Literaturnachweis - Detailanzeige
| Autor/inn/en | Chen, Guanliang; Ferreira, Rafael; Lang, David; Gasevic, Dragan |
|---|---|
| Titel | Predictors of Student Satisfaction: A Large-Scale Study of Human-Human Online Tutorial Dialogues [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019). |
| Quelle | (2019), (10 Seiten)
PDF als Volltext kostenfreie Datei |
| Zusatzinformation | Weitere Informationen |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Student Attitudes; Intelligent Tutoring Systems; Computer Software; Dialogs (Language); Computational Linguistics; Models; Correlation; Teacher Student Relationship; Tutors; Teaching Methods; Prediction |
| Abstract | For the development of successful human-agent dialogue-based tutoring systems, it is essential to understand what makes a human-human tutorial dialogue successful. While there has been much research on dialogue-based intelligent tutoring systems, there have been comparatively fewer studies on analyzing large-scale datasets of human-human online tutoring dialogues. A critical indicator of success of a tutoring dialogue can be student satisfaction, which is the focus of the study reported in the paper. Specifically, we used a large-scale dataset, which consisted of over 15,000 tutorial dialogues generated by human tutors and students in a mobile app-based tutoring service. An extensive analysis of the dataset was performed to identify factors relevant to student satisfaction in online tutoring systems. The study also engineered a set of 325 features as input to a Gradient Tree Boosting model to predict tutoring success. Experimental results revealed that (i) in a tutorial dialogue, factors such as efforts spent by both tutors and students, utterance informativeness and tutor responsiveness were positively correlated with student satisfaction; and (ii) Gradient Tree Boosting model could effectively predict tutoring success, especially with utterances from the later period of a dialogue, but more research effort is needed to improve the prediction performance. [For the full proceedings, see ED599096.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2022/4/11 |
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