Title |
Controlling item difficulty for automatic vocabulary question generation
|
---|---|
Published in |
Research and Practice in Technology Enhanced Learning, December 2017
|
DOI | 10.1186/s41039-017-0065-5 |
Pubmed ID | |
Authors |
Yuni Susanti, Takenobu Tokunaga, Hitoshi Nishikawa, Hiroyuki Obari |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 2 | 40% |
United Kingdom | 1 | 20% |
Unknown | 2 | 40% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 40% |
Members of the public | 2 | 40% |
Science communicators (journalists, bloggers, editors) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 38 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 8 | 21% |
Student > Doctoral Student | 4 | 11% |
Lecturer | 3 | 8% |
Student > Bachelor | 2 | 5% |
Researcher | 2 | 5% |
Other | 7 | 18% |
Unknown | 12 | 32% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 8 | 21% |
Social Sciences | 4 | 11% |
Linguistics | 3 | 8% |
Psychology | 3 | 8% |
Medicine and Dentistry | 2 | 5% |
Other | 6 | 16% |
Unknown | 12 | 32% |