Developing and Piloting a Literature Course Learnable Via Blackboard for EFL Literature Instruction
Bibliographic record
Abstract
The underlying purpose for this study was to describe how technology was used to teach a literature course developed by the researchers in an EFL context and to explore the effects of the online course on students’ achievement via piloting the new course and to further gain information about the skills and reactions of students who used this new literature course while employing technology in their learning. The researchers, therefore, set to employ a qualitative/quantitative approach to describe how technology was harnessed to deliver the newly piloted literature in an EFL class and explore students’ reactions to the use of technology in the EFL context. Purposeful sampling was used in selecting 30 participants for the study from Saudi students studying English as a foreign language. The features and facilities of Blackboard were fully used in the course of the study. Two semi-structured surveys were conducted with each participant, among teachers and students, during initial and final instruction weeks. As such, students’ perceptions of the use of technology in the teaching of literature in the EFL classroom were assessed. Findings of the study showed the effectiveness of the Modern Literary Movements course delivered online, called the online Literature course hence forth. Qualitative and quantitative findings also showed that learning outcomes are in alignment with the course requirements, and that course assessments are in agreement with the course content and learning objectives, assignments and evaluation procedures, and the professional presentation of the e-course on the part of the course instructors. Results also proved that the course could prove effective in enhancing the participants’ performance on pretesting compared to post testing results. The study ends on notes of recommendation and implications for further research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".