Hypocampus - Evidence based digital learning
| Reference number | |
| Coordinator | Hypocampus AB |
| Funding from Vinnova | SEK 300 000 |
| Project duration | March 2017 - October 2017 |
| Status | Completed |
| Venture | Innovative Startups |
Purpose and goal
The project aims to investigate the possibility to create a next generation of adaptive learning platforms by using Educational Data Mining, EDM, and Machine Learning, ML. In phase 1 we developed EDM and the gathered data has been analysed by a research group in ML. The analyse concludes that the data gives unique information for how learning process and cognitive profile correlates with learning outcome and knowledge level. The goals of Phase 1 are fulfilled and there is a high possibility that ML can be applied to create optimal adaptive study plans and predictive models for study results
Expected results and effects
In phase 1 we developed EDM and gathered data 20 million rows of data that has been analysed by DISA (research group in ML). The analyse concludes that the data gives unique information for how learning process and cognitive profile correlates with learning outcome and knowledge level. The goals of Phase 1 are fulfilled and we have concluded that the data collection produces unique information on which ML can be applied to create optimal adaptive study plans and predictive models for study results.
Planned approach and implementation
-Identified key parameters from previous research -Created support for big data and collected ca 20M database rows from 2400 medical students over 2 months -Found that the system in combination with the user base has unique potential compared to other products since we can get more and better data -The data has been analysed by LNU’s “Center for Learning and Knowledge Technologies”. Furthermore, suitable ML algorithms has been Identified and a plan for implementing the functionality has been constructed.