One Hint Doesn’t Fit All: Learner Characteristics and Hint Design in Digital Learning Games

• In Event: AERA Roundtable Session Saturday 1:45 pm - Gold 4
  In Roundtable Session: Designing Immersive Learning Environment

Abstract

This systematic review examined how individual learner characteristics inform the design and evaluation of hint-based support in digital learning games, based on 20 empirical studies identified through a systematic search (2010–2024). Using a PRISMA multi-phase screening process across six databases and quality appraisal tools, the review identified four major domains of learner characteristics: cognitive, demographic, affective, and metacognitive. Within these domains, Prior knowledge was the most frequently examined variable. In contrast, age, credits earned, mathematics self-efficacy, and Grade Point Average (GPA) were rarely addressed. The findings reveal limited personalization in current hint systems and call for adaptive designs that address learner characteristics. This review supports future research on learner-sensitive hints in digital game-based learning.

Authors

• Zeinab Mokhtari, Dresden University of Technology

• Mahboobeh Mehrvarz, Carnegie Mellon University

• Bruce McLaren, Carnegie Mellon University

• Seyedahmad Rahimi, University of Florida

• Esma Nur Kahveci, University of Massachusetts - Dartmouth

• Samaneh Abdoli, Farhangian University