pith. sign in

arxiv: 2601.22199 · v2 · submitted 2026-01-29 · 💻 cs.RO · cs.HC

Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines

Syed T. Mubarrat , Byung-Cheol Min , Tianyu Shao , E. Cho Smith , Bedrich Benes , Alejandra J. Magana , Christos Mousas , Dominic Kao This is my paper

Pith reviewed 2026-05-16 09:39 UTC · model grok-4.3

classification 💻 cs.RO cs.HC
keywords robotics educationgame-based learninggamificationsystematic reviewdesign guidelinescomputational thinkingpedagogylearning context
0
0 comments X

The pith

A review of 95 studies shows game-based learning suits informal robotics settings while gamification dominates formal classrooms using project-based methods.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper delivers the first PRISMA-aligned systematic review comparing game-based learning and gamification specifically in robotics education. It examines 95 studies drawn from over twelve thousand records to map how these two approaches are applied in different contexts. The analysis reveals clear couplings between the chosen method, the setting, and the teaching style, along with a widespread focus on basic tools and brief self-reported evaluations. From these patterns the authors extract a design space of best practices and outline directions for stronger future work. Readers who teach or design robotics programs can use the synthesis to match methods to their own environment and avoid common limitations.

Core claim

The review identifies three main patterns across the 95 studies: an approach-context-pedagogy coupling in which game-based learning appears more often in informal settings while gamification prevails in formal classrooms and favors project-based learning; a strong emphasis on introductory programming and modular hardware kits with limited uptake of advanced software, hardware, or immersive technologies; and short study durations that rely primarily on self-report measures.

What carries the argument

The PRISMA-aligned coding scheme that classifies each study by approach, learning context, skill level, modality, pedagogy, and outcomes.

If this is right

  • Educators should match game-based learning to informal out-of-school programs and gamification to structured classroom projects.
  • Current practice centers on introductory programming with modular kits, leaving room for wider use of advanced tools.
  • Future studies should extend beyond short time frames and incorporate objective performance measures instead of self-reports.
  • The proposed design space supplies concrete best practices and pitfalls for implementing either approach.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Hybrid designs that combine selected elements of both game-based learning and gamification could be tested for settings that sit between informal and formal contexts.
  • The identified emphasis on basic tools suggests that scaling advanced robotics platforms will require targeted training materials matched to each approach.
  • Longer-term studies could check whether the context-specific patterns persist when computational thinking gains are measured over multiple semesters.

Load-bearing premise

The 95 included studies are representative of the full field and the coding categories capture the meaningful differences without bias from the four databases or the 2014-2025 window.

What would settle it

A new search across additional databases or years that finds substantially more studies using advanced hardware or immersive technologies inside formal classrooms would alter the reported patterns of limited adoption and context coupling.

Figures

Figures reproduced from arXiv: 2601.22199 by Alejandra J. Magana, Bedrich Benes, Byung-Cheol Min, Christos Mousas, Dominic Kao, E. Cho Smith, Syed T. Mubarrat, Tianyu Shao.

Figure 1
Figure 1. Figure 1: A concept map illustrating the relationships among key concepts used for the literature review. Solid lines denote [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 4
Figure 4. Figure 4: Learning contexts utilized across different approach [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 2
Figure 2. Figure 2: Flowchart of the PRISMA-based selection process. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of the publication years of the reviewed [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 6
Figure 6. Figure 6: Left: pedagogical models used across the reviewed studies (% of studies). Right: Comparison of different pedagogical [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Levels of programming/software development and understanding of robotic systems skills taught across all reviewed [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Levels of programming/software development skills vs. understanding of robotic systems skills taught across all [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Virtual reality (VR) and haptic technology usage [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Experience level of target users across the reviewed [PITH_FULL_IMAGE:figures/full_fig_p009_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Intervention types across all reviewed studies (% of studies). [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Genre of game or gamification elements used across all reviewed studies (% of studies). [PITH_FULL_IMAGE:figures/full_fig_p010_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Genre of game or gamification elements used in different approach types across all reviewed studies (% of studies). [PITH_FULL_IMAGE:figures/full_fig_p010_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Design guidelines for game-mediated robotics learning. (a) Guidelines by approach and context, with quadrants [PITH_FULL_IMAGE:figures/full_fig_p019_14.png] view at source ↗
read the original abstract

