  Personalization and active learning are key aspects to successful learning.
These aspects are important to address in intelligent educational applications,
as they help systems to adapt and close the gap between students with varying
abilities, which becomes increasingly important in the context of online and
distance learning. We run a comparative head-to-head study of learning outcomes
for two popular online learning platforms: Platform A, which follows a
traditional model delivering content over a series of lecture videos and
multiple-choice quizzes, and Platform B, which creates a personalized learning
environment and provides problem-solving exercises and personalized feedback.
We report on the results of our study using pre- and post-assessment quizzes
with participants taking courses on an introductory data science topic on two
platforms. We observe a statistically significant increase in the learning
outcomes on Platform B, highlighting the impact of well-designed and
well-engineered technology supporting active learning and problem-based
learning in online education. Moreover, the results of the self-assessment
questionnaire, where participants reported on perceived learning gains, suggest
that participants using Platform B improve their metacognition.
