This research is focused on how to support students’ acquisition of program construction skills through worked examples. Although examples have been consistently proven to be valuable for student’s learning, the learning technology for computer science education lacks program construction examples with interactive elements that could engage students. The goal of this work is to investigate the value of the “engaging” features in programming examples. We introduce PCEX, an online tool developed to present program construction examples in an engaging fashion. We also present the results of a controlled study with a between-subject design that was conducted in a large introductory Python programming class to compare PCEX with non-interactive worked examples focused on program construction. The results of our study show the positive impact of interactive program construction examples on student’s engagement, problem-solving performance, and learning.

Educational data mining research has demonstrated that the large volume of learning data collected by modern e-learning systems could be used to recognize student behavior patterns and group students into cohorts with similar behavior. However, few attempts have been done to connect and compare behavioral patterns with known dimensions of individual differences. To what extent learner behavior is defined by known individual differences? Which of them could be a better predictor of learner engagement and performance? Could we use behavior patterns to build a data-driven model of individual differences that could be more useful for predicting critical outcomes of the learning process than traditional models? Our paper attempts to answer these questions using a large volume of learner data collected in an online practice system. We apply a sequential pattern mining approach to build individual models of learner practice behavior and reveal latent student subgroups that exhibit considerably different practice behavior. Using these models we explored the connections between learner behavior and both, the incoming and outgoing parameters of the learning process. Among incoming parameters we examined traditionally collected individual differences such as self-esteem, gender, and knowledge monitoring skills. We also attempted to bridge the gap between cluster-based behavior pattern models and traditional scale-based models of individual differences by quantifying learner behavior on a latent data-driven scale. Our research shows that this data-driven model of individual differences performs significantly better than traditional models of individual differences in predicting important parameters of the learning process, such as performance and engagement.

The prevalence of mobile devices has increased rapidly in recent years. People store valuable data like personal and financial information on those devices. In addition, applying “bring your own device (BYOD)” policy in companies has become popular. Hence, mobile devices are also source of valuable and confidential company information. Accordingly, there is a growing need for malware detection methods and tools to protection mobile devices against attacks targeting them. In this study, an automated feature-based static analysis method is applied to detect malicious mobile applications on Android devices. By utilizing the metadata of applications on the official market and an online free malware scanner, the feasibility of a mobile malware detection model using free public sources and having quite acceptable accuracy rates is shown. As opposed to previous studies considering only the requested permissions as feature set, additional market metadata including but not limited to application category, download number, developer name, and average rating are included in the analysis as the feature set for training supervised classification algorithms. Based on an experimental evaluation of the majority voting of antivirus (AV) engines on the free online AV community, applications in the training set are labeled as malicious or benign. Naïve Bayes classification algorithm is chosen as supervised learning algorithm for the detection task. In addition, as filter-based algorithms, Chi-Square, Information Gain and ReliefF feature selection methods are used for overcoming potential overfitting problems. Finally, a quick prototype for showing the feasibility of the detection model is demonstrated with sample case applications.