Robotics education fosters computational thinking, creativity, and problem-solving, but remains challenging due to technical complexity. Game-based learning (GBL) and gamification offer engagement benefits, yet their comparative impact remains unclear. We present the first PRISMA-aligned systematic review and comparative synthesis of GBL and gamification in robotics education, analyzing 95 studies from 12,485 records across four databases (2014-2025). We coded each study's approach, learning context, skill level, modality, pedagogy, and outcomes (k = .918). Three patterns emerged: (1) approach-context-pedagogy coupling (GBL more prevalent in informal settings, while gamification dominated formal classrooms [p < .001] and favored project-based learning [p = .009]); (2) emphasis on introductory programming and modular kits, with limited adoption of advanced software (~17%), advanced hardware (~5%), or immersive technologies (~22%); and (3) short study horizons, relying on self-report. We propose eight research directions and a design space outlining best practices and pitfalls, offering actionable guidance for robotics education.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 4 minor

Summary. The manuscript presents the first PRISMA-aligned systematic review comparing game-based learning (GBL) and gamification in robotics education. From 12,485 records screened across four databases (2014-2025), 95 studies were included and coded on approach, context, skill level, modality, pedagogy, and outcomes (inter-rater reliability κ = .918). Three patterns are reported: (1) statistically significant approach-context-pedagogy coupling, with GBL more common in informal settings and gamification dominant in formal classrooms (p < .001) and favoring project-based learning (p = .009); (2) heavy emphasis on introductory programming and modular kits, with limited use of advanced software (~17%), hardware (~5%), or immersive technologies (~22%); and (3) predominantly short-term studies relying on self-report measures. The paper proposes eight research directions and a design space of best practices and pitfalls.

Significance. If the synthesis holds, the review supplies a much-needed comparative overview of GBL versus gamification in robotics education, an area previously lacking structured synthesis. The use of explicit PRISMA reporting, high inter-rater reliability, and statistical tests for the key associations adds methodological rigor uncommon in many educational reviews. The resulting design guidelines and research agenda are actionable for both practitioners and researchers, directly addressing gaps such as under-adoption of advanced technologies and short evaluation horizons. These contributions can help standardize and improve future work in the field.

minor comments (4)
  1. [Abstract and Section 3.1] Abstract and Section 3.1: The search window is stated as 2014-2025; clarify whether the upper bound is inclusive and provide the exact search date to support reproducibility.
  2. [Section 4] Section 4 (Results): Percentages for technology adoption (e.g., ~17% advanced software) should be accompanied by the corresponding absolute counts (n out of 95) to allow readers to assess the base rates directly.
  3. [Section 6] Section 6 (Design Guidelines): Consider adding a summary table that explicitly maps each of the eight research directions or guidelines back to the specific coding frequencies or statistical patterns reported earlier.
  4. [Figures] Figure 2 or equivalent: Ensure all axis labels, legends, and statistical annotations (including exact p-values and test names) are legible at print size.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of our manuscript and the recommendation for minor revision. The summary accurately reflects our PRISMA-aligned approach, the statistical findings on approach-context couplings, the observed limitations in technology adoption, and the proposed research directions. We appreciate the recognition of the methodological rigor and actionable contributions.

