World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
7430
World Ranking
11958
National Ranking
465

Sabine Graf publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Sabine Graf sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 144 publications — 24th percentile

24% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Sabine Graf D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Sabine Graf sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Sabine Graf is affiliated with Athabasca University in Canada. Their research primarily spans the fields of Computer Science and Psychology, with notable contributions to subfields such as Computer Science Applications, Developmental and Educational Psychology, Artificial Intelligence, Education, and Cognitive Neuroscience.

Their work focuses on various topics within online learning and educational technology. Key areas of study include:

  • Online Learning and Analytics
  • Innovative Teaching and Learning Methods
  • E-Learning and Knowledge Management
  • Online and Blended Learning
  • Educational Innovations and Technology
  • Learning Styles and Cognitive Differences
  • Evolutionary Algorithms and Applications

Graf has published multiple papers across a variety of venues. Some of the recent publications include:

  • "Self-Regulated Learning in Massive Online Open Courses: A State-of-the-Art Review" (2020) in IEEE Access
  • "Evaluation of a learning analytics tool for supporting teachers in the creation and evaluation of accessible and quality open educational resources" (2020) in British Journal of Educational Technology
  • "Improving online education through automatic learning style identification using a multi-step architecture with ant colony system and artificial neural networks" (2022) in Applied Soft Computing
  • "Impact of personality traits on learners' navigational behavior patterns in an online course: a lag sequential analysis approach" (2023) in Frontiers in Psychology
  • "Self-supervised learning reduces label noise in sharp wave ripple classification" (2025) in Scientific Reports

The frequent venues where Graf publishes include:

  • IEEE Access
  • British Journal of Educational Technology
  • Applied Soft Computing
  • Frontiers in Psychology
  • Scientific Reports

Graf collaborates regularly with a set of co-authors, which include:

  • Silvia Baldiris
  • J Bernard
  • Jhoni Cerón
  • Jairo Quintero
  • Rainer Rubira García

Best Publications

  • Augmented Reality Trends in Education: A Systematic Review of Research and Applications.

    Jorge Bacca;Silvia Baldiris;Ramon Fabregat;Sabine Graf

  • An evaluation of open source e-learning platforms stressing adaptation issues

    S. Graf;B. List

  • Adaptivity in learning management systems focussing on learning styles

    Sabine Graf

  • Analysis of learners' navigational behaviour and their learning styles in an online course

    Sabine Graf;Tzu-Chien Liu;Kinshuk

  • Identifying Learning Styles in Learning Management Systems by Using Indications from Students' Behaviour

    S. Graf;Kinshuk;Tzu-Chien Liu

  • An Approach for Detecting Learning Styles in Learning Management Systems

    S. Graf;P. Kinshuk

  • In-Depth Analysis of the Felder-Silverman Learning Style Dimensions

    Sabine Graf;Silvia Rita Viola;Tommaso Leo;Kinshuk

  • A fully personalization strategy of E-learning scenarios

    Fathi Essalmi;Leila Jemni Ben Ayed;Mohamed Jemni;Kinshuk

  • Supporting Teachers in Identifying Students' Learning Styles in Learning Management Systems: An Automatic Student Modelling Approach

    Sabine Graf;Kinshuk;Tzu Chien Liu

  • Forming heterogeneous groups for intelligent collaborative learning systems with ant colony optimization

    Sabine Graf;Rahel Bekele

  • Learning styles and cognitive traits - Their relationship and its benefits in web-based educational systems

    Sabine Graf;Tzu-Chien Liu;Kinshuk;Nian-Shing Chen

  • Providing Adaptive Courses in Learning Management Systems with Respect to Learning Styles

    Sabine Graf;K. Kinshuk

  • Mobile Augmented Reality in Vocational Education and Training

    Jorge Bacca;Silvia Baldiris;Ramon Fabregat;Kinshuk

  • Coping with mismatched courses: students’ behaviour and performance in courses mismatched to their learning styles

    Kinshuk;Tzu Chien Liu;Sabine Graf

  • Learning style Identifier: Improving the precision of learning style identification through computational intelligence algorithms

    Jason Bernard;Ting-Wen Chang;Elvira Popescu;Sabine Graf

  • The relationship between learning styles and cognitive traits - Getting additional information for improving student modelling

    Sabine Graf;Taiyu Lin;Kinshuk

  • PLORS: a personalized learning object recommender system

    Hazra Imran;Mohammad Belghis-Zadeh;Ting-Wen Chang;Kinshuk

  • Advanced Adaptivity in Learning Management Systems by Considering Learning Styles

    Sabine Graf;Kinshuk

  • Generalized metrics for the analysis of E-learning personalization strategies

    Fathi Essalmi;Leila Jemni Ben Ayed;Mohamed Jemni;Sabine Graf

  • A Flexible Mechanism for Providing Adaptivity Based on Learning Styles in Learning Management Systems

    Sabine Graf;Kinshuk;Cindy Ives

Frequent Co-Authors

Kinshuk
Kinshuk University of North Texas
Nian-Shing Chen
Nian-Shing Chen National Taiwan Normal University
Edgar R. Weippl
Edgar R. Weippl University of Vienna
A Min Tjoa
A Min Tjoa TU Wien
Daniele Quercia
Daniele Quercia Nokia Bell Labs
Stephen J. H. Yang
Stephen J. H. Yang National Central University
Terry Anderson
Terry Anderson Athabasca University
Olfa Nasraoui
Olfa Nasraoui University of Louisville
Andreas Holzinger
Andreas Holzinger BOKU University
Lori Lockyer
Lori Lockyer Queensland University of Technology

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens up many complementary paths and online learning opportunities. For students seeking affordability, consider the cheapest data science degree options, which combine value with strong career prospects in one of tech’s fastest-growing specialties.

Those interested in the technical side of hardware and systems can look into electrical engineering degree online admissions. This pathway blends computing skills with engineering for expanded job opportunities.

For career changers or those eager for quick upskilling, there are certifications that pay well. These targeted credentials can lead quickly to high-demand roles, often without the time or expense of a full degree.

If you want to fast-track your qualification with advanced knowledge, explore the shortest online masters degree programs available. These allow you to earn a respected graduate degree in less time and accelerate your career growth.

Best Scientists Citing Sabine Graf

Trending Scientists

Recently Published Articles