  Large class sizes pose challenges to personalized learning in schools, which
educational technologies, especially intelligent tutoring systems (ITS), aim to
address. In this context, the ZPDES algorithm, based on the Learning Progress
Hypothesis (LPH) and multi-armed bandit machine learning techniques, sequences
exercises that maximize learning progress (LP). This algorithm was previously
shown in field studies to boost learning performances for a wider diversity of
students compared to a hand-designed curriculum. However, its motivational
impact was not assessed. Also, ZPDES did not allow students to express choices.
This limitation in agency is at odds with the LPH theory concerned with
modeling curiosity-driven learning. We here study how the introduction of such
choice possibilities impact both learning efficiency and motivation. The given
choice concerns dimensions that are orthogonal to exercise difficulty, acting
as a playful feature.
  In an extensive field study (265 7-8 years old children, RCT design), we
compare systems based either on ZPDES or a hand-designed curriculum, both with
and without self-choice. We first show that ZPDES improves learning performance
and produces a positive and motivating learning experience. We then show that
the addition of choice triggers intrinsic motivation and reinforces the
learning effectiveness of the LP-based personalization. In doing so, it
strengthens the links between intrinsic motivation and performance progress
during the serious game. Conversely, deleterious effects of the playful feature
are observed for hand-designed linear paths. Thus, the intrinsic motivation
elicited by a playful feature is beneficial only if the curriculum
personalization is effective for the learner. Such a result deserves great
attention due to increased use of playful features in non adaptive educational
technologies.
