Key courses of academic curriculum uncovered by data mining of students' grades

Jan Chołoniewski , Łukasz Gajewski , Janusz Hołyst


Learning is a complex cognitive process that depends not only on an individual capability of knowledge absorption but it can be also influenced by various group interactions and by the structure of an academic curriculum. We have applied methods of statistical analyses and data mining (principal component analysis and maximal spanning tree) for anonymized students' scores at Faculty of Physics, Warsaw University of Technology. A slight negative linear correlation exists between mean and variance of course grades, i.e. courses with higher mean scores tend to possess a lower scores variance. There are courses playing a central role, e.g. their scores are highly correlated to other scores and they are in the centre of corresponding maximal spanning trees. Other courses contribute significantly to students' score variance as well to the first principal component and they are responsible for differentiation of students' scores. Correlations of the first principal component to courses' mean scores and scores variance suggest that this component can be used for assigning ECTS points to a given course. The analysis is independent of declared curricula of considered courses. The proposed methodology is universal and can be applied for analysis of students' scores and academic curriculum at any faculty.
Author Jan Chołoniewski PFENS
Jan Chołoniewski,,
- Center of Physics in Economics and Social Sciences
, Łukasz Gajewski PFENS
Łukasz Gajewski,,
- Center of Physics in Economics and Social Sciences
, Janusz Hołyst PFENS
Janusz Hołyst,,
- Center of Physics in Economics and Social Sciences
Journal seriesActa Physica Polonica A, ISSN 0587-4246
Issue year2016
Publication size in sheets0.5
Keywords in EnglishCurricula; Data mining; Education; Students; Teaching; Telecommunication networks; Trees (mathematics), Cognitive process; First principal components; Group interaction; Highly-correlated; Knowledge absorptions; Linear correlation; Students' grades; Warsaw University of Technology, Principal component analysis
projectReverse EngiNeering of sOcial Information pRocessing. Project leader: Hołyst Janusz, , Phone: 22 234 7133, application date 28-04-2015, start date 01-01-2016, end date 31-12-2019, 691152, Implemented
WF Horizon 2020 [Horyzont 2020]
Languageen angielski
Key courses of academic curriculum uncovered by data.pdf 795.91 KB
Score (nominal)15
ScoreMinisterial score [Punktacja MNiSW] = 15.0, 28-11-2017, ArticleFromJournal
Ministerial score (2013-2016) [Punktacja MNiSW (2013-2016)] = 15.0, 28-11-2017, ArticleFromJournal
Publication indicators WoS Impact Factor [Impact Factor WoS]: 2016 = 0.469 (2) - 2016=0.489 (5)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.