Assistive technology for reading and writing, and coping with anxiety
Bibliographic record
Abstract
It seems that in the 21st century digital technology has evolved to help, assist, and support students with their learning. I wanted to research about anxiety in students and coping with their struggles in reading and writing because it connects with me on a personal level. I did not have the luxury to use the assistive technologies that are out there today. I wanted to find out the benefits and disadvantages of using assistive technologies with students that have anxiety. I believe researching this topic will help me in my future teaching practice because I will have gained more knowledge about my topic. To obtain some background knowledge about my topic, I had to do some research about anxiety in students and students who have difficulties in reading and writing. Through this background research I now have a better understanding about assistive technology and how it can help students. From my research findings, I learned that yes assistive technology can help students with anxiety and problems with reading and writing, but to not let the students become too dependent on the technology. I also found out to make sure the technology matches with the student because if not then the student will avoid using the technology and anxiety can increase. Another key take away from my findings was that I need to be knowledgeable about the assistive technologies I plan on using with students. If I do not have the knowledge it is not going to help a student who has certain learning needs.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".