Modeling E-Behaviour, Personality and Academic Performance with Machine Learning

The analysis of student performance involves data modelling that enables the formulation of hypotheses and insights about student behaviour and personality. We extract online behaviours as proxies to Extraversion and Conscientiousness, which have been proven to correlate with academic performance. The proxies of personalities we obtain yield significant (p<0.05) population correlation coefficients for traits against grade—0.846 for Extraversion and 0.319 for Conscientiousness. Furthermore, we demonstrate that a student’s e-behaviour and personality can be used with deep learning (LSTM) to predict and forecast whether a student is at risk of failing the year. Machine learning procedures followed in this report provide a methodology to timeously identify students who are likely to become at risk of poor academic performance. Using engineered online behaviour and personality features, we obtain a classification accuracy (κ) of students at risk of 0.51. Lastly, we show that we can design an intervention process using machine learning that supplements the existing performance analysis and intervention methods. The methodology presented in this article provides metrics that measure the factors that affect student performance and complement the existing performance evaluation and intervention systems in education.

Stimulus-dependent representational drift in primary visual cortex

To produce consistent sensory perception, neurons must maintain stable representations of sensory input. However, neurons in many regions exhibit progressive drift across days. Longitudinal studies have found stable responses to artificial stimuli across sessions in visual areas, but it is unclear whether this stability extends to naturalistic stimuli. We performed chronic 2-photon imaging of mouse V1 populations to directly compare the representational stability of artificial versus naturalistic visual stimuli over weeks. Responses to gratings were highly stable across sessions. However, neural responses to naturalistic movies exhibited progressive representational drift across sessions. Differential drift was present across cortical layers, in inhibitory interneurons, and could not be explained by differential response strength or higher order stimulus statistics. However, representational drift was accompanied by similar differential changes in local population correlation structure. These results suggest representational stability in V1 is stimulus-dependent and may relate to differences in preexisting circuit architecture of co-tuned neurons.

Demographic contraction as an indicator of the problems of single-industry municipalities

Migration processes associated with the outflow of the population from single-industry municipalities (monotowns) are becoming one of the components of global demographic changes on the territory of the Russian Federation. One of the tools to curb demographic contraction in the territory of single-industry municipalities can be the strengthening of diversification of the economy of monotowns, which should contribute to improving the quality of life of the population, including through a change in the structure of employment. The article analyzes the existing trends in the socio-economic development of single-industry towns of various types and shows that a significant number of them are characterized by demographic contraction, caused, among other things, by outflow of the working age population. Correlation analysis for a number of indicators characterizing demographic processes in single-industry municipalities showed a weak connection between them. Population survey data for 2016 and 2019 indicate a low assessment of measures to support single-industry towns by the population. The most significant factors for residents, indicating a favourable level of socio-economic development of the single-industry entity, in the opinion of the respondents, are employment opportunities, a decent level of wages, the quality of medical care, the quality of housing and utilities, and the ecological situation.

Bayesian population correlation: A probabilistic approach to inferring and comparing population distributions for detrital zircon ages

Populations of detrital zircons are shaped by geologic factors such as sediment transport, erosion mechanisms, and the zircon fertility of source areas. Zircon U-Pb age datasets are influenced both by these geologic factors and by the statistical effects of sampling. Such statistical effects introduce significant uncertainty into the inference of parent population age distributions from detrital zircon samples. This uncertainty must be accounted for in order to understand which features of sample age distributions are attributable to earth processes and which are sampling effects. Sampling effects are likely to be significant at a range of common detrital zircon sample sizes (particularly when n < 300).In order to more accurately account for the uncertainty in estimating parent population age distributions, we introduce a new method to infer probability model ensembles (PMEs) from detrital zircon samples. Each PME represents a set of the potential parent populations that are likely to have produced a given zircon age sample. PMEs form the basis of a new metric of correspondence between two detrital zircon samples, Bayesian Population Correlation (BPC), which is shown in a suite of numerical experiments to be unbiased with respect to sample size. BPC uncertainties can be directly estimated for a specific sample comparison, and BPC results conform to analytical predictions when comparing populations with known proportions of shared ages. We implement all of these features in a set of MATLAB® scripts made freely available as open-source code and as a standalone application. The robust uncertainties, lack of sample size bias, and predictability of BPC are desirable features that differentiate it from existing detrital zircon correspondence metrics. Additionally, analysis of other sample limited datasets with complex probability distributions may also benefit from our approach.

Reliability and Validity of Compulsive Buying Scale Without Middle Point

Objective: The study aims at finding out the affect of reliability and validity for the compulsive buying behavior scale by Valence, d’ Astou’s and Fortier Scale without middle point. Methodology: Responses across 5 to 9 point scales are obtained to calculate the reliability and validity of compulsive buying behavior scale by Valence, d’ Astou’s and Fortier Scale. Cronbach’s alpha is used to measure the internal reliability of the scale. To compare the reliability coefficients among different scale points Feldt test and Hakstian-Whalen test are used. Convergent validity for measuring inter-correlations between scales with different numbers of response categories is used and Fisher’s –r to –z transformation is used to test population correlation coefficient. Conclusion: From study it is concluded that there is no change in reliability and validity when the middle point is dropped from the compulsive buying scale.

Stimulus-dependent representational drift in primary visual cortex

ABSTRACTTo produce consistent sensory perception, neurons must maintain stable representations of sensory input. However, neurons in many regions exhibit progressive drift across days. Longitudinal studies have found stable responses to artificial stimuli across sessions in primary sensory areas, but it is unclear whether this stability extends to naturalistic stimuli. We performed chronic 2-photon imaging of mouse V1 populations to directly compare the representational stability of artificial versus naturalistic visual stimuli over weeks. Responses to gratings were highly stable across sessions. However, neural responses to naturalistic movies exhibited progressive representational drift across sessions. Differential drift was present across cortical layers, in inhibitory interneurons, and could not be explained by differential response magnitude or higher order stimulus statistics. However, representational drift was accompanied by similar differential changes in local population correlation structure. These results suggest representational stability in V1 is stimulus-dependent and related to differences in preexisting circuit architecture of co-tuned neurons.