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An Educational 3D Maze Game for Bulgarian Orthodox Iconography


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Thanks to their specific characteristics, educational video games make game-based learning more effective than traditional teaching approaches with regard of students’ motivation, engagement, and learning outcomes. This demonstrator presents an educational 3D maze video game introducing Bulgarian Orthodox iconography. The structure and learning content of the maze game are defined declaratively by applying several contemporary teaching methods and learning scenarios for Orthodox iconographic art study. Next, the game is generated by means of a special software platform for creation of customizable desktop video maze games for education. The demonstrator outlines both the game design and development processes and provides a tour through the generated educational maze rooms and tunnels. It shows how the game can be adapted according player’s achievements, emotions and performance.

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An Educational 3D Maze Game for Bulgarian Orthodox Iconography

  1. 1. AN EDUCATIONAL 3D MAZE GAME FOR BULGARIAN ORTHODOX ICONOGRAPHY Boyan Bontchev Dep. of Software Technologies, Fac. of Math. and Informatics, Sofia Univ., Bulgaria DIPP’2016September 28, Veliko Tarnovo, Bulgaria 1
  2. 2. AGENDA  Introduction  The ADAPTIMES project  Player-centric modeling of adaptation  Measurement of player´s characteristics  Generation of educational maze game  A maze game for Bulgarian Orthodox Iconography  Video demonstration  Conclusions September 28, Veliko Tarnovo, Bulgaria 2DIPP’2016
  3. 3. PLAYER-CENTRIC ADAPTATION Player-centric adaptation in video games needs to answer some questions:  Who – player character structural and behavioral changes => requires player modelling  How – how adaptation will be realized => requires adaptive loop design  What – which game features can be adapted => requires adaptive gameplay design  Why – advantages of game adaptation => requires analysis of outcomes in playability and learning results DIPP’2016 September 28, Veliko3
  4. 4. THE ADAPTIMES FP7 PROJECT  ADAPTIve player-centric serious video gaMES  ADAPTIMES aims at investigating how  cognitive abilities (the cognitive part),  psycho-emotional status (the affective part) and  playing style (the conative part – that of “desire, volition, and striving”) can be used in a holistic approach for efficient and effective player-centric adaptation DIPP’2016 September 28, Veliko4
  5. 5. ADAPTATION MODEL DIPP’2016 September 28, Veliko5 Game dynamics Player Player style metrics Playing styles Performance and efficiency (abilities, skills and knowledge) Emotions (flow, immersion & intrinsic motivation)
  6. 6. FLOW MENTAL STATE AS FUNCTION OF CHALLENGE AND PLAYER’S ABILITY/SKILLS (CSIKSZENTMIHALYI, 1997) DIPP’2016 September 28, Veliko6 LowChallenge/ComplexityHigh Individual average Anxiety Arousal Flow Worry Apathy Boredom Relaxation Low Skills High Control
  7. 7. MEASURING PROCESS  Measuring player’s performance – by tracking player’s results, incl. time, task difficulties and efficiency  Measuring playing styles – based on tracking specific gaming metrics describing player’s style of playing  Measuring player’s affect (emotions and arousal):  By means of analysis of psychophysiology signals (arousal)  By means of visual expressions analysis (emotional states) DIPP’2016 September 28, Veliko7
  8. 8. AFFECT INFERRED BY PSYCHOPHYSIOLOGY SIGNALS  EDA (Electro-Dermal Activity)  Custom EDA meter – custom hardware device and software (H2020 RAGE asset developed by SU) DIPP’2016 September 28, Veliko8
  9. 9. EMOTIONS INFERRED BY VISUAL EXPRESSIONS ANALYSIS  Custom emotion recognizer using Affectiva SDK  Recognizes: sadness, disgust, anger, surprise, fear, joy; engagement, attention (fixation), eye closures (saccades) DIPP’2016 September 28, Veliko9
