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Browsing Forschungsdaten by Author "Broneske, David"

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    LAMM - Learning Analytics Metadata Model
    (Otto-von-Guericke-Universität Magdeburg, 2022) Wolff, Ian; Broneske, David; Köppen, Veit
    Research data management is an emerging topic in all sciences. In this work, we introduce a metadata model called LAMM – Learning Analytics Metadata Model – for learning analytics. We derived metadata entities, by conducting a literature review in the special learning analytics field on observing collaborative programming scenarios, and together with researchers from our project context from three different universities. The main focus lies on the subject-specific metadata entities, to describe the learning analytics data provenance and learning process measurement in detail to increase third-party researcher reusability for learning analytics datasets. The result of the Version 0.1 takes metadata entities of existing metadata standards into consideration and provides the opportunity besides general descriptive metadata, to describe the learning-analytics-specific entities environment, learner, measurement instrument and learning-process-measurement and gives the option to link datasets with other related research.
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    Learning Analytics Data from Collaborative SQL Exercises using the SQLValidator
    (Otto-von-Guericke-Universität Magdeburg, 2023) Broneske, David; Obionwu, Victor; Berndt, Sarah; Hawlitschek, Anja
    This data set is part of the DiP-iT project (http://www.dip-it.ovgu.de), which tests and evaluates different didactic concepts to improve collaborative learning of programming languages. This data set resembles student data of 83 students recorded at the university of Magdeburg in the Summer Semester 2021 (2022), where the SQLValidator is used in the weekly exercises to practice SQL. It consists of student questionnaire answers, submissions of students’ task solutions (and trials of their solution), as well as their chat and submissions of their semester’s team project. The questionnaire contains items on socio-demographic characteristics (e.g. gender, age), on factors relevant to the course (e.g. subject semester, degree program), on attitudes and attitudes towards the course (e.g. self-efficacy expectations, interest) and on teamwork (e.g. experience, relevance, openness towards teamwork). The students' previous knowledge is also surveyed subjectively (e.g. previous knowledge of programming, previous knowledge of SQL) and objectively (knowledge test in the area of teamwork). In the task solutions, students test their SQL queries in the SQLValidator for the weekly exercises in a trial-and-error fashion. The aim was to analyze how students learn from the shown error messages and what problem-solving skills they employ. In the semester’s team project, a group of students is working on the tasks, chat about the tasks and submit their solutions. The aim of this part was to analyze collaboration behavior between students and factors for a successful collaboration.