TELL-OP

The chapter discusses the importance of in-service teacher training (INSET) to promote the use of open natural language processing (NLP)-based technologies (NLPTs). The first section briefly outlines the affordances of technology for second language acquisition and emphasizes the potential of open NLPTs. The second section presents the overall INSET design used in the TELL-OP ERASMUS+ project led by a team of researchers from several European universities. Section 3 provides a quantitative and qualitative analysis of the questionnaires and feedback data from Belgian French-speaking teachers (n = 86) on the TELL-OP online training module. A SWOT analysis (strengths, weaknesses, opportunities, and threats) is used to complement the teachers' feedback. In the fourth section, the authors put the course design into perspective using several theoretical models on the use of technology and open access resources in education, and provide suggestions for improving future similar INSET initiatives.

Data-driven learning (DDL) is a learner-focused approach which promotes language learners’ discovery of linguistic patterns of use and meaning by examining extensive samples of attested uses of language. Despite the emergence of mobile-assisted language learning (MALL) and its affordances, i.e. individualization and personalization, the potential of DDL in this context has not been widely explored. This study involved the creation of a mobile language learning app based on freely available natural language processing (NLP) tools, followed by a test of the app to gather the attitudes and perceptions of several groups of language learners across Europe. The results suggest a generally positive evaluation of DDL’s instant and personalized feedback and direct access to a variety of tools. Besides, suggestions for improvement were made concerning the design of the tasks, such as the addition of further built-in tools and adaptations to hardware constraints. Analyses also showed a need for specialized learner training, so as to grasp the potential of the feedback provided. This study may be construed as a first step towards creating more fleshed-out tools and further investigating the potential of combining DDL and MALL.

Combined with the ubiquity and constant connectivity of mobile devices, and with innovative approaches such as Data-Driven Learning (DDL), Natural Language Processing Technologies (NLPTs) as Open Educational Resources (OERs) could become a powerful tool for language learning as they promote individual and personalized learning. Using a questionnaire that was answered by language teachers (n= 230) in Spain and the UK, this research explores the extent to which OER NLPTs are currently known and used in adult foreign language learning. Our results suggest that teachers´ familiarity and use of OER NLPTs are very low. Although online dictionaries, collocation dictionaries and spell checkers are widely known, NLPTs appear to be generally underused in foreign language teaching. It was found that teachers prefer computer-based environments over mobile devices such as smartphones and tablets and that teachers´ qualification determines their familiarity with a wider range of OER NLPTs. This research offers insight into future applications of Language Processing Technologies as OERs in language learning.

Despite the emergence of Open Educational Resources (OER), the role of mobile learning, in general, and that of language processing technologies, in particular, has remained largely under-explored in the context of adult foreign language learning. This chapter sets out to bridge the gap between the possibilities offered by Information and Communication Technologies (ICTs) and the huge demand for ubiquitous personalised language learning opportunities. This chapter provides a state-of-the-art review of the use of mobile devices for non-formal learning and in particular adult language learning. It will consider the challenges and opportunities of recognition of learning achievements through open learning. The aim is to inform the debate concerning recognition of open learning and indicate the types of recognition practices in existence and the factors that influence them. Practices, attitudes and rationales for the types of recognition awarded are discussed, along with the factors that influence decisions and the contexts in which non-formal, open learning are recognised.

Combined with the ubiquity and constant connectivity of mobile devices, and with innovative approaches such as Data-Driven Learning (DDL), Natural Language Processing Technologies (NLPTs) as Open Educational Resources (OERs) could become a powerful tool for language learning as they promote individual and personalized learning. Using a questionnaire that was answered by language teachers (n= 230) in Spain and the UK, this research explores the extent to which OER NLPTs are currently known and used in adult foreign language learning. Our results suggest that teachers´ familiarity and use of OER NLPTs are very low. Although online dictionaries, collocation dictionaries and spell checkers are widely known, NLPTs appear to be generally underused in foreign language teaching. It was found that teachers prefer computer-based environments over mobile devices such as smartphones and tablets and that teachers´ qualification determines their familiarity with a wider range of OER NLPTs. This research offers insight into future applications of Language Processing Technologies as OERs in language learning.

This survey seeks to explore the spread and take­up of language, and/or text, processing technologies for language learning across Europe.