@misc{12993,
  abstract     = {{In computer science and related technical fields, researchers, educators, and practitioners are continuously automating recurring tasks for high efficiency in a wide variety of fields. In higher education, such tasks that educators face are the recurring review and assessment process of students' programming coursework. Thus, various attempts exist to automate the assessment and feedback generation for course homework and practicals in higher education. Those approaches for automated programming task assessment often comprise running automated tests to check for limited functional correctness and potentially style checking for various violations (LINTing). Educators familiar with large-scale automated task assessment are likely used to seeing hard-coded solutions specifically or accidentally designed to just pass the required tests, ignoring or misinterpreting the actual task requirements. Detecting such issues in arbitrary code is non-trivial and an ongoing research topic in software engineering. Software engineering research has yielded various semantic analysis frameworks, such as GitHub's CodeQL, which can be adapted for programming task assessment. We present a work-in-progress programming task analysis framework which employs CodeQL's analysis technology to identify the actual use of task-description-mandated syntactic and semantic elements such as loop structures or the use of mandated data blocks in branching conditions. This allows extending existing course work analysis frameworks to include a semantic check of an uploaded program which exceeds the relatively simple set of input-output test cases provided by unit tests. We use a running example of entry level programming tasks and several solution attempts to introduce and explain our proposed control flow and data flow -based analysis method. We discuss the benefits of including semantic analysis as an additional method in the automated programming task assessment toolbox. Our main contribution is the adaptation of an semantic analysis code framework to analyse syntactic and semantic components in students' programming coursework.}},
  author       = {{Wehmeier, Leon and Eilermann, Sebastian and Niggemann, Oliver and Deuter, Andreas}},
  booktitle    = {{FIE 2023 : College Station, TX, USA, October 18-21, 2023 : conference proceedings  / 2023 IEEE Frontiers in Education Conference (FIE)}},
  isbn         = {{979-8-3503-3643-6}},
  keywords     = {{Codes, Electronic learning, Soft sensors, Semantics, Education, Syntactics, Task analysis}},
  location     = {{Texas}},
  publisher    = {{IEEE}},
  title        = {{{Task-fidelity Assessment for Programming Tasks Using Semantic Code Analysis}}},
  doi          = {{10.1109/fie58773.2023.10342916}},
  year         = {{2024}},
}

@misc{13010,
  abstract     = {{Especially in highly interdisciplinary fields such as automation engineering, contemporary programming education with tailored assignments and individual feedback is a major challenge for educational institutions due to the increasing number of students per teacher and the ever-increasing demand for computer science professionals. To address this gap, we present ”KIAAA” an AI Assistant for Automation Engineering Teaching, a work-in-progress approach for an integrated, customized, and AI-based learning support system for automation and programming courses based on instructor-defined course objectives. Thereby in the KIAAA system, the individual knowledge level of the students is determined and individually tailored virtual learning scenarios are generated based on the knowledge and learning profile of the students. These are iteratively adapted based on the answers given. To achieve this, KIAAA uses several AI components, a hybrid rule-based scenario generation component, a Help-DKT-based cognitive model, and a solution assessor that uses a combination of traditional code analysis methods and AI-based analyses methods for automated programming task assessment. These components are the main parts of KIAAA to generate customized programming scenarios as well as visualization and simulation based on a modern game and physics engine.}},
  author       = {{Eilermann, Sebastian and Wehmeier, Leon and Niggemann, Oliver and Deuter, Andreas}},
  booktitle    = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}},
  editor       = {{Jasperneite, Jürgen}},
  isbn         = {{978-1-6654-9314-7}},
  keywords     = {{Visualization, Automation, Education, Games, Hybrid power systems, Task analysis, Artificial intelligence}},
  location     = {{Lemgo}},
  publisher    = {{IEEE}},
  title        = {{{KIAAA: An AI Assistant for Teaching Programming in the Field of Automation}}},
  doi          = {{10.1109/indin51400.2023.10218157}},
  year         = {{2023}},
}

