Mining the Change of Fuzzy Quantitative Association Rules for Summative Assessment
Chih-Hong Huang, Tony Cheng-Kui Huang, Shih-Sheng Chen
Available Online August 2013.
- https://doi.org/10.2991/icaicte.2013.36How to use a DOI?
- data mining, change mining, fuzzy association rules
- A learning management system (LSM) prevails and it accumulates an amount of data about the progress of student learning, demographic and students’ background over different sessions. Educators are very concerned about the shifts of unknown relationships among a thousand variables about students in a LMS for adjusting their teaching strategies and pedagogies. However, educators are not satisfied with the traditional reports, which are explored with limited relative variables on the issue of summative assessment and learning achievement in static sessions. The information about the changes of un-known relationships among many varia-bles cannot be produced with statistical methods in traditional reports. Our study proposes a mining change of fuzzy quan-titative association rules model to reveal the information. This model can discover the six types of changes rules from un-known relationships among many varia-bles with nominal or numerical attributes. Experiments are carried out to evaluate the proposed model. We empirical demonstrate how the model helps educa-tors understand the changing characteris-tics of students and to modify their teaching practices.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Chih-Hong Huang AU - Tony Cheng-Kui Huang AU - Shih-Sheng Chen PY - 2013/08 DA - 2013/08 TI - Mining the Change of Fuzzy Quantitative Association Rules for Summative Assessment BT - 2013 International Conference on Advanced ICT and Education (ICAICTE-13) PB - Atlantis Press SP - 169 EP - 173 SN - 1951-6851 UR - https://doi.org/10.2991/icaicte.2013.36 DO - https://doi.org/10.2991/icaicte.2013.36 ID - Huang2013/08 ER -