---
_id: '12992'
abstract:
- lang: eng
  text: In today's technology-driven world, the need for interdisciplinary skills
    is increasing. This has become challenging in tertiary education to provide students
    with applicable knowledge of various fields. Anderson's Adaptive Control of Thought
    (ACT) theory suggests that universities have traditionally focused on imparting
    declarative knowledge, which involves memorization of facts and concepts. However,
    imparting the ability to apply such knowledge on individual students and create
    procedural knowledge is the challenge. This includes teachers dealing with a diverse
    range of student abilities, particularly at university-level where they teach
    the same course content to students with different levels of prior knowledge and,
    given the structure of modern education systems, the resources required to monitor
    and provide feedback for a number of decisions and attempts independently performed
    by the students. Intelligent Tutoring Systems (ITS) have proven to be effective
    in addressing the aforementioned challenges by creating personalized learning
    environments that provide instant feedback, adapt to individual student needs,
    and promote the development of procedural knowledge. In the field of automation
    education at the university level, we are creating a 3D artificial intelligence
    (AI)-based ITS software named KIAAA (An AI Assistant for teaching in the field
    of automation), specifically designed to teach computer programming to students.
    KIAAA aims to assist students in transitioning from their abilities to procedural
    aptitude by providing personalized learning scenarios that allow them to apply
    their knowledge and receive immediate feedback. Our approach is based mainly on
    the pedagogical model of ITS, which focuses on creating a supportive and inclusive
    learning environment that promotes success for all students, regardless of their
    initial level of knowledge. One of the key aspects of our approach is the utilization
    of personalized learning. We propose a scheme that, subsequent to evaluate student's
    initial levels of procedural knowledge, creates 3D learning environments tailored
    to each individual student. By analyzing the solutions proposed by the students,
    we select the difficulty level of subsequent tasks. This approach takes into consideration
    student's discrete competence throughout the learning process, enabling them to
    progress on their prior knowledge. Additionally, the software provides customized
    feedback to each student on their performance, helping students identify areas
    that require improvement. Concepts for and implementations of ITS for a variety
    of fields, including introductory programming classes, have evolved for a long
    time. Our main contribution lies in presenting an end to end solution for ITS
    focused on teaching programming for automation students with realistically 3D
    simulated factory environments. While we strongly believe to have created a pedagogically
    sound, integrated intelligent teaching system for assisting programming classes
    in tertiary automation education, a robust user study for methodically evaluating
    our concept and implementation is still to be performed. Thus, we limit ourselves
    to presenting the underlying didactic concepts of KIAAA as a work in progress
    paper with a comprehensive evaluation to follow at a later date.
author:
- first_name: Asmar
  full_name: Ali, Asmar
  id: '78685'
  last_name: Ali
- first_name: Andreas
  full_name: Deuter, Andreas
  id: '62088'
  last_name: Deuter
  orcid: 0000-0002-6529-6215
- first_name: Leon
  full_name: Wehmeier, Leon
  id: '81257'
  last_name: Wehmeier
citation:
  ama: 'Ali A, Deuter A, Wehmeier L. <i>Personalized Learning in Automation: A 3D
    AI-Based Approach</i>. (IEEE ASEE Frontiers in Education Conference, Institute
    of Electrical and Electronics Engineers, American Society for Engineering Education,
    eds.). IEEE; 2024. doi:<a href="https://doi.org/10.1109/fie58773.2023.10343228">10.1109/fie58773.2023.10343228</a>'
  apa: 'Ali, A., Deuter, A., &#38; Wehmeier, L. (2024). Personalized Learning in Automation:
    A 3D AI-Based Approach. In IEEE ASEE Frontiers in Education Conference, Institute
    of Electrical and Electronics Engineers, &#38; American Society for Engineering
    Education (Eds.), <i>FIE 2023 : College Station, TX, USA, October 18-21, 2023 :
    conference proceedings  / 2023 IEEE Frontiers in Education Conference (FIE)</i>.
