As access to video-viewing technology has increased, so has researchers’ interest in understanding how the viewing of captioned and subtitled videos can lead to effective vocabulary learning outcomes. Previously, there has been one meta-analysis on the effects of this type of video-viewing on vocabulary acquisition. However, the variables investigated and types of vocabulary knowledge analyzed were limited. To address these issues, we conducted a mixed review that combined a scoping review and meta-analysis. We identified 139 studies in major databases, of which 34 aligned with our inclusion criteria. Results from the scoping review found that researchers have assessed productive knowledge more than receptive knowledge, and knowledge of form and meaning more than knowledge of use. Participants were given TV series to view more than any other media type. Results from the meta-analysis found that viewing any type of captioned or subtitled videos had a positive effect on vocabulary acquisition. Among all the captioned and subtitled video types, viewing videos with intralingual captions had the largest effect on vocabulary learning outcomes. Furthermore, the viewing of animations had the largest effect on vocabulary learning outcomes compared with all the other types of video viewing investigated. No statistically significant difference between intentional or incidental learning conditions was found, indicating that both conditions are suitable for developing vocabulary learning through video viewing. Additional findings and implications for teaching and research are discussed.

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Vocabulary Acquisition through Viewing Captioned and Subtitled Video: A Scoping Review and Meta-Analysis

This chapter outlines the relation between artificial intelligence (AI) / machine learning (ML) algorithms and digital games. This relation is two-fold: on one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour (i.e. actions within the game world), commercial behaviour, interaction with graphical user interface elements or messaging with other players, while games can utilise intelligent algorithms to automate testing of game levels, generate content, develop intelligent and responsive non-player characters (NPCs) or predict and respond player behaviour across a wide variety of player cultures. In this work, we discuss some of the most common and widely accepted uses of AI/ML in games and how intelligent systems can benefit from those, elaborating on estimating player experience based on expressivity and performance, and on generating proper and interesting content for a language learning game.

AI in (and for) Games.

Impact of textual enhancement and input processing on syntactic development of EFL university students in Kuwait

Background: Approaches to teacher professional development, such as learning designs (LDs), can facilitate primary school teachers' appropriation of literacy technology in the classroom. LDs are detailed learning activities and interventions designed by teachers to plan their use of technology. Methods: Using a creative design methodology to carry out a series of LD workshops with teachers, we aimed to understand how primary school teachers envision learning and teaching with two distinct technologies designed to support children's reading skills: a game and an e-reader. Employing systematic qualitative content analysis, we compared LDs developed by teachers for each technology. Results: Our study shows that while principles of teacher instruction are consistently incorporated across the LDs, the design of each technology plays an important role in how teachers plan their students' learning and focal reading skills. Further, teachers' perception of the technology is as important as the features of the design. Compared with the e-reader, the game is perceived as an individual practice activity with less opportunities to learn with peers. Finally, across both technologies, teachers envision supporting additional literacy skills, beyond those designed in the technology, highlighting the importance of explicitly facilitating LDs intended to foster within-subject learning. Conclusions: These findings raise a new set of considerations on how to support teachers to design literacy learning and teaching activities with technology, and also offer a new methodological approach to facilitate LDs in future research and teacher training. Highlights What is already known about this topic Professional development supports teachers to appropriate literacy technologies in the classroom. The learning design approach has been shown to facilitate professional development by placing teachers in the role of the designer. The learning design approach can support and complement existing pedagogical approaches in the classroom, as well as to foster socio-constructivist learning. What this paper adds In the context of literacy, teachers rely on the learning aims of the technology and its pedagogical approach to determine how and what their students will learn. Teachers embed learning with peers when the technology aligns with existing social practice in the classroom. Teachers reappropriate literacy technology designed to support reading skills to extend into other literacy skills, such as writing and oral language. Implications for theory, policy or practice The paper offers a learning design methodology to support teachers in creating activities that integrate literacy technologies in the classroom. The methodology includes strategies that support teachers to embed learning with peers alongside reflecting a broad coverage of literacy learning objectives. 

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Appropriation of literacy technologies in the classroom: reflections from creative learning design workshops with primary school teachers

https://onlinelibrary.wiley.com/doi/pdf/10.1002/tesq.3036

The Effects of Multiple-Exposure Textual Enhancement on Child L2 Learners’ Development in Derivational Morphology: A Multi-Site Study

The effects of textual enhancement on development in L2 derivational morphology: A multi-site longitudinal study

User profiling and e-learning have received great attention in the last years. In a learning environment, user profiling provides historical data of the students' performance on different learning subjects. e-Learning tools such as serious games can collect user's data and build a user profile by appropriate storing these information. Thus, by combining above techniques and analysing each user's data a teacher can provide personalized treatment to her/his students. In this paper we present our solution on storing and analysing user data on a centralized server. Our system stores data collected from users with dyslexia. We maintain one user profile per student from data taken from a) user responses on a screening test and b) from user's performance on a set of serious games. The learning process then depends on the above data, so it is important to store and analyse our resources efficiently while respecting the user's privacy. Our solution is based on the usage of the cube model in parallel with a traditional, table-based storage of log data. The services we present are part of the iLearnRW1 project, aiming to support gamified learning for children with dyslexia.

Ioan P. Mihu

Papers

The Effects of Multiple-Exposure Textual Enhancement on Child L2 Learners’ Development in Derivational Morphology: A Multi-Site Study

The effects of textual enhancement on development in L2 derivational morphology: A multi-site longitudinal study

A data-logging mechanism to support e-learning systems