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Journal

2016 | 2 (64) | 13-21

Article title

Analiza graficzna danych edukacyjnych z wykorzystaniem języka Python

Authors

Content

Title variants

EN
Graphical analysis of educational data using Python

Languages of publication

PL

Abstracts

EN
In practice, the high quality of educational processes and content can hardly be achieved and maintained without monitoring. And since tracking the results is not enough, it is necessary to find existing flaws and identify their causes. In this paper, it is argued that even small data sets such as students' test results can provide valuable information useful to improve further iterations of teaching and learning outcomes verification processes and educational materials. Next, the appropriateness of graphical analysis for this purpose is pointed out taking its simplicity even for non-statisticians and its ability to visualize entire small data sets with high precision into account. However, the primary aim of this paper is to provide practical examples showing that the Python programming language (with a selection of specialized modules) can be used in a convenient and effective manner for graphical analysis of small educational data sets. For the purpose of demonstration case study, actual test results were used. Analysis examined: correctness and confidence of students answering respective test questions, correlations between results based on question type and relevant content area, and also similarities of answers given by different students. Also, a Python-based software environment for graphical analysis has been compared to Microsoft Excel.

Journal

Year

Issue

Pages

13-21

Physical description

Contributors

author
  • University of Szczecin

References

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  • Goggins S., Xing W., Chen X., Chen B., Wadholm B., Learning Analytics at „Small” Scale: Exploring a Complexity-Grounded Model for Assessment Automation, „Journal of Universal Computer Science” 2015, Vol. 21, No. 1, s. 66-92, http://www.jucs.org/jucs_21_1/learning_analytics_at_small.
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  • Papamitsiou Z., Economides E., Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence, „Educational Technology & Society” 2014, Vol. 17, No. 4, s. 49-64, http://www.jstor.org/stable/jeductechsoci.17.4.49.
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  • TIOBE Index for April 2016, http://www.tiobe.com/tiobe_index.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.desklight-280be55c-f82c-4cbf-8224-ebe5c007558f
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