ICT & Society I: Impact of automated short-answer marking on students' learning: IndusMarker, a case study
Abstract/Description
IndusMarker is an automated short-answer marking system based on structure-editing and structure-matching rather than extensive use of linguistic features analysis. Since IndusMarker cannot guarantee 100% human-system agreement rate, the use of IndusMarker has therefore been limited to conducting practice tests. It was expected that such a use of IndusMarker will lead to improvements in student learning and instructor-student interactions. The main aim of this paper is to verify these claims. The results indicate that such a use of IndusMarker leads to improvements in both student learning and instructor-student interactions. In addition, IndusMarker is also shown to give reasonably high human-system agreement rates even after the removal of all linguistic analysis features from the software.
Keywords
Self-assessment technologies, Classroom feedback systems, Automatic assessment tools, Education
Location
Room M2
Session Theme
ICT & Society I
Session Type
Other
Session Chair
Dr. Shakeel Khoja
Start Date
14-12-2013 4:00 PM
End Date
14-12-2013 4:30 PM
Recommended Citation
Siddiqi, R. (2013). ICT & Society I: Impact of automated short-answer marking on students' learning: IndusMarker, a case study. International Conference on Information and Communication Technologies. Retrieved from https://ir.iba.edu.pk/icict/2013/2013/8
COinS
ICT & Society I: Impact of automated short-answer marking on students' learning: IndusMarker, a case study
Room M2
IndusMarker is an automated short-answer marking system based on structure-editing and structure-matching rather than extensive use of linguistic features analysis. Since IndusMarker cannot guarantee 100% human-system agreement rate, the use of IndusMarker has therefore been limited to conducting practice tests. It was expected that such a use of IndusMarker will lead to improvements in student learning and instructor-student interactions. The main aim of this paper is to verify these claims. The results indicate that such a use of IndusMarker leads to improvements in both student learning and instructor-student interactions. In addition, IndusMarker is also shown to give reasonably high human-system agreement rates even after the removal of all linguistic analysis features from the software.
