Scaffolding a Knowledge Community for High School Physics

This paper presents a design study of a collective inquiry model, where studentcontributions are captured, aggregated, tagged and represented in a coherent visualization in the context of an advanced high school physics course. We have developed a flexible new technology layer that allows the investigation of collaborative inquiry scripts to support the aggregation of peer responses, including the collection of student explanations and semantic tags. We also discuss why these scripts must take into account both the longer (macro) scripts that are enacted over a long-term curriculum, and consider how they can support (and be supported by) in-class (micro) scripts. Below we outline our rationale for inquiry design in Physics, the role scripting and orchestration play in the successful implementation of this curriculum, the role of the “smart classroom” in their enactment, and three successive iterations of our curriculum. Knowledge Communities for 21 Century Learning As we move further into the “knowledge Age” (Zuboff, 2004), today’s modern workplace is shaped by new technologies, where activities are increasingly data-driven, collaborative, and predicated on a set of fundamental skills commonly referred to as information literacies (Livingstone, 2008). This shift is particularly pronounced across STEM (Science, Technology, Engineering, and Mathematics) disciplines where workplace practices have increasingly shifted towards data-intensive practices and large, multidisciplinary collaborations across everwidening spatial and temporal scales (e.g., the Human Genome project, sea floor mapping). This transition, sometimes referred to as “science 2.0” (Gray & Szalay, 2007), highlights the need for the integration of such practices into science education. Otherwise, we risk students’ future success in STEM related careers (NSF, 2008). A theoretical perspective from the learning sciences that is well suited to learning and instruction in the 21st century is that of knowledge communities, as exemplified by the Fostering Communities of Learners (FCL) project (Brown & Campione, 1996), and Knowledge Building (Scardamalia & Bereiter, 1996), amongst others. These researchers have advanced an epistemological perspective where students come to consider learning as a social process, and value the collective knowledge of their peers. Although difficult to enact (van Aalst & Chan 2008; Sherin et al, 2004), the knowledge community approach has garnered renewed attention, partly as a result of Web 2.0 capabilities, which have the capacity to support complex pedagogical constructs (Slotta, 2010; Slotta & Najafi, 2010). One promising avenue of research is the investigation of a socially oriented, “Web 2.0” model that actively engages students as participants in a knowledge community that is active in the production, aggregation, and assessment of science topics, with an emphasis on inquiry and collaboration (Peters & Slotta, 2010). Providing students with the opportunity to contribute their own content allows them to make connections amongst the often disparate pieces of information within a domain (Ulrich et al., 2008; Ito et al., 2009). The most common way of creating such connections is by assigning meta-data, or “tags”, to discrete content elements (Mathes, 2004; Wiley, 2000). This process allows individuals to assign descriptors without the knowledge of other content elements that share the same designation. Participants can rely on the emergent collective data set, and are guided by tags that reveal meaningful connections across content elements (Hayman & Lothian, 2007). It is now possible to reveal flexible data representations in “real time” (Shirley et al., 2011) resulting in a new functionality (e.g., audience response “clicker” systems) that could not have been achieved in traditional pen and paper learning environments. Krajick et al. (1998) have identified such representations as an important element of science inquiry learning. Emerging conventions for user-contributed, tagged repositories (e.g., FlickR, YouTube) provide natural mechanisms for supporting the aggregation and refinement of student generated content (Anderson, 2008; Mathes, 2004; Wiley, 2000). Moreover, recent advances in semantic representation (e.g., PicLens or Taggraph) have enabled the development of a new media that provides personalized access to the user-created dataset. Researchers can now provide the mechanisms for students to become active members of a knowledge community, where they upload and tag elements according to prescribed content tags, or user-defined tags (i.e., as in a folksonomy). Questions remain about how such collections of content can best serve student learning and foster knowledge communities. Two aspects of such research that will be important to the present paper are those of reflection and scripting, and are discussed below. Reflection, Discourse, Scripting and Orchestration in Learning Activities An important dynamic within most inquiry or knowledge community research is that of reflection, which is typically embedded within student learning activities (Bielaczyc & Collins, 2006, Slotta & Linn, 2009). While generally accepted as an essential part of the learning process, reflection takes on particular significance in digitally mediated learning environments (Johnson & Aragon, 2003). In such environments, many interactions take place asynchronously, providing students with the opportunity to think critically about the ideas of their peers before adding their own ideas to the public discourse (Garrison, 2003). The act of placing one’s own ideas into words, for inclusion into the community discourse, also allows learners to reflect on their own understanding, construct coherent ideas, and reconcile misconceptions (Chi, 2000). By adding discourse with peers to the reflective process, we can provide students with opportunities to ask or answer questions, and elaborate on or challenge the ideas of their peers. Such activities have been shown to be an effective means for individuals to rehearse their knowledge, monitor their own understanding, and recognize and repair gaps in their knowledge (Roscoe & Chi 2007). Another topic of interest to learning scientists is the notion of scripting and orchestration (Dillenbourg, Jarvela & Fischer, 2009; Dimitriadis, 2001), where specified learning and interaction designs (i.e., “the script”) are enacted (“the orchestration”) by teachers and students. The script can be seen as a formalism that captures the pedagogical structure of a learning design. For example, each student could be required to upload two relevant videos for discussion. It could also entail collaborations, including a wide range of interaction patterns among students, their peers, and the teacher. When user-contributed materials are introduced, the script becomes more open-ended (Peters & Slotta, 2010), and any inquiry design must be left somewhat “unbounded” to allow for emergent themes, directions or content. Teachers can be seen as ”orchestrators” of the script – although this responsibility is also shared amongst students. In technology-enhanced learning environments, teachers receive real-time feedback about student ideas, resulting in opportunities for evidence-based decisions that can influence the script itself (i.e., real-time “course corrections”), and provide opportunities for teacher professional development (Dillenbourg & Jerman, 2007; Lui, Tissenbaum & Slotta, 2011; Slotta & Linn, 2009). Research Context This paper presents a design study of a collective inquiry model, where student-contributions are captured, aggregated, tagged and represented in a coherent visualization in the context of an advanced high school physics course, with a teacher who was actively engaged as a research partner. We have developed a flexible new technology layer that allows the investigation of collaborative inquiry scripts to support the aggregation of peer responses, including the collection of student explanations and semantic tags. While we have not adopted the complete pedagogical and epistemological commitment of the “knowledge community” approach, we have introduced a layer of social knowledge construction where students actively create a shared repository of physics homework problems (solved, tagged and explained), uploaded relevant examples, and other creative artifacts. In order to support these complex interactions, we have developed a new “smart classroom” technology environment that supports all student activities in the classroom, at home and in the field. In the sections below, we outline our rationale for inquiry design in Physics, the role scripting and orchestration play in the successful implementation of this curriculum, the role of the “smart classroom” in their enactment, and three successive iterations of our curriculum in a high school physics setting. In a design-based series of iterative advancements, we began in iteration one with a straightforward script for tagging, responding and explaining, which we implemented and evaluated in terms of student learning and teacher practice. In iteration two, we introduced a dimension of specialized expertise into the script, as well as new supports for teacher feedback. The first two iterations were formative, providing important information about how students collaborate using such real-time digital features. In iteration three, we dramatically expanded our designs, moving from single session smart classroom scripts, to a persistent digital layer that supported periodic inquiry and collaboration for the duration of the physics class. We worked closely with the teacher to develop designs, including a powerful new repository of user-contributed materials, and social and semantic tags. This repository facilitated the development of new scripts for teachers and students alike.