The Community for Technology Leaders
Green Image
Issue No. 02 - April-June (2018 vol. 11)
ISSN: 1939-1382
pp: 243-254
Qingtang Liu , School of Educational Information Technology, Central China Normal University, Wuhan, China
Si Zhang , School of Educational Information Technology, Central China Normal University, Wuhan, China
Qiyun Wang , National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore
Wenli Chen , National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore
ABSTRACT
Teachers’ online discussion text data shed light on their reflective thinking. With the growing scale of text data, the traditional way of manual coding, however, has been challenged. In order to process the large-scale unstructured text data, it is necessary to integrate the inductive content analysis method and educational data mining techniques. An inductive content analysis on samples taken from 17,624 posts was implemented and the categories of teachers’ reflective thinking were obtained. Based on the results of inductive content analysis, we implemented a single-label text classification algorithm to classify the sample data. Then, we applied the trained classification model on a large-scale and unexplored online discussion text data set and two types of visualizations of the results were provided. By using the categories gained from inductive content analysis to create a radar map, teachers’ reflection level was represented. In addition, a cumulative adjacency matrix was created to characterize the evolution of teachers’ reflective thinking. This study could partly explain how teachers reflected in online professional learning environments and brought awareness to educational policy makers, teacher training managers, and education researchers.
INDEX TERMS
Reflection, Data visualization, Encoding, Text categorization, Training
CITATION

Q. Liu, S. Zhang, Q. Wang and W. Chen, "Mining Online Discussion Data for Understanding Teachers’ Reflective Thinking," in IEEE Transactions on Learning Technologies, vol. 11, no. 2, pp. 243-254, 2018.
doi:10.1109/TLT.2017.2708115
1022 ms
(Ver 3.3 (11022016))