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Code reviews are an essential part of quality assurance in modern software projects. But despite their great importance, they are still carried out in a way that relies on human skills and decisions. During the last decade, there have been several publications on code reviews using eye tracking as a method, but only a few studies have focused on the performance differences between experts and novices. To get a deeper understanding of these differences, the following experiment was developed: This study surveys expertise-related differences in experts’, advanced programmers’, and novices’ eye movements during the review of eight short C++ code examples, including correct and erroneous codes. A sample of 35 participants (21 novices, 14 advanced and expert programmers) were recruited. A Tobii Spectrum 600 was used for the data collection. Measures included participants’ eye movements during the code review, demographic background data, and cued retrospective verbal comments on replays of their own eye movement recordings. Preliminary results give proof for experience-related differences between participants. Advanced and expert programmers performed significantly better in case of error detection and the eye tracking data implies a more efficient reviewing strategy.
In this paper we present our first steps in defining the type, scope and relevance of writing in higher education of software engineering. We aim to identify lacks of scientific research and raise a new and necessary research interest to push research in this area. First we clarify the relevance of writing in higher education in general. In a second step we highlight the relevance of writing in the domain of software engineering in particular. Soft skills to be taught to students of engineering professions and especially to software engineering students are highly discussed. We discuss the skill of writing from a theoretical view as well as reasons for the high relevance of this skill for future engineers. An obligation of teaching writing in the higher education is formulated.
Although there has been much speculation about the potential of Augmented Reality (AR) in teaching for learning material, there is a significant lack of empirical proof about its effectiveness and implementation in higher education. We describe a software to integrate AR using the Microsoft Hololens into UML (Unified Modeling Language) teaching. Its user interface is laid out to overcome problems of existing software. We discuss the design of the tool and report a first evaluation study. The study is based upon effectiveness as a metric for students performance and components of motivation. The study was designed as control group experiment with two groups. The experimental group had to solve tasks with the help of the AR modeling tool and the control group used a classic PC software. We identified tendencies that participants of the experimental group showed more motivation than the control group. Both groups performed equally well
Learning tasks play an important role in education and especially in higher education. However, there is a significant lack in research regarding these in higher education. A learning task shows several characteristics, whereas the didactic function of a task is mostly considered. Two characteristics of learning tasks are focused in this paper: The didactic function and the type knowledge. Existing types of learning tasks are presented as well as a proposal for learning tasks in software engineering education that considers didactic functions, like elaboration, training or application and types of knowledge, i.e. factual, conceptual, procedural and metacognitive knowledge. This paper aims to serve as a guidance for lecturers who have the purpose to create learning tasks that address both characteristics -- the didactic function and the type of knowledge.
This paper deals with the identification of learning ob-stacles using the questionnaire method. Therefore, two iterations were proceeded: The first one was part of a survey that was carried out at four lo-cations at universities of applied sciences. We asked students about obstructive facts in general providing items for five learning ob-stacle 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 Question-naire” was intended to measure students’ learning strategies, but, as the obstacle dimensions were partly derived from learning strategy classification, we chose this already validated question-naire [2]. Within this iteration, we could confirm a five-factor structure of the questionnaire that could be mapped to the five be-fore set learning obstacle dimensions.
This study is based on the work of Uwano, Nakamura, Monden and Matsumoto (2006) who tried to identify programmers’ eye movements in source code reviews by using eye tracking technology. The researchers were able to identify certain eye movement patterns but due to the technical limitations of earlier eye tracking systems and a small sample they could not find a valid proof for their existence. Now, twelve years later, the eye tracking technology has made significant improvements and is able to capture programmers’ reading behavior in an unobtrusive and precise way. Now the goal is to verify the described patterns by using eye tracking data from expert and novice programmers. In the experiment they have to detect errors in six different codes and take part in a retrospective interview. At the moment, data collections are ongoing. At the time of the conference, we will present the results of our analyses.
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.