through the No Child Left Behind legislation (NCLB, 2002) and more recently reinscribed through the Blueprint for Reform act (USDOE, 2010). Underlying these public policies is the belief that every student can achieve state educational standards when provided with sufficient instructional support and, if necessary, provisions for accommodation. Teachers are currently expected to use data from student assessments, both classroom-based and large-scale, to inform instruction for diverse students. Across these policy directives, diversity is primarily understood as differences in student ability (i.e., disability or giftedness), culture and race, and English language proficiency as well as considerations of other student characteristics including learning styles, gender, socio-economic status, sexual orientation, religious affiliation, body image, and interests. For many school districts, data-informed instruction has been enacted, in part, through a Response to Intervention (RTI) approach that emphasizes tracking of learner progress in relation to tiered interventions of instructional supports and learning environments for students working below grade level standards (Barnes & Wade-Woolley, 2007; Batsche et al., 2007). Inclusive, standards-based mandates and RTI is also a prevalent component of other educational systems including those in UK, Canada, Australia, and New Zealand (Armstrong & Cheryl, 2011; Hutchinson, 2009; Miles & Singal, 2010). Underpinning teacher effectiveness within a standards-based framework and, more specifically within a RTI approach, is teachers' ability to integrate knowledge of both educational assessment and student diversity as related to cognitive development, learning abilities, and students' social and behavioral backgrounds. Not only are teachers expected to assess student learning to satisfy accountability concerns, but they are also expected to engage in both summative and formative assessment practices to gather ongoing information about learner progress in relation to students' individual learning profiles. Accordingly, effective preparation of teachers for an inclusive, standards-based context of education must not only prepare teacher candidates with competence in the areas of student assessment and student diversity, but more importantly, in the intersection of these two aspects of teaching. However, to date, the majority of research on pre-service and early-career teachers has addressed these competencies in isolation (Cochran-Smith, Davis, & Fries, 2003; Crocker & Dibbon, 2008; Hollins & Guzman, 2005). This bifurcation in research is not surprising as teacher candidates typically learn about topics in assessment and student diversity within separate pre-service courses, that is, under a discrete course delivery model (Crocker & Dibbon, 2008; DeLuca, 2012a, 2012b; DeLuca & Klinger, 2010). Aside from their teaching placements, which are highly variable learning contexts (Darling-Hammond, 2006), there are few opportunities for teacher candidates to consolidate knowledge from these two areas, both theoretically and practically, making it difficult for teacher candidates to integrate these competencies in their teaching practice. As a basis for an integrated approach to preservice education, the purpose of this research was to begin to explore beginning teachers' conceptions on the linkages between student assessment and teaching within diversity classrooms upon completion of their preservice program. This research contributes to continued calls for enhancing the preparation of teachers in the areas of assessment and diversity (Ball & Tyson, 2011; Popham, 2004, 2011; Shepard, Hammerness, Darling-Hammond, & Rust, 2005; Zeichner & Conklin, 2008). …
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".