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Virtual reality applications in education are becoming more and more frequent. Empirical, data-based insights in the mechanisms and impacts of VR trainings are still sparse, however. With this quasi-experimental investigation, we compare the effects of a VR training game with a conventional face-to-face presence workshop in the field of vocational training. The training domain is awareness and customer interaction training for bank clerks. The results show that the VR solutions excelled the expectations of participants and the learning motivation was significantly higher as opposed to the conventional training. In the perceived effectiveness, the VR conditions achieved equal results than face-to-face workshops. The results provide evidence that VR solutions are an appropriate approach for vocational training.
Michael D. Kickmeier-Rust; Philipp Hann; Michael Leitner. Increasing Learning Motivation: An Empirical Study of VR Effects on the Vocational Training of Bank Clerks. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 111 -118.
AMA StyleMichael D. Kickmeier-Rust, Philipp Hann, Michael Leitner. Increasing Learning Motivation: An Empirical Study of VR Effects on the Vocational Training of Bank Clerks. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():111-118.
Chicago/Turabian StyleMichael D. Kickmeier-Rust; Philipp Hann; Michael Leitner. 2019. "Increasing Learning Motivation: An Empirical Study of VR Effects on the Vocational Training of Bank Clerks." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 111-118.
Recent advances in automatic machine learning (aML) allow solving problems without any human intervention. However, sometimes a human-in-the-loop can be beneficial in solving computationally hard problems. In this paper we provide new experimental insights on how we can improve computational intelligence by complementing it with human intelligence in an interactive machine learning approach (iML). For this purpose, we used the Ant Colony Optimization (ACO) framework, because this fosters multi-agent approaches with human agents in the loop. We propose unification between the human intelligence and interaction skills and the computational power of an artificial system. The ACO framework is used on a case study solving the Traveling Salesman Problem, because of its many practical implications, e.g. in the medical domain. We used ACO due to the fact that it is one of the best algorithms used in many applied intelligence problems. For the evaluation we used gamification, i.e. we implemented a snake-like game called Traveling Snakesman with the MAX–MIN Ant System (MMAS) in the background. We extended the MMAS–Algorithm in a way, that the human can directly interact and influence the ants. This is done by “traveling” with the snake across the graph. Each time the human travels over an ant, the current pheromone value of the edge is multiplied by 5. This manipulation has an impact on the ant’s behavior (the probability that this edge is taken by the ant increases). The results show that the humans performing one tour through the graphs have a significant impact on the shortest path found by the MMAS. Consequently, our experiment demonstrates that in our case human intelligence can positively influence machine intelligence. To the best of our knowledge this is the first study of this kind.
Andreas Holzinger; Markus Plass; Michael Kickmeier-Rust; Katharina Holzinger; Gloria Cerasela Crişan; Camelia-M. Pintea; Vasile Palade. Interactive machine learning: experimental evidence for the human in the algorithmic loop. Applied Intelligence 2018, 49, 2401 -2414.
AMA StyleAndreas Holzinger, Markus Plass, Michael Kickmeier-Rust, Katharina Holzinger, Gloria Cerasela Crişan, Camelia-M. Pintea, Vasile Palade. Interactive machine learning: experimental evidence for the human in the algorithmic loop. Applied Intelligence. 2018; 49 (7):2401-2414.
Chicago/Turabian StyleAndreas Holzinger; Markus Plass; Michael Kickmeier-Rust; Katharina Holzinger; Gloria Cerasela Crişan; Camelia-M. Pintea; Vasile Palade. 2018. "Interactive machine learning: experimental evidence for the human in the algorithmic loop." Applied Intelligence 49, no. 7: 2401-2414.
Learning Analytics is an important trend in education. In conventional classroom settings, however, a sound basis of digital data for analytics is lacking. Therefore, it is important to develop the methodologies and technologies to utilize the scattered and heterogeneous bits of available data as effective as possible. It is also important to deploy simple and usable tools to teachers that could help them within the context conditions and constraints of their daily work. In this paper, we introduce a prototypical approach for learning Analytics in the classroom and a simple data collection tool named Flower Tool. This tool enables collecting and comparing students’ self-assessments with teacher-lead assessments and the results of external tests. In a first field study, we gathered feedback from students and teachers about the approach, indicating a strong acceptance and a number of potential advantages for the assessment and reflection processes in the classroom.
Michael D. Kickmeier-Rust; Lenka Firtova. Learning Analytics in the Classroom: Comparing Self-assessment, Teacher Assessment and Tests. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology 2018, 131 -138.
AMA StyleMichael D. Kickmeier-Rust, Lenka Firtova. Learning Analytics in the Classroom: Comparing Self-assessment, Teacher Assessment and Tests. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. 2018; ():131-138.
Chicago/Turabian StyleMichael D. Kickmeier-Rust; Lenka Firtova. 2018. "Learning Analytics in the Classroom: Comparing Self-assessment, Teacher Assessment and Tests." Proceedings of the 2nd International Conference on Data Engineering and Communication Technology , no. : 131-138.