Using Online Textbook and In-Class Poll Data to Predict In-Class Performance
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| Title: | Using Online Textbook and In-Class Poll Data to Predict In-Class Performance |
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| Language: | English |
| Authors: | Hunt-Isaak, Noah, Cherniavsky, Peter, Snyder, Mark, Rangwala, Huzefa |
| Source: | International Educational Data Mining Society. 2020. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Peer Reviewed: | Y |
| Page Count: | 6 |
| Publication Date: | 2020 |
| Sponsoring Agency: | National Science Foundation (NSF) |
| Contract Number: | 1757064 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Textbooks, Surveys, Grade Prediction, Undergraduate Students, Introductory Courses, Computer Science Education, Data Analysis, Data Use, Electronic Publishing |
| Geographic Terms: | Virginia |
| Abstract: | National failure rates seen in undergraduate introductory CS courses are quite high. In this paper, we develop a predictive model for student in-class performance in an introductory CS course. The model can serve as an early warning system, flagging struggling students who might benefit from additional support. We use a variety of features from the first few weeks of the course such as scores on assignments, interaction with the online textbook, and participation with the in-class polling system in order to train our models. We compare the performance of a number of machine learning algorithms on predicting final exam scores as well as final course grade. We find that the Support Vector Machine and AdaBoost are the most effective, and that we can achieve increasingly accurate predictions as we use data from further into the course. The regression coefficients give us insights into which features are most correlated with student success, suggesting that certain types of assignments are more indicative of learning than others. [For the full proceedings, see ED607784.] |
| Abstractor: | As Provided |
| Entry Date: | 2020 |
| Accession Number: | ED608057 |
| Database: | ERIC |
| Abstract: | National failure rates seen in undergraduate introductory CS courses are quite high. In this paper, we develop a predictive model for student in-class performance in an introductory CS course. The model can serve as an early warning system, flagging struggling students who might benefit from additional support. We use a variety of features from the first few weeks of the course such as scores on assignments, interaction with the online textbook, and participation with the in-class polling system in order to train our models. We compare the performance of a number of machine learning algorithms on predicting final exam scores as well as final course grade. We find that the Support Vector Machine and AdaBoost are the most effective, and that we can achieve increasingly accurate predictions as we use data from further into the course. The regression coefficients give us insights into which features are most correlated with student success, suggesting that certain types of assignments are more indicative of learning than others. [For the full proceedings, see ED607784.] |
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