TY - GEN
T1 - Improving the user-friendliness of AAT through a staged evaluation
AU - Tamra, Ross
AU - Ting-Wen, Chang
AU - Cindy, Ives
AU - Nancy, Parker
AU - Andrew, Han
AU - Sabine, Graf
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/28
Y1 - 2016/11/28
N2 - With the rapid adoption of online learning technologies, there is a pedagogical imperative to evaluate the success of online courses and analyze how student interact with electronic communication and learning resources. There is substantial data in learning management systems (LMSs) to inform this research, and many software tools aimed at mining LMS data or providing statistics or visualizations about those data. However, these tools provide either overviews or are very specific to the information they show. In this paper, we introduce the latest revision of the Academic Analytics Tool (AAT), which empowers users (e.g., teachers, learning designers) to do their own investigations into complex educational data stored in LMS databases. AAT allows non-technical users to answer almost limitless questions about learners' behavior and the impact of teaching methods and learning designs. To improve the user-friendliness of AAT, which is one of the core goals of the tool, a staged, qualitative evaluation study has been conducted. By improving AAT's user-friendliness, the tool gets more effective in supporting users and enabling them to use the huge amounts of educational data to learn how students interact with courses and learning materials as well as about the effectiveness of teaching methods and learning designs.
AB - With the rapid adoption of online learning technologies, there is a pedagogical imperative to evaluate the success of online courses and analyze how student interact with electronic communication and learning resources. There is substantial data in learning management systems (LMSs) to inform this research, and many software tools aimed at mining LMS data or providing statistics or visualizations about those data. However, these tools provide either overviews or are very specific to the information they show. In this paper, we introduce the latest revision of the Academic Analytics Tool (AAT), which empowers users (e.g., teachers, learning designers) to do their own investigations into complex educational data stored in LMS databases. AAT allows non-technical users to answer almost limitless questions about learners' behavior and the impact of teaching methods and learning designs. To improve the user-friendliness of AAT, which is one of the core goals of the tool, a staged, qualitative evaluation study has been conducted. By improving AAT's user-friendliness, the tool gets more effective in supporting users and enabling them to use the huge amounts of educational data to learn how students interact with courses and learning materials as well as about the effectiveness of teaching methods and learning designs.
KW - Academic analytics
KW - Data extraction and analysis
KW - Learning management systems
UR - http://www.scopus.com/inward/record.url?scp=85006934220&partnerID=8YFLogxK
U2 - 10.1109/ICALT.2016.81
DO - 10.1109/ICALT.2016.81
M3 - Published Conference contribution
AN - SCOPUS:85006934220
T3 - Proceedings - IEEE 16th International Conference on Advanced Learning Technologies, ICALT 2016
SP - 245
EP - 249
BT - Proceedings - IEEE 16th International Conference on Advanced Learning Technologies, ICALT 2016
A2 - Spector, J. Michael
A2 - Tsai, Chin-Chung
A2 - Huang, Ronghuai
A2 - Resta, Paul
A2 - Sampson, Demetrios G
A2 - Kinshuk, null
A2 - Chen, Nian-Shing
T2 - 16th IEEE International Conference on Advanced Learning Technologies, ICALT 2016
Y2 - 25 July 2016 through 28 July 2016
ER -