Session 5D

Please see attached pdf with the original formatting preserved.

Digital game-based learning has been suggested as a potentially useful methodology to help improve literacy skills. Beyond bringing emotion and posing challenges, games have been shown to draw attention to and engage in processing of specific linguistic features. Additionally, games may allow personalization of learning, which has also been claimed to be advantageous for learners (Holmes et al. 2018). Yet little is known about how specifically games may contribute to improving bottom-up development of specific linguistic features with varying degrees of difficulty and how this, in turn, may impact reading accuracy and fluency. IRead (Infrastructure and integrated tools for personalized learning of reading skills) is an EU-funded four-year innovation project on the promotion of reading skills in first and second languages. 350 EFL students participated in the study. They were attending 6th grade (age 11-12) and used iRead 1-hour per week for six months. Lessons combined gameplay with coordinated reading from an e-reader. Games belonged to three different categories: accuracy (where they practice the feature until it is mastered), word-building, and automatization, and they included audio repetition of the features as well as audio feedback following errors. Literacy games in this project use an adaptivity algorithm integrated in the system that generates individual trajectories that may vary in amount of practice of each feature. The system covers four linguistic dimensions: phonology, morphology, morphosyntax and syntax. In this paper we report on phoneme-grapheme correspondence, a particularly challenging area for EFL students. The overall goal is to measure whether amount and quality of practice may have an impact on accuracy and fluency of reading. Pre-tests were administered regarding learners’ mastery of features, reading accuracy (word and non-word reading) and fluency (words per minute while reading aloud). Data are being currently collected and our outcome measures include the number of games played with each feature (amount of practice), time on task, number of correct answers, errors and recovery from errors. Post-tests will detect any improvements in reading accuracy and fluency. Statistical analysis will include regressions to establish the relative weight of both independent (amount of practice) and mediating factors (prior knowledge). Results will be discussed in the light of both reading (Gough & Turner, 1986) and multimodal theories (Mayer, 2009) of second language acquisition. Gough, P. and Tunmer, W. (1986). Decoding, reading, and reading disability. Remedial and Special Education, 7, 6–10. Holmes, W., Anastopoulou S., Schaumburg, H. & Mavrikis, M. (2018). Technology-enhanced personalised learning: untangling the evidence. Stuttgart: Robert Bosch Stiftung. Mayer, R. E. (2009). Multimedia Learning. Cambridge University Press.