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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
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
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  Data: 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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      – Text: English
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    Subjects:
      – SubjectFull: Textbooks
        Type: general
      – SubjectFull: Surveys
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      – SubjectFull: Grade Prediction
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      – SubjectFull: Virginia
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      – TitleFull: Using Online Textbook and In-Class Poll Data to Predict In-Class Performance
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