PART B: VALORISATION PLAN
|1||Please provide a short description of the state-of-the-art and/or current trends in the field? How does the result fit into it?|
|Educational data mining (EDM) is an emerging domain concerned with developing methods for exploring the unique types of data that come from an educational context. Given the widespread use of e-learning and the vast amount of data accumulated recently, researchers in various fields have begun to investigate data mining methods to improve e-learning systems. Educational Data Mining discipline discusses issues regarding state-of-the-art applications of data mining techniques in education. The most important data mining techniques may be used on data providing from educational environments.
Educational Data Mining (EDM) may be applied to extract patterns from student data, but it is usually employed after the course has finished, for instance to analyze student behavior. The idea of carrying out data mining as an integrated, functional part of the system has yet to be explored.
The main research area of the product is Educational Data Mining. The effort of integrating state of the art Machine Learning/Information Retrieval algorithms will create a high quality intelligent e-Learning environment.
|2||What is the problem/need/knowledge gap that the research result is responding to? How was it addressed before?|
|Educational Data Mining domain is currently one of the most interesting issues in e-Learning. There are several efforts of building intelligent and adaptive environments but the results are not yet convincing. This is due to the novelty and high difficulty of understanding and integrating state of the art machine learning/information retrieval algorithms into e-Learning.
|3||What is the potential for further research?|
|The potential of future research is tremendous. Building Machine Learning applications means building software that continuously improves the representation of data. This is usually named data modeling and the aim of the whole process is to obtain more and more accurate data models. The obtained result may be called knowledge or lately wisdom. Currently and future existing algorithms need to be adapted to work into e-Learning domain and their efficiency must be proved.|
|4||What is the proposed method of IPR-protection? (patent, license, trademark etc.)|
|The name of the product may be protected by a trademark.
The usage of the product must be under license.
The specific innovations that may be obtained from the research must be protected by patets.
|5||What are the steps that need to be taken in order to secure the IPR-protection? What is the cost of IPR-protection?|
|The product may be patented at national or European level.
The stages which should be covered for assuring the intellectual property rights are:
1. Drawing up the documentation of patent application –200 euros.
2. Recording the patent request at OSIM (State office for Inventions and Trademarks) – 35 euros.
3. Publishing by OSIM the patent request –58 euros.
4. Examination of the patent request –575 euros.
5. Granting the patent and its issuing –115 euros.
6. Maintaining in force the patent for the entire validity period –6809 euros.
For a possible protection by European patent, costs of about 27000 euros are foreseen.
|6||What is you overall assessment of the scientific maturity of the research result?|
|The overall assessment of the scientific maturity may be considered in the prototype phase. Many of-line data analysis architectures have been tested. These tests used many algorithms and different sets of features were taken into consideration.
Lately, a clustering procedure has been used to work in an on-line real time recommender system.
KEYWORDS QUANTITATIVE ASSESSMENT (0-5).