DEVELOPING PROFESSIONAL COMPETENCE OF FACULTY AND STUDENTS OF CIVIL ENGINEERING TO MEET THE GLOBAL STANDARDS
Bibliographic record
Abstract
In 21st century, the major challenge in engineering education in India is to meet the demands of technical profession and emerging job market. Now a days the education pattern, nature of jobs and services are fast changing across the world. Skill is becoming a commodity that can be bought from low-cost providers anywhere across the globe. Also in the digital world, knowledge is no longer confined to experts only, rather computer and internet connectivity has empowered every citizen to look for anything and everything. In this context, the engineering education of any country is very critical/crucial for determining its global positioning as well as ensuring the prosperity of their citizens. So engineering education system should be modified to enable our students to develop the skills like creativity and innovation, communication, critical thinking, interpersonal skills, collaboration and teamwork. Communication and collaboration are identified as an essential competencies by almost all of the organizations who are seeking competent employees. All the professional/accreditation bodies like Accreditation Board for Engineering and Technology (ABET), Washington Accord or National Board of Accreditation (NBA), India have already elaborately stated about the students learning outcomes, program educational outcomes, list of competencies, professional/ ethical responsibilities of engineers/ engineering educators/ academic institutions. The aim of this study is to define parameters for measuring competence required for engineering faculty and students in meeting the global standards. The study also focused to discuss current scenario of Indian engineering education system giving special attention to Kerala. Also, the study aspired to develop a competence measuring model.
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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".