At Home with Technology: Home Educators' Perspectives on Teaching with Technology
Bibliographic record
Abstract
The purpose of this research was to understand how and why home educators are schooling their children using technology. First, I explore how home educators use technology for homeschooling. Second, I investigate how home educators see themselves as teachers when using technology. Several themes emerged from the data revealing that home educators believe technology enables them to provide high quality curriculum and individualized instruction and to create a constructive and engaging learning environment for their children. Data were collected by convenience sampling with a survey of 316 (N = 316) home educators from 52 different territories, states, provinces, and countries across the globe, a nonrandom sample which is not representative of the entire homeschooling population. The quantitative data provide a specific picture of home education, reasons for homeschooling, and home educators’ perceptions of technology use in their homeschool. Qualitative data were obtained through open-ended questions on the questionnaire and through thirteen in-depth interviews with home educators from the United States, Canada, and the United Kingdom. Data analysis was inductive, using a constant comparative methodology to identify meanings and values held by homeschool parents providing an important part of the overall picture. The data in this study show that home educators use technology to evaluate and purchase curriculum, to deliver and supplement instruction, to offer what they see as an appropriate and personalized education, and to gain social, emotional, and professional support from other homeschoolers. Results of this study suggest that using technology to access a wide variety of curricula, to connect with and support fellow teachers, and to provide individualized instruction in an engaging environment might lead to better educational experiences for numerous students and teachers.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".