As the number of tools and platforms that have been developed to support learning programming demonstrates, learning programming is becoming more and more ubiquitous in all curricula. In this paper, we present an exploratory study that aims to identify students' programming behaviors. The analysis is based on unsupervised classification algorithms, and features have been selected from prior works on educational data mining. Six students' behaviors were identified using the k-means algorithm. ANCOVA, an extension of analysis of variance (ANOVA), was used to test the main and interaction effects of students' behaviors on their final course scores.

The most frequently used method of validating automated graders scores has been to compare them with scores awarded by instructors. While this is theoretically possible, research suggests that it is difficult to obtain a constant assessment as there are common problems in human scoring such as inattentiveness, halo effects, sequence effects, etc. The purpose of this study is to analyze the effectiveness of an automated scoring tool called Algo+ by comparing it with human scoring. Specifically, a correlational research design was used to examine the correlations between Algo+ and human raters' performance. We found that automated scores awarded by Algo+ exhibited different positive correlations with scores awarded by instructors that came from two different countries. Furthermore, better correlation was noticed with teachers' overall average scores. In most cases Algo+' behavior was similar to human instructors in awarding scores and it was indistinguishable from teachers. The Ward's hierarchical clustering methods were employed to classify types of teachers' behavior while they scored students' responses. Three types of teachers were classified - lenient, severe, and middle teachers. Algo+ was classified middle in the two exercises.