| dc.contributor.author | Singelmann, Lauren Nichole | |
| dc.description.abstract | One of the Grand Challenges for Engineering is advancing personalized learning, but challenges remain to identify and understand potential student pathways. This is especially difficult in complex, open-ended learning environments such as innovation-based learning courses. Student data from an iteration of an innovation-based learning course were analyzed using two educational data mining techniques: classification and clustering. Classification was used to predict student success in the course by creating a model that was both interpretable and robust (accuracy over 0.8 and ROC AUC of over 0.95). Clustering grouped student behavior into four main categories: Innovators, Learners, Surveyors, and Surface Level. Furthermore, noteworthy variables from each model were extracted to discover what factors were most likely to lead to course success. The work presented contributes to gaining a better understanding of how engineering students innovate and brings us closer to solving the Grand Challenge of advancing personalized learning. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU policy 190.6.2 | en_US |
| dc.title | Using Classification and Clustering to Predict and Understand Student Behavior in an Innovation-Based Learning Course | en_US |
| dc.type | Thesis | en_US |
| dc.date.accessioned | 2021-05-14T16:06:24Z | |
| dc.date.available | 2021-05-14T16:06:24Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | https://hdl.handle.net/10365/31885 | |
| dc.identifier.orcid | 0000-0003-3586-4266 | |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | en_US |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Electrical and Computer Engineering | en_US |
| ndsu.advisor | Ewert, Daniel | |
