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Abstract

The study concerns the functioning of disabled learners at the level of higher education. The period of university studies is usually associated with the change of the life routine practised so far. The changes comprise various fields and bring about the adjustment to new roles. This stage is a chance for becoming independent, for specifying one’s position in the group, for shaping one’s own “young adult world view”18. The article is aimed at presenting the typology of disabled students based on an analysis of the behaviour which they show as members of the academic community. The sources of these behaviour patterns can be sought in various interpersonal and intrapersonal factors resulting from the individual’s character. The presented authorial typology has been presented also in reference to the Personality Traits Theory formulated by Costa and McCrea. This has allowed for broadening this typology with some particular subtypes.

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Authors and Affiliations

Magdalena Bełza-Gajdzica
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Abstract

The aim of the present study was to explore the role of temporal intelligence in English as a Foreign Language (EFL) learners’ self-regulation and self-efficacy. To this end, a general temporal intelligence (GTI-S) scale was designed based on the subconstructs of time in the literature. The scale, along with the learning self-regulation questionnaire (SRQ-L) and the English self-efficacy scale was administered to 520 EFL learners. To validate the GTI-S, confirmatory factor analysis (CFA) was run. The results of Pearson product-moment correlations demonstrated significantly positive relationships between temporal intelligence and controlled self-regulation, automatic self-regulation and self-efficacy (p<.05). Moreover, the findings of multiple regressions revealed that Linearity of Time, Economicity of Time, and Multitasking are the most important subconstructs of time with relation to these variables.

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Authors and Affiliations

Elham Naji Meidani
Reza Pishghadam
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Abstract

Customer churn prediction is used to retain customers at the highest risk of churn by proactively engaging with them. Many machine learning-based data mining approaches have been previously used to predict client churn. Although, single model classifiers increase the scattering of prediction with a low model performance which degrades reliability of the model. Hence, Bag of learners based Classification is used in which learners with high performance are selected to estimate wrongly and correctly classified instances thereby increasing the robustness of model performance. Furthermore, loss of interpretability in the model during prediction leads to insufficient prediction accuracy. Hence, an Associative classifier with Apriori Algorithm is introduced as a booster that integrates classification and association rule mining to build a strong classification model in which frequent items are obtained using Apriori Algorithm. Also, accurate prediction is provided by testing wrongly classified instances from the bagging phase using generated rules in an associative classifier. The proposed models are then simulated in Python platform and the results achieved high accuracy, ROC score, precision, specificity, F-measure, and recall.
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Authors and Affiliations

Anitha M A
1
Sherly K K
2

  1. Faculty of Computer Science and Engineering, College of Engineering Cherthala, Alappuzha, Kerala, India
  2. Information Technology Department, Rajagiri School of Engineering & Technology Kochi-682039, Kerala, India

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