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I.E.I. Renţea
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With the fast integration of Machine Learning(ML) into several industries, the motivation to develop effective pedagogical strategies for teaching this complex and evolving field has become critical. Machine Learning, once mainly a topic in Computer Science Bachelor programs, is now widely integrated into various majors and introduced at earlier educational stages, including high school and secondary school. However, the reduced research focus on ML pedagogy results in a lack of standard teaching methods compared to other science-related subjects, which have established norms for topic introduction, teaching tools, and assessment methods. With inspiration from other fields, this research aims to look into using interactive visualizations in teaching ML topics, more specifically in teaching Gradient Descent and Principal Component Analysis (PCA). The research includes the development and evaluation of Jupyter Notebooks for introducing these visualizations to students. The targeted student population is composed of Computer Science and Engineering Bachelor students who have not yet followed any Machine Learning courses but have the necessary background knowledge, namely calculus, linear algebra, and statistics knowledge. The evaluation of this teaching method measures the knowledge gained and the motivation of the students, compared to a static version of the materials. The results of the evaluation have shown a significant effect of interactive visualizations on knowledge gained related to PCA. The evaluation has not identified any difference in knowledge gain for gradient descent and learning motivation for both topics. With these results, we contribute to the body of evidence-based teaching methods in Machine Learning and identify further research needed to generalize the effect of interactive visualizations as a teaching method for teaching ML basic concepts.
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With the fast integration of Machine Learning(ML) into several industries, the motivation to develop effective pedagogical strategies for teaching this complex and evolving field has become critical. Machine Learning, once mainly a topic in Computer Science Bachelor programs, is now widely integrated into various majors and introduced at earlier educational stages, including high school and secondary school. However, the reduced research focus on ML pedagogy results in a lack of standard teaching methods compared to other science-related subjects, which have established norms for topic introduction, teaching tools, and assessment methods. With inspiration from other fields, this research aims to look into using interactive visualizations in teaching ML topics, more specifically in teaching Gradient Descent and Principal Component Analysis (PCA). The research includes the development and evaluation of Jupyter Notebooks for introducing these visualizations to students. The targeted student population is composed of Computer Science and Engineering Bachelor students who have not yet followed any Machine Learning courses but have the necessary background knowledge, namely calculus, linear algebra, and statistics knowledge. The evaluation of this teaching method measures the knowledge gained and the motivation of the students, compared to a static version of the materials. The results of the evaluation have shown a significant effect of interactive visualizations on knowledge gained related to PCA. The evaluation has not identified any difference in knowledge gain for gradient descent and learning motivation for both topics. With these results, we contribute to the body of evidence-based teaching methods in Machine Learning and identify further research needed to generalize the effect of interactive visualizations as a teaching method for teaching ML basic concepts.
Commonsense knowledge is information that all humans own and use to interpret common situations and react to them accordingly. This kind of information is necessary for the training of artificial intelligence models to reach a performance as close as possible to human performance. Researchers have developed methods that use crowdsourcing to collect this kind of knowledge from the general public. This research focuses on systematically surveying the existing literature about these methods. We created a taxonomy to describe and compare the existing work based on the following three measures that were the most common ones reported: efficiency, cost, and quality.
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Commonsense knowledge is information that all humans own and use to interpret common situations and react to them accordingly. This kind of information is necessary for the training of artificial intelligence models to reach a performance as close as possible to human performance. Researchers have developed methods that use crowdsourcing to collect this kind of knowledge from the general public. This research focuses on systematically surveying the existing literature about these methods. We created a taxonomy to describe and compare the existing work based on the following three measures that were the most common ones reported: efficiency, cost, and quality.