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Beyond game effectiveness. Part II, a qualitative study of multi-role experiential learning

Fabian, Nathan D.; Tucker, Eilish M.

The present paper is the second in a series published at I/ITSEC that seeks to explain the efficacy of multi-role experiential learning employed to create engaging game-based training methods transitioned to the U.S. Army, U.S. Army Special Forces, Civil Affairs, and Psychological Operations teams. The first publication (I/ITSEC 2009) summarized findings from a quantitative study that investigated experiential learning in the multi-player, PC-based game module transitioned to PEO-STRI, DARWARS Ambush! NK (non-kinetic). The 2009 publication reported that participants of multi-role (Player and Reflective Observer/Evaluator) game-based training reported statistically significant learning and engagement. Additionally when the means of the two groups (Player and Reflective Observer/Evaluator) were compared, they were not statistically significantly different from each other. That is to say that both playing as well as observing/evaluating were engaging learning modalities. The Observer/Evaluator role was designed to provide an opportunity for real-time reflection and meta-cognitive learning during game play. Results indicated that this role was an engaging way to learn about communication, that participants learned something about cultural awareness, and that the skills they learned were helpful in problem solving and decision-making.

The present paper seeks to continue to understand what and how users of non-kinetic game-based missions learn by revisiting the 2009 quantitative study with further investigation such as stochastic player performance analysis using latent semantic analyses and graph visualizations. The results are applicable to First-Person game-based learning systems designed to enhance trainee intercultural communication, interpersonal skills, and adaptive thinking. In the full paper, we discuss results obtained from data collected from 78 research participants of diverse backgrounds who trained by engaging in tasks directly, as well as observing and evaluating peer performance in real-time. The goal is two-fold. One is to quantify and visualize detailed player performance data coming from game play transcription to give further understanding to the results in the 2009 I/ITSEC paper. The second is to develop a set of technologies from this quantification and visualization approach into a generalized application tool to be used to aid in future games’ development of player/learner models and game adaptation algorithms.

Specifically, this paper addresses questions such as, “Are there significant differences in one's experience when an experiential learning task is observed first, and then performed by the same individual?” “Are there significant differences among groups participating in different roles in non-kinetic engagement training, especially when one role requires more active participation that the other?” “What is the impact of behavior modeling on learning in games?” In answering these questions the present paper reinforces the 2009 empirical study conclusion that contrary to current trends in military game development, experiential learning is enhanced by innovative training approaches designed to facilitate trainee mastery of reflective observation and abstract conceptualization as much as performance-based skills.

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TYPE Conference YEAR 2010
OSTI

Canaries in a coal mine: Using application-level checkpoints to detect memory failures

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Widener, Patrick W.; Ferreira, Kurt B.; Levy, Scott; Fabian, Nathan D.

Memory failures in future extreme scale applications are a significant concern in the high-performance computing community and have attracted much research attention. We contend in this paper that using application checkpoint data to detect memory failures has potential benefits and is preferable to examining application memory. To support this contention, we describe the application of machine learning techniques to evaluate the veracity of checkpoint data. Our preliminary results indicate that supervised decision tree machine learning approaches can effectively detect corruption in restart files, suggesting that future extreme-scale applications and systems may benefit from incorporating such approaches in order to cope with memory failues.

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TYPE Conference Poster YEAR 2015
ScopusOSTI

Data co-processing for extreme scale analysis level II ASC milestone (4745)

Rogers, David R.; Moreland, Kenneth D.; Oldfield, Ron A.; Fabian, Nathan D.

Exascale supercomputing will embody many revolutionary changes in the hardware and software of high-performance computing. A particularly pressing issue is gaining insight into the science behind the exascale computations. Power and I/O speed con- straints will fundamentally change current visualization and analysis work ows. A traditional post-processing work ow involves storing simulation results to disk and later retrieving them for visualization and data analysis. However, at exascale, scien- tists and analysts will need a range of options for moving data to persistent storage, as the current o ine or post-processing pipelines will not be able to capture the data necessary for data analysis of these extreme scale simulations. This Milestone explores two alternate work ows, characterized as in situ and in transit, and compares them. We nd each to have its own merits and faults, and we provide information to help pick the best option for a particular use.

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TYPE SAND Report YEAR 2013
OSTIDOI

Data privacy and security considerations for personal assistantsfor learning (PAL)

International Conference on Intelligent User Interfaces, Proceedings IUI

Raybourn, Elaine M.; Fabian, Nathan D.; Davis, Warren L.; Parks, Raymond C.; McClain, Jonathan T.; Trumbo, Derek T.; Regan, Damon; Durlach, Paula J.

A hypothetical scenario is utilized to explore privacy and security considerations for intelligent systems, such as a Personal Assistant for Learning (PAL). Two categories of potential concerns are addressed: factors facilitated by user models, and factors facilitated by systems. Among the strategies presented for risk mitigation is a call for ongoing, iterative dialog among privacy, security, and personalization researchers during all stages of development, testing, and deployment.

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TYPE Conference Poster YEAR 2015
ScopusOSTI

Demonstration of a Legacy Application's Path to Exascale - ASC L2 Milestone 4467

Barrett, Brian B.; Kelly, Suzanne M.; Klundt, Ruth A.; Laros, James H.; Leung, Vitus J.; Levenhagen, Michael J.; Lofstead, