Factors Affecting the Academic Performance of Students with Hearing Impairment
Bibliographic record
Abstract
Around 5 million people in India are with hearing impairment. The higher education opportunities for students with hearing impairment are very limited. Only six institutes in the country provide undergraduate degrees to students with hearing impairment, according to accessible records. Academic achievement in the past, student background characteristics, and eLearning elements are all aspects that influence a student's academic performance. Hearing impairment-related characteristics may also need to be considered for a student with hearing impairment. Identification of these elements may aid teachers in developing individualized teaching plans for students. This paper tries to find the features that affect the performance of students with hearing impairment. The features included are socioeconomic, previous academic scores, and deafness-related factors. The dataset includes data of 224 undergraduate students who have hearing impairment. The preadmission data is used to analyze the performance of students with hearing impairment. The students are classified into different levels – low, medium, and high according to their performance. Different machine learning models are used to classify the students – Logistic Regression, Decision Tree, Support Vector Machine, KNN, Random Forest, and Naïve Bayes. The Random Forest model performed better compared to other models.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".