Learning Analytics of Beginner Programming Assignments for Teachers

Abstract

Due to the increase in student numbers, the amount of time teachers have for each individual decreases. To keep the quality of the program the same, learning analytics can be utilized to assist teachers to give a better overview of problems which the students are struggling the most with. This work makes use of WebLab data of the Introduction to Python course. To provide more useful information for teachers in introductory programming assignments, this work applies learning analytics to revision history of the student submissions of this course. The revision history provides more insight into the progression of the students throughout the assignment compared to only the final solutions. With these data, this work clusters and analyzes student submissions to provide instructors with information about the types of submitted solutions, students who struggled with the assignment, and the biggest hurdles for the students. Results show that the clustering of student submissions is 90\% accurate in comparison to manually clustered solutions, however the identification of students who struggled with the assignment should take student behaviour more into account. Because, student behaviour has been discussed in the focus group as being a major factor in whether data can indicate whether a student is struggling or not. Further research should be directed into expanding the proposed algorithms to more complex programming assignments and improving the way the data is visualized to the teachers.