TY - GEN
T1 - Teacher interventions to enhance the quality of student comments and their effect on prediction performance
AU - Sorour, Shaymaa E.
AU - El Rahman, Shaymaa Abd
AU - Mine, Tsunenori
N1 - Funding Information:
This work was supported in party by JSPS KAKENHI Grant No. 26540183, 26350357 and 16H02926.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/28
Y1 - 2016/11/28
N2 - Today, the use of learning analytics is becoming more crucial in the learning environment for the purpose of understanding and optimizing students' learning situations. The purpose of this paper is to examine the impacts of Teacher Interventions (TIs) on students' attitudes and achievements involved with the lesson by analyzing their freestyle comment data after every lesson. The current study proposes a new method for building an accessible prediction model, which represents students' activities, situations and viewpoints; the method classifies words in the student comments into six attribute types and indicates the most important types that affect the prediction results. Further, the prediction results are compared with the topic-based statistical method that uses Latent Dirichlet Allocation and Support Vector Machine models. The results proved that there were positive correlations between TIs and the quality of writing comments that affect on improving the prediction results.
AB - Today, the use of learning analytics is becoming more crucial in the learning environment for the purpose of understanding and optimizing students' learning situations. The purpose of this paper is to examine the impacts of Teacher Interventions (TIs) on students' attitudes and achievements involved with the lesson by analyzing their freestyle comment data after every lesson. The current study proposes a new method for building an accessible prediction model, which represents students' activities, situations and viewpoints; the method classifies words in the student comments into six attribute types and indicates the most important types that affect the prediction results. Further, the prediction results are compared with the topic-based statistical method that uses Latent Dirichlet Allocation and Support Vector Machine models. The results proved that there were positive correlations between TIs and the quality of writing comments that affect on improving the prediction results.
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U2 - 10.1109/FIE.2016.7757736
DO - 10.1109/FIE.2016.7757736
M3 - Conference contribution
AN - SCOPUS:85006790233
T3 - Proceedings - Frontiers in Education Conference, FIE
BT - FIE 2016 - Frontiers in Education 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 46th Annual Frontiers in Education Conference, FIE 2016
Y2 - 12 October 2016 through 15 October 2016
ER -