Constructing Dynamic Dialogic Spaces Through a Technology-Enhanced Civic Learning Curriculum

• In Event: Technology-Enhanced Dialogic Instruction

Abstract

Objectives and Framework

Teachers and students construct dialogic spaces that mediate classroom learning. Dialogic space involves multiple voices constructed between individuals, within individuals, or from texts/media (Wegerif, 2007). Supported by technological tools, classroom dialogic spaces can expand from traditional face-to-face or person-text interactions to online virtual spaces (Authors, 2018). Technology also allows flexibility to flow between different dialogic spaces with varying dialogic properties in response to instructional goals and needs.

We hypothesize that technology-enhanced dialogic instruction that immerses students in dynamic dialogic spaces can foster students’ collective efficacy (CE) and social perspective-taking (SPT). Technology-enhanced dialogic instruction can provide students enriched opportunities to construct and coordinate multiple voices from teacher, peers, self, and media/text. This then enhances their sense of efficacy for collaboration and ability to take multiple perspectives.

Methods and Data

Data were drawn from our two-year effort to iteratively develop a technology-enhanced dialogic instruction in the context of civic learning. Participants were 153 fourth and fifth-grade students (46% girls) from eight classrooms in two Midwestern suburban districts. DCL consists of four 10-day units, each introducing provocative, multifaceted social issues. Students collaborated in immersive learning activities to engage in collective decision-making and digital concept journals (Appendix A). They engaged in collaborative social reasoning (CSR) discussions (Authors, 2022) featuring collaborative argumentation, mutual respect, and equal participation. Asynchronous dialogue occurred in online discussion boards and a videorecording platform (Appendix B). Teachers orchestrated whole-class discussions using multimedia and cloud-based technologies.

We conducted an activity analysis to code dialogic properties of each instructional activity. Target audience identifies the individual or material a student dialogically interacts with (Sun et al., 2022). Interpretive authority identifies who evaluates students’ comments for accuracy or plausibility (Chinn et al., 2001). Discussion stance identifies the orientation of the dialogue about the material (Murphy et al., 2009). We identified prevalent combinations of these dialogic properties that characterized a dialogic space. Students rated their CE (Glassman et al., 2021) and SPT (Davis, 1996) pre- and post-intervention.

Findings and Significance

Four dialogic spaces (DS) were identified: CSR (DS1), immersive problem solving (DS2), whole-class instruction (DS3), whole-class instruction with media (DS4). DS involving interactions with teachers (DS3) tend to be efferent and have teachers as the authority. However, when media is integrated into the space (DS4), media helped generate critical-analytic dialogue and emotional connections. DS2 capitalizes on media and fictional characters to bring perspectives from outside the classroom space to deepen students’ understanding of the issues. DS1 opens dialogic spaces that allow students the interpretive authority to engage in critical-analytic and expressive dialogue with peers. Students constantly moved across the four DS, engaging in argumentation and emotional reflections with the teacher, peers, self, and media. Repeated-measures analysis of variance indicated students’ CE and SPT were higher at post-intervention than pre-intervention (ps<.001).

DCL offers a dynamic flow across multiple dialogic spaces throughout each curricular day. Technology makes the power structure in the dialogic classroom more fluid, and dialogue around efferent, critical-analytic, and expressive stances more connected. These affordances further students’ CE and SPT.

Authors

• Monica Lu, The Ohio State University
  Presenting Author

• Adriana I Martinez Calvin, The Ohio State University
  Non-Presenting Author

• August Masonheimer, The Ohio State University
  Non-Presenting Author

• Tzu-Jung Lin, The Ohio State University
  Non-Presenting Author

• Michael Glassman, The Ohio State University
  Non-Presenting Author

• Eric M. Anderman, The Ohio State University
  Non-Presenting Author

• Wonjoon Cha, The Ohio State University
  Non-Presenting Author

• Kimiko Ching, The Ohio State University
  Non-Presenting Author

• Yilun Huang, The Ohio State University
  Non-Presenting Author

• Saes Byul Kim, The Ohio State University
  Non-Presenting Author

• Manisha Naresh Nagpal, The Ohio State University
  Non-Presenting Author

• Julie Parrot
  Non-Presenting Author

• Shantanu Tilak, Chesapeake Bay Academy
  Non-Presenting Author

• Ziye Wen, The Ohio State University
  Non-Presenting Author

• Nava Yekita, The Ohio State University
  Non-Presenting Author

• Yue Sheng, The Ohio State University
  Non-Presenting Author