Visualizing Revision: Leveraging Student-Generated Between-Draft Diagramming Data in Support of Academic Writing Development

Abstract

Once writers complete a first draft, they are often encouraged to evaluate their writing and prioritize what to revise. Yet, this process can be both daunting and difficult. This study looks at how students used a semantic concept mapping tool to re-present the content and organization of their initial draft of an informational text. We examine the processes of students at two different schools as they remediated their own texts and how those processes impacted the development of their rhetorical, conceptual, and communicative capacities. Our analysis suggests that students creating visualizations of their completed first drafts scaffolded self-evaluation. The mapping tool aided visualization by converting compositions into discrete persistent visual data elements that represented concepts and connections. This often led to students' meta-awareness of what was missing or misaligned in their draft. Our findings have implications for how students approach, educators perceive, and designers support the drafting and revision process.

Education 2.0: Artificial Intelligence and the End of the Test

“…Cope, Kalantzis, Abd-El-Khalick, & Bagley (2013) -Coded annotations, supported by machine learning where users train the system to recognize higher order thinking.5 -Ontology-referenced maps that prompt knowledge creators and reviewers to add a second layer of meaning to text, image and data; this is direct support to learners, as well as machine learning training data. Olmanson et al (2016) We need to broaden the range of data types and data points for assessment. The dominance of select response assessments is based on the ease of their mechanization (Kalantzis & Cope, 2012 "bubble tests."…”

(Neo-) Marxism

“…Since the late 1990s, teachers have worked to integrate new media and aspects of new literacies into the curriculum. The range of integration rationales includes an interest in leveraging platform affinity and novelty to inject excitement into content areas (Olmanson and Abrams 2013), rethinking student participation in learning spaces (Vasudevan 2010), encouraging the expression of student identities (Rust 2015), closing the digital divide, and mirroring collaborative ecologies of the twenty-first-century workplace and better facilitating the inclusion of multimodality in academic texts to fulfill evolving State and national expectations (Olmanson et al 2015).…”

Big Data Comes to School

“…In one example of visual markup, we have created in our "Scholar" environment a tool whereby students highlight sections of information texts (readings, their own texts, their peers' texts) in different colors in order to identify CCSS information text ideas of concept, definition, fact, example, and opinion. This creates nodes for a diagram beside the text in which they outline the structure of the 6 information presentation (Olmanson et al, 2015). Additional user structuring directly supports the assessment process.…”

“…In our Scholar research and development, we have created a tool that traces learner thinking in the form of a sequence of moves as users create a visualization of the underlying logic of their information and argument texts. The question then is, what patterns of thinking predict successful or less successful written texts (Olmanson et al, 2015)?…”