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Algorithmic versus teacher-led sequencing in a digital serious game and the development of second language reading fluency and accuracy
British Journal of Educational Technology ( IF 2.951 ) Pub Date : 2021-06-10 , DOI: 10.1111/bjet.13131
Judit Serra, Roger Gilabert

Recent years have seen a surge of calls for personalization of education. Automatised adaptivity in serious games has been advocated as a potential instantiation of such calls. Yet little is known about the extent to which personalised learning through automatised adaptivity poses an advantage for language learning over generalised teacher-led sequencing in digital, game-based learning environments. The goal of this paper is to address this question by comparing the learning outcomes in reading accuracy and fluency of didactic sequences designed by EFL teachers or by an adaptive algorithm. A total of 67 participants completed several proficiency and reading skills pretest and posttest and used the iRead system for 6 months. Results showed that all learners made progress in reading skills, but no significant differences were found between the two sequences in relation to the development of reading skills. It was also shown that adaptivity works best if it leads to increase in the number of games per feature. Results are discussed in the context of previous findings, and the role of adaptivity and sequencing is critically assessed.

中文翻译:

数字严肃游戏中的算法与教师主导的排序以及第二语言阅读流畅性和准确性的发展

近年来,要求个性化教育的呼声激增。严肃游戏中的自动化自适应被认为是此类调用的潜在实例。然而,关于在基于游戏的数字学习环境中,通过自动化适应性进行个性化学习在多大程度上为语言学习带来优势,而不是由教师主导的广义排序,我们知之甚少。本文的目的是通过比较 EFL 教师或自适应算法设计的教学序列在阅读准确性和流畅性方面的学习成果来解决这个问题。共有 67 名参与者完成了多项能力和阅读技能的前测和后测,并使用了 iRead 系统 6 个月。结果表明,所有学习者在阅读技能方面都有进步,但在阅读技能的发展方面,两个序列之间没有发现显着差异。还表明,如果适应性导致每个功能的游戏数量增加,则效果最佳。结果在先前发现的背景下进行了讨论,并对适应性和排序的作用进行了批判性评估。
更新日期:2021-06-10
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