TY - JOUR
T1 - No exam: assessment of third-year engineering students on the basis of self-generated Statistics cases
AU - Litvak, Nelly
AU - Kula, Fulya
PY - 2022/3/16
Y1 - 2022/3/16
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.
KW - UT-Hybrid-D
U2 - 10.1080/0020739X.2021.1982041
DO - 10.1080/0020739X.2021.1982041
M3 - Article
SN - 0020-739X
VL - 53
SP - 647
EP - 655
JO - International Journal of Mathematical Education in Science and Technology
JF - International Journal of Mathematical Education in Science and Technology
IS - 3
ER -