Potential Abstract: In the postindustrial era, educational institutions are increasingly turning to digital platforms such as Slack channels to facilitate communication and collaboration among students and instructors. This context-laden study explores the use of educational data mining techniques within Slack channels to uncover patterns of student engagement and interaction. Drawing on a rich dataset of student interactions in a variety of academic settings, we employ advanced analytics to identify key factors influencing student engagement and performance. Our findings shed light on the potential of leveraging Slack channels as a valuable resource for enhancing student learning outcomes in the rapidly evolving educational landscape. Furthermore, we examine how these insights can inform policy decisions in the context of postindustrial educational environments, where the integration of technology and data-driven approaches is becoming increasingly prevalent. This research contributes to the emerging field of context-laden educational data mining by offering a nuanced perspective on the intricate relationship between digital platforms, student engagement, and policy frameworks.
Potential References:
- Learning analytics in collaborative learning supported by Slack: From the perspective of engagement
- Flip & slack–Active flipped classroom learning with collaborative slack interactions
- Using learning analytics to explore the multifaceted engagement in collaborative learning
- Learning analytics and educational data mining
- Characterizing student engagement moods for dropout prediction in question pool websites
