The Use of Debates as an Approach to Deliver the Course Entitled “The Impact of US Policy on Integration Processes in Europe in the Post-Bipolar Era”
Bibliographic record
Abstract
The purpose of this research was to identify how the use of a debate-based course delivery approach merged with a flipped classroom model influenced the students’ academic outcomes and motivation in relation to their intelligence type and how the sampled students perceived the course delivery approach and certain debate-related activities. Sampled students’ academic performance records, an evaluation survey to obtain students’ feedback on both the course delivery approach and the effectiveness of the activities like ‘Think-Pair-Share’, ‘Write-Pair-Share’, ‘Illogical story-telling’, ‘Treasure Hunt’, case-study, ‘One Minute Paper’, ‘Attitude/motivation test battery’ as intelligence type-based diagnostics of learners’ motivation, and a focus-group semi-structured interview were used as the instruments. SPSS 10.0.5 computer statistical package was used to process data. The use of debates to deliver the instructional content to the tertiary students can be considered a three-vector approach capable to bring a positive change to learning motivation, cognitive (intellectual) activity, self-esteem (self-efficacy) of a student and the overall quality of the vocational training system of the historians and lawyers-to-be. This study boosts the methodology of vocational training of the students majoring in humanities like History/Law in terms of fostering the 21st century-competencies and it adds a different perspective to the theory on relation between the type of intelligence and skills. This approach fosters learner autonomy and positive perception of challenging educational activities. It was found that it was prerequisite for the success of the above approach that there was a well-trained debate moderator, and debate-procedure-aware and trained students. The further research is needed in purposeful introduction of NLP training into the above model and examination its impacts.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".