Learners’ non-cognitive skills and behavioral patterns of programming: A sequential analysis

Abstract

The interest in artificial intelligence (AI) education is growing exponentially; nevertheless, how to learn about AI, particularly Natural Language Processing (NLP), has been a challenging problem for educators and researchers worldwide. This study used a graphical programming platform Snap! to facilitate learning by allowing learners to explore AI and its NLP techniques in class. Data from 18,452 logged events were collected and Lag Sequential Analysis (LSA) was used to examine how learners behaved and learned sequentially. Non-cognitive factors were used to group learners as detailed and subtle behavior sequences that did not occur by chance could be uncovered. The results showed that five groups of learners, that is Passive Learners, Performers, Adaptive Learners, Interested Learners, and Dedicated Learners. They presented varied learning behavior patterns, which should be considered further in designing personalized and intelligent learning platforms to support AI education.