dAn-oNo Learning Environment for Data Journalists Teaching Data Analytics Principles

Christina Stoiber, Štefan Emrich, Sonja Radkohl, Eva Goldgruber, Wolfgang Aigner

Room: 109

2023-10-22T22:00:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
{\rtf1\ansi\ansicpg1252\cocoartf2709 \cocoatextscaling0\cocoaplatform0{\fonttbl\f0\fswiss\fcharset0 ArialMT;\f1\froman\fcharset0 Times-Roman;} {\colortbl;\red255\green255\blue255;\red0\green0\blue0;} {\*\expandedcolortbl;;\cssrgb\c0\c0\c0;} \paperw11900\paperh16840\margl1440\margr1440\vieww11520\viewh8400\viewkind0 \deftab720 \pard\pardeftab720\sa320\partightenfactor0 \f0\fs29\fsmilli14667 \cf0 \expnd0\expndtw0\kerning0 \outl0\strokewidth0 \strokec2 Automated data set analysis using the Python library pandas_profiling delivering extensive insight, often overwhelming for DA novices \f1\fs24 \ \f0\fs29\fsmilli14667 Design Process of the dAn-oNo learning environment: (1) selection of an easy-to-understand data set & exploration and validation of technical possibilities for the implementation to overcome the hurdle of coding, support understanding of pitfalls and challenges of data analytics, easy access, and step-wise instructions and feedback. We meet the needs of the journalists by utilizing Jupyter and Markdown, binder, and GitHub, the learning environment. (2) The second step was the development of the structure and content, including the following steps: importing data to the notebook, inspecting the data set, statistical analysis, and in-depth analysis. (3) The final step was implementing the dAn-oNo learning environment, including developing the core component of translating the warnings and information delivered by the automated profiler into human-understandable information. \f1\fs24 \ }

To derive narratives from data, several journalistic abilities are required, including the skill to discover and construct compelling stories (data storytelling), employ data-driven techniques to research and analyze information (data literacy), utilize visualization methods effectively (visualization literacy), and approaching data with a combination of creativity and critical thinking. Despite their expertise in journalism, journalists often encounter challenges in comprehending and utilizing novel visual representations or understanding data analysis methods. The main objective of the dAn-oNo learning environment is to guide journalists through the data analytics process by removing coding hurdles and allowing them to experiment with and understand the code. The learning environment utilizes a Jupyter notebook with Markdown sections and incorporates a step-by-step approach, covering various stages of the data analytics workflow, such as data importing, inspection, statistical analysis, and in-depth analysis. The learning environment also includes an automated profiler that translates warnings and information into human-understandable insights. The design and implementation of the dAn-oNo learning environment were informed by a literature review, user research, interviews with Austrian data journalists, a phase of exploring different technical possibilities for the learning environment, and rapid prototyping. The prototype is accessible here: https://github.com/stemrich/SEVA\_DA-Onboarding-Tool