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ERIC Number: ED560772
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 4
Abstractor: As Provided
Reference Count: 8
ISBN: N/A
ISSN: N/A
Predicting Student Grade Based on Free-Style Comments Using Word2Vec and ANN by Considering Prediction Results Obtained in Consecutive Lessons
Luo, Jingyi; Sorour, Shaymaa E.; Goda, Kazumasa; Mine, Tsunenori
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
Continuously tracking students during a whole semester plays a vital role to enable a teacher to grasp their learning situation, attitude and motivation. It also helps to give correct assessment and useful feedback to them. To this end, we ask students to write their comments just after each lesson, because student comments reflect their learning attitude towards the lesson, understanding of course contents, and difficulties of learning. In this paper, we propose a new method to predict final student grades. The method employs Word2Vec and Artifical Neural Network (ANN) to predict student grade in each lesson based on their comments freely written just after the lesson. In addition, we apply a window function to the predicted results obtained in consecutive lessons to keep track of each student's learning situation. The experiment results show that the prediction correct rate reached 80% by considering the predicted student grades from six consecutive lessons, and a final rate became 94% from all 15 lessons. The results illustrate that our proposed method continuously tracked student learning situation and improved prediction performance of final student grades as the lessons go by. [For complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: Japan Society for the Promotion of Science (JSPS)
Authoring Institution: International Educational Data Mining Society