Computational Social Science (CSS) is developing rapidly as a paradigm that allows for causal computer modelling of behavioural processes, ranging from processes within the individual to society at large, hence providing an interdisciplinary approach to the social sciences (Cioffi-Revilla 2014). This approach implies the causal modelling of processes within and between people using computer-formalised algorithms. In the current digital era, with an increasingly complex and turbulent society, DEMAND IS RISING FOR SOCIAL SCIENTISTS CAPABLE OF ANALYSING BEHAVIOURAL DYNAMICS, be it in PUBLIC POLICY MAKING, IN SCIENTIFIC PROJECTS, OR IN KNOWLEDGE-INTENSIVE BUSINESS SECTORS. Traditional methods used by social scientists or market researchers (e.g. surveys) are not sufficient to understand complex problems. On the other hand, CSS is increasingly being used as a tool to systematically explore complex behavioural dynamics in social systems. Its formal nature also allows linkages to other formal models (e.g. ecological/economical models). This contributes to bringing social scientific knowledge into projects which address multidisciplinary challenges, such as adaptation to climate change, opinion dynamics, transitions in energy use and food consumption and dealing with migration (e.g., Jager 2017). At the same time THIS APPROACH IS STILL HARDLY FOUND IN PROGRAMMES at bachelor or master levels in Europe (sociology, social or cognitive psychology programs). Hence most students are not aware of developments in modelling societal dynamics, automated information extraction (Big Data) and modelling the complexity of individual behaviour and cognition. Our project will help tackle this mismatch by INTRODUCING SOCIAL SCIENCE STUDENTSTO THE WORLD OF COMPUTATIONAL SOCIAL SCIENCES IN AN ENGAGING AND EASILY ACCESSIBLE MANNER. We will develop a MASSIVE OPEN ONLINE COURSE PROGRAMME (MOOC PROGRAMME) that introduces key concepts and methods of computational social sciences. In order to produce the MOOC programme, we will develop diverse digital learning materials such as lecture videos and real-life showcases, e.g. interviews with practitioners, scripts, exercises, quizzes, reading lists. The MOOC programme will: -consist of an introductory course and four thematic courses that can be used independently -NOT require prior knowledge of any advanced mathematics. E.g.students will be able to learn some basic concepts and tools of Social Network Analysis without learning mathematical graph theory first -be set on Futurelearn platform We plan to launch the first edition in Jan/Feb 2021 and it will be closely monitored and evaluated before the end of the project. To increase accessibility subtitles for the lecture videos will be available in four languages. TARGET GROUPS: -STUDENTS OF SOCIAL SCIENCES, esp. those beginning their studies Thanks to the accessibility of our course, STUDENTS will be able to learn basic concepts of computational social sciences, even if they have a high level of mathematics anxiety. More importantly, the success associated with completing the course can reduce their fears and make it easier and more attractive for them to participate in further courses during their educational career that they would not have even considered before. Hence, our MOOC programme can become a real careerchanger for them. We estimate that in the first edition of the MOOC PROGRAMME we will have appr. 4,000 participants in the introductory course and 2,000 to each of the follow-up courses (appr. 20% of MOOC students are student-aged learners). -ACADEMIC TEACHERS who are interested in teaching computational social sciences during their own classes TEACHERS will benefit from our MOOC by gaining access to materials which are easily accessible for their students and easy to incorporate into their didactic work. Apart from the MOOC platform, all the course materials will also be available on the project website (it is crucial that the teachers can access the materials whenever necessary). We will also organise a conference in order to promote the project's results. We plan that at least 50 teachers will use the materials before the end of the project. Consequently, appr. 1,000 students will access the program materials via their teachers. MAIN OUTCOMES: -Raising awareness of the emerging field of COMPUTATIONAL SOCIAL SCIENCES and its potential both within the academia and outside the academia (STUDENTS AND ACADEMIC TEACHERS) -Acquiring knowledge and skills: -namely learning basic concepts and tools of computational social sciences that are highly relevant both inside academia and the job market (STUDENTS) -Acquiring skills related to the production of engaging, online teaching content (TEAM MEMBERS AND PARTNER INSTITUTIONS) IN THE LONG-TERM, THIS WILL INCREASE THE NUMBER OF PEOPLE WHO USE COMPUTATIONAL METHODS IN AND OUT OF ACADEMIA.