Circularity Check

0 steps flagged

No circularity: literature synthesis with external data only

full rationale

This is a PRISMA systematic review that codes and statistically summarizes 95 external studies drawn from database searches. No equations, fitted parameters, derivations, or self-citations serve as load-bearing premises for the reported patterns; the three observed associations (approach-context coupling, technology adoption rates, study duration) are computed directly from the coded attributes of the included papers. Inter-rater reliability (k=.918) and p-values are reported on the external corpus, with no reduction of any claim to the authors' own prior inputs or definitions. The review is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The synthesis rests on standard systematic review methodology and the assumption that included studies accurately reflect field practices; no free parameters or new entities are introduced.

axioms (2)
  • standard math PRISMA guidelines provide a reliable framework for conducting and reporting systematic reviews
    Invoked as the alignment standard for the review process.
  • domain assumption Cohen's kappa of 0.918 indicates sufficient coding consistency across studies
    Used to support reliability of the approach-context-pedagogy classifications.

pith-pipeline@v0.9.0 · 5527 in / 1602 out tokens · 43008 ms · 2026-05-16T09:39:17.533913+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

194 extracted references · 194 canonical work pages

  1. [1]

    Surattana Adipat, Kittisak Laksana, Kanrawee Busayanon, Alongkorn Asawa- sowan, and Boonlit Adipat. 2021. Engaging students in the learning process with game-based learning: The fundamental concepts.International Journal of Technology in Education4, 3 (2021), 542–552. Publisher: ERIC

    work page 2021

  2. [2]

    Filippo Agalbato and Daniele Loiacono. 2018. Robo 3: A Puzzle Game to Learn Coding. In2018 IEEE Games, Entertainment, Media Conference (GEM). IEEE, 359–366

    work page 2018

  3. [3]

    Louis Alfieri, Ross Higashi, Robin Shoop, and Christian D Schunn. 2015. Case studies of a robot-based game to shape interests and hone proportional reasoning skills.International Journal of STEM Education2 (2015), 1–13. Publisher: Springer

    work page 2015

  4. [4]

    Dimitris Alimisis. 2013. Educational robotics: Open questions and new chal- lenges.Themes in Science and Technology Education6, 1 (2013), 63–71. Publisher: ERIC

    work page 2013

  5. [5]

    Maximilian Altmeyer, Pascal Lessel, and Antonio Krüger. 2018. Investigating gamification for seniors aged 75+. InProceedings of the 2018 Designing Interactive Systems Conference. 453–458

    work page 2018

  6. [6]

    Rafael Alves Heinze, Regan L Mandryk, and Madison Klarkowski. 2024. Explor- ing the Role of Action Mechanics in Game-Based Stress Recovery.Proceedings of the ACM on Human-Computer Interaction8, CHI PLAY (2024), 1–32

    work page 2024

  7. [7]

    Pamela Andrade, Effie Lai-Chong Law, Juan Carlos Farah, and Denis Gillet

    work page

  8. [8]

    InProceedings of the 32nd Australian Conference on Human-Computer Interaction

    Evaluating the effects of introducing three gamification elements in STEM educational software for secondary schools. InProceedings of the 32nd Australian Conference on Human-Computer Interaction. 220–232

    work page

  9. [9]

    Julian M Angel-Fernandez and Markus Vincze. 2018. Towards a definition of educational robotics. InAustrian Robotics Workshop 2018, Vol. 37

    work page 2018

  10. [10]

    Gizem Ateş. 2022. Work in Progress: Learning Fundamental Robotics Concepts Through Games at Bachelor Level. In2022 IEEE Global Engineering Education Conference (EDUCON). IEEE, 1956–1958

    work page 2022

  11. [11]

    Soumela Atmatzidou and Stavros Demetriadis. 2016. A didactical model for educational robotics activities: A study on improving skills through strong or minimal guidance. InInternational Conference EduRobotics 2016. Springer, 58–72

    work page 2016

  12. [12]

    Serdar As,ut. 2024. Developing a Hybrid Learning Environment for Architectural Robotics. InProceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe. eCAADe, 695–704

    work page 2024

  13. [13]

    Abdul Syafiq Bahrin, Mohd Shahrizal Sunar, and Azizul Azman. 2021. Enjoyment as gamified experience for informal learning in virtual reality. InInternational Conference on Intelligent Technologies for Interactive Entertainment. Springer, 383–399

    work page 2021

  14. [14]

    Jacky Baltes, Reinhard Gerndt, Saeed Saeedvand, Soroush Sadeghnejad, Petr Čížek, and Jan Faigl. 2023. Learning Through Competitions—The FIRA Youth Mission Impossible Competition. InInternational Conference on Robotics in Education (RiE). Springer, 383–394

    work page 2023

  15. [15]

    İremsu Baş, Demir Alp, Ceren Dolu, Melis Alsan, Andy Emre Koçak, Irmak Atılgan, and Sedat Yalçın. 2023. Enhancing Prototyping Skills of K-12 Students Through Lemon: A Bio-Inspired Robotics Kit. InInternational Conference on Human-Computer Interaction. Springer, 264–270

    work page 2023

  16. [16]

    Marina Umaschi Bers, Louise Flannery, Elizabeth R Kazakoff, and Amanda Sullivan. 2014. Computational thinking and tinkering: Exploration of an early childhood robotics curriculum.Computers & education72 (2014), 145–157. Publisher: Elsevier

    work page 2014

  17. [17]

    Max V Birk, Regan L Mandryk, and Cheralyn Atkins. 2016. The motivational push of games: The interplay of intrinsic motivation and external rewards in games for training. InProceedings of the 2016 Annual Symposium on Computer- Human Interaction in Play. 291–303

    work page 2016

  18. [18]

    Mayara Bonani, Raquel Oliveira, Filipa Correia, André Rodrigues, Tiago Guer- reiro, and Ana Paiva. 2018. What my eyes can’t see, a robot can show me: Exploring the collaboration between blind people and robots. InProceedings of the 20th International ACM SIGACCESS Conference on Computers and Accessibil- ity. 15–27

    work page 2018

  19. [19]

    Elise Buckley, Joseph D Monaco, Kevin M Schultz, Robert W Chalmers, Armin Hadzic, Kechen Zhang, Grace M Hwang, and M Dwight Carr. 2022. An interdis- ciplinary approach to high school curriculum development: Swarming Powered by Neuroscience. In2022 IEEE Integrated STEM Education Conference (ISEC). IEEE, 1–8

    work page 2022

  20. [20]

    Ridho Dedy Arief Budiman, Herman Dwi Surjono, and Edi Hidayat. 2025. Gam- ification: Enhancing Constructivist Skills through Line-Following Robots.Inter- national Journal of Online & Biomedical Engineering21, 1 (2025)

    work page 2025

  21. [21]

    José M Cañas, Diego Martín-Martín, Pedro Arias, Julio Vega, David Roldán- Álvarez, Lía García-Pérez, and Jesús Fernández-Conde. 2020. Open-source drone programming course for distance engineering education.Electronics9, 12 (2020),

    work page 2020

  22. [22]

    José M Cañas, Eduardo Perdices, Lía García-Pérez, and Jesús Fernández-Conde

    work page

  23. [23]

    Publisher: MDPI

    A ROS-based open tool for intelligent robotics education.Applied Sciences 10, 21 (2020), 7419. Publisher: MDPI

    work page 2020

  24. [24]

    Tan-I Chen, Shih-Kai Lin, and Hung-Chang Chung. 2023. Gamified Educational Robots Lead an Increase in Motivation and Creativity in STEM Education. Journal of Baltic Science Education22, 3 (2023), 427–438. Publisher: Scientia Socialis Ltd. 29 K. Donelaicio Street, LT-78115 Siauliai, Republic

    work page 2023

  25. [25]

    Chien-Hsing Chou, Yu-Sheng Su, and Hui-Ju Chen. 2019. Interactive teaching aids integrating building blocks and programming logic.Journal of Internet Technology20, 6 (2019), 1709–1720

    work page 2019

  26. [26]

