Educational games have been increasingly used to improve students’ computational thinking. However, most existing games have focused on the theoretical knowledge of computational thinking, ignoring ...the development of computational thinking skills. Moreover, there is a lack of integration of adaptivity into educational computer games for computational thinking, which is crucial to addressing individual needs in developing computational thinking skills. In this study, we present an adaptive educational computer game, called AutoThinking, for developing students’ computational thinking skills in addition to their conceptual knowledge. To evaluate the effects of the game, we conducted an experimental study with 79 elementary school students in Estonia, where the experimental group learned with AutoThinking, while the control group used a traditional technology-enhanced learning approach. Our findings show that learning with the adaptive educational computer game significantly improved students’ computational thinking related to both conceptual knowledge and skills. Moreover, students using the adaptive educational computer game showed a significantly higher level of interest, satisfaction, flow state, and technology acceptance in learning computational thinking. Implications of the findings are also discussed.
In this paper we are reporting the finding on the use of a static analysis of C source code written by students learning to program. Two different tools for static code analysis were used to analyze ...the solutions submitted by the students on the partial exams and exams from the introductory course in programming in a three year period. We have collected, analyzed and compared most common errors reported by both tools. We further investigate if the available checks provided by these tools, often used in professional software development practices to find bugs and improve the code quality, can also help novice programmers in tracking down and resolving their problems in the code or have any other value in the process of learning programming.
A common approach for solving multi-label classification problems using problem-transformation methods and dichotomizing classifiers is the pair-wise decomposition strategy. One of the problems with ...this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming especially in classification problems with large number of labels. To tackle this problem we propose a two stage voting architecture (TSVA) for efficient pair-wise multiclass voting to the multi-label setting, which is closely related to the calibrated label ranking method. Four different real-world datasets (enron, yeast, scene and emotions) were used to evaluate the performance of the TSVA. The performance of this architecture was compared with the calibrated label ranking method with majority voting strategy and the quick weighted voting algorithm (QWeighted) for pair-wise multi-label classification. The results from the experiments suggest that the TSVA significantly outperforms the concurrent algorithms in term of testing speed while keeping comparable or offering better prediction performance.
This paper presents the design and implementation of a mobile application along with a web server for geo-tagging favorite and interesting places and sharing them with the community. The design and ...architecture shows some key aspects and issues concerning this kind of system. The mobile application is implemented in J2ME and tested on GPS enabled Nokia phones and the web server is implemented on cloud infrastructure implementation, the Google App Engine. The system was evaluated with real devices and a proof of concept was made that applications such as Place-Tags has its place in the mobile world.
