About this paper

Appears in:
Pages: 4617-4625
Publication year: 2019
ISBN: 978-84-09-14755-7
ISSN: 2340-1095
doi: 10.21125/iceri.2019.1143

Conference name: 12th annual International Conference of Education, Research and Innovation
Dates: 11-13 November, 2019
Location: Seville, Spain

CLASSIFICATION OF STUDENTS BY SUCCESS PREDICTORS IN THE SPOC PROGRAMMING COURSE

M. Mrázek, J. Basler

Palacký University Olomouc, Faculty of Education (CZECH REPUBLIC)
Small private online courses (SPOC) are modern ways of online study methods based on small groups of students with the same or very similar specific study orientation. The present paper describes the partial results of a research study performed in the context of including the SPOC programming course in Basics of programming course designed for university training of future teachers of information science subjects in elementary and secondary schools in academic year 2018-2019. The aim of the research was to determine which groups of students can be identified by success predictors in the SPOC course with an emphasis on the basics of programming. The design of the research included quantitative data collection and assessment methods. The data collection was performed by means of a questionnaire of the authors’ own design with a total of 110 scale items (predictors), where the respondents indicated predictor significance on a scale (1 completely insignificant – 7 significant). Moreover, the Eysenck Personality Questionnaire (EPQ) and VARK Questionnaire ver. 7.8. were used. The questionnaire items focused on the following areas: student characteristics, teacher characteristics, educational environment characteristics, technological areas. A total of 13 respondents completed the whole survey but regarding the range of questionnaire items, the relevance of the study matches a quantitative analysis. The number of respondents fully corresponds with the possibilities of a research analysis of a SPOC course. These types of courses usually have a low number of students. The present research had the maximum possible number of students in the current study programme. The data were analysed by means of multidimensional statistical methods, primarily by means of cluster analysis methods, which provide graphical group models, but also a valid verification of group existence. The orientation model of possible groups of students was developed by means of a hierarchical cluster analysis. The orientation model was verified by means of the globalized cluster analysis K-means and ANOVA analysis of variance. The results suggest two groups of students based on predictors. Significant differences between the two groups are suggested by 26.5 % of predictors of the total number of 110. Based on the research findings and limitations, the results should not be generalized to include all SPOC courses with different specializations. The results are characteristic for a specific group of students with a specific study orientation at Palacký University Olomouc.
@InProceedings{MRAZEK2019CLA,
author = {Mr{\'{a}}zek, M. and Basler, J.},
title = {CLASSIFICATION OF STUDENTS BY SUCCESS PREDICTORS IN THE SPOC PROGRAMMING COURSE},
series = {12th annual International Conference of Education, Research and Innovation},
booktitle = {ICERI2019 Proceedings},
isbn = {978-84-09-14755-7},
issn = {2340-1095},
doi = {10.21125/iceri.2019.1143},
url = {http://dx.doi.org/10.21125/iceri.2019.1143},
publisher = {IATED},
location = {Seville, Spain},
month = {11-13 November, 2019},
year = {2019},
pages = {4617-4625}}
TY - CONF
AU - M. Mrázek AU - J. Basler
TI - CLASSIFICATION OF STUDENTS BY SUCCESS PREDICTORS IN THE SPOC PROGRAMMING COURSE
SN - 978-84-09-14755-7/2340-1095
DO - 10.21125/iceri.2019.1143
PY - 2019
Y1 - 11-13 November, 2019
CI - Seville, Spain
JO - 12th annual International Conference of Education, Research and Innovation
JA - ICERI2019 Proceedings
SP - 4617
EP - 4625
ER -
M. Mrázek, J. Basler (2019) CLASSIFICATION OF STUDENTS BY SUCCESS PREDICTORS IN THE SPOC PROGRAMMING COURSE, ICERI2019 Proceedings, pp. 4617-4625.
User:
Pass: