GANDALF · Gamification and Datafication towards Learning Forecasting
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2019-10-07 → 2021-10-06
- EU contribution
- €190,681
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Gamification and Datafication towards Learning Forecasting
GANDALF (GAmification aNd DAtafication towards Learning Forecasting) experimentally designed and researched gamification and datafication solutions that improved learning outcomes, aiming to address the need for civic understanding of data which is crucial for sustainable societal decision-making. During the last decade, the world's data is doubling every two years, and the pace at which humans create data is expected to increase exponentially. Thanks to technological growth, data is ‘exploding’, offering us today “a gift from yesterday, to make our future better” (Acuff, 2017). But this gift is only as beneficial as the insights that we gain from it. Despite the data explosion, education in the context of statistics and STEM remains a difficult field for citizens or even students to engage with. A possible solution to this need for data education arises from games. The number of gamers worldwide keeps increasing, showing the potential of game experiences and gamification to affect human behavior and motivation, even in contexts that are more difficult to engage such as data analytics and forecasting education (Legaki et al., 2020; Legaki et al., 2021). The main objective of GANDALF was to investigate the effects of different types of gamification on learning outcomes in the educational context of exploratory data analysis (EDA) and in the construction of world-views based on data. To this end, a web-based, publicly available, educational, and interactive application was designed and implemented, supporting challenge-, immersion-, social-based gamification and a non-gamified version, to experimentally examine the behavioural and psychological effects of individual affordances (linked to different gamification types) on learning basic concepts of EDA and promote a fact-based worldview related to challenging social problems (e.g., data related to Sustainable Development Goals SDGs2030, COVID-19). A series of online experiments have been conducted with participants selected among both students and the general public worldwide (N=478). Preliminary statistical analysis of the results shows the effectiveness of the designed application and the promising effects of an interactive learning environment regarding the learning outcomes, without noticing statistically significant differences among the different types of gamification. Further data analysis is still to be done related to participants' attitudes, and their gameful experience.
Data: CORDIS, © European Union
Project objective
A full 90% of all the data in the world has been produced over the last two years and the challenge of our society is to turn a world full of data into a data-driven world. Whereas the impressive progress in predictive analytics let us have insight on everything, our motivational resources are limited to learn and understand our reality. Today, gamification is regarded as a promising technique to improve motivational affordance in domains where people have difficulty of engagement such as data analytics and forecasting education.In this regard, the GANDALF project proposes the design of gamification and datafication solutions, which aims at improvement of learning outcomes in the context of forecasting course and demonstrate a world view based on facts. GANDALF aims to experimentally investigate gamification effects on educational process of a forecasting techniques’ course by addressing three novel objectives: (1) Design, implement and use a web-based, modular, educational platform in order to teach exploratory data analytics (module 1) and forecasting time series methods (module 2); (2) Investigate behavioural and psychological effects of individual affordances on learning exploratory analysis and promote fact-based world-view; (3) Examine behavioural and psychological outcomes in the context of a forecasting time series methods course. GANDALF employs controlled experimental research methods, with pre-, post-test design and forecasting problems to gain knowledge on the gamification effect on learning, user-experience and users’ forecasting accuracy based on their characteristics as well.GANDALF, along with its innovative points, contributes not only to civic understanding of data, but also to investigate how to effectively motivate users to explore data insights. In parallel, GANDALF advances my professional career and personal development in interdisciplinary field of teaching and learning methodologies for forecasting courses with gamification strategies.
Original text from CORDIS.
Participants
- TAMPEREEN KORKEAKOULUSAATIO SR · TampereCoordinatorFinland
Links
Data: CORDIS, © European Union