Co-Adaptation Processes of Syntactic Complexity in Real-Time Kindergarten Teacher-Student Interactions

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Co-Adaptation Processes of Syntactic Complexity in Real-Time Kindergarten Teacher-Student Interactions 1 Nonlinear Dynamics, Psychology, and Life Sciences, Vol. 23, No. 2, pp. 229-260. 2 © 2019 Society for Chaos Theory in Psychology & Life Sciences 3 Co-Adaptation Processes of Syntactic Complexity in 4 Real-Time Kindergarten Teacher-Student Interactions 5 Astrid Menninga, Marijn van Dijkler1, Ralf Cox, Henderien Steenbeek 6 and Paul van Geert, University of Groningen 7 Abstract: Under the premise that language learning is bidirectional in nature, 8 this study aimed to investigate syntactic coordination within teacher-student 9 interactions by using cross-recurrence quantification analysis (CRQA). Seven 10 teachers’ and a group of their students’ interactions were repeatedly measured in 11 the course of an intervention in early science education. Results showed changes 12 in the proportion of recurrent points; in case of simple sentences teachers and 13 students became less coordinated over time, whereas in case of complex sentences 14 teachers and students showed increasing coordination. Results also revealed less 15 rigid (more flexible) syntactic coordination, although there were no changes in 16 the relative contribution of teacher and students to this. In the light of the 17 intervention under investigation this is an important result. This means that 18 teachers and students learn to use more complex language and coordinate their 19 language complexity better in order to co-construct science discourse. The 20 application of CRQA provides new insights and contributes to better 21 understanding of the dynamics of syntactic coordination. 22 23 Key Words: cross-recurrence quantification analysis (CRQA), syntactic 24 coordination, adaptation processes, kindergarten teacher-student interaction, 25 intervention 26 INTRODUCTION 27 The bidirectional properties of language learning have been 28 demonstrated in several studies (e.g., Bronfenbrenner & Morris, 2006; Sameroff 29 & MacKenzie, 2003; Van Dijk et al., 2013; Van Geert, Steenbeek, & van Dijk, 30 2011). In addition, many researchers agree on the importance of social interaction 31 for language development (Dickinson & Porsche, 2011; Powell, Diamond, 32 Burchinal & Koehler, 2010). In classrooms settings, the teacher-student 33 interaction provides a unique entry point for educational interventions in that 34 improving this interaction can be the direct focus of the intervention (Barber & 35 Mourshed, 2007) or that the interaction may be regarded as a contributing factor 36 to successful implementation of an intervention. The intervention that is described 37 in this paper aimed at improving the quality of the teacher-student interaction and 1 Correspondence address: Marijn van Dijk, Heymans Institute of Psychological Research, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, The Netherlands. E- mail: [email protected] 229 230 NDPLS, 23(2), Menninga et al. 38 used coding of real-time observations to evaluate the effectiveness. Therefore, it 39 is important to use novel analysis techniques that allow capturing the complexity 40 of language use in interaction. Traditional evaluation analyses – such as 41 randomized control trials – are often used to study whether an intervention works, 42 but these analyses do not provide information about how and when change occurs 43 at the micro level. In order to gain insight into possible changes in the dynamics 44 of the teacher-student interaction innovative techniques are required. 45 One of the main reasons why change processes associated with 46 interventions were not widely studied was the lack of appropriate methodology to 47 study them effectively (Granic & Hollenstein, 2003). Recently, more studies have 48 focused on processes of change while taking into account inter- and intra- 49 individual variability (Granic, O’Hara, Pepler, & Lewis, 2007; Lichtwarck- 50 Aschoff, Hasselman, Cox, Pepler, & Granic, 2012; Turner, Christensen, Kackar- 51 Cam, Trucano, & Fulmer, 2014; Van Vondel, Steenbeek, van Dijk, & van Geert, 52 2016). In part, these studies investigate profiles and pathways of change in the 53 structure of adult-child interactions by quantifying the dynamics within these 54 interactions. The aim of the current paper is to examine the dynamics of language 55 use in real-time teacher-student interactions and to analyze whether this changes 56 in the course of an intervention called “Language as a Tool for learning science” 57 (LaT). This intervention is professionalization training for teachers based on video 58 feedback coaching. The LaT aims to improve the quality of kindergarten science 59 lessons with a specific focus on language interaction since science education both 60 demands and supports complex language (Glass & Oliveira, 2014; Wellington & 61 Osborne, 2001). In this paper, we investigate the dynamics of teacher-student 62 interaction – which is measured by sentences of the same syntactic category – and 63 possible changes therein in the course of the intervention. 