  10. 10. WORKFLOW OF EMOTION-BASED GAME DYNAMIC ADAPTATION CONTROL DIPP’2016 September 28, Veliko10 Implicit Emotional Indicators (via sensors & other devices) Facial expr. GSR Heart Rhythm ECG EEG Pressure … Change Procedures Time Velocity Acceleration Route Size Defeat range Luminosity Contrast … Real-Time Explicit Player Feedback through Adaptation Control Panel Analysi s Change Visuali- zation Triggering Events on Behavior Templates
  11. 11. IN-GAME EXPLICIT PLAYER FEEDBACK  using a built-in Adaptation Control Panel DIPP’2016 September 28, Veliko11
  12. 12. STYLES BASED ON KOLB LEARNING PROCESS DIPP’2016 September 28, Veliko12 Concrete Experience (feeling) Abstract Conceptualization (thinking) Active Experimentation (doing) Reflective Observation (watching) Competitor Dreamer Logician Srategist Learning styles of Kolb versus styles of Honey and Mumford (Bontchev & Vassileva, 2012) and ADOPTA playing styles
  13. 13. PLAYING STYLE MODELS DIPP’2016 September 28, Veliko13 Relationships between Honey and Mumford (1982) learning styles and playing styles of ADOPTA (Aleksieva et al., 2011) and Bartle (1996) Honey and Mumford Learns/plays by: ADOPTA Bartle Activist Hand–eye coordination, planning and strategizing, problem-solving, teamwork and the ability to think quickly Competitor Killer Theorist Logically entering problems step-by- step, with spatial awareness and verbal & numeracy skills Logician Achiever Pragmatist Planning, decision-making, testing hypotheses, strategic thinking, management skills Strategist Explorer Reflector Observing and watching reflectively Dreamer Socializer
  14. 14. PLAYER STYLE MEASUREMENT 1/2  Explicitly - though self-report  A 40 items questionnaire for measuring Competitor, Discoverer (Dreamer), Logician and Strategist style of playing  Experimented together with the 40 items questionnaire for measuring Honey and Mumford learning styles DIPP’2016 September 28, Veliko14 Correlations Playing style/ Learning style Competitor/ Activist Discoverer/ Reflector Logician/ Theorist Strategist/ Pragmatist r 0,75562 0,71113 0,83883 0,70487 p-value 0,00000 0,00000 0,00000 0,00003 N 152 Playing style Competitor Discoverer Logician Strategist Cronbach’s alpha 0,63606 0,80414 0,78935 0,70201 N 260
  15. 15. PLAYER STYLE MEASUREMENT 2/2  Implicitly - during game time of playing specific mini-game  Based on player’s metrics for specific game tasks including performance (result), efficiency (result/effort), task difficulty and play time  Linear regression coefficients determined by:  Qualitative study (structured interviews with gamers)  Adjusted by the least squares method (LSM)  Calculation incorporated into an adaptive action video game DIPP’2016 September 28, Veliko15
  16. 16. EXPERIMENTAL SETUP DIPP’2016 September 28, Veliko16 Brainstorm eStudio Rush for Gold Video maze 3D quiz assessment 3D zoom ordering Rooms RoomsRooms Rooms RoomsTunnels Affectiva SDK Emotion recognizer RAGE arousal asset RAGE arousal asset.NET USB TCP sockets
  17. 17. A PLATFORM FOR GENERATION OF CUSTOMIZABLE VIDEO MAZES FOR EDUCATION DIPP’2016 September 28, Veliko17 Maze Editor Transitions table Property Editor Titles … Texts … Graphics … Python Scripts Brainstorm API 3D Maze Game Engine Brainstorm Run Time Environment Embedded Mini-Games 3D video game Property Metadata Graph Editor
  18. 18. SAMPLE MAZE STRUCTURE (BULGARIAN ORTHODOX ICONOGRAPHY GAME) DIPP’2016 September 28, Veliko Tarnovo, Bulgaria 18
  19. 19. INTRODUCTION ROOM OF THE MAZE DIPP’2016 September 28, Veliko Tarnovo, Bulgaria 19
  20. 20. INSIDE A TUNNEL OF THE MAZE DIPP’2016 September 28, Veliko Tarnovo, Bulgaria 20
  21. 21. VIDEO DEMONSTRATION  See gameplay video at:  September 28, Veliko Tarnovo, BulgariaDIPP’2016 21
  22. 22. CONCLUSIONS  We plan to apply game features in further applications related to pilgrimage tourism and cultural heritage literacy.  Gamer style-based adaptations and customization will be considered.  Web-based releases of both the generation platform and educational games, which will result in massive use of this approach for creation of customized maze games and game-based learning. September 28, Veliko Tarnovo, Bulgaria 22DIPP’2016
  23. 23. THANK YOU FOR YOUR ATTENTION!  Questions?  Boyan Bontchev, September 28, Veliko Tarnovo, Bulgaria 23DIPP’2016