Advances in Teaching and Learning Technologies

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    What Students Want: Using A Need-based Innovation Approach for Digitizing Higher Education
    ( 2022-01-04) Brenk, Sebastian
    Incumbent organizations like universities lack capabilities of how to make sense of student learning needs to guide digitization activities that develop and implement new e-learning technologies. This paper contributes a methodological approach based on the jobs-to-be-done theory that assists universities in digitizing their learning and teaching processes. This study applies the Outcome Driven Innovation (ODI) approach to assess students’ learning needs in the learning process of higher education. The results from the ODI methods are valuable to create need-based data intelligence that supports sensemaking about digitization strategies at universities and fosters the development of e-learning platforms and analytics.
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    Towards Routinely Using Virtual Reality in Higher Education
    ( 2022-01-04) Majchrzak, Tim A. ; Radianti, Jaziar ; Fromm, Jennifer ; Gau, Michael
    Virtual reality promises to be a tool that can improve higher education. Immersive virtual environments offer the chance to enrich courses with experiential learning experiences. The technological possibilities evolve rapidly and more and more researchers report on adopting virtual reality for learning – albeit such work often has a more or less experimental character. However, the base of knowledge on using virtual reality in higher education is growing; educators who want to employ virtual reality to amend courses, to extend the curriculum with experiential learning, or who want to offer new content enabled through virtual reality, find increasingly rich advice. With this article, we contribute to this advice by providing insights from three research cases. Although these were experimental, their embedding into a larger project enables us to propose recommendations for educators. The ultimate aim of our work is the routine use of virtual reality in higher education.
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    Towards Better Support for Machine-Assisted Human Grading of Short-Text Answers
    ( 2022-01-04) Neumann, Gustaf ; Meisl, Alexander
    This paper aims at tools to help teachers to grade short text answers submitted by students. While many published approaches for short-text answer grading target on a fully automated process suggesting a grading result, we focus on supporting a teacher. The goal is rather to help a human grader and to improve transparency rather than replacing the human by an Oracle. This paper provides a literature overview of the numerous approaches of short text answer grading which were proposed throughout the years. This paper presents two novel approaches (answer completeness and natural variability) and evaluates these based on published exam data and several assessments collected at our university.
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    The Virtual Tutor: Combining Conversational Agents with Learning Analytics to support Formative Assessment in Online Collaborative Learning
    ( 2022-01-04) Lenk, Florian ; Schmidt, Sebastian
    The objective of this design science research project is to combine Learning Analytics data with a conversational agent communication interface, the Virtual Tutor, which is able to support formative assessment for educators and learners in online collaborative learning (OCL) environments. The main benefit for educators is providing user-adaptable Learning Analytics data requests to fit the information needs for formative assessment. Learners receive semi-automated feedback on their platform activity in form of reports, which shall trigger self-reflection processes. By extracting requirements from the potential users and deriving design principles, a conversational agent is implemented and evaluated in an online collaborative learning course. The results indicate that the Virtual Tutor reduces the task load of educators, supports formative assessment and gives scaffolded guidance to the learners by reflecting their performance, thus triggering self-reflection processes. This research provides a first step towards data supported (semi-)automated feedback systems for formative assessment in OCL courses.
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    The Relationship Between Students' Flow Experience and Their Behavior Data in Gamified Educational Systems
    ( 2022-01-04) Oliveira, Wilk ; Tenório, Kamilla ; Hamari, Juho ; Isotani, Seiji
    The number of students using online educational systems is increasing, especially after the growth of the use of this type of system due to the social isolation caused by the Covid-19 pandemic. This situation highlighted the challenge of analyzing the users' experience in this type of system, especially when evaluating more complex experiences, such as the flow experience. One of the most promising innovative alternatives is to use the behavior data logs produced by students in educational systems to analyze their experiences. In this paper, we conducted a study (N = 24) to analyze the relationships between the behavior data logs produced by students when using a gamified educational system and their flow experience during the system usage. Our results contribute to the automatic users' experience analysis in educational systems.