HIGHER EDUCATION Study on behalf of the working group Curriculum 4.0 - PowerPoint PPT Presentation

FUTURE SKILLS: APPROACHES FOR TEACHING DATA LITERACY IN HIGHER EDUCATION Study on behalf of the working group “Curriculum 4.0” of the “ Hochschulforum Digitalisierung ” Dr. Jens Heidrich Pascal Bauer Hochschulforum Digitalisierung is a joint initiative by Fraunhofer IESE Stifterverband, CHE Centre for Higher Education and the German Rectors’ Conference (HRK). It is financed by Daniel Krupka Germany’s Federal Ministry of Education and Research Gesellschaft für Informatik e.V. (BMBF). October 30, 2018 1

Objective and focus • Objective: Compile actionable knowledge Key questions: for the implementation of curricula for data 1. What is meant by data literacy and literacy what is the main focus? 2. How is data literacy integrated into • Focus: European and international best disciplines and curricula and how do practice examples of offers for cross- you create incentives for teachers? disciplinary education of data literacy 3. What is a transdisciplinary set of basic competencies and what are special competences? • Scope: Scope was on the education of data literacy in different application 4. What are requirements on graduates domains and not on data science for the society, job market and education research? 5. What are factors of success and failure of the curricular implementation? October 30, 2018 2

Overview 1. Desk Research 4. Documentation • Research und classification of 89 • Stan-of-the-art handout courses (of studies) • 100-page final report • Summary of 17 state-of-the-art literature sources 2. Interviews and Survey • Selection and detailed classification of 15 cases • Interviews with representatives of 6 cases (21 questions) • Survey with 69 participants (16 questions) 3. Workshop • Conduction of an international workshop with 19 experts October 30, 2018 3

Key question 1: What is meant by data literacy and what is the main focus? Agreement with Definition of Data “Data Literacy is defined as the ability to Literacy collect, manage, evaluate and apply data 4% 2% in a critical manner” [Ridsdale et al.] • Expert interviews as well as survey fully or I do not agree partially agreed to that definition (100% I partially agree and 94%, respectively) 45% 49% I totally agree Don't know • The missing aspects usually affect and emphasize individual competence areas of data literacy Overlapping of Data Literacy Term • There is a significant overlapping with the Big Data terms “Information Literacy” as well as Data Management with adjacent terms such as “Data Information Literacy Information Literacy”, “Science Data 0 20 40 60 80 Literacy”, or “Statistical Literacy” 5 4 3 2 1 Don't Know October 30, 2018 4

Key question 2: How is data literacy integrated into disciplines and curricula and how do you create incentives for teachers 1. Acquisition of competences in the field 5. A national research, education and of data literacy should start as early as training agenda is required as well as possible (for example at post-secondary the development of national institutions) infrastructures 2. Awareness of the importance has to be 6. Different models of integration raised for students as well as imaginable: Online offers, a central organizations (universities, institutes) introductory course with advanced modules, or approaches fully integrated 3. Any offer must be adapted to different in existing courses (of studies) educational levels and to specifics of disciplines, such as the general context, 7. Successful offers modular and make terminology, workflows, and problems use of modern teaching formats (such as hands-on and project-based 4. It is recommended to establish an learning) independent institution/unit, which involves experts from different 8. Motivation of teachers to participate in disciplines for developing educational joint offers mostly based on personal programs interest and broadening their own skills October 30, 2018 5

Key question 3: What is a multidisciplinary set of basic competencies and what are special competencies? Conceptual Introduction to Data • Basic and advanced competences Framework Conceptual depend on purpose of data literacy Data Collection Data Discovery and Collection Evaluating and Ensuring Quality of Data and Sources education Data Management Data Organization • Within the workshop to different main Data Manipulation Data Conversion (from format to format) purposes were discussed: Metadata Creation and Use Data Curation, Security, and Re-Use 1. Teaching of mature educated Data Preservation citizens: requires a cross- Data Evaluation Data Tools Core Basic Data Analysis disciplinary, generic, basic, Data Interpretation (Understanding Data) broad set of competences Identifying Problems Using Data Data Visualization 2. Teaching data literacy Presenting Data (Verbally) competence for a specific Data Driven Decisions Making (DDM) (Making decisions based on data) discipline: requires more Advanced Data Application Critical Thinking specialized, in-depth Data Culture Data Ethics competences with adaptations Data Citation Data Sharing Evaluating Decisions Based on Data October 30, 2018 6 [C. Ridsdale et al., „Strategies and Best Practices for Data Literacy Education: Knowledge Synthesis Report“, Report, 2015 .]

