Blackboard as a Motivator for Saudi EFL Students: A Psycholinguistic Study
Bibliographic record
Abstract
Blackboard is a learning management system (LMS) that provides students with an opportunity to access the course at their convenience. Blackboard makes it possible for students to do online activities, interact with other students and their teachers, review instructional materials, and listen to the recorded classes. Students can also attend live lectures and discussion via virtual classes (collaborative blackboard) from homes, cafés, or from anywhere they choose. All of these features can be accessed by students anytime and anywhere. This facility might motivate English as a Foreign Language (EFL) students to study effectively and efficiently. The purpose of this research is to investigate the use of Blackboard system in teaching and learning and evaluate its influence on motivating Saudi English students. It looks at the role of Blackboard on English students’ motivation from their own point of view and tries to determine whether or not students encounter issues that affect their motivation. El-Seoud et al. (2014, p. 2) believe that “the success or failure of online instruction is perhaps related to student motivation”. This study is conducted at the University of Bisha in Saudi Arabia. The sample consisted of 80 students from the English Department. This is a mixed method study of the students who use Blackboard for online learning. The study found that using Blackboard motivated students to work harder and learn better than traditional methods of learning. It also found that students believe that Blackboard is a motivating factor. Some demotivating factors have also been identified in this study.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".