Catch the Open! A Gamified Interactive Immersion Into Open Educational Practices for Higher Education Educators.

Open Education (OE) opens up learning opportunities to, potentially, every person in the world. Additionally, it allows teachers, researchers, and practitioners to find, share, reuse, and improve existing resources under a dependable legal framework. Aiming to spread and foster the introduction of open policies in Higher Education (HE) institutions, the gamified interactive learning experience Catch the Open! was developed. Catch the Open! targets HE educators who wish to learn, or who wish to deepen their existing knowledge, about OE and Open Educational Practices (OEP). Within the gamified learning experience, the user becomes an educator, Alex, the game character, who receives a task from the Rector: to investigate how to best include OE and OEP in teaching and learning practice within the institution. Alex proceeds to explore and gather information in a web-based 2D virtual HE institution where students, colleagues, and guest researchers will help her to develop a comprehensive understanding of OE and the practical application of OEP. The educational content within Catch the Open! is underpinned by an OE competences framework for HE educators, developed in a previous phase of the Erasmus+ OpenGame project. In this paper, the design process to link pedagogical and technological approaches, which results in the Catch the Open! gamified web-based interactive application, is presented as well as the application itself. Moreover, two phases of piloting with 153 HE educators from six different HE institutions are presented. The obtained findings showed that the gamified environment helped in learning about OE. On the other hand, learners also suggested several improvement aspects of the gamified environment, such as the length of finishing a learning mission while playing.

Probabilistic finite-state machines--part I.

Probabilistic finite-state machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked: computational linguistics, machine learning, time series analysis, circuit testing, computational biology, speech recognition, and machine translation are some of them. In Part I of this paper, we survey these generative objects and study their definitions and properties. In Part II, we will study the relation of probabilistic finite-state automata with other well-known devices that generate strings as hidden Markov models and n-grams and provide theorems, algorithms, and properties that represent a current state of the art of these objects.

Probabilistic finite-state machines--part II.

Probabilistic finite-state machines are used today in a variety of areas in pattern recognition or in fields to which pattern recognition is linked. In Part I of this paper, we surveyed these objects and studied their properties. In this Part II, we study the relations between probabilistic finite-state automata and other well-known devices that generate strings like hidden Markov models and n-grams and provide theorems, algorithms, and properties that represent a current state of the art of these objects.