Learning analytics on identifying difficulties in coding tasks, performance levels and learning styles of students in a programming course | |
| Author | Phway Thant Thant Soe Lin |
| Call Number | AIT RSPR no.DSAI-22-03 |
| Subject(s) | Computer science--Study and teaching Learning-- Data processing |
| Note | A research study submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Data Science and Artificial Intelligence |
| Publisher | Asian Institute of Technology |
| Abstract | Investigating factors that influence the learning process of students is important, especially in online education. Computer programming courses are essential in computer science education and other engineering fields. Programming courses often have high dropout rates and lower success rate. The investigation of the learners’ progress and difficulties in programming language learning is important to optimize their learning path effectively. This study focused on identifying common errors and topic difficulty as their programming difficulties, performance levels and learning styles in relation to the learning performance in a Java programming course supported by Moodle Learning Management System. The errors were analyzed based on the compiler error messages in a topic-specific basis. As a result, the common errors that the students likely to make are mostly syntax errors. Students encounter major difficulties in “Class programming”, “Inheritance” and “Recursion”, so they should be encouraged to practice more on these topics. This study also found that most of the students showed a good performance but it is still necessary to enhance the learning performance. This study also confirmed that different learning styles of the students did not influence the learning performance. Considering that, students can adapt the current course design and providing more concrete examples and experimentations in a visual way would add value in designing a motivate learning environment for a programming course. |
| Year | 2022 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Data Science and Artificial Intelligence (DSAI) |
| Chairperson(s) | Chutiporn Anutariya; |
| Examination Committee(s) | Chaklam Silpasuwanchai;Vatcharaporn Esichaikul; |
| Scholarship Donor(s) | AIT Scholoarships; |
| Degree | Research Studies Project Report (M. Eng.) - Asian Institute of Technology, 2022 |