Do Country Stereotypes Exist in PISA? A Clustering Approach for Large, Sparse, and Weighted Data
Saarela, M., & Kärkkäinen, T. (2015). Do Country Stereotypes Exist in PISA? A Clustering Approach for Large, Sparse, and Weighted Data. In O. Santos, J. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, . . . M. Desmarais (Eds.), EDM 2015 : Proceedings of the 8th International Conference on Educational Data Mining (pp. 156-163). International Educational Data Mining Society,. Retrieved from http://www.educationaldatamining.org/EDM2015/uploads/papers/paper_92.p...
© the Authors & International Educational Data Mining Society, 2015.
Certain stereotypes can be associated with people from different countries. For example, the Italians are expected to be emotional, the Germans functional, and the Chinese hard-working. In this study, we cluster all 15-year-old students representing the 68 different nations and territories that participated in the latest Programme for International Student Assessment (PISA 2012). The hypothesis is that the students will start to form their own country groups when clustered according to the scale indices that summarize many of the students’ characteristics. In order to meet PISA data analysis requirements, we use a novel combination of our previously published algorithmic components to realize a weighted sparse data clustering approach. This enables us to work with around half a million observations with large number of missing values, which represent the population of more than 24 million students globally. Three internal cluster indices suitable for sparse data are used to determine the number of clusters and the whole procedure is repeated recursively to end up with a set of clusters on three different refinement levels. The results show that our final clusters can indeed be explained by the actual student performance but only to a marginal degree by the country. ...
PublisherInternational Educational Data Mining Society
Is part of publicationEDM 2015 : Proceedings of the 8th International Conference on Educational Data Mining, ISBN 978-84-606-9425-0
MetadataShow full item record
Showing items with similar title or keywords.
Saarela, Mirka; Hämäläinen, Joonas; Kärkkäinen, Tommi (Springer International Publishing, 2017)A clustering result needs to be interpreted and evaluated for knowledge discovery. When clustered data represents a sample from a population with known sample-to-population alignment weights, both the clustering and the ...
Saarela, Mirka; Kärkkäinen, Tommi (ESANN, 2015)Clustering as an unsupervised technique is predominantly used in unweighted settings. In this paper, we present an efficient version of a robust clustering algorithm for sparse educational data that takes the weights, ...
Body weight dissatisfaction and communication with parents among adolescents in 24 countries: international cross-sectional survey Al Sabbah, Haleama; Vereecken, Carine A.; Elgar, Frank J.; Nansel, Tonja; Aasvee, Katrin; Abdeen, Ziad; Ojala, Kristiina; Ahluwalia, Namanjeet; Maes, Lea (BioMed Central, 2009)BACKGROUND: Parents have significant influence on behaviors and perceptions surrounding eating, body image and weight in adolescents. The aim of this study was to examine the prevalence of body weight dissatisfaction, ...
Lozano Garcia, Ana Maria (2017)This research is grounded in the planning concept of Integrated Solid Waste Management (ISWM). ISWM was developed to promote sustainability in the waste management for developing countries. Waste managers and policy makers ...
Comparing the outcomes of two different approaches to CEFR-based rating of students’ writing performances across two European countries Holzknecht, Franz; Huhta, Ari; Lamprianou, Iasonas (Pergamon Press, 2018)This study investigated to what extent two teams of experienced raters from different European countries (Finland and Austria), using their own CEFR-based rating scale (one holistic and one analytic), agreed on the CEFR ...