Jakub Swacha

While gamification has been paired with computer programming education on numerous occasions, most of the scientific reports covering the evaluation of its learning outcomes pertain to single-case specialized gamification applications with none or little chance of reuse in other institutions and courses; thus, they offer only limited replicability and comparability of results. In this work, we aim to address this gap by investigating the learning outcomes of a programming course based on the FGPE (Framework for Gamified Programming Education) platform, an open-source, fully configurable gamification platform developed specifically to support teaching and learning computer programming that can be used by any institution to support any programming-related course. This is, to the best of our knowledge, the very first study evaluating the learning outcomes of FGPE-supported programming education. Moreover, we address the question of whether students learning with gamified platforms limited to programming practice can benefit from additionally using non-gamified MOOCs by comparing the results attained in three groups differing in the choice of learning support tools (FGPE and MOOC vs. FGPE only vs. MOOC only).

Since the emergence of the first web development frameworks at the turn of the 21st century, many of them have made a name for themselves and rose to wide popularity only to be later ousted by newer frameworks and sometimes even fall to obscurity. In our paper, we would like to depict the last fifteen years of the changing popularity of web development frameworks by observing the number of newly created repositories in open-source projects hosted at GitHub that were based on them as well as the number of questions posted on Stack Overflow regarding the respective frameworks. We analyze the correspondence between these two indicators and put them in the context of the popularity of the programming languages that the respective frameworks are based on, as measured by the TIOBE Programming Community index, in an effort to check whether one source could be used to forecast another.