Distance learning with a personalized system of instruction
Bibliographic record
Abstract
Distance learning is growing everywhere. In Brazil distance learning courses are becoming more common and educational institutions are authorized to develop distance learning programs. Nevertheless, distance learning courses as any other teaching procedures, will be effective only if teaching contingencies are carefully planned and implemented. Behavior analysis as a discipline has accumulated technology that is suitable for distance learning, based on the Personalized System of Instruction (PSI), first developed by Keller, in 1968. PSI courses are characterized by: course content is broken down in small units, learning goals are previously established, studying pace depends on the student, mastery is a requisite on each unit, emphasis on written material, immediate feedback for students, proctors. A distance learning program that uses the internet, called Computer-aided Personalized System of Instruction (CAPSI), developed at the University of Manitoba, Canada, some 20 years, has been applied to many disciplines with promising results. CAPSI courses have all the characteristics of PSI courses and are taken by students through the internet. The system makes tests and exams available to the student, records students performances and progress, and manages aspects of the course, such as sending tests for correction. Tests are taken when students apply for them and are marked by teachers, instructors and/or proctors (advanced students). Tests become eligible when students master previous tests. This study was conducted to test the generality of previous research on CAPSI with Brazilian students, with a course on Behavior Analysis Principles. 77 students (62 from the same teaching institution) and the others from other state were enrolled, but only for 33 of them the course was initially mandatory (as part of their professional training). The mandatory status was changed on the 9th week of the course. The following variables are considered as the course results: dropouts, students performances on tests and exams, level of difficulty of tests, students activities as proctors, precision and content of feedbacks to students, feedback effects, and students assessment of the course. Results showed a larger number of dropouts when compared with the literature. Other results are consistent with the literature: students grades are high and students evaluation of the course is similar to those reported previously. The higher percentage of dropouts is discussed as s a probable function of students previous history as well as the elective character of the course
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".