Anzeige
Erschienen in:
2011 | OriginalPaper | Buchkapitel
Analysis of Students’ Learning Activities through Quantifying Time-Series Comments
verfasst von : Kazumasa Goda, Tsunenori Mine
Erschienen in: Knowlege-Based and Intelligent Information and Engineering Systems
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
These days, many university teachers are concerned about the increasing number of students whose motivation is declining. Some of them fall into a situation that they cannot recover from by themselves, and require assistance, but they hesitate to call for help. In order to recognize such students quickly and give guidance to them in class, we have collected time-series comments in the classroom and analyzed them. In the analysis, we divided the comments into the three time slots: P (Previous), C (Current), and N (Next), and quantify them so that we can infer the learning behaviors between the previous and the current classes. We call this analysis method the PCN method. The PCN method is useful for grasping students’ learning status in the class. Some of our case studies illustrate the validity of the PCN method.