Creating Creative Thinking in Students: A Business Research Perspective
Bibliographic record
Abstract
The objective of this study is to investigate how to create creative thinking in students through encouraging students’ logical thinking, motivation, and collaborative learning. The study also attempts to find suitable teaching procedures for the research subject. This study is based on qualitative research. Participants were graduate students studying business research methods. The results indicate that logical thinking affects the analytical skill. This skill, in turn, affects students’ creative thinking. A model of creating creative thinking in students is proposed from the research findings. Instructors may consider using the modeling to boost creative thinking in students. In addition, the findings suggest that the main teaching processes should be as follows: Instructors should encourage students to use their logical reasoning during the conceptual framework development. Workshops on students’ research projects should be conducted so students can practice doing research. Students should make oral presentations of their projects and experts invited to comment on them. Collaborative technologies need to be introduced so that instructors and students can communicate with each other on assignments. Apart from collaborative tools, instructors can set up additional sessions after hours to allow students to discuss problems they are facing. Research classes should incorporate in the coursework three student presentations: problem statement, research proposal, and completed research report. Finally, instructors should form students into groups and establish roles for the members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| 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".