@inproceedings{2005,
  abstract     = {{We present a method for the fast and robust linear classification of badly conditioned data. In our considerations, badly conditioned data are such data which are numerically difficult to handle. Due to, e.g. a large number of features or a large number of objects representing classes as well as noise, outliers or incompleteness, the common software computation of the discriminating linear combination of features between classes fails or is extremely time consuming. The theoretical foundations of our approach are based on the single feature ranking, which allows fast calculation of the approximative initial classification boundary. For the increasing of classification accuracy of this boundary, the refinement is performed in the lower dimensional space. Our approach is tested on several datasets from UCI Reposi-tiory. Experimental results indicate high classification accuracy of the approach. For the modern real industrial applications such a method is especially suitable in the Cyber-Physical-System environments and provides a part of the workflow for the automated classifier design}},
  author       = {{Dörksen, Helene and Lohweg, Volker}},
  booktitle    = {{23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  keywords     = {{Task analysis, Software, Linear discriminant analysis, Dimensionality reduction, Mathematical model, Covariance matrices, Measurement}},
  location     = {{ Turin, Italy }},
  title        = {{{Linear Classification of Badly Conditioned Data. }}},
  doi          = {{10.1109/ETFA.2018.8502485}},
  year         = {{2018}},
}

@inproceedings{10668,
  abstract     = {{Digitalization has a significant impact on our working life and it allows whole industries to rethink their value chains. This paper examines how digitalization relates to complexity in work systems with respect to relevant organizational fields of work organization. 23 semi-structured interviews with experts from science and economy were conducted and analyzed. Key findings are that digitalization has far-reaching, interrelated implications for all organizational fields. Moreover, digitalization-related aspects were identified which have the potential to increase complexity in work systems.}},
  author       = {{Latos, Benedikt and Harlacher, Markus and Przybysz, Philipp M. and Mutze-Niewohner, Susanne}},
  booktitle    = {{2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)}},
  issn         = {{2157-362X}},
  keywords     = {{Complexity theory, Interviews, Organizations, Industries, Task analysis, Acceleration}},
  location     = {{Singapore}},
  publisher    = {{IEEE}},
  title        = {{{Transformation of working environments through digitalization: Exploration and systematization of complexity drivers}}},
  doi          = {{10.1109/ieem.2017.8290059}},
  year         = {{2018}},
}

@inproceedings{265,
  abstract     = {{The maintenance of a tool for injection molding or forming is usually accompanied by its disassembly and assembly. The duration of the assembly activities is often a large part of the total activity time for the maintenance of the tool. The degree of performance of the employees in the execution of these disassembly and assembly activities is often low. In addition, allowances occur (e.g. searching for work equipment). At the Industrial Engineering Lab of the Ostwestfalen-Lippe University of Applied Sciences, a prototype of an assistance system was developed to support the assembly activities in toolmaking. With the help of this system, the operator is guided step by step through the assembly process. The economic potential of the system exists in the reduction of training times, the avoidance of assembly errors and the increase of labor productivity.}},
  author       = {{Hinrichsen, Sven and Riediger, Daniel and Unrau, Alexander}},
  booktitle    = {{2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)}},
  isbn         = {{978-1-5386-0948-4 }},
  keywords     = {{injection moulding, machine tools, maintenance engineering, productivity, projection-based assistance system, injection molding tools, assembly activities, assembly process, assembly errors, tool maintenance, disassembly activities, economic potential, Industrial Engineering Lab, Ostwestfalen-Lippe University of Applied Sciences, toolmaking, Tools, Injection molding, Maintenance engineering, Usability, Task analysis, Workstations, Morphology, assembly assistance systems, assistance systems, maintenance of injection molding tools, manual assembly}},
  location     = {{Singapore}},
  number       = {{1}},
  pages        = {{1571--1575}},
  title        = {{{Development of a Projection-Based Assistance System for Maintaining Injection Molding Tools}}},
  doi          = {{https://doi.org/10.1109/IEEM.2017.8290157}},
  year         = {{2017}},
}