    Chan-Jin Chung, Shannan Palonis, Elmer Santos, Pamela Sparks, and Christo- pher Cartwright. 2021. Design, implementation, and assessment of synchronized worldwide online robotics competitions for engineering and computing educa- tion. In2021 IEEE Frontiers in Education Conference (FIE). IEEE, 1–8

    work page 2021

  27. [27]

    Douglas B Clark, Emily E Tanner-Smith, and Stephen S Killingsworth. 2016. Digital games, design, and learning: A systematic review and meta-analysis. Review of educational research86, 1 (2016), 79–122. Publisher: Sage Publications Sage CA: Los Angeles, CA

    work page 2016

  28. [28]

    Jacob Cohen. 1960. A coefficient of agreement for nominal scales.Educational and psychological measurement20, 1 (1960), 37–46

    work page 1960

  29. [29]

    Joana M Costa. 2023. Using game concepts to improve Programming Learning: A multi-level meta-analysis.Computer Applications in Engineering Education31, 4 (2023), 1098–1110. Publisher: Wiley Online Library

    work page 2023

  30. [30]

    Valter Costa, Tiago Cunha, Miguel Oliveira, Heber Sobreira, and Armando Sousa. 2015. Robotics: using a competition mindset as a tool for learning ROS. InRobot 2015: Second Iberian Robotics Conference: Advances in Robotics, Volume

    work page 2015

  31. [31]

    Raúl Crespo, René García, and Samuel Quiroz. 2015. Virtual reality simulator for robotics learning. In2015 International Conference on interactive collaborative and blended learning (ICBL). IEEE, 61–65

    work page 2015

  32. [32]

    Raúl Crespo, René García, and Samuel Quiroz. 2015. Virtual reality application for simulation and off-line programming of the mitsubishi movemaster RV-M1 robot integrated with the oculus rift to improve students training.Procedia Computer Science75 (2015), 107–112. Publisher: Elsevier

    work page 2015

  33. [33]

    Milan Dahal, Lydia Kresin, and Chris Rogers. 2023. Introductory activities for teaching robotics with smartmotors. InInternational Conference on Robotics in Education (RiE). Springer, 229–241

    work page 2023

  34. [34]

    Thabatta Moreira Alves De Araújo, Anderson Ribeiro De Oliveira Santos Silva, Alisson Marques Da Silva, Eduardo Habib Bechelane Maia, and Michel Pires Da Silva. 2023. Prorobot: An experience report of an introduction to program- ming and robotics course. In2023 Latin American Robotics Symposium (LARS), 2023 Brazilian Symposium on Robotics (SBR), and 2023 W...

    work page 2023

  35. [35]

    Bruno Henrique De Paula, Andrew Burn, Richard Noss, and José Armando Valente. 2018. Playing Beowulf: Bridging computational thinking, arts and liter- ature through game-making.International journal of child-computer interaction 16 (2018), 39–46. Publisher: Elsevier

    work page 2018

  36. [36]

    Chris Dede. 2009. Immersive interfaces for engagement and learning.science 323, 5910 (2009), 66–69. Publisher: American Association for the Advancement of Science

    work page 2009

  37. [37]

    gamification

    Sebastian Deterding, Dan Dixon, Rilla Khaled, and Lennart Nacke. 2011. From game design elements to gamefulness: defining" gamification". InProceedings of the 15th international academic MindTrek conference: Envisioning future media environments. 9–15

    work page 2011

  38. [38]

    D Dewantara, M Misbah, S Haryandi, and S Mahtari. 2021. Game-based learning for the mastery of HOTS in prospective physics teachers in digital electron- ics courses. InJournal of Physics: Conference Series, Vol. 1869. IOP Publishing, 012153

    work page 2021

  39. [39]

    2000.Robots for kids: exploring new tech- nologies for learning

    Allison Druin and James A Hendler. 2000.Robots for kids: exploring new tech- nologies for learning. Morgan Kaufmann

    work page 2000

  40. [40]

