Michael Spranger

Mobile communication devices are a popular means of planning, commissioning and carrying out criminal offenses. In particular, data from messengers such as WhatsApp or Telegram often contain conclusive information. Organized crime also usually involves many devices, but not all of them contain the full history of communication. Rather, it is heavily fragmented due to individual deletions of messages or different joining times to groups. A singular evaluation of individual devices is therefore often not expedient, since important relationships cannot be recognized. Furthermore, communication is often distributed across different channels and modalities and can only be fully and correctly understood through a joint semantic analysis. The linking of related communications of different devices enables an almost complete reconstruction of the communication with a simultaneous reduction in reading effort by merging identical messages. Grouping coherent messages into conversations enables efficient comparison with a knowledge model. Building such a model is complex, but can be supported by a term recommender system. In this paper, MoNA is presented as a platform that implements these approaches and enables an assisted analysis of mobile communications.

The digital transformation of higher education demands effective and efficient methods for learning assessment. Research has shown that tests have the potential to be an appropriate medium not only to diagnose learning success but to improve learning outcomes/processes in general. In this study, we examine possible connections between the use of digital tests and learning success. Based on a survey (n=71) and a quantitative analysis (n=214) with logging and exam assessment data, we find three points that indicate positive correlation: Firstly, students perceive digital tests as motivating and useful. Secondly, we detect a positive relation between the points students achieved in the online tests and the exam results (Spearman correlation coefficient of 0.39), and thirdly, students who participated continually in the digital testing system on average succeeded and passed the exam. The results show the importance of systematic digital testing in learning management systems, but also the need for more research with respect to other contexts and domains.

Named entity recognition (NER) constitutes an important step in the processing of unstructured text content for the extraction of information as well as for the computer-supported analysis of large amounts of digital data via machine learning methods. However, NER often relies on domain-specific knowledge, being conducted manually in a time- and human-resource-intensive process. These can be reduced with statistical models performing NER automatically. The current work investigates whether Conditional Random Fields (CRF) can be efficiently trained for NER in German texts, by means of an iterative procedure combining self-learning with a manual annotation–active learning–component. The training dataset increases continuously with the iterative procedure. Whilst self-learning did not markedly improve the performance of the CRF for NER, the manual annotation of sentences with the lowest probability of correct prediction clearly improved the model F1-score and simultaneously reduced the amount of manual annotation required to train the model. A model with an F1-score of 0.885 was able to be trained in 11.4 h.