No abstract

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.

Wikis are a website technology for mass collaborative authoring. Today, wikis are increasingly used for educational purposes. Basically, the most important asset of wikis is free and easy access for end users: everybody can contribute, comment and edit-following the principles of Universal access. Consequently, wikis are ideally suited for collaborative learning and a number of studies reported a great success of wikis in terms of active participation, collaboration, and a rapidly growing content. However, the wikis success in education was often linked either to direct incentives or even pressure. This paper strongly argues that this contradicts the original intentions of wikis and, furthermore, weakens the psycho-pedagogical impact. A study is presented which focuses on investigating the success of wikis in higher education, when students are neither enforced to contribute nor directly rewarded similar to the principles of Wikipedia. Amazingly, the results show that, in total, none of the N = 287 students created new articles or edited existing ones during a whole semester. It is concluded that the use of Wiki-Systems in educational settings is much more complicated, and it needs more time to develop a kind of ''give-and-take'' generation.

To find a balance between learning analytics research and individual privacy, learning analytics initiatives need to appropriately address ethical, privacy, and data protection issues. A range of general guidelines, model codes, and principles for handling ethical issues and for appropriate data and privacy protection are available, which may serve the consideration of these topics in a learning analytics context. The importance and significance of data security and protection are also reflected in national and international laws and directives, where data protection is usually considered as a fundamental right. Existing guidelines, approaches, and regulations served as a basis for elaborating a comprehensive privacy and data protection framework for the LEA's BOX project. It comprises a set of eight principles to derive implications for ensuring ethical treatment of personal data in a learning analytics platform and its services. The privacy and data protection policy set out in the framework is translated into the learning analytics technologies and tools that were developed in the project and may be used as best practice for other learning analytics projects.

The idea of utilizing the rich potential of today's computer games for educational purposes excites educators, scientists and technicians. Despite the significant hype over digital gamebased learning, the genre is currently at an early stage. One of the most significant challenges for research and development in this area is establishing intelligent mechanisms to support and guide the learner, and to realize a subtle balance between learning and gaming, and between challenge and ability on an individual basis. In contrast to traditional approaches of adaptive and intelligent tutoring, the key advantage of games is their immersive and motivational potential. Because of this, the psycho-pedagogical and didactic measures must not compromise gaming experience, immersion and flow. In the present paper, we introduce the concept of micro-adaptivity, an approach that enables an educational game to intelligently monitor and interpret the learner's behaviour in the game's virtual world in a non-invasive manner. On this basis, micro-adaptivity enables interventions, support, guidance or feedback in a meaningful, personalized way that is embedded in the game's flow. The presented approach was developed in the context of the European Enhanced Learning Experience and Knowledge TRAnsfer project. This project also realized a prototype game, demonstrating the capabilities, strengths and weaknesses of micro-adaptivity.