7 research outputs found
Key courses of academic curriculum uncovered by data mining of students' grades
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 analyse
is independent from declared curricula of considered courses. The proposed
methodology is universal and can be applied for analysis of student's scores
and academic curriculum at any faculty
The role of emotional variables in the classification and prediction of collective social dynamics
We demonstrate the power of data mining techniques for the analysis of
collective social dynamics within British Tweets during the Olympic Games 2012.
The classification accuracy of online activities related to the successes of
British athletes significantly improved when emotional components of tweets
were taken into account, but employing emotional variables for activity
prediction decreased the classifiers' quality. The approach could be easily
adopted for any prediction or classification study with a set of
problem-specific variables.Comment: 16 pages, 9 figures, 2 tables and 1 appendi
Temporal Taylor's scaling of facial electromyography and electrodermal activity in the course of emotional stimulation
High frequency psychophysiological data create a challenge for quantitative
modeling based on Big Data tools since they reflect the complexity of processes
taking place in human body and its responses to external events. Here we
present studies of fluctuations in facial electromyography (fEMG) and
electrodermal activity (EDA) massive time series and changes of such signals in
the course of emotional stimulation. Zygomaticus major (ZYG, "smiling" muscle)
activity, corrugator supercilii (COR, "frowning"bmuscle) activity, and phasic
skin conductance (PHSC, sweating) levels of 65 participants were recorded
during experiments that involved exposure to emotional stimuli (i.e., IAPS
images, reading and writing messages on an artificial online discussion board).
Temporal Taylor's fluctuations scaling were found when signals for various
participants and during various types of emotional events were compared. Values
of scaling exponents were close to 1, suggesting an external origin of system
dynamics and/or strong interactions between system's basic elements (e.g.,
muscle fibres). Our statistical analysis shows that the scaling exponents
enable identification of high valence and arousal levels in ZYG and COR
signals
A calibrated measure to compare fluctuations of different entities across timescales
© 2020 The Authors. Published by Springer. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1038/s41598-020-77660-4A common way to learn about a system’s properties is to analyze temporal fluctuations in associated variables. However, conclusions based on fluctuations from a single entity can be misleading when used without proper reference to other comparable entities or when examined only on one timescale. Here we introduce a method that uses predictions from a fluctuation scaling law as a benchmark for the observed standard deviations. Differences from the benchmark (residuals) are aggregated across multiple timescales using Principal Component Analysis to reduce data dimensionality. The first component score is a calibrated measure of fluctuations—the reactivityRA of a given entity. We apply our method to activity records from the media industry using data from the Event Registry news aggregator—over 32M articles on selected topics published by over 8000 news outlets. Our approach distinguishes between different news outlet reporting styles: high reactivity points to activity fluctuations larger than expected, reflecting a bursty reporting style, whereas low reactivity suggests a relatively stable reporting style. Combining our method with the political bias detector Media Bias/Fact Check we quantify the relative reporting styles for different topics of mainly US media sources grouped by political orientation. The results suggest that news outlets with a liberal bias tended to be the least reactive while conservative news outlets were the most reactive.The work was partially supported as RENOIR Project by the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska–Curie Grant Agreement No. 691152 and by Ministry of Science and Higher Education (Poland), Grant Nos. 34/H2020/2016, 329025/PnH/2016 and by National Science Centre, Poland Grant No. 2015/19/B/ST6/02612. J.A.H. was partially supported by the Russian Scientific Foundation, Agreement #17-71-30029 with co-financing of Bank Saint Petersburg and by POB Research Centre Cybersecurity and Data Science of Warsaw University of Technology within the Excellence Initiative Program—Research University (IDUB).Published onlin