The effects of enhanced word processing on the journal writing of middle school students with learning disabilities
Bibliographic record
Abstract
The purpose of this investigation was to examine whether the use of word processors, enhanced with speech synthesis and word prediction software, assisted the transcription skills and word recognition abilities of Middle School students with learning disabilities, while writing dialogue journals. An ABAB single-case research design, with probes, was implemented in an inclusive classroom setting with four students, ages eleven and twelve, with learning disabilities and severe writing problems. Effects of the intervention were examined during baseline sessions, when the students utilized a regular word processor, and during the treatment sessions, when the students used an enhanced word processor. The results were analyzed to find what effect, if any, the intervention, using a word processor enhanced with speech synthesis and word prediction software when dialogue journal writing, had on (a) the number of words in the students' dialogue journal writings, (b) the transcription quality of these students' dialogue journal entries, (c) the number of words recognized by the students when reading their dialogue journal entries, ( d) the students' composing rates, ( e) the students' transcription and word recognition skills when writing their subsequent journal writings on word processors (without enhancements) in the classroom, (f) the students' transcription and word recognition skills when writing their dialogue journals on word processors (without enhancements) one week and three weeks after the second treatment phase, and (g) the students' transcription skills when writing (by hand) in their daily agenda books. Data were analyzed on an individual basis and across participants. None of the participants improved the quantity of words they wrote when using an enhanced word processor. Three of the four participants' word quantity remained constant and one participant decreased the number of words he/she was able to write during the fifteen-minute sessions. All of the participants showed enhanced transcription skills, that is, the proportions of readable words, correctly spelled words, readable word sequences, and correctly spelled word sequences when they wrote their dialogue journals on the enhanced word processor. The word recognition baseline session scores for all the participants were near, or at the ceiling; consequently, treatment effect was minimal. Two of the four students' composing rates decreased when they composed their dialogue journals on the enriched word processor; whereas, the other two participants' composing rates were unaffected by the treatment. Using an enhanced word processor did not have any effect on the students' transcription skills when writing subsequent dialogue journal writings on word processors (without enhancements) or when writing (by hand) in their daily agenda books. Recommendations for future research included identifying (a) other populations and age groups that might benefit from an enhanced word processor, (b) other types of writing tasks that might be supported by using an enhanced word processor, (c) other academic areas that may benefit from use of an enhanced word processor such as spelling skills, (d) and whether or not using an enhanced word processor when dialogue journal writing over a longer period of time would affect the students' transcription skills when using a regular word processor. A recommendation for practice suggested that Middle School teachers use assistive technology to aid the writing skills of students with learning disabilities.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".