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  <doc>
    <id>1271</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>503</pageFirst>
    <pageLast>512</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-02-07</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A multi dimensional view of the Graves value systems model on teaching and learning leading to a students-centered learning: Graves model revisited</title>
    <abstract language="eng">A cultural change at the university eco-system is possible with diverse learning approaches in faculties. Diverse learning offers will cope with the diversity of students regarding their value systems. Currently teaching at universities is dominated by "teacher-centered teaching", also there are approaches to use different methods to accelerate and intensify the teaching and learning process. Nevertheless these approaches do often not show the desired impact with all students. This paper is offering insights how that comes using the Graves value systems model and is proposing a set of methods which fits to different value systems of students.</abstract>
    <parentTitle language="eng">IEEE Global Engineering Education Conference (EDUCON), 10-13 April 2016, Abu Dhabi, UAE</parentTitle>
    <identifier type="doi">10.1109/EDUCON.2016.7474600</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Jürgen Mottok</author>
    <author>Josef Merk</author>
    <author>Thomas Falter</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Graves Value Systems Model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Teaching methods</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Students-centered teaching</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Inside-outside classroom learning     ,</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Scaffolding</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cultural set up</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>8804</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1775</pageFirst>
    <pageLast>1784</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IATED</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">LEVERAGING FIVE QUESTIONNAIRES TO ANALYZE STUDENT LEARNING STRATEGIES AND GENERATE AI-POWERED INDIVIDUALIZED LEARNING PATHS</title>
    <abstract language="eng">Background:&#13;
The COVID-19 pandemic has significantly accelerated the shift toward online and blended learning in higher education, placing renewed emphasis on the individualization of learning content to meet diverse student needs. Even high-quality learning materials may fail to engage learners if they do not align with students’ personal preferences and learning styles. Identifying these learner preferences, therefore, emerges as a critical challenge.&#13;
&#13;
Objectives:&#13;
This paper presents ongoing work within a larger research project aimed at employing artificial intelligence to recommend optimal learning path for students in specific courses. Beyond mere optimization, the goal is to ensure the best possible fit between learning materials and individual learners.&#13;
&#13;
Sample &amp; Methods:&#13;
A total of 27 students from technical degree programs took part in this survey. All participation was voluntary, and data were handled in full compliance with GDPR regulations. Although our broader project integrates fine-grained learning analytics from Moodle, the present abstract focuses exclusively on the self-report questionnaire results. Participants completed five instruments:&#13;
1. Index of Learning Styles (ILS)&#13;
2. LIST-K (Learning and Study Strategies Inventory – Short version)&#13;
3. BFI-10 (Big Five Inventory – 10 items)&#13;
4. Custom Preferences Instrument, capturing preferences for specific learning elements (e.g. instructional videos, lecture notes, summaries) and basic demographic data&#13;
5. Motivational Value Systems Questionnaire (MVSQ), piloted last semester to assess value orientations and motivational drivers&#13;
&#13;
Results:&#13;
Preliminary analyses of the questionnaire data reveal:&#13;
- Learning Styles (ILS): The majority lean toward the visual learning type (M = 5.740, SD = 3.430).&#13;
- Learning Strategies (LIST-K): High scores on metacognitive strategies (M = 3.000; SD = 0.520) and collaboration with peers (M = 3.190; SD = 0.540).&#13;
- Preferred Learning Elements: Summaries, overviews, and self-checks are most favored.&#13;
- Value Orientations (MVSQ): Students are primarily driven by the pursuit of personal achievement (M = 4.400; SD = 11.140).&#13;
&#13;
Conclusion &amp; Significance:&#13;
By integrating these five standardized questionnaires, we gain valuable insights into student learning preferences—insights that complement our Moodle analytics in the broader project. Observed trends suggest that learning materials should be concise and designed to facilitate peer interaction and knowledge deepening. These findings will guide the refinement of our AI-driven recommendation engine, enhancing its ability to deliver personalized learning paths that boost both engagement and effectiveness.</abstract>
    <parentTitle language="eng">ICERI2025 Proceedings</parentTitle>
    <identifier type="doi">10.21125/iceri.2025.0658</identifier>
    <enrichment key="ConferenceStatement">18th annual International Conference of Education, Research and Innovation, 10.11.2025 - 11.11.2025, Seville, Spain</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Florian Hauser</author>
    <author>Susanne Staufer</author>
    <author>Simon Röhrl</author>
    <author>Vamsi Krishna Nadimpalli</author>
    <author>Timur Ezer</author>
    <author>Lisa Grabinger</author>
    <author>Jürgen Mottok</author>
    <author>Thomas Falter</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>AI in higher education</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>learning management system</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>adaptive learning</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>personalized learning paths</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>online and blended learning</value>
    </subject>
    <collection role="institutes" number="FakBW">Fakultät Business and Management</collection>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
    <collection role="DFGFachsystematik" number="2">Geistes- und Sozialwissenschaften</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Digitale Transformation</collection>
  </doc>
</export-example>
