Divna Krpan

Educational data mining for grouping students in e-learning system

The popularity and the use of e-learning systems increased through last decades. Students produce a lot of data through their interactions with the system, which is often not exploited. Since there is a vast amount of such data, data mining techniques were used as the appropriate solution. In this paper we show practical experience with specific e-learning system and applied data mining technique for the analysis which served as a tool for grouping students with similar characteristics.

Mediated transfer from visual to high-level programming language

Visual programming languages (VPLs) are becoming more popular and making the transition from the informal to conventional educational settings. One of the important features of VPL is that novices are not required to remember a list of commands or complex syntax since everything they need is just there in the environment. The objective of introductory computer programming courses at the university is to teach students how to develop solutions in high-level computer programming languages such as C#. However, they also need to acquire problem-solving skills. Since computer programming and problem-solving are both challenging, schools and universities often make use of VPLs combined with game-based programming. Students will eventually need to transfer programming concepts learnt from VPL into a high-level programming language. A transition from VPL to the text-based high-level programming language is not seamless and additional tools and efforts are required. This paper presents prototypes we have developed for undergraduate university students to enable mediated transfer from VPL to high-level programming language by using the idea of mini-languages.