A Quality Assurance Scheme of Higher Engineering Education Accreditation Based on Expert Opinions Integration
Yujie Feng, Bin Duan, Xiaoxiao Guo
Available Online August 2016.
- https://doi.org/10.2991/iceemt-16.2016.23How to use a DOI?
- Coordinated decision making mechanism, Expert opinions integration, Knowledge automation, Machine learning, Teaching evaluation.
- With the constantly advancing of university engineering education accreditation, in the curricular of assessment of teaching quality, establishing an effective quality guarantee system that meets the standard of engineering education accreditation becomes one of issues that need to be addressed urgently in higher education reform. The paper proposes a new Higher Engineering Education Accreditation evaluation scheme to investigate the issue of teaching evaluation in higher education where decision management cycle makes a decision based on integrated expert opinions and generated knowledge in which machine learns, analyzes huge chunks of education data and makes predictions. A collaboration decision mechanism is implemented to assist to make multi-target cooperative decisions for evaluation of teaching, which provides more high-quality expert opinions for teaching. A practical case study has been successfully validated for the adaptability and practicability of the quality assurance scheme where Bayesian techniques process many variables and large of observations to achieve expert opinions integration. The scheme reached a degree of predictive ability, generalization ability, and self-improvement and it can improve the efficiency of teaching management decision.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Yujie Feng AU - Bin Duan AU - Xiaoxiao Guo PY - 2016/08 DA - 2016/08 TI - A Quality Assurance Scheme of Higher Engineering Education Accreditation Based on Expert Opinions Integration BT - 2016 International Conference on Education, E-learning and Management Technology PB - Atlantis Press SP - 120 EP - 125 SN - 2352-5398 UR - https://doi.org/10.2991/iceemt-16.2016.23 DO - https://doi.org/10.2991/iceemt-16.2016.23 ID - Feng2016/08 ER -