A Dual-Scaffolding Framework Integrating Peer-Scaffolding and Cognitive-Scaffolding for an Augmented Reality-Based Educational Board Game: An Analysis of Learners’ Collective Flow State and Collaborative Learning Behavioral Patterns

The design and application of educational board games have been emphasized in game-based learning. The integration of educational board games and augmented reality (AR) can help provide extensive cognitive-scaffolding for learners. This study proposed a dual-scaffolding framework that integrated peer-scaffolding and cognitive-scaffolding for an AR educational board game. This study also conducted an empirical analysis to evaluate this framework. Forty-four college students participated in this study. The researchers investigated these learners’ flow, acceptance, and their collaborative learning behavioral patterns with the sequential analysis. Moreover, this study explored the correlation of flow and acceptance and investigated learners’ behavioral pattern differences between high collective flow groups and low collective flow groups (collective flow referred to the mean of flow from group members). The results showed that there was a positive correlation between learners’ flow and acceptance. These learners’ behavioral patterns also showed that both peer-scaffolding and cognitive-scaffolding facilitated their problem-solving process. Moreover, the study found that high collective flow groups had more reflection and analysis behaviors than low collective flow groups in game-based learning.

Exact and Efficient Inference for Collective Flow Diffusion Model via Minimum Convex Cost Flow Algorithm

Collective Flow Diffusion Model (CFDM) is a general framework to find the hidden movements underlying aggregated population data. The key procedure in CFDM analysis is MAP inference of hidden variables. Unfortunately, existing approaches fail to offer exact MAP inferences, only approximate versions, and take a lot of computation time when applied to large scale problems. In this paper, we propose an exact and efficient method for MAP inference in CFDM. Our key idea is formulating the MAP inference problem as a combinatorial optimization problem called Minimum Convex Cost Flow Problem (C-MCFP) with no approximation or continuous relaxation. On the basis of this formulation, we propose an efficient inference method that employs the C-MCFP algorithm as a subroutine. Our experiments on synthetic and real datasets show that the proposed method is effective both in single MAP inference and people flow estimation with EM algorithm.

“Ngeli”: Flowing together in a Gamelan ensemble

The purpose of this study was to examine the collective flow experiences of participants while playing in a Gamelan ensemble. Participants were 15 members of a Gamelan ensemble in Singapore who were prompted to articulate their phenomenological experiences through extensive semi-structured interviews. Their responses were then transcribed and analyzed for emergent themes with initial codes guided by flow and collective flow theories. Three themes emerged from the data: community, chemistry, and collective peak. The Javanese term “ ngeli” surfaced from the interviews as a parallel notion to the Western concept of flow. Implications for theory and practice were proffered in light of the findings.