Search

The main objective of this work is to show the potentialities of recently developed approaches for automatic knowledge extraction directly from the universities’ websites. The information automatically extracted can be potentially updated with a frequency higher than once per year, and be safe from manipulations or misinterpretations. Moreover, this approach allows us flexibility in collecting indicators about the efficiency of universities’ websites and their effectiveness in disseminating key contents. These new indicators can complement traditional indicators of scientific research (e.g. number of articles and number of citations) and teaching (e.g. number of students and graduates) by introducing further dimensions to allow new insights for “profiling” the analyzed universities.

Educational Institutions data constitute the basis for several important analyses on the educational systems; however they often contain not negligible shares of missing values, for several reasons. We consider in this work the relevant case of the European Tertiary Education Register (ETER), describing the Educational Institutions of Europe. The presence of missing values prevents the full exploitation of this database, since several types of analyses that could be performed are currently impracticable. The imputation of artificial data, reconstructed with the aim of being statistically equivalent to the (unknown) missing data, would allow to overcome these problems. A main complication in the imputation of this type of data is given by the correlations that exist among all the variables. We propose several imputation techniques designed to deal with the different types of missing values appearing in these interconnected data. We use these techniques to impute the database. Moreover, we evaluate the accuracy of the proposed approach by artificially introducing missing data, by imputing them, and by comparing imputed and original values. Results show that the information reconstruction does not introduce statistically significant changes in the data and that the imputed values are close enough to the original values. • Imputation methodology for educational data using information reconstruction and donors. • Designed to satisfactory impute the difficult case of interconnected time series. • Application to an important real case: European Tertiary Education Register (ETER). • Uses a formal and data-driven approach, hence applicable in other contexts. • Analysis of the imputation accuracy by using artificial missing data. [ABSTRACT FROM AUTHOR]