Generating Engagement : A Content Analysis of the Motivational Qualities in EFL Teachers’ Descriptions of Motivating Activities

Students’ declining motivation to learn English in school presents a major challenge for teachers (Ushioda, 2013) and motivation researchers need to help them “find ways of eliciting, enhancing, and sustaining students’ motivation” (Guilloteaux, 2013: 3). While recent publications (e.g. Dörnyei & Kubanyiova, 2014; Gregersen & Macintyre, 2014) offer theoretically-based, practically-oriented guidance on generating motivation, focus tends to be on the development of language-speaker/user self-concepts over longer timescales. In the research literature on materials design, itself an under-explored area (Gilmore, 2012), focus has been directed to the effectiveness of tasks as classroom input, rather than their motivational qualities. Faced with a daily need to deliver motivating activities, teachers are poorly supported by principled advice. With the aim of contributing to the development of effective practice, and recognizing that “the accumulated wisdom of best practices in the teaching profession considerably exceeds the significance of the findings of empirical investigations” (Dörnyei, 2009: 267), the purpose of the research presented here is to examine the language learning activities teachers themselves consider motivational. An electronic questionnaire was distributed to 325 teachers of English from a randomly-drawn sample of 65 secondary schools in Sweden. An open-ended question invited teachers to describe an activity carried out in the current/previous term which they experienced as generating student motivation. Quantitative and qualitative content analysis techniques were employed in the analysis of these descriptions. Drawing on activity-relevant motivational factors outlined by Dörnyei and Csizér (1998), Ushioda’s (2011) principles for motivating learners ‘to speak as themselves’, and Henry’s (2013) identification of the motivational potential of activities with ‘affinity space’ features (Gee, 2007), analyses were guided by five overarching motivat