Technology-facilitated oral homework: leveraging technology to get students speaking outside the classroom

It is understandable why speaking and pronunciation work might fall by the wayside in some language learning contexts. Limited classroom contact time, pressure to cover curriculum content, high student-to-teacher ratios, challenges in monitoring speaking activities, and the need for one-to-one time for effective oral assessment are just some of the reasons why oral work might drop down the priority list. Peer pressure and Foreign Language Anxiety (FLA) can also make speaking the target language a daunting prospect for students. To compound matters, homework in our subject has traditionally focused predominantly upon the written word. Limited in-lesson opportunities to practise orally and little-tono chance to do so between formal lessons could leave some students lacking

in oral confidence, with low levels of L2 decoding ability and low levels of motivation for language learning more generally.
TFOH is an approach that aims to overcome some of these common challenges and develop students' oral confidences by providing additional opportunities for practising in a 'lower stakes', self-regulated environment. The goal is that this practice could lead to a decrease in debilitative FLA, as well as increases in students' oral confidences, L2 decoding abilities, and motivation.
TFOH does not currently appear to be a widespread practice at the secondary school level. The research base is also limited, and focuses on adult learners in higher education settings (e.g. Correa & Grim, 2014;Guanoluisa, 2017;Méndez, 2010). The proliferation of Internet-connected mobile devices over the last decade and the concurrent emergence of more affordable, user-friendly digital technologies (that include audio and video recording facilities), however, mean that TFOH is becoming an increasingly viable option for a wider range of settings.

Example
As part of their French course, a class of Year 9 (13-14 years old) students in London, UK took part in 'Let's Talk Homework', a case study involving an intervention of five TFOH tasks (Shanks, 2018). In place of their usual weekly written or learning homework, students were assigned a TFOH task, in response to which they recorded and uploaded an audio file to a private online bulletin board set up and managed by their teacher through a free Padlet account (see Figure 1). Task types were varied in structure and focus. Read-aloud activities invited students to practise sound-symbol correspondences and develop confidence in pronunciation. More extended tasks invited students to create language themselves, for example, by describing a photograph.
All students managed to submit audio, with the vast majority submitting directly through the free Padlet app from a mobile device. A small number of students uploaded a link to an audio file recorded via another method, such as their mobile phone's native voice recorder app or the recording app Vocaroo. That the case study took place successfully in a state school with above national levels of socioeconomic disadvantage is encouraging in terms of the feasibility of using TFOH in a wider range of contexts. Many other websites, apps, or digital tools can be used to run TFOH, and there are pockets of innovative practice being shared online, for example through the #MFLTwitterati and #MFLChat networks.

Benefits
The main benefit of TFOH is that it allows students increased opportunities to practise orally -something any language teacher would want for their students. It can let students more regularly go through the important and confidence-building process of physically producing new target language sounds, words, and sentences. TFOH can provide a safer, lower-stakes environment in which to practise, away from the peer pressure of classmates and the teacher. In the 'Let's Talk Homework' case study, it became clear that TFOH can provide a space for students to monitor their own oral performance and self-regulate their learning at their own speed: " I recorded my voice once to listen to how I did it and then I didn't post it, because I just practised it and then I did it again" (Student 14 " for the tongue twisters I had to record like ten different times" (Student 13).
The teacher involved in the same case study also observed that TFOH can have a positive impact upon oral confidence, pronunciation, and anxiety reduction:

Potential issues
Whilst conversation-like tasks are currently possible (e.g. by the teacher recording sequences of questions or using Qwiqr's conversation feature), the currently asynchronous nature of TFOH means that it is not yet possible to replicate elements of spontaneity, listenership, and support in the ways that are possible with authentic face-to-face oral practice. It is also important to consider equality of access to the requisite technology, especially when working with schools that serve socio-economically disadvantaged students. Support or providing alternative ways of completing TFOH could be considered, e.g. morning or after-school homework clubs with hardware access, use of a shared class devices, signposting to the library or computer room facilities, suggesting use of a parent's or sibling's phone, placing files on the school's student file server, or changing the app or method of submission used. Time, expertise, and support needs to be available for helping teachers and students develop the technological knowledge of the digital tools required in each context.

Looking to the future
There is an increasing number of digital tools that can be used for TFOH. As such, TFOH could realistically become a much more widely used type of homework task, used regularly by language teachers. TFOH will likely be most effective when the tasks are carefully designed, embedded regularly within courses, and are accompanied by feedback to support learners.
We increasingly interact orally with technology through Intelligent Virtual Assistants such as Siri, Cortana, and Alexa (see Underwood, this volume). Similar voice recognition and artificially intelligent technologies could be put to use in language learning contexts. It is possible to imagine increasingly complex oral human-machine interactions that soon resemble real conversations, with the appropriate machine responses based upon the learner's voice input guiding the learning conversations. Analysis of the learner's performance, automated feedback, and remedial tasks could also be integrated into such interactions. Google's "Human-like Open-Domain Chatbot" Meena is already providing interesting insights into this area.