Literaturnachweis - Detailanzeige
| Autor/inn/en | Fateen, Menna; Mine, Tsunenori |
|---|---|
| Titel | Predicting Student Performance Using Teacher Observation Reports [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021). |
| Quelle | (2021), (6 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Grade Prediction; Academic Achievement; Observation; Middle School Students; Artificial Intelligence; Foreign Countries; Tutorial Programs; Japan |
| Abstract | Studying for entrance examinations can be a distressing period for numerous students. Consequently, many students decide to attend cram schools to assist them in preparing for these exams. For such schools and for all educational institutes, it is necessary to obtain the best tools to provide the highest quality of learning and guidance. Performance prediction is one tool that can serve as a resource for insights that are valuable to all educational stakeholders. With accurate predictions of their grades, students can be further guided and fostered in order to achieve their optimal learning goals. In this regard, we target middle school students to be able to guide them on their educational journey as early as possible. We propose a method to predict the students' performance in entrance examinations using the comments that cram school teachers made throughout the lessons. Teachers in cram schools observe their student's behavior closely and give reports on the efforts taken in their subject material. We show that the teachers' comments are qualified to construct a tool that is capable of predicting students' grades efficiently. This is a new method because previous studies focus on predicting grades mainly using student data such as their reflection comments or earlier scores. Experimental results show that using readily available feedback from teachers can remarkably contribute to the accuracy of student performance prediction. [For the full proceedings, see ED615472.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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