Assessment of Quipper as Learning Management System of Saint Paul University Surigao
Bibliographic record
Abstract
<strong>ABSTRACT</strong>: A school learning management system (LMS) is extremely important in the current educational landscape since it transforms how educational institutions administer and deliver their courses. LMS gives educators a strong platform to produce, organize, and distribute instructional content while enabling students to access resources, participate in interactive learning activities, and collaborate with peers by seamlessly integrating technology into the learning process. This study was conducted to assess the level of utilization, level of satisfaction, and level of effectiveness with Quipper as an LMS in Saint Paul University Surigao (SPUS), Surigao City, Philippines. The research design was quantitative, and researchers used a survey questionnaire to gather data from the college faculty of SPUS during the academic year 2022-2023. To ensure reliable results, 33 college teachers were purposefully and conveniently chosen as respondents. The data were analyzed using various parametric and non-parametric statistical tools considering the normality of the data. The study revealed that variables such as sex, age, department, highest educational attainment, and years of teaching experience did not significantly impact Quipper's utilization and satisfaction among teachers. The profile of college faculty members also did not significantly affect Quipper's effectiveness as a learning management system. However, the department variable significantly influenced Quipper's performance, with frequent utilization leading to higher satisfaction and effectiveness. Overall, Quipper was widely used, effective, and met the needs of the college faculty at SPUS. The study suggested room for improvement, and the administration could establish clear implementation goals, offer incentives to consistent users, provide proper training and guidance, and encourage teachers to explore more features to better support students' learning.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".