Details
| Translated title of the contribution | Konzeptkarten für die formative Beurteilung: Erstellung und Implementierung einer automatischen und intelligenten Bewertungsmethode |
|---|---|
| Original language | English |
| Pages (from-to) | 433-447 |
| Number of pages | 15 |
| Journal | Knowledge Management & E-Learning |
| Volume | 15 |
| Issue number | 3 |
| Publication status | Published - 2023 |
Abstract
diagnostic information. On this basis, further learning or further teaching should
be adaptive and, in the best case, optimized. However, this aspect is difficult to
implement in reality, as teachers work with a large number of students and the
whole process of formative assessment, especially the evaluation of student
performance takes a lot of time. To address this problem, this paper presents an
approach in which student performance is collected through a concept map and
quickly evaluated using Machine Learning techniques. For this purpose, a
concept map on the topic of mechanics was developed and used in 14 physics
classes in Germany. After the student maps were analysed by two human raters
on the basis of a four-level feedback scheme, a supervised Machine Learning
algorithm was trained on the data. The results show a very good agreement
between the human and Machine Learning evaluation. Based on these results, an
embedding in everyday school life is conceivable, especially as support for
teachers. In this way, the teacher can use and interpret the automatic evaluation
and use it in the classroom.
Keywords
- Concept maps, Feedback, Formative assessment, Machine learning
ASJC Scopus subject areas
- Social Sciences(all)
- Education
- Business, Management and Accounting(all)
- Management of Technology and Innovation
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Knowledge Management & E-Learning, Vol. 15, No. 3, 2023, p. 433-447.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Concept maps for formative assessment: Creation and implementation of an automatic and intelligent evaluation method
AU - Bleckmann, Tom
AU - Friege, Gunnar
N1 - This work has been partly supported by the Ministry of Science and Education of Lower Saxony, Germany, through the Graduate training network “LernMINT: Data-assisted classroom teaching in the STEM subjects” (project no. 51410078).
PY - 2023
Y1 - 2023
N2 - Formative assessment is about providing and using feedback and diagnostic information. On this basis, further learning or further teaching should be adaptive and, in the best case, optimized. However, this aspect is difficult to implement in reality, as teachers work with a large number of students and the whole process of formative assessment, especially the evaluation of student performance takes a lot of time. To address this problem, this paper presents an approach in which student performance is collected through a concept map and quickly evaluated using Machine Learning techniques. For this purpose, a concept map on the topic of mechanics was developed and used in 14 physics classes in Germany. After the student maps were analysed by two human raters on the basis of a four-level feedback scheme, a supervised Machine Learning algorithm was trained on the data. The results show a very good agreement between the human and Machine Learning evaluation. Based on these results, an embedding in everyday school life is conceivable, especially as support for teachers. In this way, the teacher can use and interpret the automatic evaluation and use it in the classroom.
AB - Formative assessment is about providing and using feedback and diagnostic information. On this basis, further learning or further teaching should be adaptive and, in the best case, optimized. However, this aspect is difficult to implement in reality, as teachers work with a large number of students and the whole process of formative assessment, especially the evaluation of student performance takes a lot of time. To address this problem, this paper presents an approach in which student performance is collected through a concept map and quickly evaluated using Machine Learning techniques. For this purpose, a concept map on the topic of mechanics was developed and used in 14 physics classes in Germany. After the student maps were analysed by two human raters on the basis of a four-level feedback scheme, a supervised Machine Learning algorithm was trained on the data. The results show a very good agreement between the human and Machine Learning evaluation. Based on these results, an embedding in everyday school life is conceivable, especially as support for teachers. In this way, the teacher can use and interpret the automatic evaluation and use it in the classroom.
KW - Concept maps
KW - Feedback
KW - Formative assessment
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85173934739&partnerID=8YFLogxK
U2 - 10.34105/j.kmel.2023.15.025
DO - 10.34105/j.kmel.2023.15.025
M3 - Article
VL - 15
SP - 433
EP - 447
JO - Knowledge Management & E-Learning
JF - Knowledge Management & E-Learning
SN - 2073-7904
IS - 3
ER -