Jupyter Notebook-Based Diagnostic E-Assessment Model for Novice Programmer

Abstract

Programming is one of the most difficult subjects for new students, and an important effort to improve these skills is by adjusting their cognitive abilities as well as learning styles. Therefore, this study aimed to improve student's learning outcomes through the use of Diagnostic E-Assessment Model. It determines the effectiveness of the model in improving student learning outcomes. Furthermore, programming teachers need to relate authentic information about uncleared topics to the students since their skills are grouped at the SOLO taxonomy level. Data grouping was conducted using the K-means clustering method which is often used in data mining analysis. It measures students' programming skills using an AMS application in line with the Jupyter Notebook engine. The data was obtained twice, after the completion of learning and after the teacher followed up on the assessment results. Also, the differences in the first and second assessments were analyzed using the N-Gains method. The results showed a significant increase in programming skills. This was indicated by the significant movement of numbers from the low to the highest level group such as Relational. Therefore, this model can also be used for other lessons as well.