One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.
The COVID-19 crisis influenced universities worldwide in early 2020. In Austria, all universities were closed in March 2020 as a preventive measure, and meetings with over 100 people were banned and a curfew was imposed. This development also had a massive impact on teaching, which in Austria takes place largely face-to-face. In this paper we would like to describe the situation of an Austrian university regarding e-learning before and during the first three weeks of the changeover of the teaching system, using the example of Graz University of Technology (TU Graz). The authors provide insights into the internal procedures, processes and decisions of their university and present figures on the changed usage behaviour of their students and teachers. As a theoretical reference, the article uses the e-learning readiness assessment according to Alshaher (2013), which provides a framework for describing the status of the situation regarding e-learning before the crisis. The paper concludes with a description of enablers, barriers and bottlenecks from the perspective of the members of the Educational Technology department.
The influence of digital technologies as well as the World Wide Web on education rises dramatically. In former years Learning Management Systems (LMS) were introduced on educational institutes to address the needs both their institutions and their lecturers. Nowadays a shift from an institution-centered approach to a learner-centered one becomes necessary to allow individuality through the learning process and to think about learning strategies in general. In this paper a first approach of a Personal Learning Environment (PLE) is described. The technological concept is pointed out as well as a study about the graphical user-interface done at Graz University of Technology (TU Graz). It can be concluded that PLEs are the next generation environments, which help to improve the learning and teaching behavio
Technology Enhanced Learning has become more popular in recent times and many organizations and universities use it as a key instrument in various teaching and training scenarios. At the University of Technology of Graz, some courses require randomized quizzes where question variables can be assigned by arbitrary mathematical functions and this feature is missing in the current solutions. This article describes the development of a quiz application that can be integrated into Moodle by utilization of the Learning Tools Interoperability protocol (LTI). The PHP application is built to support programmable questions that can contain JavaScript and HTML code. Teachers are able to build interactive, randomized quizzes, in which the random variables can be assigned with complex mathematical functions. Furthermore, the application provides a programmable grading mechanism. With this mechanism, it is possible for students to self-assess their performance, as well as for teachers to formally assess their students' learning success automatically and send the results back to Moodle (or other LTI compatible consumer applications).
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