AKOVIA is Automated Knowledge Visualization and Assessment
Although HIMATT has already been used by several researchers, it has two design problems worth mentioning. On the one hand, the user interface was accepted by researchers and subjects alike, and it even had a good usability (Pirnay-Dummer, Ifenthaler, & Spector, 2010). On the other hand, it was a web service which integrated both the data collection and the analysis. Researchers understandably wanted to integrate the data collection into their experiments and studies. However, subjects needed to log into HIMATT in order to input their data as text or draw graphs. They needed to enter another login, username, and password, which might have disturbed the experimental setting in some cases. The second design problem results from the first: We were often given raw data to upload into the HIMATT system so that the researchers could use the analysis facilities on their data. After following this procedure more often than the system had been used through the “front door,” we felt it was time for a complete redesign of the blended methods.
AKOVIA supports two different model input formats: 1. Re-representations on graphs (e.g. list form), 2. Re-representations as text
AKOVIA transforms the text into the list form. For several technical reasons, MS Excel® files are used to input data into AKOVIA. Although it is unconventional and usually XML is used, we found that the Excel format has several benefits, especially when character sets in plain text sometimes raise incompatibilities. Moreover, in some methodologies the list forms of models are hand coded and researchers find it easier to work with Open Office and/or Excel to input data. However, in the future we will also work on a stable XML input format to ensure better connectivity with other computer programs.
AKOVIA places no explicit limits on the size of data which can be investigated and analyzed. Large concurrent analyses used to slow our servers down to the point where the browser experienced time outs. Therefore, we separated the topology of the small analysis grid into theupload server, which takes in the files, and the analysis servers. The latter access the upload server and process the tickets offline. Afterwards, the results are uploaded to the upload server and the user is notified. Depending on the number and size of concurrent jobs, a response may take hours or sometimes even days. The figure below shows a simplification of the server topology.
A documentation is available here: AKOVIA Documentation
Please refer to the following works when using AKOVIA:
Ifenthaler, D. (2010). Scope of graphical indices in educational diagnostics. In D. Ifenthaler, P. Pirnay-Dummer & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge (pp. 213-234). New York: Springer.
Ifenthaler, D., Pirnay-Dummer, P., & Seel, N. M. (Eds.). (2010). Computer-based diagnostics and systematic analysis of knowledge. New York: Springer.
Pirnay-Dummer, P., Ifenthaler, D., & Spector, J. M. (2010). Highly integrated model assessment technology and tools. Educational Technology Research and Development, 58(1), 3-18. doi: 10.1007/s11423-009-9119-8
Pirnay-Dummer, P., & Ifenthaler, D. (2010). Automated knowledge visualization and assessment. In D. Ifenthaler, P. Pirnay-Dummer & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge (pp. 77-115). New York: Springer.