The main objective of this project is to investigate the feasibility of using Big Data in Science Teaching, taking as mediators the computer and the public and free of Big Data tools such as Google Correlate.
Of course, "correlation does not imply causation" (Field, 2003, p. 10). However, we believe that there is potential for these Big Data tools to find unexpected correlations, even unusual ones, which may, however, serve as clues to interesting phenomena, from the scientific point of view.
After observing a new phenomenon - the correlation - students need to deepen their research from various other sources for a causal explanation - the causation - to confirm or refute it, what would be extremely productive in terms of Science learning, especially in understanding the phenomenon, observation, measurement, physical laws, and theory concepts, among others.
According to (Mattmann 2013), to solve the challenges of Big Data, both in scientific and technical and ethical terms, a new breed of scientists called 'data scientists' is required. But Kate Mueller (Dumbill et al., 2013) considers that saying that only computer science people make good Big Data experts is a mistake.
Our didactic proposal is based on Papert's constructionism, which emphasizes the importance of tools, media, and contexts in human development and on how theirs dialogues with artifacts promote self-learning and facilitate the construction of new knowledge (Ackermann, 2001).
On the other hand, this proposal has epistemological focus because more than merely training students in computational infrastructure or predictive analysis, the goal of teaching activities to be performed during this project is to provide science students, especially physics students, a preparation, both in technical and ethical terms, to the scientific challenges posed by Big Data to the real world in which they will exercise their professions, as well as a better understanding, based on the practice of Big Data, on their construction of physical knowledge.
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