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  <doc>
    <id>2655</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>470</pageFirst>
    <pageLast>474</pageLast>
    <pageNumber>5</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
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    <title language="eng">Investigating Factors Influencing Students’ Assessment of Conceptual Models</title>
    <parentTitle language="eng">Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering</parentTitle>
    <identifier type="isbn">9798400700446</identifier>
    <identifier type="doi">10.1145/3593434.3593960</identifier>
    <enrichment key="opus.import.data">@inproceedingsdaun_investigating_2023, address = New York, NY, USA, series = EASE ’23, title = Investigating Factors Influencing Students’ Assessment of Conceptual Models, isbn = 9798400700446, url = https://doi.org/10.1145/3593434.3593960, doi = 10.1145/3593434.3593960, abstract = This paper discusses the challenges in evaluating the quality of conceptual models in educational settings. While automated grading techniques may work for simplistic modeling tasks, realistic modeling tasks that allow for a wide variety of solutions cannot be evaluated using automated techniques. However, the traditional approach of having instructors grade the exercises may not be feasible in larger courses. To address this issue, alternative approaches, such as educating students to assess the quality of their own solutions or using calibrated peer reviews, can be used. Therefore, it is crucial to identify the quality of feedback a student can deliver on their own. As a first step, this paper reports on the results of controlled experiments with 368 participants to investigate factors that influence students’ model comprehension and to identify ways to distinguish good student assessments from bad ones., urldate = 2023-05-31, booktitle = Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, publisher = Association for Computing Machinery, author = Daun, Marian and Brings, Jennifer, month = jun, year = 2023, keywords = conceptual modeling, controlled experiment, model comprehension, student assessment, pages = 470–474,</enrichment>
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    <enrichment key="opus.import.date">2023-06-06T14:17:28+00:00</enrichment>
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    <enrichment key="opus.import.id">647f3ff8e049a4.08559578</enrichment>
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    <author>Marian Daun</author>
    <author>Jennifer Brings</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>conceptual modeling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>controlled experiment</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>model comprehension</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>student assessment</value>
    </subject>
    <collection role="Autoren" number="daun">Marian Daun</collection>
  </doc>
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