    IEEE. <a href="https://doi.org/10.1109/fie58773.2023.10343228">https://doi.org/10.1109/fie58773.2023.10343228</a>'
  bjps: '<b>Ali A, Deuter A and Wehmeier L</b> (2024) <i>Personalized Learning in
    Automation: A 3D AI-Based Approach</i>, IEEE ASEE Frontiers in Education Conference,
    Institute of Electrical and Electronics Engineers, and American Society for Engineering
    Education (eds). [Piscataway, NJ]: IEEE.'
  chicago: 'Ali, Asmar, Andreas Deuter, and Leon Wehmeier. <i>Personalized Learning
    in Automation: A 3D AI-Based Approach</i>. Edited by IEEE ASEE Frontiers in Education
    Conference, Institute of Electrical and Electronics Engineers, and American Society
    for Engineering Education. <i>FIE 2023 : College Station, TX, USA, October 18-21,
    2023 : Conference Proceedings  / 2023 IEEE Frontiers in Education Conference (FIE)</i>.
    [Piscataway, NJ]: IEEE, 2024. <a href="https://doi.org/10.1109/fie58773.2023.10343228">https://doi.org/10.1109/fie58773.2023.10343228</a>.'
  chicago-de: 'Ali, Asmar, Andreas Deuter und Leon Wehmeier. 2024. <i>Personalized
    Learning in Automation: A 3D AI-Based Approach</i>. Hg. von IEEE ASEE Frontiers
    in Education Conference, Institute of Electrical and Electronics Engineers, und
    American Society for Engineering Education. <i>FIE 2023 : College Station, TX,
    USA, October 18-21, 2023 : conference proceedings  / 2023 IEEE Frontiers in Education
    Conference (FIE)</i>. [Piscataway, NJ]: IEEE. doi:<a href="https://doi.org/10.1109/fie58773.2023.10343228">10.1109/fie58773.2023.10343228</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Ali, Asmar</span> ; <span style="font-variant:small-caps;">Deuter,
    Andreas</span> ; <span style="font-variant:small-caps;">Wehmeier, Leon</span>
    ; <span style="font-variant:small-caps;">IEEE ASEE Frontiers in Education Conference</span>
    ; <span style="font-variant:small-caps;">Institute of Electrical and Electronics
    Engineers</span> ; <span style="font-variant:small-caps;">American Society for
    Engineering Education</span> (Hrsg.): <i>Personalized Learning in Automation:
    A 3D AI-Based Approach</i>. [Piscataway, NJ] : IEEE, 2024'
  havard: 'A. Ali, A. Deuter, L. Wehmeier, Personalized Learning in Automation: A
    3D AI-Based Approach, IEEE, [Piscataway, NJ], 2024.'
  ieee: 'A. Ali, A. Deuter, and L. Wehmeier, <i>Personalized Learning in Automation:
    A 3D AI-Based Approach</i>. [Piscataway, NJ]: IEEE, 2024. doi: <a href="https://doi.org/10.1109/fie58773.2023.10343228">10.1109/fie58773.2023.10343228</a>.'
  mla: 'Ali, Asmar, et al. “Personalized Learning in Automation: A 3D AI-Based Approach.”
    <i>FIE 2023 : College Station, TX, USA, October 18-21, 2023 : Conference Proceedings 
    / 2023 IEEE Frontiers in Education Conference (FIE)</i>, edited by IEEE ASEE Frontiers
    in Education Conference et al., IEEE, 2024, <a href="https://doi.org/10.1109/fie58773.2023.10343228">https://doi.org/10.1109/fie58773.2023.10343228</a>.'
  short: 'A. Ali, A. Deuter, L. Wehmeier, Personalized Learning in Automation: A 3D
    AI-Based Approach, IEEE, [Piscataway, NJ], 2024.'
  ufg: '<b>Ali, Asmar/Deuter, Andreas/Wehmeier, Leon</b>: Personalized Learning in
    Automation: A 3D AI-Based Approach, hg. von IEEE ASEE Frontiers in Education Conference/Institute
    of Electrical and Electronics Engineers, American Society for Engineering Education,
    [Piscataway, NJ] 2024.'
  van: 'Ali A, Deuter A, Wehmeier L. Personalized Learning in Automation: A 3D AI-Based
    Approach. IEEE ASEE Frontiers in Education Conference, Institute of Electrical
    and Electronics Engineers, American Society for Engineering Education, editors.
