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This paper explores the significance of interoperability advancements in learning management systems (LMS) and the demand for customizable, flexible learning environments. We present a system that integrates learning resources from an LMS into a highly customizable frontend, including the embedded TaskAssessment, a programming task evaluating software. By incorporating standards such as Learning Tools Interoperability (LTI) and Experience API (xAPI), seamless interoperability across various systems is ensured. The goal is to contribute to the advancement of personalized learning experiences by learning element recommendation, automatic assessment and targeted feedback, while improving system interoperability.
This paper introduces a software tool designed to elevate the assessment of student-submitted freeform text through a node-based graph approach leveraging large language models and customizable natural language processing (NLP) techniques. It combines directed feedback for students with a scoring system to provide a comprehensive understanding of the submitted responses. The node-based approach further allows educators to create tailored assignments with reusable evaluation strategies. This tool aims to scale up individualized feedback efficiently, addressing the challenges posed by large student numbers, thus alleviating the time investment of educators. This capability is particularly vital in scenarios where manual evaluation of numerous freeform submissions is impractical or unfeasible. This paper focuses on the software’s architecture and the integration of nodebased NLP practices in freeform text analysis.
The aim of this paper is to describe the evaluation process and findings of an AI-based Adaptive Learning System for the Computer Science discipline at two different German universities and discuss an array of methods in regard to assessing such a system. The primary objectives have been twofold: Firstly, to examine the reception of selected learning elements, which were conceptually outlined based on relevant literature, among the student body; and secondly, to investigate the efficacy of individualized adaptive learning paths. These paths were generated by employing a variety of algorithms to analyze student’s learning style tendencies, with a particular emphasis on adaptive navigational techniques. The used algorithms encompassed a modified version of a literature based adaptive mechanism, an Ant-Colony-Algorithm and a Genetic Algorithm, alongside a lecturer-recommended learning path for a nonadaptive comparison. While the system suggested suitable learning paths based on student data, it never forced the individuals to give up their self-directed learning. The evaluation criteria revolved around the evolution of student motivation, interest levels, and knowledge acquisition during the time they spent working in the system. The evaluation sought to facilitate comparative analyses and assess algorithmic fitness for proficient learning path generation. The methods included both quantitative and qualitative approaches to gather data, seeking to strike a balance between being student-friendly and scientifically informative. They ranged from Likert Scale self-assessments to screen and video observations with retrospective interviews. Since the purpose of adaptive learning systems is intertwined with personalized learning it seems imperative to already take the preferences and opinions of students into account while the system is still in development. This complexity underscores the challenge of evaluating such systems, as significant constraints on student choice - though simplifying evaluation - directly oppose the ethos of individualized, self-directed learning. Initial findings suggest that the underlying theoretical considerations on sequencing and structuring of learning elements are confirmed, coupled with providing adequate flexibility to meet diverse learning needs. Cross-site evaluation of the literature-based learning elements indicated a high comprehensibility and positive student ratings. While significant positive trends were observed regarding knowledge acquisition, they cannot be definitively attributed to a specific method of learning path generation. Motivation and interest analyses show no significant differences among learning path types, albeit heavily limited by sample size. Similarly, emotion measurements, though limited, hint at positive impacts from HASKI system use. Despite limitations, early indications suggest student acceptance and potential effectiveness of learning paths, highlighting the need for larger sample sizes for validation and expansion. Ensuring alignment with student needs and user-friendly design are crucial considerations.
With the increasing complexity of software systems, it is becom- ing more and more relevant for students in software engineering education to learn how to elicitate requirements from customers or stakeholders and visualize them in a diagram. This continues to present different challenges to novice modellers. To counter- act these, educators and researchers have developed support tools. These offer various functionalities to address different challenges. However, it is unclear what functionality needs to be provided to address all of these issues and not just individual ones. Therefore, this paper extracts such functional requirements and summarizes them. It also shows how they can be translated into activities during an exercise lesson.
Learning how to model software systems or components is con- sidered to be a central part in the education of future computer scientists. Students are usually introduced to this topic in software engineering courses. As de facto standard, most of them teach the Unified Modeling Language (UML) for creating diagrams in spe- cific contexts. This task regularly presents students with significant challenges, as has been widely discussed in literature. Therefore, it is not surprising that there are some tool-supported approaches provided by educators. These are often based on observations. How- ever, comprehensive research on actual students’ requirements and aspirations for such tools has so been missing. The contribution of this paper will be a qualitative requirements elicitation for students requirements for tools to support teaching of UML diagrams in software engineering education.
Development of an Authoring Tool for the Creation of Individual 3D Game-Based Learning Environments
(2023)
Game-based learning in general and serious games in particular have a promising potential in higher education. In this article we going to show the capability of serious games with regard to current challenges in higher education. The focus of this article is the presentation of the AdLer authoring tool, which offers lecturers the possibility to design and generate virtual 3D learning environments in which students can interact with learning content according to the principles of game-based learning.
Flipped teaching is an approach which is becoming increasingly popular. There are already various field reports and scientific findings in different educational institutions and subjects. The approach of flipped teaching also appears to be promising in the university subject of software engineering. This paper explores the question of how this approach can be evaluated in teaching areas such as software engineering in comparison to traditional eaching approaches. A long-term study is presented in which courses with flipped teaching and courses without flipped teaching are compared. In total, 11 courses were analyzed over a period of six years (N=157). The results show that flipped teaching in software engineering has great potential and is rated better by students than traditional teaching in many areas. At the same time, the study also raises questions, for example, it is not clear from the results that active involvement is higher in flipped teaching than in traditional teaching.
This paper aims to gather tools from the scientific literature that can be used to teach software design patterns and software architecture patterns. The underlying procedure is a systematic literture review based on Kitchenham’s methodology. In order to improve categorization and clarify the objectives and focus, this systematic literature review was classified according to Cooper’s taxonomy. The presentation of the results following the underlying research questions of the tools found and listed includes the general description, the presentation of the functionalities, the support of the tools in teaching as well as a classification of the functionalities into the five areas: description, detection, selection, application, and implementation.
Planning adaptive learning paths for students’ progress throughout a course can be a challenging task, although it can be helpful for their learning progress. Within the HASKI-System, students should be able to get their own, personalized learning paths. In this paper, we present an approach towards the learning path sequencing problem. This idea is based on a novel proposal for arranging learning objects in a multidimensional space, bringing the relationship and similarities of these objects into a new relationship. We show, that we can use both, the Ant Colony Optimization Algorithm and the Genetic Algorithm with the idea of the Traveling-Salesman-Problem and get results, that are comparable with a proposed literature-based adaption mechanism. Nevertheless, the learning paths are all personalized based on the Felder & Silverman Learning Style Model and the hyperspace model will allow us later on to include more dimensions for other influencing factors.
This paper describes an investigation into the possibility of defining a customized course in Moodle, a popular learning management system. The aim was to define a course independently of Moodle, and to implement the course without making any modifications
to the configuration of the Moodle system. Two methods were considered: The Moodle REST-API and the restore functionality of Moodle. The latter method was chosen, and the paper details the structure of Moodle backup zip (MBZ) files and the creation of a generator that builds the required file and folder structure. The generator reads a Domain-Specific Language (DSL) document, parses it, and creates the required backup folders and XML files, which are then packed into an MBZ file. This paper provides technical details about the MBZ file structure and the implementation of the generator, as well as a flow chart that describes the steps needed to create an H5P-File into the MBZ file structure. This work has implications for the development of customized courses in Moodle without modifying the Moodle instance.