Modern education through Learning Management Systems (LMSs) provides learners with personalized learning paths. This is achieved by first querying the learning style according to the theory of Felder and Silverman to recommend suitable learning content. However, a rigid learning style representation is lacking of adaptability to the learners' choices. Therefore, the present study evaluates the idea of providing adaption to the representation of learning styles by using Hidden Markov Models (HMMs). Thus, data is collected from participants out of the Higher Education Area. The Index of Learning Styles questionnaire is used to obtain the learning style based on the theory of Felder and Silverman. Also, a questionnaire that asks the respondents to create a preferred learning path with the sequence length of nine learning elements is provided. From the given data, we initially evaluate the probability relationships between learning styles and learning elements. Then, we use the Viterbi algorithm in HMMs to identify alterations in learning styles from the provided learning paths. The alignment is then quantified by introducing a metric called support value. The findings imply that our concept can be used to adapt the learning style based on the user's real choice of learning elements. Thus, the proposed model also offers a way to integrate a feedback loop within LMSs leading to an improvement of learning path recommendation algorithms.

Most recently, the number of students dropping out of universities or higher education institutions increased dramatically. This might be partly because of the students’ limited capability exploring their own learning paths within a certain course. The introduction of adaptive learning management systems could be a potential solution to this issue. Based on individual’s learning styles, these systems recommend customised and tailored learning paths. These learning styles are commonly identified using questionnaires and learning analytics, but both methods are prone to errors. While questionnaires potentially give superficial answers due to e.g. time constraints, Learning Analytics cannot mirror offline behaviour. This paper proposes an alternative to classify the learning style of individuals through the integration and combination of eye tracking and artificial intelligence algorithms. The eye movement data, which is collected in a study including more than 100 participants, is processed with different methodolgies such as data scaling and subsequently classified using various models ranging from Logistic Regression to Neural Network. Moreover, this experiment setting discovers the interplay between preprocessing and classification techniques based on complex eye tracking metrics in order to determine the most promising solutions for learning style identification. Ultimately, this comprehensive analysis not only enables the understanding of individuals’ subconscious processes, but could also lead to improved educational outcomes.

This study uses holistic models of image perception originating from radiology and psychology to analyze and interpret eye movements during code reviews in the C++ programming language. The study design is based on former experiments, but is supplemented by approaches from expertise research. The study utilizes a sample of 34 subjects whose eye movements are recorded by a Tobii Pro Spectrum 600 Hz. The results show that the holistic models of image perception are suitable for application to source code. In addition, it can be observed that the code reviews are conducted in phases, which are characterized by certain strategies (e.g. scan, error detection,...). Furthermore, experience-related differences can be detected between experts and novices, which emphasize that experts use elaborate strategies and have a comparatively better ability to collect and process information from source code.

In the course Eye Tracking in Engineering Science, students develop a variety of skills. These competencies include both, theoretical knowledge and practical experience: in empirical research (i.e., the design of experiments and the writing of research papers), in eye tracking technology, and in applying computer science and statistics for data analysis. This competence building is done with a teaching concept based on pair-teaching and research-based learning - described in detail in the present article.

Abstract In the field of software engineering, graph‐based models are used for a variety of applications. Usually, the layout of those graphs is determined at the discretion of the user. This article empirically investigates whether different layouts affect the comprehensibility or popularity of a graph and whether one can predict the perception of certain aspects in the graph using basic graphical laws from psychology (i.e., Gestalt principles). Data on three distinct layouts of one causal graph is collected from 29 subjects using eye tracking and a print questionnaire. The evaluation of the collected data suggests that the layout of a graph does matter and that the Gestalt principles are a valuable tool for assessing partial aspects of a layout.

In modern vehicles, system complexity and technical capabilities are constantly growing. As a result, manufacturers and regulators are both increasingly challenged to ensure the reliability, safety, and intended behavior of these systems. With current methodologies, it is difficult to address the various interactions between vehicle components and environmental factors. However, model-based engineering offers a solution by allowing to abstract reality and enhancing communication among engineers and stakeholders. Applying this method requires a model format that is machine-processable, human-understandable, and mathematically sound. In addition, the model format needs to support probabilistic reasoning to account for incomplete data and knowledge about a problem domain. We propose structural causal models as a suitable framework for addressing these demands. In this article, we show how to combine data from different sources into an inferable causal model for an advanced driver-assistance system. We then consider the developed causal model for scenario-based testing to illustrate how a model-based approach can improve industrial system development processes. We conclude this paper by discussing the ongoing challenges to our approach and provide pointers for future work.

This study uses holistic models of image perception to analyze and interpret eye movements during a code review. 23 participants (15 novices and 8 experts) take part in the experiment. The subjects’ task is to review six short code examples in C programming language and identify possible errors. During the experiment, their eye movements are recorded by an SMI 250 REDmobile. Additional data is collected through questionnaires and retrospective interviews. The results implicate that holistic models of image perception provide a suitable theoretical background for the analysis and interpretation of eye movements during code reviews. The assumptions of these models are particularly evident for expert programmers. Their approach can be divided into different phases with characteristic eye movement patterns. It is best described as switching between scans of the code example (global viewing) and the detailed examination of errors (focal viewing).

The discipline of causal inference uses so-called causal graphs to model cause and effect relations of random variables. As those graphs only encode a relation structure there is no hard rule concerning their alignment. The present paper presents a study with the aim of working out the optimal alignment of causal graphs with respect to comprehensibility and interestingness. In addition, the study examines whether the central gestalt principles of psychology apply for causal graphs. Data from 29 participants is acquired by triangulating eye tracking with a questionnaire. The results of the study suggest that causal graphs should be aligned downwards. Moreover, the gestalt principles proximity, similarity and closure are shown to hold true for causal graphs.