The phrase "as educational data" is correct and usable in written English.
It can be used when referring to information or statistics that are related to education or learning.
Example: "The research findings were analyzed and presented as educational data to inform policy decisions."
Alternatives: "in the context of educational information" or "as learning-related data".
As such, educational data mining (EDM) has emerged as a field in itself to resolve such research issues (Romero and Ventura 2010).
Although underrepresentation of controls in the lowest educational level cannot be ruled out, this could not be proven as educational information for the 40ties and 50ties were not available because census data were collected starting from year 1975 [ 35].
The first part included background information, i.e. demographic, social and educational data, economic status, as well as asking whether they had ever heard about osteoporosis.
This type of technology is especially useful for mining educational data as it is known for its universality in many applications and for its high performance (Mansmann et al. 2014).
Therefore, it can be concluded that VQ_fk_nps is suitable for educational data sets as compared to the others which have been examined.
This is because Hathaway and Bezdek [9] has taken into account the ubiquity of incomplete data in the same way as we found in the educational data sets earlier discussed in Sect.
University College London has confirmed that analysis of educational data such as those in the present analysis is exempt from requiring ethical permission (see http://ethics.grad.ucl.ac.uk/exemptions.php).php
Here, we describe our environment Inq-ITS (inquiry intelligent tutoring system), that uses educational data mining to assess science inquiry skills, as described as 21st century skills.
The resulting algorithm, named VQ_fk_nps, is our novel incomplete educational data clustering approach as compared to the algorithms K_nps and S_nps which have been achieved in [24].
As shown through the experimental results on real educational data sets, the clusters from our solution have better cluster quality as compared to some existing approaches.
