Gonzalo Gabriel Méndez

iVoLVER: Interactive Visual Language for Visualization Extraction and Reconstruction

We present the design and implementation of iVoLVER, a tool that allows users to create visualizations without textual programming. iVoLVER is designed to enable flexible acquisition of many types of data (text, colors, shapes, quantities, dates) from multiple source types (bitmap charts, webpages, photographs, SVGs, CSV files) and, within the same canvas, supports transformation of that data through simple widgets to construct interactive animated visuals. Aside from the tool, which is web-based and designed for pen and touch, we contribute the design of the interactive visual language and widgets for extraction, transformation, and representation of data. We demonstrate the flexibility and expressive power of the tool through a set of scenarios, and discuss some of the challenges encountered and how the tool fits within the current infovis tool landscape.

Expertise estimation based on simple multimodal features

Multimodal Learning Analytics is a field that studies how to process learning data from dissimilar sources in order to automatically find useful information to give feedback to the learning process. This work processes video, audio and pen strokes information included in the Math Data Corpus, a set of multimodal resources provided to the participants of the Second International Workshop on Multimodal Learning Analytics. The result of this processing is a set of simple features that could discriminate between experts and non-experts in groups of students solving mathematical problems. The main finding is that several of those simple features, namely the percentage of time that the students use the calculator, the speed at which the student writes or draws and the percentage of time that the student mentions numbers or mathematical terms, are good discriminators be- tween experts and non-experts students. Precision levels of 63% are obtained for individual problems and up to 80% when full sessions (aggregation of 16 problems) are analyzed. While the results are specific for the recorded settings, the methodology used to obtain and analyze the features could be used to create discriminations models for other contexts.

Techniques for data-driven curriculum analysis

One of the key promises of Learning Analytics research is to create tools that could help educational institutions to gain a better insight of the inner workings of their programs, in order to tune or correct them. This work presents a set of simple techniques that applied to readily available historical academic data could provide such insights. The techniques described are real course difficulty estimation, dependance estimation, curriculum coherence, dropout paths and load/performance graph. The description of these techniques is accompanied by its application to real academic data from a Computer Science program. The results of the analysis are used to obtain recommendations for curriculum re-design.

Bottom-up vs. Top-down: Trade-offs in Efficiency, Understanding, Freedom and Creativity with InfoVis Tools

The emergence of tools that support fast-and-easy visualization creation by non-experts has made the benefits of InfoVis widely accessible. Key features of these tools include attribute-level operations, automated mappings, and visualization templates. However, these features shield people from lower-level visualization design steps, such as the specific mapping of data points to visuals. In contrast, recent research promotes constructive visualization where individual data units and visuals are directly manipulated. We present a qualitative study comparing peoples visualization processes using two visualization tools: one promoting a top-down approach to visualization construction (Tableau Desktop) and one implementing a bottom-up constructive visualization approach (iVoLVER). Our results show how the two approaches influence: 1) the visualization process, 2) decisions on the visualization design, 3) the feeling of control and authorship, and 4) the willingness to explore alternative designs. We discuss the complex trade-offs between the two approaches and outline considerations for designing better visualization tools.

Tools for opportunistic information visualization: Visual analysis with non-traditional data sources

Information Visualization (InfoVis) often supports the analysis of structured data that is organized in documents with specific formats such as databases, Excel tables, or comma-separated files. Informal analyses that take place without anticipation and away from the desktop, however, might involve the use of data contained in digital artifacts that lack this structure (e.g., photographs, bitmaps, web pages). Such artifacts cannot provide immediate input for most existing visualization systems, as the data they contain does not exist as a set of variables with associated values. This research seeks to explore new opportunities in the design and implementation spaces of InfoVis authoring tools to support visualization in opportunistic scenarios. This document briefly defines the Opportunistic Visualization (OpportuVis) domain and describes iVoLVER, a research prototype that supports the construction of interactive visuals from non-traditional data sources. Future stages of this endeavor include the evaluation of iVoLVER from two perspectives: its analytical support and its usability features.

Constructing Interactive Visualizations with iVoLVER

iVoLVER, the Interactive Visual Language for Visualization Extraction and Reconstruction, is a web-based pen and touch system that graphically supports the construction of interactive visualizations and allows the extraction of data from different types of digital artifacts and photographs. Together, these features enable the creation of visualizations from data that is not structured in traditional formats without the need of textual programming. This demonstration shows how iVoLVER visualizations are constructed and illustrates an interactive example that can be used in teaching and educational contexts.

iVoLVER: A Visual Language for Constructing Visualizations from In-the-Wild Data

iVoLVER, the Interactive Visual Language for Visualization Extraction and Reconstruction, is a web-based pen-and-touch interface that graphically supports construction of interactive visualizations. iVoLVER is designed to enable data extraction from different types of artifacts (e.g., photos) and to use that data to generate original representations of that data. People can create visualizations from data that is not structured in traditional formats without the need of textual programming or sitting at their desk. This demonstration shows how iVoLVER visualizations are constructed and also demonstrates the possible uses of iVoLVER in several contexts.

Opportunistic visualization with iVoLVER

Proposed as “data analysis anywhere, anytime, from anything”, Opportunistic Information Visualization (Opportu-Vis) [1] seeks to provide analytical support in scenarios where the data of interest is not explicitly available and has to be retrieved from digital artifacts that are not traditionally used as data sources. Examples include raster images, web pages, vector files, and photographs. This showpiece presents how iVoLVER, the Interactive Visual Language for Visualization Extraction and Reconstruction, provides support in such settings. We briefly describe the overall construction approach of the tool in scenarios where different digital artifacts are used to compose interactive visuals. All of this becomes possible by using the data extraction capabilities of iVoLVER together with the elements of its visual language.