64 Complex Dynamic Systems Approach 65 The complex dynamic systems approach proposes to study evolving 66 patterns within micro-level real-time interactions in educational settings, 67 specifically also focusing on language (Kunnen & van Geert, 2012; Steenbeek & 68 van Geert, 2013; Van Geert, 1994; 2003; Cox & van Dijk, 2013). From a complex 69 dynamic systems point of view, it is argued that the emergence of new patterns or 70 pattern transitions occurs through even small changes of relevant parameters of 71 systems functioning (Heinzel, Tominschek, & Schiepek, 2014). Language, in this 72 context, can be seen as the product of a transactional process between interacting 73 and adapting complex dynamic systems, in this case teacher and students, which 74 is characterized by iterativity, variability, nonlinearity, and self-organization 75 (Cameron & Larsen-Freeman, 2007; Van Geert, 2003; Van Dijk et al., 2013). 76 More coordinated language interactions are expected to be related to more optimal 77 developmental outcomes. The amount and structure of variability in the language 78 interaction is related to the coordination between teacher and students (Menninga, 79 van Dijk, Steenbeek, & van Geert, 2017). Analyzing the patterns of variability by 80 using nonlinear time series techniques, provides a window on the coupled 81 dynamics of teacher and students language interaction. Changes in these patterns NDPLS, 23(2), Syntactic Coordination 231 82 over time, for instance due to an intervention, can be detected in this way, and can 83 be linked to outcome measures, like intervention effectiveness, and provide clues 84 on the underlying developmental processes (e.g., Heinzel et al., 2014; Lichtwarck- 85 Aschoff et al., 2012). 86 Coordination of Syntactic Complexity 87 Language is of great importance in (early) science lessons (Snow, 2014; 88 Wellington & Osborne, 2001). The acquisition of sophisticated forms of language 89 use, including increasing syntactic complexity, is required to appropriately “speak 90 science” (Gee & Green, 1998). This is a process of co-constructing complex 91 science discourse, which entails coordination between teacher and students in a 92 way that enables students to sufficiently understand and contribute to the 93 conversation (Clark, 1996; Garrod & Pickering, 2004; Mercer, 1995). To achieve 94 this contribution, they align their words, grammar and sounds in order to 95 ultimately create mutual understanding (Brown-Schmidt & Tanenhaus, 2008; 96 Pickering & Garrot, 2004). 97 Many studies have emphasized the crucial role of adaptive processes in 98 language development of children (e.g., Snow, 1972; Van Dijk et al., 2013). 99 Language can be seen as an ordered, coupled and meaningful sequence of 100 utterances of the individual in a specific interactional context (e.g., teacher- 101 student discourse during science lesson). Typically, the utterances of the 102 interaction partners are co-constructed and coordinated, to create meaning and 103 understanding. Both teacher and students adapt their individual utterances to the 104 other, and the probability that certain combinations of utterances will recur within 105 and across multiple interactions increases, creating more predictable patterns of 106 language use. From previous studies, we know that speakers coordinate their 107 language use on various levels; for instance on vocalisations (Goldstein, King, & 108 West, 2003), speech characteristics (Ko, Seidl, Cristia, Reimchen, & Soderstrom, 109 2015; Reuzel et al., 2013, 2014), verbal expressions (Brennan & Clark, 1996; 110 Garrod & Anderson, 1987; Tamis-LeMonda & Bornstein, 2002), syntactic 111 complexity (Branigan, Pickering, & Cleland, 2000; Dale & Spivey, 2006; 112 Hopkins, Yuill, & Keller, 2016; Van Dijk et al., 2013) but also in gestures, gazes 113 and body posture during conversation (Abney, Warlaumont, Haussman, Ross & 114 Wallot, 2014; de Jonge-Hoekstra, Van der Steen, Van Geert, & Cox, 2016; 115 Golden-Meadow, 1998; Richardson, Dale & Kirkham, 2007; Shockley, Baker, 116 Richardson, Fowler, 2007). Coordination of syntactic complexity in the teacher- 117 student interaction is the focus of the current study. In this paper, coordination is 118 defined as the on-going and (un)intentional process of mutual interpersonal 119 adaptation (Garrod & Pickering, 2009; Van Dijk, Cox, & van Geert, 2016). This 120 means that within an on-going interaction, input as well as (effective) contingent 121 responses of both teachers and students shape their language toward syntactic 122 coordination (Dale & Spivey, 2006):
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