Data mining techniques for the study of online learning from an extended approach
Submitted: 2019-03-07
|Accepted: 2019-04-23
|Published: 2019-05-20
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Keywords:
Cognition, connectivism, data mining, e-learning, extended mind, machine learning
Supporting agencies:
Abstract:
In the latest years information technologies have impacted society changing the way human beings learn, and because of that it is necessary to study the intimate relationship between humans and their technological tools. On this path the extended mind thesis posits human cognition as a process that occurs in conjunction between biological and non-biological components, furthermore Connectivism is stated as a learning theory for the digital age. Based on such approaches this work presents a summary of a research whose objective was to know how people extend their cognitive processes with the aim of learning through the internet. Methodologically, an artificial intelligence algorithm for supervised learning (J48) was used to analyze the data of 336 participants with the aim of obtaining classification rules (patterns) of internet use. Finally, the results show that people who report visiting specialized websites, read electronic books and take into account the spelling of the resources they are looking at on the internet are the ones with optimal strategies for learning online.
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