ACTISS-ACTION FOR COMPUTATIONAL THINKING IN SOCIAL SCIENCES is an educational project that introduced a teaching innovation in the area of computational social sciences by developing a comprehensive open online course programme, consisting of five introductory courses on the subject. CONTEXT: Computational Social Science (CSS) is developing rapidly as a paradigm that allows for causal computer modelling of behavioural processes, ranging from processes within the individual to society at large, hence providing an interdisciplinary approach to the social sciences (Cioffi-Revilla 2014). In the current digital era, with an increasingly complex and turbulent society, DEMAND IS RISING FOR SOCIAL SCIENTISTS CAPABLE OF ANALYSING BEHAVIOURAL DYNAMICS, be it in PUBLIC POLICY MAKING, IN SCIENTIFIC PROJECTS, OR IN BUSINESS SECTORS. Traditional methods used by social scientists or market researchers (e.g. surveys) are not sufficient to understand complex problems. CSS enables to systematically explore complex behavioural dynamics in social systems and allows for interdisciplinary linkages. This contributes to addressing multidisciplinary challenges (Jager 2017). At the same time this approach is hardly found in social sciences programme and social sciences students are not aware of developments in modelling and social simulations and often they are apprehensive towards this approach, fearing that it requires advanced mathematical and programming skills. OBJECTIVE Our project helps tackle this mismatch by developing educational resources INTRODUCING SOCIAL SCIENCE STUDENTS TO THE WORLD OF COMPUTATIONAL SOCIAL SCIENCES IN AN ENGAGING AND EASILY ACCESSIBLE MANNER. MAIN RESULTS: -a MOOC programme that consists of an introductory course and four thematic courses that can be used independently = a vast and comprehensive set of high-quality OPEN EDUCATIONAL RESOURCES: 5 open online courses, each consisting of diverse digital materials (videos, interviews, articles, exercises, quizzes, educational models): -->People, Networks and Neighbours: understanding social dynamics (main introductory course) -->Social Network Analysis: The networks connecting people -->Understanding Human Behaviour: Introduction to Game Theory and Shared Resources -->Why do ghettos form in a tolerant society? Schelling's model and the introduction of cellular automata -->Decision Making in a Complex World: Using Computer Simulations to Understand Human Behaviour -the programme does NOT require prior knowledge of any advanced mathematics. E.g.students can learn some basic concepts and tools of Social Network Analysis without learning mathematical graph theory first -the programme is available on FUTURELEARN and on the project’s website (15 units-weeks of materials, equivalent to a 2 ECTS points course) -60 educational videos on ACTISS YouTube channel and 21 educational NetLogo models in ACTISS model repository -the programme is INNOVATIVE because it is an introductory programme on computational social sciences TAILORED to the needs of people interested in SOCIAL SCIENCES, esp. social sciences students beginning their studies. Learners can take the courses without any prior mathematical or programming background. -a TEACHER'S GUIDE prepared for each course containing the main information about the course and additional materials to be used in a classroom setting or as homework. With these additional materials the course may be equivalent to 3 or 4 ECTS points -a CURRICULUM describing the whole programme -MOOC participants- over 2000 participants of Futurelearn courses in 2021 and a prospect of reaching out a much bigger audience in future -students at UW and UG learning from ACTISS materials on courses and workshops each year -a study visit in the HIIG-Alexander von Humboldt Institute for Internet and Society that helped the team gain skills related to the production of high-quality attractive digital educational content -management activities that helped build a strong team spirit and achieve the project goals despite pandemic-related difficulties -newly acquired skills of the team, related to the educational design, to international collaboration, organisational and managerial skills -publications, conference presentations and blog posts about the project For more details, please visit: https://actiss-edu.eu/ MAIN OUTCOMES: -Raising awareness of the emerging field of COMPUTATIONAL SOCIAL SCIENCES and its potential both within and outside the academia (STUDENTS, ACADEMIC TEACHERS, GENERAL AUDIENCE) -Acquiring knowledge and skills: learning basic concepts and tools of computational social sciences that are highly relevant both inside academia and the job market (STUDENTS and GENERAL AUDIENCE) -Acquiring skills related to the production of engaging, online teaching content (TEAM MEMBERS AND PARTNER INSTITUTIONS) In the long term we hope that ACTISS will increase the number of people who use computational methods in and out of academia.