Creative collaborative learning for macroeconomics: C-span video clips in MBA classroom
Bibliographic record
Abstract
Creative collaborative learning (CCL) is attempted in a classroom environment for studying macroeconomics for a global economy where the frontier of models and theories are often shaped by decision makers in global and national institutions. The methodology is suitable for student-centered learning MBA students who must put themselves through realistic situations, asking right questions and making decisions. Traditional top-down methodology of emphasizing model building and mathematical proofs in studying macroeconomics are not suitable at the MBA level. The proposed CCL model in this study entails the joint efforts of three groups of players' the professionals, the students and the instructor. Constructive knowledge is acquired not by drill and memorization of definitions, but by learning from the contexts in which terminologies are pragmatically applied, utilizing critical thinking. Students in an MBA class were asked to form country-focus teams, identifying country macroeconomic indicators as well as specific issues affecting infrastructure and performance of a country. Specific video clips were searched and reviewed in C-span video library. This search and review exercise were analyzed by evaluating their effectiveness in motivating interests, learning of abstract terminologies, professional manner and articulation method, and recognizing the role of important institutions through the speaking professionals. Our research shows that if students hear a terminology from a professional in a particular field, they connect the term with an experience of listening to the person and also with a face of the person and with the institution where he/she is affiliated with. An abstract concept becomes easier, more pragmatic, and more fun to learn beyond memorization. On that dimension, the researchers' classroom experiment achieved good success. However, CCL demands evaluations for "in-the-moment" expressions and quotations that can "elevate thinking" in a student-centered learning environment. CCL is effective for some clips but not generally. Our research also looks into how design of learning activities can better achieve CCL. Keywords: Creative Collaborative Learning, C-Span Video Clips, MBA Macroeconomics
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".