Mechanix: A natural sketch interface tool for teaching truss analysis and free-body diagrams. Issue 2 (16th May 2014)
- Record Type:
- Journal Article
- Title:
- Mechanix: A natural sketch interface tool for teaching truss analysis and free-body diagrams. Issue 2 (16th May 2014)
- Main Title:
- Mechanix: A natural sketch interface tool for teaching truss analysis and free-body diagrams
- Authors:
- Hammond, Tracy
Linsey, Julie
Atilola, Olufunmilola
Valentine, Stephanie
Kim, Hong-Hoe
Turner, David
McTigue, Erin
Hammond, Tracy
Linsey, Julie - Abstract:
- <abstract abstract-type="normal"> <title>Abstract</title> <p>Massive open online courses, online tutoring systems, and other computer homework systems are rapidly changing engineering education by providing increased student feedback and capitalizing upon online systems' scalability. While online homework systems provide great benefits, a growing concern among engineering educators is that students are losing both the critical art of sketching and the ability to take a real system and reduce it to an accurate but simplified free-body diagram (FBD). For example, some online systems allow the drag and drop of forces onto FBDs, but they do not allow the user to sketch the FBDs, which is a vital part of the learning process. In this paper, we discuss Mechanix, a sketch recognition tool that provides an efficient means for engineering students to learn how to draw truss FBDs and solve truss problems. The system allows students to sketch FBDs into a tablet computer or by using a mouse and a standard computer monitor. Using artificial intelligence, Mechanix can determine not only the component shapes and features of the diagram but also the relationships between those shapes and features. Because Mechanix is domain specific, it can use those relationships to determine not only whether a student's work is correct but also <italic>why</italic> it is incorrect. Mechanix is then able to provide immediate, constructive feedback to students without providing final answers. Within this<abstract abstract-type="normal"> <title>Abstract</title> <p>Massive open online courses, online tutoring systems, and other computer homework systems are rapidly changing engineering education by providing increased student feedback and capitalizing upon online systems' scalability. While online homework systems provide great benefits, a growing concern among engineering educators is that students are losing both the critical art of sketching and the ability to take a real system and reduce it to an accurate but simplified free-body diagram (FBD). For example, some online systems allow the drag and drop of forces onto FBDs, but they do not allow the user to sketch the FBDs, which is a vital part of the learning process. In this paper, we discuss Mechanix, a sketch recognition tool that provides an efficient means for engineering students to learn how to draw truss FBDs and solve truss problems. The system allows students to sketch FBDs into a tablet computer or by using a mouse and a standard computer monitor. Using artificial intelligence, Mechanix can determine not only the component shapes and features of the diagram but also the relationships between those shapes and features. Because Mechanix is domain specific, it can use those relationships to determine not only whether a student's work is correct but also <italic>why</italic> it is incorrect. Mechanix is then able to provide immediate, constructive feedback to students without providing final answers. Within this manuscript, we document the inner workings of Mechanix, including the artificial intelligence behind the scenes, and present studies of the effects on student learning. The evaluations have shown that Mechanix is as effective as paper-and-pencil-based homework for teaching method of joints truss analysis; focus groups with students who used the program have revealed that they believe Mechanix enhances their learning and that they are highly engaged while using it.</p> </abstract> … (more)
- Is Part Of:
- AI EDAM. Volume 28:Issue 2(2014)
- Journal:
- AI EDAM
- Issue:
- Volume 28:Issue 2(2014)
- Issue Display:
- Volume 28, Issue 2 (2014)
- Year:
- 2014
- Volume:
- 28
- Issue:
- 2
- Issue Sort Value:
- 2014-0028-0002-0000
- Page Start:
- 169
- Page End:
- 192
- Publication Date:
- 2014-05-16
- Subjects:
- Engineering design -- Data processing -- Periodicals
Artificial intelligence -- Periodicals
Expert systems (Computer science) -- Periodicals
620.00420285 - Journal URLs:
- http://www.journals.cambridge.org/jid%5FAIE ↗
- DOI:
- 10.1017/S0890060414000079 ↗
- Languages:
- English
- ISSNs:
- 0890-0604
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 4297.xml