Detecting Latent Training Needs Using Large Datasets
Event details
| Date | 02.07.2018 |
| Hour | 10:00 › 12:00 |
| Speaker | Ramtin Yazdanian |
| Location | |
| Category | Conferences - Seminars |
EDIC candidacy exam
Exam president: Prof. Karl Aberer
Thesis advisor: Prof. Pierre Dillenbourg
Thesis co-advisor: Prof. Robert West
Co-examiner: Prof. Daniel Gatica-Perez
Abstract
Training Need Analysis (TNA) is a field in management, concerned with detecting, understanding and subsequently addressing the learning needs of individuals, organisations, and entire professions. The traditional processes used for TNA are slow and run the risk of falling behind rapidly changing training needs. In this document, we present our research plan for addressing TNA - with a focus on the profession level - using large, existing and continually updated datasets. We believe that our methods should be capable of significantly speeding up the existing processes by providing various informative summaries of relevant data sources to human decision makers, who have the final say in the strategic decisions regarding the creation of training programs.
Background papers
What are mobile developers asking about? A large scale study using stack overflow, by Rosen, C., Shihab, E.
Training needs analysis. A literature review and reappraisal, by Gould, D., et al.
Machine Beats Experts: Automatic Discovery of Skill Models for Data-Driven Online Course Refinement, by Matsuda, B., et al.
Exam president: Prof. Karl Aberer
Thesis advisor: Prof. Pierre Dillenbourg
Thesis co-advisor: Prof. Robert West
Co-examiner: Prof. Daniel Gatica-Perez
Abstract
Training Need Analysis (TNA) is a field in management, concerned with detecting, understanding and subsequently addressing the learning needs of individuals, organisations, and entire professions. The traditional processes used for TNA are slow and run the risk of falling behind rapidly changing training needs. In this document, we present our research plan for addressing TNA - with a focus on the profession level - using large, existing and continually updated datasets. We believe that our methods should be capable of significantly speeding up the existing processes by providing various informative summaries of relevant data sources to human decision makers, who have the final say in the strategic decisions regarding the creation of training programs.
Background papers
What are mobile developers asking about? A large scale study using stack overflow, by Rosen, C., Shihab, E.
Training needs analysis. A literature review and reappraisal, by Gould, D., et al.
Machine Beats Experts: Automatic Discovery of Skill Models for Data-Driven Online Course Refinement, by Matsuda, B., et al.
Practical information
- General public
- Free
Contact
- EDIC - [email protected]