Determining the Difficulty Level of Tasks in Online Courses
Bibliographic record
Abstract
Online courses on different platforms provide thousands of students with the knowledge and skills they need. This paper presents the results of a survey of students, during which they expressed their opinion on the use of electronic resources in teaching. The survey showed that students are more motivated to study when they understand how their knowledge will be used in their professional activities. The survey results also showed that the objectivity of knowledge control is essential. Students are usually familiar with the criteria for assessing the performance of the task. Knowing the criteria for evaluating the task itself, understanding why it is possible to get this particular number of points for completing the task will help students to approach their studies more responsibly. We analyzed the tasks that will be offered to students in the course of learning the MAXScript language. These tasks are assessed according to factors that affect their complexity and the maximum number of points that students can receive for their correct performance. The resulting complexity value can be adjusted after analyzing the scripts written and the trainees' time. This approach to assessing tasks can be applied in the study of information technology and other disciplines.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".