AI Summary & Key Takeaways
AI InsightThe findings of the qualitative and quantitative strands are overlapping, and the responses elicited from the participants seem to shed some light on the rating behavior of the writing faculty.
📌 3 Key Takeaways
Abstract
This study examined the faculty perspectives towards the use of electronic rubrics and their rating behavior in a freshman composition course. A mixed-methods approach has been employed for data collection and analysis. The data for faculty perspectives were collected from nine instructors through semi-structured interviews and for their behavior, six instructors teaching the same course in Fall 2019, shared their students’ essay scores with the researchers. Many facet Rasch model (MFRM) was employed for quantitative data analysis. According to the findings of the quantitative data, the instructors differed in their degree of leniency and severity, one instructor being more lenient and one being more severe than the others. Another interesting finding was one instructor turned out to be an inconsistent user of the e-rubric. The findings of the qualitative data showed that writing faculty think e-rubrics come with great advantages such as facilitating scoring, ensuring standardization, and reducing student complaints and grade appeals. However, they view the impact of e-rubrics on student writing with cautious optimism. The findings of the qualitative and quantitative strands are overlapping, and the responses elicited from the participants seem to shed some light on the rating behavior of the writing faculty.
Özet
This study examined the faculty perspectives towards the use of electronic rubrics and their rating behavior in a freshman composition course. A mixed-methods approach has been employed for data collection and analysis. The data for faculty perspectives were collected from nine instructors through semi-structured interviews and for their behavior, six instructors teaching the same course in Fall 2019, shared their students’ essay scores with the researchers. Many facet Rasch model (MFRM) was employed for quantitative data analysis. According to the findings of the quantitative data, the instructors differed in their degree of leniency and severity, one instructor being more lenient and one being more severe than the others. Another interesting finding was one instructor turned out to be an inconsistent user of the e-rubric. The findings of the qualitative data showed that writing faculty think e-rubrics come with great advantages such as facilitating scoring, ensuring standardization, and reducing student complaints and grade appeals. However, they view the impact of e-rubrics on student writing with cautious optimism. The findings of the qualitative and quantitative strands are overlapping, and the responses elicited from the participants seem to shed some light on the rating behavior of the writing faculty.