Name
Abstract | ||
|---|---|---|
The aim of this study is to predict the achievement degree of each student at the end of a lecture, based on a simple questionnaire result which regularly surveys degree of the subjective understanding conducted to students in a class. In this study, the feedforward neural networks (FNNs) and decision tree are used for the prediction and the classification. A FNN which is with a multiple input and multiple output structure is well known that it has high performance for multidimensional data prediction or classification. Therefore, FNNs are considered to be suitable for the problem dealt with in this study such that student classification based on multiple questionnaire results. Additionally, it is possible to analyze students' learning process in detail by using a decision tree that can obtain student classification rules in an explicit form. This study conducts an experiment using data of a classification six times questionnaire surveys and a final examination and constructed a system for student classification based on the the answers of three questionnaire. Experiment has succeeded in roughly classifying learners into three clusters based on achievement degree. It means that the proposed method is predict the potential comprehension degree of a student. Sequentially, by providing additional education for the students who are classified as a low degree, it is expected to be able to take countermeasures for not becoming " dropout" in early stage. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/FIE.2018.8658760 | Frontiers in Education Conference |
Keywords | Field | DocType |
Learning system,neural networks,feature extraction,classification of learners | Countermeasure,Histogram,Decision tree,Feedforward neural network,Sociology,Knowledge management,Feature extraction,Artificial intelligence,Artificial neural network,Machine learning,Comprehension,Final examination | Conference |
ISSN | Citations | PageRank |
0190-5848 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomohiro Hayashida | 1 | 29 | 11.56 |
| Toru Yamamoto | 2 | 56 | 30.31 |
| Shin Wakitani | 3 | 15 | 8.25 |
| Ichiro Nishizaki | 4 | 443 | 42.37 |
| Shinya Sekizaki | 5 | 0 | 2.37 |
| Yusuke Tanimoto | 6 | 0 | 0.34 |