Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with Crystal Island
作者：
Highlights：
• Used multi-level modeling to assess performance on in-game assessments with Crystal Island.
• Better performance when reading fewer total books, but reading each book more frequently.
• Better performance with low proportions of fixations on book content and book concept matrices.
• Highest performance with fewer books and low proportions of fixations on books and matrices.
• Implications for designing GBLEs that model efficient behavior leading to greater performance.
摘要
•Used multi-level modeling to assess performance on in-game assessments with Crystal Island.•Better performance when reading fewer total books, but reading each book more frequently.•Better performance with low proportions of fixations on book content and book concept matrices.•Highest performance with fewer books and low proportions of fixations on books and matrices.•Implications for designing GBLEs that model efficient behavior leading to greater performance.
论文关键词：Cognitive strategies,Metacognitive monitoring,Game-based learning environments,Eye tracking,Log files,Self-regulated learning
论文评审过程：Received 1 July 2016, Revised 12 January 2017, Accepted 22 January 2017, Available online 7 February 2017, Version of Record 9 September 2017.
论文官网地址：https://doi.org/10.1016/j.chb.2017.01.038
