Enhancing Academic Writing Skills in Freshmen: Evaluating the Impact of Blackboard Platform in the Classroom
Bibliographic record
Abstract
This study examines the impact of the Blackboard learning management system on the academic writing development of freshmen enrolled in a university-level writing course.Guided by an interpretive/constructivist paradigm, the research explores how technology-mediated instruction supports writing improvement through sustained drafting, continual feedback, and collaborative interaction.A mixed-methods case study design was employed to track eleven undergraduate students over one academic semester as they completed three major writing assignments, each requiring the submission of multiple drafts.Quantitative data were generated through systematic error analysis of all drafts and final submissions, focusing on linguistic, mechanical, and structural errors.A Repeated Measures ANOVA was used to determine whether statistically significant improvements occurred across drafts.Complementary qualitative data drawn from discussion board posts, email exchanges, and instructor observations were analyzed to identify patterns in student engagement, collaboration, and perceptions of the Blackboard-assisted learning environment.Findings show a significant reduction in writing mechanics errors over time, demonstrating measurable gains in writing fluency and technical accuracy.The study also highlights the value of Blackboard features such as discussion forums, peer-review tools, external resource links, and wordprocessing functions in promoting recursive revision, autonomous learning, and a collaborative environment.Students reported increased confidence, motivation, and appreciation for the platform's flexibility and constant accessibility.However, the platform had limited influence on substantive and cognitive writing skills, including idea development and content elaboration.Few participants also reported technical difficulties and challenges adapting to the platform.Overall, the study concludes that Blackboard is an effective tool for fostering foundational writing skills and supporting student-centered instruction.It underscores the importance of structured guidance and consistent feedback, and it advocates for the strategic use of learning management systems as complementary components in freshman writing pedagogy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".