Pros & Cons of Using Blackboard Collaborate for Blended Learning on Students Learning Outcomes
Bibliographic record
Abstract
Blackboard Collaborate was introduced to King Khalid University recently in the last decade, instructors and students were trained to use it in an effective way. The objective of this study is to find pros & cons of using Blackboard Collaborate for Blended Learning and its effect on students learning outcomes. The researcher used the experimental and descriptive methods to conduct this study; the population of this study was twenty two female-students studying at College of Science & Arts/Muhayil, English Department in the 5th level who were studying Speech Workshop Course in the 1st semester 2016-2017. Two instruments were used to collect the data of this study: 1) Paper test and electronic test. 2) Students’ questionnaire. The findings of this study are: 1) Students’ results in the electronic test are much better than the paper test. 2) In spite of students’ preference to traditional classroom lectures, the students agreed that using blackboard for blended learning helped them: a) Get lesson-materials or to watch recorded lectures in case they are absent. b) Learn from their classmates’ mistakes in discussion blogs. c) Learn according to their learning styles. D) Feel dependent and secure to have regular contact with their instructor and get quick feedback for their questions and confidential tests grade results. The findings above helped to reinforce students’ motivation towards learning and affect their learning outcomes positively, however, bad access of net affects using Blackboard Collaborate in blended learning negatively.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".