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There is a need for a social semiotic multimodal take on meaning-making through technology use in research and practice in a comprehensive frame of understanding (Schnaider, Gu & Rantatalo, 2020). Contemporary social semiotic multimodal research has identified the multiplicity in meaning-making through technology use within different semiotic systems. Some components have specifically been highlighted such as activities, actions on different levels of mediation and modes of representations in relation to hardware and software technologies, functional properties, and sign-systems that moves across configurations of technologies and users (Adami, 2010; 2014; Djonov & van Leeuwen, 2011; 2013; 2018; Jewitt, 2005; Norris, 2002; Ravelli & van Leeuwen, 2018; The Swedish national agency for education, 2018; Vigild Poulsen, 2018; Zhao & van Leeuwen, 2014; Zhao and Zappavigna, 2018). With a concert of different actors in learning settings such as schools, current research has also pointed out that teacher use is overlooked (Schnaider et al., 2020). Although some considerable contributions to understanding meaning-making through technology use by pinpointing essential aspects have been made, research has not yet united and comprehensively theorized the components important when studying meaning-making through technology focusing on learning settings and actors in school. Previous research is limited in equally exploring the nature of technologies and meaning-making practices. To amend existing research gaps, a multimodal layer (ML) perspective was created that unites the technologies to the meaning-making of different actors in school (Schnaider et al., 2020). In this paper, the ML framework will be theoretically developed and refined from the research question; what multimodal principles can guide a comprehensive understanding of technology use in learning settings?

The findings of three previous empirical studies on teachers’ and students’ meaning-making through technology use from the ML perspective will be synthesized, developed, and refined by methods of qualitative meta-synthesis (Finfgeld-Connett, 2018) from the five components: technologies (configurations of hardware/software) (Ravelli et al., 2018), technologies functional (the taxonomy, Wartofsky, 1979) and semiotic properties (Jewitt, 2017), modes of representation and activities (Bezemer & Kress, 2016; Kress, 2010; Kress et al., 2014).

The ML framework offers a lens through which the variations between the five components can be identified. By developing the framework, the distinction and overlaps that were found to exist between the layers’ components can be clarified, and how the layers vary in emphasis between activities, users, and technologies. The ML can offer new comprehensive insights into how teachers and students variously mediate meaning in learning settings through different configurations of technologies and representational forms. Detailed knowledge on the nature of the technologies and their relations to teachers’ and students’ meaning-making activities is important since it can guide both design thinking and learning design and model future technology use and implementation.