Algorithmic versus teacher‐led sequencing in a digital serious game and the development of second language reading fluency and accuracy

Abstract

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. What is already known about this topic? Serious games have the potential to aid learning but empirical research is needed. Findings about the efficiency of serious games are mixed. Current and reviewed versions of the Simple View of Reading constitute a suitable framework to measure reading acquisition. What this paper adds? It contributes to the growing corpus of research on digital serious games. It provides empirical evidence on the use of an adaptive system in formal education. Comparing a teacher‐led sequence to an algorithmic adaptive sequence on the same digital serious game has never been done before. The paper shows the need to obtain both system‐internal and system‐external data in order to capture the impact of gameplay on the development of L2 reading skills. Implications for practise and/or policy It sheds some light on how certain game designs may actually help practise with different degrees of intervention by teachers. It is interesting for teachers to use an adaptive sequence that they can check and intervene in if needed.

Key Competences for Lifelong Learning through the “Animal Crossing: New Horizons” Video Game

“…We found that playing the game can enhance the knowledge of users regarding didactic materials on natural sciences, the acquisition of second languages, economics and business entrepreneurship, and mathematical concepts, as has already been examined in previous studies in relation to other games focused on education [37,41,43,44,47]. Playing this video game can also contribute to reading comprehension [47] and help change behaviour in relation to climate change [49].…”

“…Moreover, these products can improve players' skills. Research shows evidence of improvements in the skills of health care professionals [45], social skills in companies [46], and reading comprehension skills [47]. There is also research showing that video games are a tool that can contribute to behavioural changes or to raising awareness of certain issues that affect society.…”

Research trends of game‐based language learning in K‐12 education: A systematic review of SSCI articles during 2009–2022

“…The most frequently reported benefits were enhancing four language skills, communicative skills, perceptions and learning attitudes. It could be well documented that GBLL could not only facilitate K‐12 students' listening (Hwang et al, 2017), speaking (e.g., Khodabandeh, 2023; Wu et al, 2014), reading (e.g., Hooshyar et al, 2017; Serra & Gilabert, 2021), writing (e.g., Allen et al, 2014; Chang et al, 2021), grammar (e.g., Chu et al, 2019; Kasprowicz et al, 2019), and vocabulary (e.g., Chen et al, 2021; Elaish et al, 2019); but also support instant feedback and collaborative learning (e.g., Lawrence & Sherry, 2021; Wen, 2021), improve K‐12 students' attention span (e.g., Yu & Tsuei, 2022), decrease their negative emotions, e.g., anxiety, high cognitive load and fear of failure (e.g., Hsu, 2019; Raffone, 2022; Yang et al, 2021), and provide authentic learning contexts (e.g., Dalton & Devitt, 2016; Lan, 2015), among others. Various challenges should be cautioned as well, such as technical issues, difficult learning tasks, high cognitive load and anxiety, and difficulty in integrating GBLL into K‐12 curriculum.…”

Technology‐mediated personalised learning for younger learners: Concepts, design, methods and practice

“…Different taxonomies (Bernacki et al, 2021;Plass & Pawar, 2020) showed the large diversity among technology-mediated personalised learning environments. For example, it is not yet clear whether the sequencing of tasks is more effective when relying on algorithms which automatically decide on the sequencing of tasks than when teachers can change the sequence of the tasks themselves based on their own estimation of a child's ability, which Serra et al (2021) investigated within the context of a digital serious game. Participants used the learning tool for 6 months, and the effects of the sequencing (by the teacher or system) were examined through pre and posttests in terms of children's performance.…”