TY - GEN
T1 - Automated capture of paper-based evaluations to provide early feedback to students
AU - Jurado, David
AU - Maya, Ricardo
AU - Dominguez, Federico
AU - Ochoa, Xavier
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/1/4
Y1 - 2018/1/4
N2 - Current Learning Management Systems (LMS) are able to use the data automatically captured from the actions of their users to provide immediate feedback to students and to provide a rich dataset to be mined or analyzed to understand and optimize the learning process. However, in traditional education, not all, or even the majority, of learning products are created or processed through the LMS. Traditional education still uses paper-based assignments and assessments as an integral part of the process. In these cases, the data contained in the LMS is often incomplete and do not provide a holistic view of the students' activities. To alleviate this problem, this work describes SARA, a system to automatically capture paper-based assignments and evaluations while the instructor is writing feedback and grading them. This information is uploaded automatically to the LMS to become part of both, the feedback provided to students and the data available for analyzing the learning process. This system is based on low-cost hardware and requires little configuration and intervention from the final user to work. An initial evaluation of the system provides evidence of the feasibility and usefulness of SARA in real-world learning environments.
AB - Current Learning Management Systems (LMS) are able to use the data automatically captured from the actions of their users to provide immediate feedback to students and to provide a rich dataset to be mined or analyzed to understand and optimize the learning process. However, in traditional education, not all, or even the majority, of learning products are created or processed through the LMS. Traditional education still uses paper-based assignments and assessments as an integral part of the process. In these cases, the data contained in the LMS is often incomplete and do not provide a holistic view of the students' activities. To alleviate this problem, this work describes SARA, a system to automatically capture paper-based assignments and evaluations while the instructor is writing feedback and grading them. This information is uploaded automatically to the LMS to become part of both, the feedback provided to students and the data available for analyzing the learning process. This system is based on low-cost hardware and requires little configuration and intervention from the final user to work. An initial evaluation of the system provides evidence of the feasibility and usefulness of SARA in real-world learning environments.
KW - Embedded System
KW - LMS
KW - computer vision
KW - evaluation feedback
KW - fiducial mark
KW - paper based assessment
UR - http://www.scopus.com/inward/record.url?scp=85045726401&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045726401&partnerID=8YFLogxK
U2 - 10.1109/ETCM.2017.8247489
DO - 10.1109/ETCM.2017.8247489
M3 - Conference contribution
AN - SCOPUS:85045726401
T3 - 2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017
SP - 1
EP - 6
BT - 2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Ecuador Technical Chapters Meeting, ETCM 2017
Y2 - 16 October 2017 through 20 October 2017
ER -