A learning analytic platform for systematic intervention on computer programming courses - Piriya Utamachant; Chutiporn Anutariya; Vatcharaporn Esichaikul; Huynh, Trung Luong; National Science and Technology Development Agency (NSTDA), Thailand; Royal Thai Government; AIT Fellowship (Pathum Thani, Thailand : Asian Institute of Technology, 2023)
The high non-progress rates of students in introductory programming courses have been continuously reported and become a persistent issue worldwide. Educators have been striving to improve instructional design and delivery methods aiming to en hance students{u2019} comprehension and ultimately increase their success rate. Neverthe less, it is essential to recognize that each class has its distinct characteristics, and while good instructional design and delivery are crucial, they can only contribute to part of the overall success. Instructors require competent intervention strategies to handle and elevate unforeseen situations that may arise in each course. This, in turn, presents another challenge of implementing effective intervention. Most instructors struggle to identify at-risk students to determine a proper intervention approach, to trace and to evaluate whether the intervention works. This process requires extensive effort and commitment, which is impractical, especially for large classes with few instructors. This dissertation proposes a platform, namely i-Ntervene, that integrates a Learning Management System (LMS), an automatic code grader, and learning analytic fea tures which can empower systematic learning intervention for large programming classes. The platform iteratively assesses student engagement levels and subject un derstanding to identify at-risk students and suggests aspects of intervention based on their lagging in these areas. Students{u2019} post-intervention data are traced and evaluated quantitatively to determine effective intervention approaches aligning with the evi dence-based research design. The i-Ntervene prototype was experimented with a Java programming course, delivered to 253 first-year undergraduate students. The result was satisfactory, as the instructors could successfully perform and evaluate 12 interventions throughout the semester with minimal administrative efforts. In addition to the proposed platform, the research introduces a longitudinal analysis to investigate the underlying issue of the high student failure rate in introductory programing courses. It examines the student-related factors that differentiate stu dents who failed the course for the first time and those who repeatedly failed from their successful peers, based on the longitudinal observation. These factors encom pass various aspects including student{u2019}s demographics, learning motivation & strategies, and engagement levels in learning activities. In this study, we analyzed three consecutive semesters of student data. The longitudinal analysis highlights three common learning motivations and strategies that should be enhanced for all non-progressing students: (1) self-efficacy, (2) effective time & environment man agement, and (3) the perception of the course{u2019}s importance and utility. For new stu dents enrolling for the first time, the results indicate that instructors should give pri ority to strengthening their intrinsic goal orientation and control of learning belief, while repeating students should concentrate on promoting their metacognitive self regulation. In terms of learning activities, instructors should primarily emphasize activities that involve coding practices, such as assignments and in-class exercises. Additionally, assistance should be easily accessible for students who encounter chal lenges during coding practices. The findings from the study provide a valuable con tribution to the research community by precisely addressing the issues of high stu dent failure in introductory programming courses.
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Management, School of Engineering and Technology
Thesis (Ph.D.) - Asian Institute of Technology, 2023
Keywords: Education -- Data processing; Longitudinal method; Educational tests and measurements; Application software -- Development; Computer programming -- Study and teaching