    Dalila Alves Durães. 2015. Gaming and robotics to transforming learning. In Methodologies and intelligent systems for technology enhanced learning. Springer, 51–56

    work page 2015

  41. [41]

    Belkis Díaz-Lauzurica and David Moreno-Salinas. 2019. Computational thinking and robotics: A teaching experience in compulsory secondary education with students with high degree of apathy and demotivation.Sustainability11, 18 (2019), 5109. Publisher: MDPI

    work page 2019

  42. [42]

    Amy Eguchi. 2014. Robotics as a learning tool for educational transformation. In Proceeding of 4th international workshop teaching robotics, teaching with robotics & 5th international conference robotics in education Padova (Italy), Vol. 1. 1–6

    work page 2014

  43. [43]

    Fivia Eliza, Muhammad Hakiki, Mughnil Muhtaj, Della Asmaria Putri, Yayuk Hidayah, Ade Fricticarani, Jamal Fakhri, Iqbal Arpannudin, Desty Endrawati Subroto, and Kelik Sussolaikah. 2025. Game-D: Development of an educational game using a line follower robot on straight motion material.International Journal of Information and Education Technology15, 1 (2025)

    work page 2025

  44. [44]

    Francisco J Estrada. 2017. Practical robotics in computer science using the LEGO NXT: An experience report. InProceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education. 329–334

    work page 2017

  45. [45]

    Salomi Evripidou, Kyriakoula Georgiou, Lefteris Doitsidis, Angelos A Amana- tiadis, Zinon Zinonos, and Savvas A Chatzichristofis. 2020. Educational robotics: Platforms, competitions and expected learning outcomes.IEEE access8 (2020), Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines CHI ’26, April 13–17, 202...

    work page 2020

  46. [46]

    Chiara Fante, Fabrizio Ravicchio, and Flavio Manganello. 2024. Navigating the Evolution of Game-Based Educational Approaches in Secondary STEM Education: A Decade of Innovations and Challenges.Education Sciences14, 6 (2024), 662. Publisher: MDPI

    work page 2024

  47. [47]

    Raúl Fernández-Ruiz, Daniel Palacios-Alonso, José María Cañas-Plaza, and David Roldán-Álvarez. 2022. Automatic competitions in the unibotics open online robot programming web. InIberian Robotics conference. Springer, 463–474

    work page 2022

  48. [48]

    Guo Freeman, Dane Acena, Nathan J McNeese, and Kelsea Schulenberg. 2022. Working together apart through embodiment: Engaging in everyday collabo- rative activities in social Virtual Reality.Proceedings of the ACM on Human- Computer Interaction6, GROUP (2022), 1–25

    work page 2022

  49. [49]

    Guo Freeman and Divine Maloney. 2021. Body, avatar, and me: The presentation and perception of self in social virtual reality.Proceedings of the ACM on human- computer interaction4, CSCW3 (2021), 1–27

    work page 2021

  50. [50]

    Maximilian Achim Friehs, Martin Dechant, Sarah Vedress, Christian Frings, and Regan Lee Mandryk. 2020. Effective gamification of the stop-signal task: two controlled laboratory experiments.JMIR Serious Games8, 3 (2020), e17810

    work page 2020

  51. [51]

    Lorella Gabriele, Davide Marocco, Francesca Bertacchini, Pietro Pantano, and Eleonora Bilotta. 2017. An educational robotics lab to investigate cognitive strategies and to foster learning in an arts and humanities course degree.Inter- national Journal of Online Engineering13, 4 (2017)

    work page 2017

  52. [52]

    William W Gaver. 1991. Technology affordances. InProceedings of the SIGCHI conference on Human factors in computing systems. 79–84

    work page 1991

  53. [53]

    Mohamed Gharib, Tala Katbeh, G Benjamin Cieslinski, and Brady Creel. 2021. A Novel Trilogy of e-STEM Programs. InASME International Mechanical Engi- neering Congress and Exposition, Vol. 85659. American Society of Mechanical Engineers, V009T09A041

    work page 2021

  54. [54]

