EFL Instructors’ Perceptions of Blackboard Learning Management System (LMS) at University Level
Bibliographic record
Abstract
The present paper aims to investigate EFL instructors’ perceptions of Blackboard learning management system (LMS) at Taif University in Saudi Arabia. To achieve this purposes, the researcher attempted to answer two questions. The first question investigates EFL instructors’ perceptions of Blackboard LMS. The second question aims to identify instructors’ suggestions to overcome difficulties encountered while using the system. A questionnaire of 4- Likert Scale was used to gather data from one hundred and two instructors to answer the first question, and content analysis was used to answer the second question. The collected data were analyzed in the form of descriptive statistics. The results, on one hand, revealed that 75% of the instructors have not used Blackboard technology before coming to university, which would affect their perceptions of the system. It was also evident that most of the instructors believe that the different features of Blackboard LMS are either poor or very poor. In addition, the instructors, in most of their responses to the functionalities of using the Blackboard LMS, rarely or never used the system. On the other hand, the results revealed that the instructors have a positive attitude towards the system in terms of its impact on learning, which can be the starting point to help them be familiarized more with the system’s features and functionalities through professional development. Based on the results, the researcher presented a number of conclusions and recommendations.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".