Interactive videos promote learner engagement by transforming the passive activity of watching videos into an active learning process. LIVE, a web platform developed in previous research that allows for the integration of interactive elements into videos, also supports embedding programming tasks. However, at the beginning of our research, the output of the submitted solution was automatically compared to the reference output, but the code itself still had to be checked manually, as no automatic process was available to verify the solution and provide feedback. To address this, a stand-alone application was developed that automatically evaluates students’ solutions to Python programming tasks using a self-hosted large language model. This application was then integrated into the existing platform. To test this approach, a massive open online course containing four interactive videos with several programming tasks and quizzes was created. Students’ performance was analyzed using quiz results, task submissions, self-assessments, and user statistics provided by the course platform and LIVE. The results indicate that utilizing a large language model for automated feedback can positively impact learning outcomes. However, some limitations must be taken into account, such as the need to correct some of the model’s generated outputs manually due to insufficient feedback quality for certain tasks. This suggests that the combination of automated and manual evaluation seems to be the most effective approach for now.
Zum Diktieren von einfachen Texten habe ich bis jetzt immer eine Online App verwendet. Da ich aber nicht mehr möchte, dass meine Stimme auf irgendwelchen Servern verarbeitet wird, habe ich eine einfache App entwickelt, welche rein lokal arbeitet.
In den letzten Jahren entwickelte ich gemeinsam mit Masterstudierenden und Kollegen von der Pädagogischen Hochschule Steiermark 3 Python-Kurse für absolute Anfänger in der Form von MOOCs (Massive Open Online Courses). Diese Kurse werden über die Plattform iMooX.at kostenlos und mit einer offenen Lizenz (CC BY 4.0) bereitgestellt.
This paper presents the refactoring and evaluation of IDeRBlog, an educational blog platform designed to support German language acquisition in primary schools. Originally developed as part of an EU Erasmus project, IDeRBlog aims to improve students’ spelling skills through a combination of intelligent feedback and blogging features. The recent redevelopment focused on updating the user interface for mobile compatibility and integrating modern web technologies. To assess the impact of these improvements on user experience, two test classes used the IDeRBlog system during regular German lessons. Students received automated feedback on their writing, while teachers tracked progress using learning analytics to identify common error categories. The evaluation demonstrates significant improvements in usability and user experience due to the refactoring, highlighting its positive impact on both student engagement and learning outcomes.
The importance of digital technologies, especially videos, increased over the course of the last few years. Whether they are used to watching the latest news or for entertainment purposes, one could say they have become almost omnipresent in everyday’s life. This development has also influenced education. Therefore, we want to examine the effects of interactive overlays such as programming tasks in interactive learning videos. For this purpose, a MOOC course was created to gain information about participants` learning success. To evaluate whether the interactive elements have a positive effect, two test groups completed the course. The results revealed that the test group with overlays performed better on average than the group without. Some limitations had to be considered, such as the smaller peer group that watched the videos without interactive elements.
This paper focuses on enhancing touch-typing skills through state-of-the-art deep learning technologies. It has been demonstrated that writing posture can be assessed using integrated keyboard detection and hand pose estimation in real-time browser environments. A unique dataset was created for training the object detection model to recognize individual keyboards. The object detection models were trained using various architectures and then assessed for both inference time and accuracy. In addition, a hand pose estimation was implemented to precisely recognize 21 knuckle points of the hand and compute their exact positions. In order to implement object detection and hand pose estimation, a stream transmission of the keyboard and hand scene is required. For this purpose, server-client communication via a QR code connection is implemented to transfer the stream between two mobile devices. Based on these deep learning technologies, a mobile web app was developed that offers 170 learning courses for touch-typing training. With the help of the Typing Learning Academy prototype, a study was conducted as part of this paper to evaluate the usability and utility of the learning app. This research demonstrates the potential for enhancing the development of writing skills through touch typing through the utilization of advanced deep learning technologies.
Last month I started experimenting with a Raspberry Pi Pico. I am using it with Micro Python and at the beginning the projects were very little. This means that the first projects consisted only of blinking LEDs and push buttons. Later on a distance sensor came into play.
Digital learning technologies are becoming increasingly important for our modern educational system. In addition to teaching methods that incorporate interactivity, these approaches benefit students’ overall learning experience and success by enhancing their attention and fostering a positive attitude towards the learning content being presented. Interactivity comes in various forms, and while a combination of distinct activities is beneficial, some are more effective at engaging students. Using digital technologies in an educational environment opens up new possibilities for students, teachers, and researchers. It provides new insights into learning behavior and enables the collection of interaction information. This data could, for example, show how often a video was paused or at what point students lost interest and left, but gaining such knowledge requires further processing. The use of visualizations that depict behavior, such as the change of attention over time, can be an effective way to present extracted information. Therefore, our research focuses on developing an application that enables us to generate various visualizations from the collected data. A single command-line input will be sufficient to create them. Furthermore, a video course was created from which we collected behavioral data. Our results aim to showcase the benefits of interactivity, and that the created figures can be used for data evaluation verifies the versatility of the generated visualizations.
The two mobile operating systems Android and i(Pad)OS have dominated the smartphone and tablet market for years and app providers have to offer their apps for both systems in most cases in order to be competitive or to be able to reach the majority of potential customers. In native app development, separate applications have to be written and maintained for each platform. Often, apps are developed for one platform first and the second app is developed at a later stage, after some feedback could be collected. This porting from one system to the other can be either (partially) automated or manual, but in any case, it has its challenges. Both systems were designed with different approaches and differ greatly in some parts from each other – not only visually, but also in terms of the underlying structure. To illustrate the porting process, the Android app “Schoolstart Screening App”, which was developed for the Federal Ministry of Education, Science and Research of Austria by Graz University of Technology at the OU Educational Technology, was ported so that it can be used also on iPads. Automated approaches were discussed and the chosen process is explained to get a good overview of the topic.
This work deals with the maintenance of information systems—specifically, with the maintenance of information systems that have a focus on teaching and learning. Depending on the context of an information system, there are different influencing factors for the maintenance of these systems. This work clarifies how the maintenance activities and their influencing factors differ in an information system for teaching and learning from other information systems, or why some influencing factors are particularly more important. The first step is to understand what maintenance means, why there is a need for maintenance, and which maintenance strategies can be used. Finally, the defined factors of influencing the maintenance of information systems for teaching and learning are evaluated during interviews with experts in order to be able to determine their relevance. A further part of this document is the influence of the General Data Protection Regulation (GDPR) guidelines on information systems maintenance, which came into force on May 25, 2018. These guidelines of the GDPR affect a large part of all information systems that process data—in particular, the processing of personal data. The GDPR regulates, among other things, the rights and obligations of data processing.