Accelrated Reader Program: What do Teachers Really Think
Bibliographic record
Abstract
What do teachers really think about the Accelerated Reader program, a widely used supplemental, independent reading program in which their students read fiction and non-fiction books of their choice and take brief online comprehension quizzes about the books? The Accelerated Reader (AR) program was designed by Renaissance Learning Company to increase students’ motivation to read and students’ achievement in reading; however, a review of the literature reveals inconsistent findings about its outcomes. Very few studies have been conducted seeking teacher input as to whether the program to achieves its intended outcomes. The goal of this study is to survey teachers (Grades 3 – 8) who use AR as a curricular component of their literacy program. We sought to learn about how teachers use the program and perceive its effectiveness as well as how it impacts their students’ interest and achievement in reading. We gathered data using an online questionnaire from teachers in urban, rural, exurban and suburban school settings in both elementary and middle schools. Teachers were asked to respond to items based on a 4 –point scale from strongly disagree to strongly agree including an open-ended response section.The respondents were primarily from suburban and exurban districts and they have been using the program between 1-15 years. Most of the teachers indicated their students enjoy the program and most teachers require their students to take the AR quizzes.Results indicate most teachers believe that Accelerated Reader program motivates their students to read; however, they also recognize that AR is largely an accountability measure ensuring that their students read independently. Additionally, teachers recognize that AR measures comprehension at knowledge-recall level and is not an overall strong indicator of reading comprehension. Therefore, some teachers have made their own modifications to the program.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".