Hagit Gabbay

Effective learning with ATFs (Automated Testing and Feedback system) - Examining the impact of integrating ATFs and calibrating its characteristics to enhance learning effectiveness and outcomes in online programming courses. 

PI: Dr. Anat Cohen

In programming courses, practicing of writing and running code is the basis for acquiring programming language. Getting timely, detailed and accurate feedback on the correctness and quality of the code is a crucial element of the learning process. In large scale courses, and especially in online courses, it is almost impossible for instructors to assess the great volume of submissions and to provide feedback on each exercise. Thus, Automated Testing and Feedback systems (ATFs) are often suggested, providing students an immediate feedback on their code exercises and allowing short learning cycle. 

In our study we examine the impact of embedding an ATF system in online and hybrid programming courses from pedagogical point of view and in terms of achieving learning goals.

In the focus of the study is an online course for learning Python, taught in various formats, with or without an ATF system. Using quantitative research approach, various data resources and learning analytic tools, we explore the relations between the characteristics of the system, feedback’ structure, learner pattern of usage and learning outcomes. We will ground our findings in relevant learning theories, in order to derived principles for integrating ATF system effectively, adapted to the course and the learners.

About Me

I hold a B.Sc and M.Sc in Applied Mathematics from the Technion, Haifa and completed the continuous study track in the department of Mathematics, Science and Technology Education in Tel Aviv University School of Education. After years of software development and project management, I took a turn and worked for 10 years as a learning and training manager in public organizations. I am currently a Ph.D. student under the supervision of Dr. Anat Cohen.