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.

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.

We briefly describe a tutorial on the application of Eye-Tracking technology for Software Engineering Education. We will showcase our setup of a large-scale Eye-Tracking-Classroom and its usage for real-time improvement of traditional learning scenarios in Software Engineering Education. We will focus on the integration of gaze data into modern integrated development environments (IDEs) and demonstrate a complete workflow for its usage in co-located multi-user Eye-Tracking-Environments.

We investigated the potential of augmented reality (AR) to enable visualization of abstract concepts and present the first iteration of a teaching experiment that evaluates the use of AR as support for abstraction skills. Students were confronted with the task to present and explain information to different groups of stakeholders at the example of a coffee machine. Results show that students find it helpful to have a visual app-prototype and especially one that can be disassembled in different levels. The main goal was to sensitize students for the need to think about and to abstract information for certain roles and perspectives.

This paper deals with the validation and modification of the German questionnaire "F-Komp". In its original version, it was intended to measure university students' research competences. In the beginning of this study, there were only a few tools available which were reliable. For the purposes of this study, they were not suitable. At the same time, there was no validated version of the F-Komp available, which made the whole validation process for further usage necessary. This questionnaire is based on a structure, which consist of different skills and knowledge and is focused on measuring research competence in general. The validation and modification of the F-Komp is therefore the aim of our contribution as well as a revised version of the questionnaire. We proceeded an explorative factor and a reliability analysis to do a general evaluation of the tool. Some modifications were done in the questionnaire to make it more suitable to the requirements of technical oriented universities of applied sciences [5]. Our revised version is slightly longer and contains several items to gather data about the participants demographics. The modified questionnaire is based on a more appropriate factor structure. This structure is more practically oriented and pays attention to ethical issues. In future cases, this questionnaire will be used in research oriented courses to measure students' progress in acquiring the knowledge and methods which are necessary to perform as a scientist in different research areas.

This paper deals with the identification of learning obstacles using the questionnaire method. Therefore, two iterations were proceeded: The first one was part of a survey that was carried out at four locations at universities of applied sciences. We asked students about obstructive facts in general providing items for five learning obstacle dimensions that were set up before; emotional/motivational, epistemological/cognitive, didactical, resource-related and meta-cognitive learning obstacle dimensions. After the general part, we asked them to answer the same question, but in relation to the - in their opinion - most difficult learning content. With this question, we aim to get indications regarding to epistemological obstacles. In a second step, we used the "Motivated Strategies for Learning Questionnaire", which was developed by Pintrich [1] as a basis to develop a questionnaire that extracts learning obstacles. In its original version, the "Motivated Strategies for Learning Questionnaire" was intended to measure students' learning strategies, but, as the obstacle dimensions were partly derived from learning strategy classification, we chose this already validated questionnaire [2]. Within this iteration, we could confirm a five-factor structure of the questionnaire that could be mapped to the five before set learning obstacle dimensions.

Program comprehension and the ability to find program errors are key skills of software engineering. The aim of this pilot study was to examine the visual processes of novice and advanced programmers in authentic tasks. Fifteen novices and eight advanced programmers were given eight short pieces of code. Their task was to either identify an error or give the output of the code. Eye movements and keyboard activity were recorded. On average, the novices spent more time reading the code than composing the response, whereas the more advanced programmers started composing the response sooner and spent more time on it. In general, the advanced programmers had shorter fixations and saccades. The results suggest that the advanced programmers are quicker to grasp the essence of the code and able to see more details in it. The advanced programmers had shorter fixations and saccade lengths during the second phase which might indicate the process of chunking.