Embracing digital learning: Benefits and challenges of using Canvas in education
Bibliographic record
Abstract
The use of Canvas as a Learning Management System (LMS) in educational settings involves several benefits and drawbacks. Canvas' design advances asynchronous learning, granting students to engage with materials at their own pace; thus, accommodating diverse learning needs and schedules. Integrated learning tools and collaborative features, including discussion forums and group projects, ground an interactive learning environment, enhancing student engagement, and mimicking real-world teamwork scenarios. Additionally, Canvas' data analytics grant instructors valuable student performance and engagement insights. This enables them to develop targeted interventions based on the student’s needs. However, technical issues, accessibility barriers, content readability challenges for dyslexic and non-native English speakers, depersonalization, and privacy concerns have emerged as significant drawbacks. This review is the first review that contrasts Canvas with other LMS platforms like Blackboard and Moodle. In order to maximize its educational benefits, we highlighted the differences in user satisfaction and ease of use and implied the importance of strategic implementation and support. This comprehensive and unbiased analysis will also be added to aid in developing the enhanced optimized practices for Canvas implementation. This includes instructor training, technical support, and strategies to foster online community and engagement, leveraging Canvas’ strengths while mitigating its limitations to enhance educational outcomes and students’ satisfaction.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".