Remote interpreter API model for supporting computer programming adaptive learning - Rosihan Ari Yuana; Ignasius Agus Leonardo; Cucuk Wawan Budiyanto (TELKOMNIKA (Telecommunication Computing Electronics and Control), 2019)
Despite the adoption of Learning Management Systems (LMS) has been continuously growing in the last decade few, if any, scholars addressed the lack of the interactivity in online learning systems. The requirement for an interactive learning model has been increasingly prominent as content providers realize the importance of personalization of content suits to learners’ learning progress. This paper demonstrates the adoption of adaptive learning into existing LMS engine to overcome the limitation of either systems plugins or server specification pertinent to students’ psychomotor abilities in computer programming learning. In this regard,  the psychomotor is understood as the ability of students to write the program code as correctly as expected. In this study, a web API model that works to run an interpreter based-program code remotely was developed. The web API model can be utilized by LMS so it becomes the solution to the problem. The structure of the web API model has been adapted to the needs of the learning assessment. The implementation of the developed API web model is done in Python and PHP programming languages. The performance test was done by submitting 10 to 100 program codes simultaneously indicated no significant difference to the required resources (CPU usage and memory usage) to run the program code. Furthermore, for response time, the average time needed to run Python and PHP program code is also no significant difference. The average of CPU usage required by the web API to run a Python program code is 0.2058% with 0.5973 seconds as a response time. Meanwhile, to run the PHP program code, the average CPU usage required is 0.8074% with 0.3110 seconds response time. It can be concluded that the web API performance does not overburden the server.