Daniel K. Schneider is an associate professor at TECFA, a research and teaching unit in the faculty of psychology and education, University of Geneva. Holding a PhD in political science, he has been working in educational technology since 1988 and participated in various innovative pedagogical and technological projects. He has been a prime mover towards the introduction of creative pedagogical strategies and ICT technologies. His long-term R&amP;D interests focus on modular, flexible and open Internet architectures supporting rich and effective educational designs. His current interests include digital design and fabrication (e.g. 3D printing), learning process analytics and learning in citizen science. Within TECFA's "blended" master program in educational technology (MALTT), he teaches educational information & communication systems, foundations of educational technology, and research methodology.
Eugène Horber was, before he retired, professor of methodology at the Department of political science and International relations, University of Geneva. He holds a PhD degree in Political Science and has taught social science methodology (both quantitative and qualitative), applied computer science, and statistics at the University of Geneva, as well as Exploratory Data Analysis at INSEE/ENSAI (Rennes). He is the director of the Swiss Summer School (Social Science Methodology); main teaching activities in the past include the Essex Summer School, the Carcassonne Summer School, the PRESTA Progamme (EU programme for South America), Eurostat/TES and ENSAE (Paris). His research interests and publications are in the area of statistical methodology (data exploration, visual data analysis), survey research and aggregate data analysis, as well as applied computer science (didactical software, hypertext) and computer assisted qualitative data analysis. He is the author of a software package for exploratory data analysis, as well as the author of several on-line teaching web-sites.
Studying any topic in any scientific field entails the acquisition of theoretical knowledge and concepts, analytical and practical skills, as well as hands-on experience using real-world examples. Given a problem to solve, a scientist should be able to formulate the problem conceptually (theory, hypotheses, theoretical models), select an appropriate scientific approach (e.g. experiment, survey, interviews) to operationalize and measure the concepts to gather data (quality, bias, validity, reliability), data that will be analysed using appropriate methods and tools (e.g. statistical tools, qualitative techniques), available through software (data management, analysis) in order to produce results that need to be interpreted and communicated to various kinds of publics (scientific community, contract research, general public); adopting at the same time a scientific and responsible attitude (critical, systematic, ethical), while following the methodological standards for the specific scientific field. These various aspects of a whole, are frequently taught separately, hoping that students will be able to integrate them on their own. Many forms of teaching and learning suffer from an inefficient and artificial separation of these aspects. Traditional courses tend to provide theoretical concepts and background, without much learner involvement. Others (for instance methodological seminars) are very technical, but suffer from weak links to theoretical content and real-world applications. The result is frequently low motivation for the learner and a partial, artificial, isolated view of the topics taught, although one of the goals clearly is to acquire knowledge and skills enabling students to learn to do research on their own.
This workshop proposes to lay the foundations of an approach, integrating a substantive topic (theories, concepts) with the acquisition of analytical and practical skills using real-world data and appropriate analytical tools. Some examples might illustrate this: Teaching electoral behaviour based on theories related to the topic and hands-on experience using electoral survey data analysed with some statistical software (e.g. SPSS); introducing theories of inequality and empirically studying income data using statistical techniques; comparing educational policies based on PISA data; studying the perception of the role of women in family and society using in-depth interviews (analysis based on qualitative software).
The proposed approach can be applied to most topics studied in any scientific field; it can take the form of a traditional class augmented and enhanced with material encouraging the learner to acquire skills with data and software, an on-line only course or any combination ("blended learning"). It can be ambitious and cover all topics studied or more modest covering some specific, central aspect to be learned (e.g. software skills, worked examples etc). The target public may be beginners at any level (college, undergraduates, advanced students or learners outside academia.
The workshop will cover the following themes:
At the end of the week, participants will have a good overview of what is available, acquire skills to develop their own didactical material, while being able to assess realistically the effort and time needed to produce that material.
Participants in this workshop will work in groups to develop a project for a specific topic; the discussion and presentation of these projects will be an essential part of the workshop. When the workshop is confirmed by mid-June, we will contact registered participants, to find out about their (academic) interests, background and experience.
None, except interest for didactical issues and motivation to learn and get involved.