Title of dissertation: TECHNOLOGY IN THEIR HANDS: STUDENTS ’ VOICES FROM A NOOK SUMMER READING PROGRAM FOR NON-PROFICIENT FIFTH-GRADE STUDENTS
Bibliographic record
Abstract
Researchers have documented a “summer reading setback ” where a demonstrated achievement gap between proficient and struggling readers expands during the summer months (Allington & McGill-Franzen, 2003). Educators need to devise a plan to foster diverse independent reading (Byrnes, 2000) by providing students access to texts of interest (Ivey & Broaddus, 2001; Hughes-Hassell & Rodge, 2007) and researchers suggest when given opportunities to read e-books, students read more (Fasimpaur, 2004). This study was designed to reveal students ’ perceptions of a Nook summer reading program granting the students access to a wide variety of eBooks, paying particular attention to the non-proficient fifth-grade students ’ reported summer reading behaviors and the influences for students ’ summer reading. Using a qualitative exploratory approach, I studied 20 students who participated in a summer independent reading program using Nook digital readers. I was able to analyze and interpret the student voices regarding their summer reading experiences using an online book log, student questionnaires, focus group interviews, and through two individual student case studies. I analyzed and interpreted the data through an interpretive mosaic focused on four overarching themes and the intersection of those themes which included: the reader, access to
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".