The Learning Effectiveness Analysis of JAVA Programming with Automatic Grading System
| Autor: | Chorng-Shiuh Koong, Yi-Yang Hsu, Hsin-Ying Tsai, Yeh-Cheng Chen |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Java
Artificial neural network business.industry Computer science media_common.quotation_subject Big data 020207 software engineering 02 engineering and technology Field (computer science) Data modeling Naive Bayes classifier ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering Mathematics education 020201 artificial intelligence & image processing Quality (business) Student learning business TUTOR Grading (education) computer media_common computer.programming_language |
| Zdroj: | COMPSAC (2) |
| DOI: | 10.1109/compsac.2018.10210 |
| Popis: | With the development of science and technology, analysis of large data is used more and more widely, such as electronic commerce, biotechnology, retail sale business, finance, education are inseparable from our life. Big data can show its value with data mining, moreover, it plays an important role in the field of education. Most researches on the exploration of education data have implications in predicting students' learning effectiveness, which can predict students' mid-term grades, final grades and semester grades with the big data analysis. The exploration of education data has been used by many research to apply the existing predictive model, thus can predict students who failed the semester exam. When there are some students who have a tendency, teachers can early to care about their learning conditions, find out the reason why they are poor in the performance, which can improve the quality of student learning. This study extends JAVA automatic grading system that our laboratory developed, increases students' operation behaviors and forecasting analysis models on the system, cooperates with four algorithms like random forest, supporting vector machine, neural network, Naive Bayes to predict and analyze the student's drop point of semester grades. This study can predict semester grade with 77% accuracy. Furthermore, this study also makes behavior analysis based on the collected data to find out the behavior model for students with low grades. The result of the study analysis can help teachers to early tutor students and improve their quality of teaching. Moreover, it plays an important role in reference value. |
| Databáze: | OpenAIRE |
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