Abstract: We introduce a novel approach to measuring how
humans learn based on techniques from information theory and
apply it to the oriental game of Go. We show that the total amount
of information observable in human strategies, called the strategic
information, remains constant for populations of players of differing
skill levels for well studied patterns of play. This is despite the very
large amount of knowledge required to progress from the recreational
players at one end of our spectrum to the very best and most
experienced players in the world at the other and is in contrast to
the idea that having more knowledge might imply more 'certainty'
in what move to play next. We show this is true for very local
up to medium sized board patterns, across a variety of different
moves using 80,000 game records. Consequences for theoretical and
practical AI are outlined.
Abstract: Internet computer games turn to be more and more
attractive within the context of technology enhanced learning.
Educational games as quizzes and quests have gained significant
success in appealing and motivating learners to study in a different
way and provoke steadily increasing interest in new methods of
application. Board games are specific group of games where figures
are manipulated in competitive play mode with race conditions on a
surface according predefined rules. The article represents a new,
formalized model of traditional quizzes, puzzles and quests shown as
multimedia board games which facilitates the construction process of
such games. Authors provide different examples of quizzes and their
models in order to demonstrate the model is quite general and does
support not only quizzes, mazes and quests but also any set of
teaching activities. The execution process of such models is
explained and, as well, how they can be useful for creation and
delivery of adaptive e-learning courseware.