Wikiglass: a learning analytic tool for visualizing collaborative wikis of secondary school students

Poster SessionThis demo presents Wikiglass, a learning analytic tool for visualizing the statistics and timelines of collaborative Wikis built by secondary school students during their group project in inquiry-based learning. The tool adopts a modular structure for the flexibility of reuse with different data sources. The client side is built with the Model-View-Controller framework and the AngularJS library whereas the server side manages the database and data sources. The tool is currently used by secondary teachers in Hong Kong and is undergoing evaluation and improvement.published_or_final_versio

Learning Analytics Dashboard for Teaching with Twitter

As social media takes root in our society, more University instructors are incorporating platforms like Twitter into their classroom. However, few of the current Learning Analytics (LA) systems process social media data for instructional interventions and evaluation. As a result, instructors who are using social media cannot easily assess their students’ learning progress or use the data to adjust their lessons in real time. We surveyed 54 university instructors to better understand how they use social media in the classroom; we then used these results to design and evaluate our own Twitter-centric LA dashboard. The overarching goals for this project were to 1) assist instructors in determining whether their particular use of Twitter met their teaching objectives, and 2) help system designers navigate the nuance of designing LA dashboards for social media platforms

New features in Wikiglass, a learning analytic tool for visualizing collaborative work on wikis

Wikiglass is a learning analytic tool for visualizing collaborative work on Wikis built by groups of secondary or primary school students. This poster presents new features of Wikiglass developed recently based on requests from teachers, including flexible selection of date range, revision network, and thinking order detection. Currently the new features are used and evaluated in two secondary schools in Hong Kong

Sensemaking with learning analytics visualizations: Investigating dashboard comprehension and effects on learning strategy

In the provision of just-in-time feedback, student-facing learning analytics dashboards (LADs) are meant to aid decision-making during the process of learning. Unlike summative feedback received at its conclusion, this formative feedback may help learners pivot their learning strategies while still engaged in the learning activity. To turn this feedback into actionable insights however, learners must understand LADs well enough to make accurate judgements of learning with them. For these learners, LADs could become an integral part of their self-regulatory learning strategy. This dissertation presents a multifaceted examination of learners’ sensemaking processes with LADs designed to support self-regulatory learning. The in-situ studies detailed therein examine learners’ understanding of the data visualized in LADs and the effects of this understanding on their performance-related mental models. Trace data, surveys, semi-structured in-depth qualitative interviews, and retrospective cued recall methods were used to identify why, when, and how learners used LADs to guide their learning. Learners’ qualitative accounts of their experience explained and contextualized the quantitative data collected from the observed activities. Learners preferred less complex LADs, finding them more useful and aesthetically appealing, despite lower gist recall with simpler visualizations. During an early investigation of how LADs were used to make learning judgments in situ, we observed learners’ tendency to act upon brief LAD interactions. This inspired us to operationalize gist as a form of measurement, describing learners’ ability to make sense of a LAD after a brief visual interrogation. Subsequent comparisons of the accuracy and descriptiveness of learners’ gist estimates to those of laypeople repeatedly showed that laypeople were more apt than learners to produce accurate and complete gist descriptions. This dissertation culminates in a final study examining the evolution of learners’ mental models of their performance due to repeated LAD interaction, followed by a discussion of the contextual factors that contributed to what was observed. Trends observed across this work suggest that learners were more apt to “get the gist” with LAD after repeated interaction. This dissertation contributes a novel method for evaluating learners’ interpretation of LADs, while our findings offer insight into how LADs shape learners’ sensemaking processes