  Various parameters affect the performance of students in online coding
competitions. Students' behavior, approach, emotions, and problem difficulty
levels significantly impact their performance in online coding competitions. We
have organized two coding competitions to understand the effects of the above
parameters. We have done the online survey at the end of each coding
competition, and it contains questions related to the behavior, approach, and
emotions of students during online coding competitions. Students are evaluated
based on the time and status of the submissions. We have carried out a detailed
analysis to address the impact of students' approach, behavior, and emotions on
the learning process in online coding competitions. Two difficulty levels are
proposed based on the time and status of submissions. The impact of difficulty
levels on machine learning-based performance prediction is presented in this
research work. Based on time, the coding solution submissions have two classes
"Less than 15 minutes" and "More than 15 minutes". There are three classes,
"Complete solution", "Partial solution", and "Not submitted at all," based on
the submission status. The appropriate approaches are found for both the coding
competitions to submit the solution within 15 minutes. Machine learning
classifiers are trained and evaluated for the above classification problems.
The impacts of mood, emotions, and difficulty levels on the learning process
are also assessed by comparing the results of machine learning models for both
coding competitions.