Key question 3: What is a multidisciplinary set of basic competencies and what are special competencies? • Opinions regarding the classification of Classification of Data Literacy Competences competences differed widely among 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% expert; they only agreed on “introduction 1 Introduction to Data to data” and “basic data analysis” for 2.1 Data Discovery and Collection 2.2 Evaluation and Ensuring Quality of Data and… being basic competences 3.1 Data Organization 3.2 Data Manipulation • The survey showed that “introduction to 3.3 Data Conversion (from format to format) data” is seen by 95% as being a basic 3.4 Metadata Creation and Use 3.5 Data Curation, Security and Reuse competence, followed by “data 3.6 Data Preservation 4.1 Data Tools representation (verbally)” with 90% and 4.2 Basic Data Analysis “critical thinking” with 85% 4.3 Data Interpretation 4.4 Identifying Problems Using Data • The least basic competences were “data 4.5 Data Visualization 4.6 Presenting Data (Verbally) conversion” at 10% and “data 4.7 Data Driven Decision Making 5.1 Critical Thinking preservation” at 15% 5.2 Data Culture 5.3 Data Ethics • All other areas of competences were rated 5.4 Data Citation 5.5 Data Sharing by at least 35% of respondents as being 5.6 Evaluating Decisions Based on Data basic Basic Advanced Irrelevant Don't Know October 30, 2018 7

Key question 4: What are requirements on graduates for the society, job market and research? • According to the survey, “critical thinking”, Importance of Data Literacy Competences “data ethics”, and “data sharing” plays an 0 5 10 15 20 25 30 important role for society 1 Introduction to Data • For the job market, “data conversion”, “data - 2.1 Data Discovery and Collection 2.2 Evaluation and Ensuring Quality of Data and… driven- decision making” and “data tools” are 3.1 Data Organization 3.2 Data Manipulation most relevant 3.3 Data Conversion (from format to format) 3.4 Metadata Creation and Use • In the research sector, “data citation” plays a 3.5 Data Curation, Security and Reuse major role alongside “data discovery and 3.6 Data Preservation 4.1 Data Tools collection” 4.2 Basic Data Analysis 4.3 Data Interpretation • Expert interviews showed that for the 4.4 Identifying Problems Using Data 4.5 Data Visualization society, competencies related to data ethics, 4.6 Presenting Data (Verbally) 4.7 Data Driven Decision Making for the job market, skills focusing on 5.1 Critical Thinking technical competencies, and for research, a 5.2 Data Culture 5.3 Data Ethics broader set of competencies is necessary 5.4 Data Citation 5.5 Data Sharing 5.6 Evaluating Decisions Based on Data Society Job market Research October 30, 2018 8

Key question 5: Challenges and measures from literature and interviews Structures & Collaboration Competences & Integration Teaching/Training Challenges  Collaborations with others  Create awareness as early as  Attracting enough competent (breaking silos) possible trainers and teachers  Availability of resources  Identifying relevant  Diversity of participants  Initial funding  Application-oriented teaching competencies  Different educational levels  Build up collaborations with  Start at school level  Modern learning and teaching Measures  Basic skills already for non- other faculties, institutions, concepts (e.g., mixed teams)  Lean based on real-world and industry graduates  Bundle competencies  Offer standalone and data  Scholarships for cross- across disciplines interdisciplinary courses  Shared pool of assets  Integration of competencies discipline work  Overarching centers  Create opportunities for into existing disciplines  Create a national strategy  Tailor offer to the needs of the teachers  Train-the-trainer offers and infrastructure target groups October 30, 2018 9