    FIE 2023 : College Station, TX, USA, October 18-21, 2023 : conference proceedings 
    / 2023 IEEE Frontiers in Education Conference (FIE). [Piscataway, NJ]: IEEE; 2024.'
conference:
  end_date: 2023-10-21
  location: Texas
  name: 2023 IEEE Frontiers in Education Conference (FIE)
  start_date: 2023-10-18
corporate_editor:
- IEEE ASEE Frontiers in Education Conference
- Institute of Electrical and Electronics Engineers
- American Society for Engineering Education
date_created: 2025-06-18T12:57:37Z
date_updated: 2025-06-18T13:01:55Z
department:
- _id: DEP7022
- _id: DEP1306
- _id: DEP7001
doi: 10.1109/fie58773.2023.10343228
language:
- iso: eng
place: '[Piscataway, NJ]'
publication: 'FIE 2023 : College Station, TX, USA, October 18-21, 2023 : conference
  proceedings  / 2023 IEEE Frontiers in Education Conference (FIE)'
publication_identifier:
  eisbn:
  - 979-8-3503-3642-9
  isbn:
  - 979-8-3503-3643-6
publication_status: published
publisher: IEEE
status: public
title: 'Personalized Learning in Automation: A 3D AI-Based Approach'
type: conference_editor_article
user_id: '83781'
year: '2024'
...
---
_id: '12993'
abstract:
- lang: eng
  text: 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:
- first_name: Leon
  full_name: Wehmeier, Leon
  id: '81257'
  last_name: Wehmeier
- first_name: Sebastian
  full_name: Eilermann, Sebastian
  last_name: Eilermann
- first_name: Oliver
  full_name: Niggemann, Oliver
  id: '10876'
  last_name: Niggemann
- first_name: Andreas
  full_name: Deuter, Andreas
  id: '62088'
  last_name: Deuter
  orcid: 0000-0002-6529-6215
citation:
  ama: Wehmeier L, Eilermann S, Niggemann O, Deuter A. <i>Task-Fidelity Assessment
    for Programming Tasks Using Semantic Code Analysis</i>. (IEEE ASEE Frontiers in
    Education Conference, Institute of Electrical and Electronics Engineers, American
    Society for Engineering Education, eds.). IEEE; 2024. doi:<a href="https://doi.org/10.1109/fie58773.2023.10342916">10.1109/fie58773.2023.10342916</a>
  apa: 'Wehmeier, L., Eilermann, S., Niggemann, O., &#38; Deuter, A. (2024). Task-fidelity
    Assessment for Programming Tasks Using Semantic Code Analysis. In IEEE ASEE Frontiers
    in Education Conference, Institute of Electrical and Electronics Engineers, &#38;
    American Society for Engineering Education (Eds.), <i>FIE 2023 : College Station,
    TX, USA, October 18-21, 2023 : conference proceedings  / 2023 IEEE Frontiers in
    Education Conference (FIE)</i>. IEEE. <a href="https://doi.org/10.1109/fie58773.2023.10342916">https://doi.org/10.1109/fie58773.2023.10342916</a>'
  bjps: '<b>Wehmeier L <i>et al.</i></b> (2024) <i>Task-Fidelity Assessment for Programming
    Tasks Using Semantic Code Analysis</i>, IEEE ASEE Frontiers in Education Conference,
    Institute of Electrical and Electronics Engineers, and American Society for Engineering
    Education (eds). [Piscataway, NJ]: IEEE.'
  chicago: 'Wehmeier, Leon, Sebastian Eilermann, Oliver Niggemann, and Andreas Deuter.
    <i>Task-Fidelity Assessment for Programming Tasks Using Semantic Code Analysis</i>.
    Edited by IEEE ASEE Frontiers in Education Conference, Institute of Electrical
    and Electronics Engineers, and American Society for Engineering Education. <i>FIE
    2023 : College Station, TX, USA, October 18-21, 2023 : Conference Proceedings 
    / 2023 IEEE Frontiers in Education Conference (FIE)</i>. [Piscataway, NJ]: IEEE,
    2024. <a href="https://doi.org/10.1109/fie58773.2023.10342916">https://doi.org/10.1109/fie58773.2023.10342916</a>.'
  chicago-de: 'Wehmeier, Leon, Sebastian Eilermann, Oliver Niggemann und Andreas Deuter.
    2024. <i>Task-fidelity Assessment for Programming Tasks Using Semantic Code Analysis</i>.
