Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.19334 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917638662258688 |
|---|---|
| author | Egetenmeier, Armin Strickroth, Sven |
| author_facet | Egetenmeier, Armin Strickroth, Sven |
| contents | Detailed feedback on courses and lecture content is essential for their improvement and also serves as a tool for reflection. However, feedback methods are often only used sporadically, especially in mass courses, because collecting and analyzing feedback in a timely manner is often a challenge for teachers. Moreover, the current situation of the students or the changing workload during the semester are usually not taken into account either. For a holistic investigation, the article used a digital survey format as formative feedback which attempts to measure student stress in a quantitative part and to address the participants' reflection in a qualitative part, as well as to collect general suggestions for improvement (based on the so-called One-Minute Paper) at two educational institutions. The feedback during the semester is evaluated qualitatively and discussed on a meta-level and special features (e.g. reflections on student work ethic or other courses) are addressed. The results show a low, but constant rate of feedback. Responses mostly cover topics of the lecture content or organizational aspects and were intensively used to report issues within the lecture. In addition, artificial intelligence (AI) support in the form of a large language model was tested and showed promising results in summarizing the open-ended responses for the teacher. Finally, the experiences from the lecturers are reflected upon and the results as well as possibilities for improvement are discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_19334 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Scalable Two-Minute Feedback: Digital, Lecture-Accompanying Survey as a Continuous Feedback Instrument Egetenmeier, Armin Strickroth, Sven Computers and Society Detailed feedback on courses and lecture content is essential for their improvement and also serves as a tool for reflection. However, feedback methods are often only used sporadically, especially in mass courses, because collecting and analyzing feedback in a timely manner is often a challenge for teachers. Moreover, the current situation of the students or the changing workload during the semester are usually not taken into account either. For a holistic investigation, the article used a digital survey format as formative feedback which attempts to measure student stress in a quantitative part and to address the participants' reflection in a qualitative part, as well as to collect general suggestions for improvement (based on the so-called One-Minute Paper) at two educational institutions. The feedback during the semester is evaluated qualitatively and discussed on a meta-level and special features (e.g. reflections on student work ethic or other courses) are addressed. The results show a low, but constant rate of feedback. Responses mostly cover topics of the lecture content or organizational aspects and were intensively used to report issues within the lecture. In addition, artificial intelligence (AI) support in the form of a large language model was tested and showed promising results in summarizing the open-ended responses for the teacher. Finally, the experiences from the lecturers are reflected upon and the results as well as possibilities for improvement are discussed. |
| title | Scalable Two-Minute Feedback: Digital, Lecture-Accompanying Survey as a Continuous Feedback Instrument |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2310.19334 |