Predicting Performance of Students in a Flipped Classroom Using Machine Learning: Towards Automated Data-Driven Formative Feedback
Jalal Nouri, Mohammed Saqr, Uno Fors
Learning analytics (LA) is a relatively new research
discipline that uses data to try to improve learning,
optimizing the learning process and develop the
environment in which learning occurs. One of the
objectives of LA is to monitor students activities and early
predict performance to improve retention, offer
personalized feedback and facilitate the provision of
support to the students. Flipped classroom is one of the
pedagogical methods that find strength in the combination
of physical and digital environments i.e. blended
learning environments. Flipped classroom often make use
of learning management systems in which video-recorded
lectures and digital material is made available, which thus
generates data about students interactions with these
materials. In this paper, we report on a study conducted
with focus on a flipped learning course in research
methodology. Based on data regarding how students
interact with course material (video recorded lectures and
reading material), how they interact with teachers and
other peers in discussion forums, and how they perform on
a digital assessment (digital quiz), we apply machine
learning methods (i.e. Neural Networks, Nave Bayes,
Random Forest, kNN, and Logistic regression) in order to
predict students overall performance on the course. The
final predictive model that we present in this paper could
with fairly high accuracy predict low- and high achievers
in the course based on activity and early assessment data.
Using this approach, we are given opportunities to develop
learning management systems that provide automatic datadriven
formative feedback that can help students to selfregulate
as well as inform teachers where and how to
intervene and scaffold students. Full Text
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