Visualization of potential differences in comprehension by distribution of notes and questions in online programming courses

This study presents an innovative approach to understanding differences in student comprehension in online programming courses by analyzing note-taking and questioning patterns and the distribution of grades. It enables instructing educators to understand learners' learning behaviors better. We designed and developed a system to support online learning that enables students to take notes and ask questions while viewing online courses and allows instructors to grasp overall feedback. We used a visualization method based on the location of notes and questions as they appear in the learning videos to enable instructors to capture their learners' learning better. Visualizing the distribution of notes and questions shows differences among students' note-taking patterns and learning strategies. The underlying comprehension patterns involved lead to differences in the patterns of notes and questions. We compared data from learners in different score bands to verify the correlation between note types and scores. By visualizing and comparing notes with the same type of correlation, the results show the correlation between note types and grades and the potential of the visualization method to analyze students' learning strategies and learning personalities. It provides valuable insights into student engagement, comprehension, and learning strategies, which can inform the development of more effective teaching methods. This study is relevant in the contemporary educational landscape, particularly as many institutions transition to online formats. Detecting student engagement and comprehension levels in these contexts is crucial.

I. Introduction

In recent years, the rapid shift from the traditional classroom to online learning drastically affects educational practices across disciplines. The flexibility and convenience of online learning have made it an essential part of the modern education system. In programming education, it demands more evidence than ever that online learners and instructors should learn how to acquire knowledge quickly and effectively [1]. However, the effectiveness of online learning depends on complex variables, including student engagement, learning strategies, and their interaction with the educational content [2], [3]. In an online environment, instructors often need help directly observing and guiding students' learning process as in a physical classroom. Therefore, it becomes a great challenge to grasp the learning status of learners in the online environment and to understand the behavioral patterns of students.