Hochschule Weserbergland

With factors like the COVID-19 pandemic, digitalization and climate change, hybrid work and education models with in-person and remote participation have gained popularity. According to existing studies, the hybrid format comes with a set of challenges in comparison to pure online or in-person lectures. Using a small setup with products from Sennheiser electronic SE & Co. KG as an example, the influence of hybrid lectures on the student experience was measured. The Kruskal-Wallis test was used to evaluate the influence of the format, while the difference between online and in person participation was evaluated using t- and U- tests. In addition, qualitative data was collected in the form of interviews, observations, and brief feedback integrated into the surveys. The data showed that to avoid a disadvantage of the hybrid format, careful preparations with a focus on sound quality of the lecturer is necessary. The experience of online and in-person participants only differed in comprehensibility of the used technology.

The domain of Location-based Augmented Reality (LBAR) in education is growing rapidly with the increasing ubiquity of mobile devices and their advanced capabilities. LBAR enables the superimposition of digital content onto physical environments and has been recognized for its transformative potential in educational contexts. Despite its advantages, the adoption of LBAR in education remains limited, hindered by several challenges. This paper presents a systematic review of peer-reviewed literature from 2000 to 2023 to assess the current state of LBAR in education. It explores 1) the state of the art in LBAR research, 2) its use cases and reported impacts across educational levels, and 3) the challenges and limitations identified in existing studies. Among the 50 studies included in the review, 38% of investigated studies indicate a positive acceptance of LBAR and evident enthusiasm regarding LBAR’s application for learning purposes. However, multidimensional complexities are revealed at the methodological, technological, pedagogical, and at content and organisational levels. While LBAR tools present strong potential to enhance learning experiences, that is still not realised, their long-term value depend on how well they are integrated into established pedagogical practices, learning content, and educational contexts.

Modern video-based eye trackers have reached a level of accuracy sufficient for gaze-based human-computer interaction. In many but not all applications, accuracy even allows for replacing a computer mouse with gaze-based pointing. The Multi-Modal Interaction Concept for Efficient Input (M<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>ice) tackles accuracy problems with an on-demand hybrid linear-fisheye magnifier. This paper explores how semantic analysis of screen content can be used as part of an alternative solution to the accuracy problem. This solution integrates machine-learning-based near real-time image analysis into the M<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>ice interaction concept and adds semantic highlighting to screen elements near the user's interaction point of gaze. Semantic highlighting identifies clickable screen elements and adds a number as well as a bounding box to each of these elements. This allows users to select elements with a keystroke. It thus combines visual magnification with visual semantic enrichment. The image analysis is based on a machine learning model specifically trained for the respective use case. A user study <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{n}=20)$</tex> showed no significant speed difference with the use of semantic highlighting but a 94.3 % decrease in interaction errors (misclicks) with the use of semantic highlighting compared to the traditional selection and magnification approaches.

Location-based games are a highly technology-dependent game genre that has witnessed an exponential increase in popularity with the democratisation of smartphones as well as ubiquitous mobile data and access to satellite navigation. Moving forward into the future, location-based games can be expected to evolve as the technologies underlying the genre improve. In this conceptual work, we review the current state of the art in location-based games, and identify key trajectories and trends. We discovered 12 trends, based on which we jump ten years into the future and evaluate how current technology trends may end up influencing location-based gaming. For example, we propose that in the year 2035 through improvements in map data services and sensor data coverage, we will see locative games that are increasingly connected to elements in the physical world. We also expect to see gameplay that moves away from solely taking place on a smartphone screen to the adoption of multiple forms of interactions with location-based game worlds, especially as head-mounted displays and other wearables become more commonplace.

Augmented reality (AR) is increasingly substituting traditional methods in urban planning, revolutionizing how we approach this field. This study assesses the mental workload in augmented reality (AR)-based urban planning education compared to traditional methods like paper plans and environmental cues. We employed standardized evaluation instruments, including the NASA Task Load Index (NASATLX), Subjectively Perceived Effort, and Technology Acceptance Model (TAM), in an exploratory pilot study. Focus group interviews revealed the strong potential of the AR application, but quantitative questionnaire responses indicated a high mental workload associated with AR use. Our contribution involves incorporating AR in typical lecture settings, engaging regular students within an outdoor environment. This approach enhances the study’s relevance and applicability to real-world educational contexts. Overall, our study highlights the potential challenges in implementation of AR in real-world educational practices.

Spaced repetition is a learning technique that is proven to having a positive effect on long-term retention of knowledge. Game-based learning on the other hand is known for keeping the motivation of the learners at a high level by taking the boredom out of repetition-based learning. Therefore, combining both approaches looks like a promising environment for the best possible learning success. While this success is always based on individual definitions, there are indicators which can show the impact of using spaced repetition learning in a mobile learning game on a previously defined learning success. After building a detailed groundwork for measuring this impact over several years, this paper presents selected results of a data analysis, which shows that playing the used learning game according to the intervals calculated by the SM2 spaced repetition algorithm leads to a better learning success compared to just randomly playing it based on a previously made definition.

Evaluating the success of learning technologies with respect to improvement in the learners' abilities and knowledge is not an easy task. The problem is formed by the existence of many different definitions with different perspectives like grades on the one hand and workplace performance on the other. This paper reviews definitions from the literature with the aim to find a suitable definition for the evaluation of learning success in spaced repetition based mobile learning for knowledge improvement. It also borrows approaches from learning analytics to tackle the fact that learner groups are heterogeneous which leads to the need of analyzing learning success differently in different groups of learners.