Abstract
We propose a system that allows university teachers to check the effectiveness of their lecture videos and to grasp points for improvement in the lectures. The system offers two functions: time-series graphing, which visualizes real-time changes in students' evaluation during a lecture, and teaching behavior estimation, which shows teachers information on their own teaching behaviors estimated from the overall evaluation by students of a lecture. The system was developed and evaluation experiments of each function were conducted. The subjective evaluation of each function by teachers showed the following: (1) the time series graph function was useful to narrow down which portion of the lecture videos contained points for improvement and (2) the teaching behavior estimation function was useful to determine the tendency of teaching behavior in a lecture.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 79-88 |
| Number of pages | 10 |
| Volume | 8018 LNCS |
| Edition | PART 3 |
| DOIs | |
| Publication status | Published - 2013 |
| Externally published | Yes |
| Event | 15th International Conference on Human Interface and the Management of Information: Information and Interaction for Learning, Culture, Collaboration and Business, HCI 2013 - Las Vegas, NV, United States Duration: 2013 Jul 21 → 2013 Jul 26 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 3 |
| Volume | 8018 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 15th International Conference on Human Interface and the Management of Information: Information and Interaction for Learning, Culture, Collaboration and Business, HCI 2013 |
|---|---|
| Country | United States |
| City | Las Vegas, NV |
| Period | 13/7/21 → 13/7/26 |
Fingerprint
Keywords
- Lecture video
- Overall evaluation
- Sequential evaluation
- Student evaluation
- Teaching behavior
- Teaching improvement
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Video feedback system for teaching improvement using students' sequential and overall teaching evaluations. / Kometani, Yusuke; Tomoto, Takahito; Furuta, Takehiro; Akakura, Takako.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8018 LNCS PART 3. ed. 2013. p. 79-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8018 LNCS, No. PART 3).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Video feedback system for teaching improvement using students' sequential and overall teaching evaluations
AU - Kometani, Yusuke
AU - Tomoto, Takahito
AU - Furuta, Takehiro
AU - Akakura, Takako
PY - 2013
Y1 - 2013
N2 - We propose a system that allows university teachers to check the effectiveness of their lecture videos and to grasp points for improvement in the lectures. The system offers two functions: time-series graphing, which visualizes real-time changes in students' evaluation during a lecture, and teaching behavior estimation, which shows teachers information on their own teaching behaviors estimated from the overall evaluation by students of a lecture. The system was developed and evaluation experiments of each function were conducted. The subjective evaluation of each function by teachers showed the following: (1) the time series graph function was useful to narrow down which portion of the lecture videos contained points for improvement and (2) the teaching behavior estimation function was useful to determine the tendency of teaching behavior in a lecture.
AB - We propose a system that allows university teachers to check the effectiveness of their lecture videos and to grasp points for improvement in the lectures. The system offers two functions: time-series graphing, which visualizes real-time changes in students' evaluation during a lecture, and teaching behavior estimation, which shows teachers information on their own teaching behaviors estimated from the overall evaluation by students of a lecture. The system was developed and evaluation experiments of each function were conducted. The subjective evaluation of each function by teachers showed the following: (1) the time series graph function was useful to narrow down which portion of the lecture videos contained points for improvement and (2) the teaching behavior estimation function was useful to determine the tendency of teaching behavior in a lecture.
KW - Lecture video
KW - Overall evaluation
KW - Sequential evaluation
KW - Student evaluation
KW - Teaching behavior
KW - Teaching improvement
UR - http://www.scopus.com/inward/record.url?scp=84880865582&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84880865582&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-39226-9_10
DO - 10.1007/978-3-642-39226-9_10
M3 - Conference contribution
SN - 9783642392252
VL - 8018 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 79
EP - 88
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -