While the amount of gamified exercises to be developed in the FGPE project (480) seemed to be sufficient, considering its originally planned scope of application (mostly for introductory courses of various programming languages), with the whole education moved to e-learning, there is a need to provide more online courses involving the use of programming languages in a gamified form. We want therefore to respond to this need by extending the set of open-source gamified programming exercises with 520 more programming exercises. This IO is however not only about quantity. One example of a course planned to be included in this collection is Machine Learning with Python, Keras, and Tensorflow.
During the last years, the machine learning techniques pervaded the most part of popular software applications (by popular we mean used by a wide range of people not IT specialists only) providing such a revolution in the user experience and interaction that the technology itself became somewhat invisible with the effects of its application felt as something that is simply given. An example of this is the automatic tagging of friends’ pictures uploaded to social networks, or, maybe even more persuasive, the intense and devoted usage of voice-controlled personal assistants by people who would never consider themselves tech-savvy. The use of artificial intelligence (AI) and its embedding in everyday technology is unstoppable and destined to be more supporting and supportive. The young generations consider the augmentation of their everyday life provided by AI as something that just works often forgetting that it is just a technology tool that could be, or, should me, mastered in order to earn the knowledge that could be useful for a future career. Thanks to the accessibility of off-the-shelf or remote computing power, the availability of the training datasets for pattern recognition (images, voice, music, behavior, sentiment) and of tools and programming languages enabling the application of AI techniques in an easy way, using learning machine learning techniques with Python and supporting libraries could be straightforward and even challenging as a game.
This context sets the idea of developing a gamified course focused on Machine Learning with Tensorflow, Keras, and Python, bringing the students to challenge the datasets in order to discover the hidden features, at the same time collecting and assimilating new knowledge about the programming tools used for this purpose. In our opinion, gamified courses aimed at such topics giving wide gamification opportunities, even though focused rather on applications of programming languages than on them alone, apart from its primary educational use, can also play a major role in promoting the FGPE+ project as such and all its results among people originally not interested in computer programming as such.
The target group for this IO are both programming instructors interested in making use of gamification who cannot or do not want to bother with gamifying their own exercises, and the students, learning using the exercises developed within the project, especially self-studying ones, for whom the access to other gamified courses could be limited.
The innovativeness of this IO lies in selection of novel courses to be gamified and the original ways they will be gamified. We strongly tend to exploit intrinsic gameful traits of respective course topics to implement content gamification (i.e. the application of game elements, game mechanics and game thinking to alter content to make it more game-like) rather than mere structural gamification (i.e. the application of game-elements to propel a learner through content with no alteration or changes to the content).
The expected impact of this IO is significant, as it extends (actually, more than doubles) the range of provided open-access gamified exercises and thus also the target groups of programming instructors and students whose courses are partly or fully covered in the FGPE-compliant set of gamified exercises.
