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Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students. To encourage CAD programs to build in assistance to students, we used data generated from students using a free, open-source CAD software called Aladdin to demonstrate how student data combined with machine learning techniques can predict how well a particular student will perform in a design task. We challenged students to design a house that consumed zero net energy as part of an introductory engineering technology undergraduate course. Using data from 128 students, along with the scikit-learn Python machine learning library, we tested our models using both total counts of design actions and sequences of design actions as inputs. We found that our models using early design sequence actions are particularly valuable for prediction. Our logistic regression model achieved a >60% chance of predicting if a student would succeed in designing a zero net energy house. Our results suggest that it would be feasible for Aladdin to provide useful feedback to students when they are approximately halfway through their design. Further improvements to these models could lead to earlier predictions and thus provide students feedback sooner to enhance their learning.
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Designing Playful Intelligent Tutoring Software to Support Engaging and Effective Algebra Learning
In designing learning technology, it is critical that the technology supports both learning and engagement of students. However, achieving both aspects in a single technology design is challenging. We report on the design and evaluation of Gwynnette, intelligent tutoring software for early algebra. Gwynnette was deliberately designed to enhance students’ algebra learning and engagement, integrating several playful interaction and gamification features such as drag-and-drop interactions, an alien character, and sound effects. A virtual classroom experiment with 60 students showed that the system significantly enhanced both engagement and conceptual learning in early algebra, compared to the older version of the same software. Log data analyses gave insights into how the design might have affected the out-comes. This study demonstrates that a deliberate design of learning technology can help students learn and engage well in an unpopular subject such as algebra, a challenging dual goal in designing learning technologies.
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- Award ID(s):
- 1760922
- PAR ID:
- 10472799
- Editor(s):
- Hilliger, I; Muñoz-Merino, P. J.; De Laet, T.; Ortega-Arranz, A.; Farrell, T.
- Publisher / Repository:
- Proceedings of ECTEL 2022: the 17th European Conference on Technology-Enhanced Learning
- Date Published:
- Journal Name:
- Proceedings of ECTEL 2022: the 17th European Conference on Technology-Enhanced Learning
- Edition / Version:
- 18
- Page Range / eLocation ID:
- 258-271
- Subject(s) / Keyword(s):
- Intelligent Tutoring System, Engagement, Gamification, Algebra
- Format(s):
- Medium: X
- Location:
- Cham
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-031-16290-9_19
