Bibliographic record
Abstract
Learning and teaching are quite interrelated. Teaching can't take place unless the students learn. Teaching is a bi-polar activity. Learning barriers or causes of learning difficulties are quite natural phenomenon in an educational setting. But, when it comes to exert a very adverse effect it becomes crucial. Thus, it is unavoidable to ignore those factors which adversely affect the entire learning process. It is found that the following barriers are some of those that are very influential: social, cultural, parental, attitudinal, motivational, psychological, personal and pedagogical factors that include teacher, action research, teaching strategies, teaching resource, administration etc. The present paper is going to focus on the following: motivation of the students, motivation of the teachers, dedication and commitment, teacher's role, teachers’ preparedness, teaching strategies, training and professional development etc. The case of Saudi Arabia is very important for many reasons. The government is spending a lot of money on education. But, the achievement of learning is not up to the mark. Therefore, it is important to study the effect of such factors on academic achievement. Keywords: Barriers, socio cultural factors, psychological factors, pedagogical factors, the teacher , motivation.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".