TPACK model: A means to innovate the educational process considering data science and machine learning?
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Abstract
Purpose - to analyze the design and use of the Web Application Conditional Probability (AWSPC) in the educational process considering the tpack model, data science and machine learning.
Methodology - this study uses the quantitative and qualitative approach to assess the impact of the AWSPC in the field of statistics. The sample consists of 61 students who took the Statistical Instrumentation for Business subject during the 2018 school year.
Results - the results of the machine learning (50%, 60% and 70% of training) allow affirming that the AWSPC application improves the teaching-learning process. On the other hand, data science establishes various predictive models on the use of the AWSPC application in the educational field (decision tree technique).
Limitations - these include that the awspc application presents the simulation of the conditional and intersection probability on the parts supply; the contents of this technological tool are in Spanish. Therefore, future research can design and build educational web systems that present the contents in several languages and use different exercises during the simulations. For example, the use of artificial intelligence in the educational context would allow the personalization of the themes.
Findings - the TPACK model allows the construction of new digital tools such as the awspc application through technological, pedagogical and disciplinary knowledge. This study recommends the use of the tpack model at different educational levels to improve the teaching-learning conditions.
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