E-SIP: Website-Based Scheduling Information System to Increase the Effectivity of Lecturer's Performance and Learning Process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The development of internet and information technology made a system to the practical, effective, and efficient. One of the impacts is ease to make scheduling information system in university. This research is research and development (R&D) models which have aim to develop the website-based scheduling information system to increase the effectivity of lecturer’s performance and learning process in IAIN Tulungagung. E-SIP program develop by using software development life cycle in term of waterfall model. Waterfall model was selected because it was easy and efficient. This system development consisted of system need analysis, system design, implementation, testing, dissemination, and maintenance. Data collecting system using literature review, field study, and interview. Furthermore, data also collected from questionnaire scores of E-SIP validations conducted by the validator and respondents, in terms of admins. After doing some development phase and trial, the E-SIP was developed and ready to use to make scheduling process in IAIN Tulungagung. E-SIP possibly runs with using any browser supporting the java-script system. The development result of E-SIP is in terms of login page for users in three levels. The first level is Super Admin which is responsible to input all databases needed for E-SIP. The second level is Department Admin, which is responsible to arrange the individual schedule of departments. The third level is Faculty Admin, which only able to see the schedule arranged by Department Admin. The similarity of all levels is to print and see the schedule. The advantage of E-SIP is that the scheduling system is able to be controlled by online and performed simultaneously at one time as well as to check overlapping or nonautomatic schedules in the website. Besides, the disadvantage happens when there is a bug or an error resulting in that the system does not function properly and needs to be immediately resolved.
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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 | 0.003 |
| 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 it