Various similarity measures for source code have been proposed, many rely on edit- or tree-distance. To support a lecturer in quickly assessing live or online exercises with respect to "approaches taken by the students," we compare source code on a more abstract, semantic level. Even if novice student's solutions follow the same idea,…

Programming education often imposes a high cognitive burden on novice programmers, requiring them to master syntax, logic, and problem-solving while simultaneously managing debugging tasks. Prior knowledge is a critical factor influencing programming learning performance. A lack of foundational knowledge limits students' self-regulated learning…

Students completing undergraduate majors in chemistry are not typically required to undergo formal training in computer programming or coding. As a result, many chemistry students are graduating without skills in understanding, writing, or manipulating computer code. This skills gap places students at a disadvantage, considering the widespread and…

As students learn how to program, both their programming code and their understanding of it evolves over time. In this work, we present a general data-driven approach, named "Temporal-ASTNN" for modeling student learning progression in open-ended programming domains. Temporal-ASTNN combines a novel neural network model based on abstract…