From Signals to Action: Explainable AI for
Engagement-Responsive Instructional Support in Digital
Higher Education
Andino Maseleno1,*, Meinhaj Hussain2, Aygul Z. Ibatova3
1Institut Bakti Nusantara, Lampung, Indonesia
2Rennier University, Ireland
3Tyumen Industrial University, Russia
Emails: andino.maseleno@ibnus.ac.id; meinhaj@rennier.online; aigoul@rambler.ru
Abstract
Artificial intelligence is increasingly used to monitor learning processes in higher education;
however, many analytics pipelines still terminate at prediction and provide limited
support for instructional action. The research establishes an explainable artificial intelligence
framework which utilizes digital learning environment behavioral data and contextual
information to create customized instructional support solutions. The analysis uses xAPIEdu-
Data dataset which contains 480 records to build engagement index and create support
profiles and predict multiclass performance through rule based action allocation. The study
tests three classification models using stratified cross validation. The study selects Random
Forest as the most effective system because it delivers superior results across all tests. The
selected model demonstrates 0.8021 accuracy and 0.8204 macro precision and 0.8010 macro
recall and 0.8084 macro F1 score and 0.9140 macro area under the curve on the hold-out
sample. The analysis shows that student absence and composite engagement index and
gender and student-guardian relationship and support profile and digital resource access are
the most important factors that determine student performance. The final decision layer
manages student assignment to instructional support plans which contain attendance-first
intervention and adaptive engagement support and family-engagement reinforcement and
structured progression coaching and challenge-and-extend pathways. The study develops
an analytical framework which connects explainable artificial intelligence to digital higher
education instructional decision support systems.
Keywords: Artificial intelligence in education; Learning analytics; Higher education;
explainable AI; Educational technology; Student support