    Christian Giang, Morgane Chevalier, Lucio Negrini, Ran Peleg, Evgeniia Bonnet, Alberto Piatti, and Francesco Mondada. 2018. Exploring escape games as a teaching tool in educational robotics. InInternational Conference EduRobotics

    work page 2018

  55. [55]

    Barry Golding, Mike Brown, and Annette Foley. 2009. Informal learning: A dis- cussion around defining and researching its breadth and importance.Australian Journal of Adult Learning49, 1 (2009), 34–56

    work page 2009

  56. [56]

    Brianna L Grant, Paul C Yielder, Tracey A Patrick, Bill Kapralos, Michael Williams-Bell, and Bernadette A Murphy. 2019. Audiohaptic feedback enhances motor performance in a low-fidelity simulated drilling task.Brain sciences10, 1 (2019), 21. Publisher: MDPI

    work page 2019

  57. [57]

    Heiko Hamann, Carlo Pinciroli, and Sebastian Von Mammen. 2018. A gamifica- tion concept for teaching swarm robotics. In2018 12th European Workshop on Microelectronics Education (EWME). IEEE, 83–88

    work page 2018

  58. [58]

    Robin Han, Dominik Auer, Sarah Edenhofer, and Sebastian von Mammen. 2016. SkyNetz: A Playful Experiential Robotics Simulator. In2016 8th International Conference on Games and Virtual Worlds for Serious Applications (VS-GAMES). IEEE, 1–4

    work page 2016

  59. [59]

    Carlo Harvey, Elmedin Selmanović, Jake O’Connor, and Malek Chahin. 2021. A comparison between expert and beginner learning for motor skill develop- ment in a virtual reality serious game.The Visual Computer37, 1 (2021), 3–17. Publisher: Springer

    work page 2021

  60. [60]

    Katriina Heljakka, Pirita Ihamäki, Pauliina Tuomi, and Petri Saarikoski. 2019. Gamified coding: Toy robots and playful learning in early education. In2019 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 800–805

    work page 2019

  61. [61]

    Nico Hempe and Jürgen Rossmann. 2015. The eRobotics Approach as a Uni- fying Concept for eLearning and eSystems Engineering. In2015 International Conference on Developments of E-Systems Engineering (DeSE). IEEE, 293–299

    work page 2015

  62. [62]

    Ross Higashi, Erik Harpstead, Jaemarie Solyst, Jonaya Kemper, Judith Odili Uchidiuno, and Jessica Hammer. 2021. The design of co-robotic games for computer science education. InExtended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play. 111–116

    work page 2021

  63. [63]

    Quan Nha Hong, Pierre Pluye, Sergi Fàbregues, Gillian Bartlett, Felicity Board- man, Margaret Cargo, Pierre Dagenais, Marie-Pierre Gagnon, Frances Griffiths, Belinda Nicolau, et al. 2018. Mixed methods appraisal tool (MMAT), version 2018.Registration of copyright1148552, 10 (2018), 1–7

    work page 2018

  64. [64]

    J Hrbacek and L Stuchlikova. 2018. Step by Step to the World of Science and Technology. In2018 16th International Conference on Emerging eLearning Technologies and Applications (ICETA). IEEE, 197–204

    work page 2018

  65. [65]

    Ting-Chia Hsu, Shao-Chen Chang, and Yu-Ting Hung. 2018. How to learn and how to teach computational thinking: Suggestions based on a review of the literature.Computers & Education126 (2018), 296–310. Publisher: Elsevier

    work page 2018

  66. [66]

    Shu-Hsien HUANG, Chia-Chun TSENG, and SHIH Ju-Ling. 2017. The design and evaluation of a STEM interdisciplinary game-based learning about the great voyage. InInternational Conference on Computers in Education

    work page 2017

  67. [67]

