In technology enhanced language learning, many pedagogical activities involve students in online discussion such as synchronous chat, in order to help them practice their language skills. Besides developing the language competency of students, it is also crucial to nurture their teamwork competencies for today's global and complex environment. Language communication is an important glue of teamwork. In order to assess the language of chat for teamwork dimensions, several text mining methods are pos sible. However, difficulties arise such as pre-processing being a black box and classification approaches and algorithms being dependent on the context. To address these issues, the study will evaluate and explain preprocessing and classification methods used to analyze teamwork dialogue from a dataset of chat data. Analytics methods evaluated in this study provide a direction for assessing the language of chat for teamwork dialogue and can help extend the work of technology enhanced language learning to n ot only focus on academic competency, but on the communication aspect too.
Teamwork, Pre-processing, Supervised machine learning, Text mining, Learning analytics
Databases and Information Systems | Data Storage Systems
Data Management and Analytics
Educational Technology and Society
International Forum of Educational Technology and Society / International Forum of Educational Technology & Society
SHIBANI, Antonette; KOH, Elizabeth; LAI, Vivian; and SHIM, Kyong Jin.
Assessing the language of chat for teamwork dialogue. (2017). Educational Technology and Society. 20, (2), 224-237. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3799
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.