A Reading Tutor for Low-Literacy Adults
Bibliographic record
Abstract
According to the Organization for Economic Cooperation and Development (OECD), the mean proficiency in literacy among adults in the US and Canada is at Level 2. Adults at this level cannot process dense texts, eliminate irrelevant information, perform multi-step operations, or evaluate the reliability of a source. The Reading Tutor is a website that was created to help low-literacy adults improve their English. It will be free to use the website that is personalized to the literacy level of every user. Creating a website allows people to increase their literacy levels without facing the stigma that comes with attending a class in person. Adults are inclined to improve their English because it often affects their career potential, socio-economic status, and health. The Reading Tutor has two major components: the passages and the scenarios. Passages are stories that the user can read and answer questions about. Scenarios are plots with questions that the user must answer to move on. In recent work, the information for each scenario was organized into spreadsheets to simplify the process of entering data into the code.
 The system architecture consists of HTML, CSS, Javascript, MySQL, Python, and Django. The newest development in this project was the improvement of the user intake experience. Before starting the passages and scenarios, the website collects information from each adult. The user "interests" pages are the latest additions to the site and these pages ask about the user’s hobbies. That data will then be used to incorporate their interests into later questions. It was important to add this feature to the website because relevance is a motivator for the user demographic. The next steps for the website are to log the user’s interests into the database. Future enhancements also include the creation of more scenarios to accommodate to the different user interests.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".