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1.	Palmquist, Adam, 1983, et al. (författare) AUTOMATON : A Gamification Machine Learning Project 2023 Ingår i: Encyclopedia of Data Science and Machine Learning. - : IGI Global. - 9781799892205 - 1799892204 - 9781799892212 ; , s. 3090-3101 Bokkapitel (refereegranskat)abstract This article displays a design ethnographic case study on an ongoing machine learning project at a Scandinavian gamification start-up company. From late 2020 until early 2021, the project produced a machine learning proof of concept, later implemented in the gamification start-up´s application programming interface to offer smart gamification. The initial results show promise in using prediction models to automate the cluster model selection affording more functional, autonomous, and scalable user segments that are faster to implement. The finding provides opportunities for gamification (e.g., in learning analytics and health informatics). An identified challenge was performance; the neural networks required hyperparameter fine-tuning, which is time-consuming and limits scalability. Interesting further investigations should consider the neural network fine-tuning process, but also attempt to verify the effectiveness of the cluster models selection compared with a control group.

Palmquist, Adam, 1983, et al. (författare)
AUTOMATON : A Gamification Machine Learning Project
2023
Ingår i: Encyclopedia of Data Science and Machine Learning. - : IGI Global. - 9781799892205 - 1799892204 - 9781799892212 ; , s. 3090-3101
Bokkapitel (refereegranskat)abstract
- This article displays a design ethnographic case study on an ongoing machine learning project at a Scandinavian gamification start-up company. From late 2020 until early 2021, the project produced a machine learning proof of concept, later implemented in the gamification start-up´s application programming interface to offer smart gamification. The initial results show promise in using prediction models to automate the cluster model selection affording more functional, autonomous, and scalable user segments that are faster to implement. The finding provides opportunities for gamification (e.g., in learning analytics and health informatics). An identified challenge was performance; the neural networks required hyperparameter fine-tuning, which is time-consuming and limits scalability. Interesting further investigations should consider the neural network fine-tuning process, but also attempt to verify the effectiveness of the cluster models selection compared with a control group.

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