Abstract
Collaboration is a 21st Century skill as well as an effective method for learning, so detection of collaboration is important for both assessment and instruction. Speech-based collaboration detection can be quite accurate but collecting the speech of students in classrooms can raise privacy issues. An alternative is to send only whether or not the student is speaking. That is, the speech signal is processed at the microphone by a voice activity detector before being transmitted to the collaboration detector. Because the transmitted signal is binary (1 = speaking, 0 = silence), this method mitigates privacy issues. However, it may harm the accuracy of collaboration detection. To find out how much harm is done, this study compared the relative effectiveness of collaboration detectors based either on the binary signal or high-quality audio. Pairs of students were asked to work together on solving complex math problems. Three qualitative levels of interactivity was distinguished: Interaction, Cooperation and Other. Human coders used richer data (several audio and video streams) to choose the code for each episode. Machine learning was used to induce a detector to assign a code for every episode based on the features. The binary-based collaboration detectors delivered only slightly less accuracy than collaboration detectors based on the high quality audio signal.
| Original language | English (US) |
|---|---|
| Title of host publication | Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings |
| Editors | Seiji Isotani, Peter Hastings, Amy Ogan, Bruce McLaren, Eva Millán, Rose Luckin |
| Publisher | Springer Verlag |
| Pages | 507-517 |
| Number of pages | 11 |
| ISBN (Print) | 9783030232030 |
| DOIs | |
| State | Published - Jan 1 2019 |
| Event | 20th International Conference on Artificial Intelligence in Education, AIED 2019 - Chicago, United States Duration: Jun 25 2019 → Jun 29 2019 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11625 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 20th International Conference on Artificial Intelligence in Education, AIED 2019 |
|---|---|
| Country | United States |
| City | Chicago |
| Period | 6/25/19 → 6/29/19 |
Fingerprint
Keywords
- Collaborative learning
- Learning analytics
- Machine learning
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Collaboration detection that preserves privacy of students’ speech. / Viswanathan, Sree Aurovindh; VanLehn, Kurt.
Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings. ed. / Seiji Isotani; Peter Hastings; Amy Ogan; Bruce McLaren; Eva Millán; Rose Luckin. Springer Verlag, 2019. p. 507-517 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11625 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Collaboration detection that preserves privacy of students’ speech
AU - Viswanathan, Sree Aurovindh
AU - VanLehn, Kurt
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Collaboration is a 21st Century skill as well as an effective method for learning, so detection of collaboration is important for both assessment and instruction. Speech-based collaboration detection can be quite accurate but collecting the speech of students in classrooms can raise privacy issues. An alternative is to send only whether or not the student is speaking. That is, the speech signal is processed at the microphone by a voice activity detector before being transmitted to the collaboration detector. Because the transmitted signal is binary (1 = speaking, 0 = silence), this method mitigates privacy issues. However, it may harm the accuracy of collaboration detection. To find out how much harm is done, this study compared the relative effectiveness of collaboration detectors based either on the binary signal or high-quality audio. Pairs of students were asked to work together on solving complex math problems. Three qualitative levels of interactivity was distinguished: Interaction, Cooperation and Other. Human coders used richer data (several audio and video streams) to choose the code for each episode. Machine learning was used to induce a detector to assign a code for every episode based on the features. The binary-based collaboration detectors delivered only slightly less accuracy than collaboration detectors based on the high quality audio signal.
AB - Collaboration is a 21st Century skill as well as an effective method for learning, so detection of collaboration is important for both assessment and instruction. Speech-based collaboration detection can be quite accurate but collecting the speech of students in classrooms can raise privacy issues. An alternative is to send only whether or not the student is speaking. That is, the speech signal is processed at the microphone by a voice activity detector before being transmitted to the collaboration detector. Because the transmitted signal is binary (1 = speaking, 0 = silence), this method mitigates privacy issues. However, it may harm the accuracy of collaboration detection. To find out how much harm is done, this study compared the relative effectiveness of collaboration detectors based either on the binary signal or high-quality audio. Pairs of students were asked to work together on solving complex math problems. Three qualitative levels of interactivity was distinguished: Interaction, Cooperation and Other. Human coders used richer data (several audio and video streams) to choose the code for each episode. Machine learning was used to induce a detector to assign a code for every episode based on the features. The binary-based collaboration detectors delivered only slightly less accuracy than collaboration detectors based on the high quality audio signal.
KW - Collaborative learning
KW - Learning analytics
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85068336671&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068336671&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-23204-7_42
DO - 10.1007/978-3-030-23204-7_42
M3 - Conference contribution
AN - SCOPUS:85068336671
SN - 9783030232030
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 507
EP - 517
BT - Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings
A2 - Isotani, Seiji
A2 - Hastings, Peter
A2 - Ogan, Amy
A2 - McLaren, Bruce
A2 - Millán, Eva
A2 - Luckin, Rose
PB - Springer Verlag
ER -