**Author details**

Afterwards, the cognitive walkthrough may be repeated. We chose the cognitive

Our evaluation was performed in two steps. First, we performed a cognitive walkthrough in a collaborative meeting with three experienced experts: **Expert 1** is a very experienced professor and since many years Char of Area of Multimedia and Internet Application in the Department of Mathematics and Computer Science at FernUniversität in Hagen. **Expert 2** is a PhD, significantly responsible for the concept and design of KM-EP. **Expert 3** is a PhD student, researching in the area of

First, the menu structure of SNERC was navigated exploratively, to simulate the

Within the second cognitive walkthrough all typical steps where performed, as a potential user would do it. There were no further defects detected. Expert 4 pointed to the problem of unrealistic performance indicators due to overfitting. This could be disproved with the possibility to supervise and edit the automatically generated testing data within the NER Model Manager. A further note was, SNERC may not be suitable to deal with huge data sets, because of its web-based GUI architecture. As KM-EP does not deal with such huge data sets this is not a real problem for our

We saw the informal evaluation method lead to many results with a limited amount of time and resources. Nevertheless, an empirical evaluation with a bigger group of potential users should be done, to prove the usability and robustness of the

In this research, we presented a system for named entity recognition and automatic document classification that was integrated into an innovative Knowledge Management System for Applied Gaming. After presenting various real-word use case scenarios, we demonstrated, that it is possible to support users in the process of automatic document classification by combining techniques, such as, semantic analysis, natural language processing techniques (like named entity recognition) and a rule-based expert system. Our NER system was validated using the standard metrics for machine learning models. We demonstrated the portability of this system by using standard text corpus for model training and testing in various domains. Our overall system consisting of both, the NER and document classification system, has been successfully integrated into the target environment and was validated using Cognitive Walkthrough. A future evaluation with a bigger group of potential users may help to gather further insights about the usage, usability and

navigation of a potential user in the system. Then each SNERC component was tested. Finally, the creation of an automated classification was evaluated. Within these steps, there were overall eight defects detected, which needed to be fixed. Then, a second evaluation was performed. We extended the expert group by two new evaluators: **Expert 4** is a PhD student, researching in the medical area and emerging named entity recognition. **Expert 5** is a PhD student, researching in the

walkthrough as an appropriate evaluation method for our system.

area of advanced visual interfaces and artificial intelligence.

serious games and named entity recognition.

*The Role of Gamification in Software Development Lifecycle*

approach.

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system further.

**5. Conclusion and final discussion**

error handling of the entire system.

Philippe Tamla<sup>1</sup> \*†, Florian Freund1† and Matthias Hemmje<sup>2</sup>

1 Faculty of Multimedia and Computer Science, Hagen University, Germany

2 Research Institute for Telekommunikation and Cooperation, Dortmund, Germany

\*Address all correspondence to: philippe.tamla@fernuni-hagen.de

† These authors contributed equally.

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
