TY - CHAP
T1 - No exam
T2 - assessment of third-year engineering students on the basis of self-generated statistics cases
AU - Litvak, Nelly
AU - Kula, Fulya
PY - 2024
Y1 - 2024
N2 - Through the global pandemic, the single greatest challenge at universities has been the move towards digital assessment. In this classroom note, we describe our new no-exam assessment setup in a Statistics course for third-year bachelor Mechanical Engineering students. The main idea is to assess the students based on self-generated problems. Crucially, we supported students by providing a very clear structure of the course material, learning objectives, and requirements. At the same time, we left sufficient space for the students to tune the final assignment to their interests, creativity, and mathematical skills. In our experience, this setup makes assessment meaningful and enjoyable for both the students and the teacher and does not need to demand excessive time investment on the teacher’s side. We strongly believe that approaches like ours will have potential lasting effects on the diversity of assessment, quality of learning, and, last but not least, the appeal of Statistics for future engineers.
AB - Through the global pandemic, the single greatest challenge at universities has been the move towards digital assessment. In this classroom note, we describe our new no-exam assessment setup in a Statistics course for third-year bachelor Mechanical Engineering students. The main idea is to assess the students based on self-generated problems. Crucially, we supported students by providing a very clear structure of the course material, learning objectives, and requirements. At the same time, we left sufficient space for the students to tune the final assignment to their interests, creativity, and mathematical skills. In our experience, this setup makes assessment meaningful and enjoyable for both the students and the teacher and does not need to demand excessive time investment on the teacher’s side. We strongly believe that approaches like ours will have potential lasting effects on the diversity of assessment, quality of learning, and, last but not least, the appeal of Statistics for future engineers.
U2 - 10.4324/9781032627496-11
DO - 10.4324/9781032627496-11
M3 - Chapter
SN - 9781032627472
BT - Takeaways from Teaching through a Pandemic
A2 - Seaton, Katherine
A2 - Loch, Birgit
A2 - Lugosi, Elizabeth
PB - Routledge
ER -