    Suprabha Jadhav, Kalind Karia, Prasad Trimukhe, Sourav Jena, and Kavi Arya

    work page

  68. [68]

    In2021 IEEE Global Engineering Education Conference (EDUCON)

    PBL approach in online robotics competition in resource-poor environ- ments: maze solver robot. In2021 IEEE Global Engineering Education Conference (EDUCON). IEEE, 492–498

    work page

  69. [69]

    Georg Jäggle, Richard Balogh, and Markus Vincze. 2023. The Effectiveness of Ed- ucational Robotics Simulations in Enhancing Student Learning. InInternational Conference on Robotics in Education (RiE). Springer, 325–334

    work page 2023

  70. [70]

    Nizwardi Jalinus, Rahmat Azis Nabawi, and Aznil Mardin. 2017. The seven steps of project based learning model to enhance productive competences of vocational students. InInternational Conference on Technology and Vocational Teachers (ICTVT 2017). Atlantis Press, 251–256

    work page 2017

  71. [71]

    Computer Science in Mechanical Engi- neering

    Daniela Janßen, Daniel Schilberg, Anja Richert, and Sabina Jeschke. 2016. Pump it up!–An Online Game in the Lecture “Computer Science in Mechanical Engi- neering”.Automation, Communication and Cybernetics in Science and Engineering 2015/2016(2016), 309–315. Publisher: Springer

    work page 2016

  72. [72]

    Martin Johnson and Dominika Majewska. 2022. Formal, Non-Formal, and Informal Learning: What Are They, and How Can We Research Them? Research Report.Cambridge University Press & Assessment(2022). Publisher: ERIC

    work page 2022

  73. [73]

    Michail Kalogiannakis, Effransia Tzagaraki, Stamatios Papadakis, et al. 2021. A systematic review of the use of BBC micro: bit in primary school. InConference Proceedings. New Perspectives in Science Education 2021

    work page 2021

  74. [74]

    Dominic Kao, Christos Mousas, Alejandra J Magana, D Fox Harrell, Rabindra Ratan, Edward F Melcer, Brett Sherrick, Paul Parsons, and Dmitri A Gusev

    work page

  75. [75]

    VR: A programming game in virtual reality.arXiv preprint arXiv:2007.04495(2020)

    Hack. VR: A programming game in virtual reality.arXiv preprint arXiv:2007.04495(2020)

    work page arXiv 2007

  76. [76]

    Zoi Karageorgiou, Eirini Mavrommati, and Panagiotis Fotaris. 2019. Escape room design as a game-based learning process for STEAM education. InECGBL 2019 13th European Conference on Game-Based Learning. Academic Conferences and publishing limited, 378

    work page 2019

  77. [77]

    Sukran Karaosmanoglu, Sebastian Cmentowski, Lennart E Nacke, and Frank Steinicke. 2024. Born to run, programmed to play: Mapping the extended reality exergames landscape. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 1–28

    work page 2024

  78. [78]

    Nicos Kasenides, Andriani Piki, and Nearchos Paspallis. 2024. The Edifying Im- pact of Blending Game-Based Learning with Educational Robotics: A Systematic Review of Empirical Evidence. InInternational Conference on Human-Computer Interaction. Springer, 97–115

    work page 2024

  79. [79]

    Saail Kashinath Narvekar, Rucmenya Bessariya, Arjun Sadananda, and Kavi Arya. 2020. Learn, build and compete: An aquatic robot-fish challenge. In Proceedings of the 2020 3rd International Conference on Education Technology Management. 60–65

    work page 2020

  80. [80]

    Vinayak Khade, Nafiseh Masoudi, Dane Acena, Guo Freeman, Rahul Rai, David Gorsich, Denise Rizzo, and Matt Castanier. 2022. Requirements elicitation: Impacts of gamification on variety, novelty, and completeness. InASME Inter- national Mechanical Engineering Congress and Exposition, Vol. 86663. American Society of Mechanical Engineers, V004T06A019

    work page 2022

Showing first 80 references.