The Effect of School Improvement Planning on Student Achievement
Bibliographic record
Abstract
Under the No Child Left Behind (NCLB) Act of 2001, schools identified as not making adequate progress are to submit a school improvement plan (SIP). SIPs were designed to close achievement gaps and raise levels of student achievement (White, 2009). Although not required for all schools, Fernandez (2009) found that by 2000, most schools were writing formal plans for improvement. States have recognized the importance of school accountability and are using student achievement and the process of school improvement planning as a method of distinguishing effective and ineffective schools (Phelps & Addonizio, 2006). Given the use of SIPs for decision making, it is critical to examine whether SIP quality is related to student achievement.A review of the literature on characteristics of effective SIPs indicates the importance of targeted areas for improvement, integration of specific intervention strategies, frequent monitoring of student data, and identification of persons responsible for implementation of each strategy (Fernandez, 2009; Reeves, 2004; White, 2009). Other areas necessary for systemic improvement, yet often missing from SIPs, include leadership strategies, data analysis techniques, decision making practices, and an evaluation of a school's readiness to change along with the process for improvement (Beach & Lindahl, 2004; Hall & Hord, 2011; Reeves, 2004; White, 2009). Without the integration of these steps and a frequent formal evaluation of the improvement process, sustained improvement is unlikely (Webb, 2007; White & Smith, 2010).School improvement efforts have been documented since the 1970s, and it is surprising that a clear agreement on exactly how to carry out the improvement efforts has yet to emerge (White & Smith, 2010). Despite recommendations on the content for SIPs, evidence suggests that plans often fall short. To date, there still is no required format for an SIP. Mclnerney and Leach ( 1992) and Webb (2007) have found, within the process of planning there is the chance that schools will set goals that are inappropriate or fail to meet specific subgroup needs. Additionally, if administrators only create a SIP because it is required (rather than because it is a valued process in a school), they are unlikely to build in effective strategies for achieving goals, or mechanisms for frequent monitoring of goal progress.Evidence of Effectiveness of SIPsGiven that SIPs are required in some cases, and that they have been used in decision making about schools, it is important to ask whether differences in quality correlate with student achievement. Only three of the studies have provided evidence on the effectiveness of school improvement planning. One study that examined the role of SIPs and student achievement was by Curry (2007). The study involved a content analysis of SIPs for 67 middle and high schools. The results showed significant negative correlations for student achievement with the number of math strategies found in plans and the number of writing operational action steps. These findings are consistent with Reeves' (2004) and White's (2009) recommendation to limit the number of goals and strategies.Two additional studies were more closely related to the current study. Reeves' (2011) planning, implementation, and monitoring (PIM) study and Fernandez's (2009) study on effectiveness of school improvement plans provide a framework for the current study. Both studies used similar rubrics to examine specific characteristics of SIPs in an effort to quantify the plans' effectiveness. The PIM study (Reeves, 2011) included 2,000 schools in the United States and Canada using achievement data for more than 1.5 million students. The participants in this study represented a very diverse group including both urban and rural districts spanning levels from elementary to high school. The study included double-blind reviews of SIPs in an attempt to see what components of a plan focusing on leadership practices, could be associated with increases in student achievement. …
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".