Determinant of Optimal and Inhibited Mobile Language Learning Activity: Quiz Level Length

J. Byrne
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Abstract

Learner analytic driven research concerned with mobile app learner survivability. An app was developed to indicate productiveness of quiz app usage for global mobile assisted language learning (MALL). The research focused on inhibiting and optimizing effects of level length on total questions answered. How many questions per level leads to the highest total unique questions being answered? The research focused on nine-day cohort timeframes and included three phases: a small-scale pilot study to establish parameters, an exploratory stage undertaken within the parameters and finally the use of a quadratic regression predictive model. The null hypothesis (H0) was rejected. A one-way analysis of variance found a statistically significant difference in the mean productivity of the lengths.  Results of a Tukey post hoc test (p < .05) suggests question sets with less than eight questions, or more than 15 questions, appear to inhibit MALL autonomous learning. Optimal level question sets appear to be between lengths 8-14. The results visually encapsulated by a quadratic regression model broadly support H1 and H2. Set 12 is the statistically most significant optimal load. Gains of up to 107% are reported for switching from suboptimal to optimal approaches.
最佳和抑制移动语言学习活动的决定因素:测验水平长度
学习者分析驱动的移动应用学习者生存能力研究。开发了一款应用程序,以表明测试应用程序在全球移动辅助语言学习(MALL)中的使用效率。研究的重点是水平长度对总答题量的抑制和优化效应。每个级别有多少个问题导致最多的唯一问题被回答?研究的重点是9天的队列时间框架,包括三个阶段:建立参数的小规模试点研究,在参数范围内进行的探索阶段,最后使用二次回归预测模型。零假设(H0)被拒绝。单向方差分析发现,长度的平均生产率存在统计学上的显著差异。Tukey事后检验的结果(p < 0.05)表明,少于8个问题或多于15个问题的问题集似乎抑制了MALL的自主学习。最佳的问题集长度应该在8-14之间。由二次回归模型可视化封装的结果广泛支持H1和H2。集合12是统计上最显著的最优负载。据报道,从次优方法切换到最优方法的增益高达107%。
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