    Hg. von IEEE ASEE Frontiers in Education Conference, Institute of Electrical and
    Electronics Engineers, und American Society for Engineering Education. <i>FIE
    2023 : College Station, TX, USA, October 18-21, 2023 : conference proceedings 
    / 2023 IEEE Frontiers in Education Conference (FIE)</i>. [Piscataway, NJ]: IEEE.
    doi:<a href="https://doi.org/10.1109/fie58773.2023.10342916">10.1109/fie58773.2023.10342916</a>,
    .'
  din1505-2-1: '<span style="font-variant:small-caps;">Wehmeier, Leon</span> ; <span
    style="font-variant:small-caps;">Eilermann, Sebastian</span> ; <span style="font-variant:small-caps;">Niggemann,
    Oliver</span> ; <span style="font-variant:small-caps;">Deuter, Andreas</span>
    ; <span style="font-variant:small-caps;">IEEE ASEE Frontiers in Education Conference</span>
    ; <span style="font-variant:small-caps;">Institute of Electrical and Electronics
    Engineers</span> ; <span style="font-variant:small-caps;">American Society for
    Engineering Education</span> (Hrsg.): <i>Task-fidelity Assessment for Programming
    Tasks Using Semantic Code Analysis</i>. [Piscataway, NJ] : IEEE, 2024'
  havard: L. Wehmeier, S. Eilermann, O. Niggemann, A. Deuter, Task-fidelity Assessment
    for Programming Tasks Using Semantic Code Analysis, IEEE, [Piscataway, NJ], 2024.
  ieee: 'L. Wehmeier, S. Eilermann, O. Niggemann, and A. Deuter, <i>Task-fidelity
    Assessment for Programming Tasks Using Semantic Code Analysis</i>. [Piscataway,
    NJ]: IEEE, 2024. doi: <a href="https://doi.org/10.1109/fie58773.2023.10342916">10.1109/fie58773.2023.10342916</a>.'
  mla: 'Wehmeier, Leon, et al. “Task-Fidelity Assessment for Programming Tasks Using
    Semantic Code Analysis.” <i>FIE 2023 : College Station, TX, USA, October 18-21,
    2023 : Conference Proceedings  / 2023 IEEE Frontiers in Education Conference (FIE)</i>,
    edited by IEEE ASEE Frontiers in Education Conference et al., IEEE, 2024, <a href="https://doi.org/10.1109/fie58773.2023.10342916">https://doi.org/10.1109/fie58773.2023.10342916</a>.'
  short: L. Wehmeier, S. Eilermann, O. Niggemann, A. Deuter, Task-Fidelity Assessment
    for Programming Tasks Using Semantic Code Analysis, IEEE, [Piscataway, NJ], 2024.
  ufg: '<b>Wehmeier, Leon u. a.</b>: Task-fidelity Assessment for Programming Tasks
    Using Semantic Code Analysis, hg. von IEEE ASEE Frontiers in Education Conference/Institute
    of Electrical and Electronics Engineers, American Society for Engineering Education,
    [Piscataway, NJ] 2024.'
  van: 'Wehmeier L, Eilermann S, Niggemann O, Deuter A. Task-fidelity Assessment for
    Programming Tasks Using Semantic Code Analysis. IEEE ASEE Frontiers in Education
    Conference, Institute of Electrical and Electronics Engineers, American Society
    for Engineering Education, editors. FIE 2023 : College Station, TX, USA, October
    18-21, 2023 : conference proceedings  / 2023 IEEE Frontiers in Education Conference
    (FIE). [Piscataway, NJ]: IEEE; 2024.'
conference:
  end_date: 2023-10-21
  location: Texas
  name: 2023 IEEE Frontiers in Education Conference (FIE)
  start_date: 2023-10-18
corporate_editor:
- IEEE ASEE Frontiers in Education Conference
- Institute of Electrical and Electronics Engineers
- American Society for Engineering Education
date_created: 2025-06-18T13:05:11Z
date_updated: 2025-06-18T13:23:56Z
department:
- _id: DEP7022
- _id: DEP1306
- _id: DEP7001
doi: 10.1109/fie58773.2023.10342916
keyword:
- Codes
- Electronic learning
- Soft sensors
- Semantics
- Education
- Syntactics
- Task analysis
language:
- iso: eng
place: '[Piscataway, NJ]'
publication: 'FIE 2023 : College Station, TX, USA, October 18-21, 2023 : conference
  proceedings  / 2023 IEEE Frontiers in Education Conference (FIE)'
publication_identifier:
  eisbn:
  - 979-8-3503-3642-9
  isbn:
  - 979-8-3503-3643-6
publication_status: published
publisher: IEEE
status: public
title: Task-fidelity Assessment for Programming Tasks Using Semantic Code Analysis
type: conference_editor_article
user_id: '83781'
year: '2024'
...