Developing ACT-R Model for Key Concept Recall in a Multilayered K-12 Educational Game

This study proposes an intertwined Multilayered Educational Game framework (iMEG C-Framework) paired with an ACT-R cognitive model to examine how different game-based learning structures affect memory encoding, retrieval, and retention among K-12 students. The authors argue that merging gameplay with instruction often produces cognitive overload or superficial engagement, and that separating game mechanics, instructional content, and feedback into distinct layers may improve instructional clarity and cognitive efficiency. The paper's central aim is to build a theoretically grounded, computationally simulated approach to understanding how such designs influence learners over time.

The work is currently at the design and validation stage. The authors present ACT-R equations for memory activation, decay that adjusts dynamically with prior activation, and a logistic retrieval probability function, but state that the model has not yet been simulated or empirically calibrated. The planned evaluation is a within-subjects, counterbalanced experiment with 39 seventh-grade students across three conditions: traditional learning, a classic educational game, and the iMEG design. Each condition involves a 75-minute session on board game design mechanics, with retention assessed after one day and seven days using 15-item multiple-choice tests.

The paper is explicit that no results are yet available, so its contribution at this stage is methodological and theoretical: a proposed experimental protocol, a mapping from experimental phases to the computational model, and research questions concerning both comparative effectiveness across conditions and the predictive accuracy of the ACT-R model. Stated limitations include the restriction to one subject area and grade level, and the absence of affective or motivational variables from the modeling.