Bridging the Immersion Gap Between Tabletop RPGs and Online RPGs
Bibliographic record
Abstract
The improvement of online tools has led to a resurgence in Tabletop Role-Playing Games.With new tools for tele-play such as Roll20 and Fantasy Grounds, old-fashioned pen-and-paper games are being pushed into the new age of interconnectivity and digital interactions.Previous work in board-game and video-game research states that co-located games are ranked far more enjoyable than their digital counterparts, discovering an existing gap between the experiences.We explored the possibility of improving the rank of digital games, by creating a more immersive experience.Is it possible to close the current experience gap between online and offline versions of TRPGs?In this study, we explore an augmented digital approach, using real-time responsive technology.By combining the efforts of previous research, we attempt to bring the benefits of digital avatar communication, into the world of online Tabletop Role-Playing Games.We maintain the benefits of non-verbal video communication, by implementing real-time face recognition avatars that react live to players' emotions and facial expressions during gameplay.The results were far different from the expected.Leveraging previous game experience research tools and data visualization, we discovered the importance of player agency as the most important factor of immersion and enjoyment.Data shows that immersion is not necessary always a positive outcome as negative feelings can be increased by higher immersion as well.Finally, we found that players' satisfaction in-game is determined not by the level of immersion, but by the user's ability to impact the world.1-ii | P a g e II.Acknowledgments I want to dedicate this work to the memory of Dr. Anthony Whitehead, an unwavering mentor, who believed in me since before my enrollment in the program, he encouraged me and always directed my efforts in the right direction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".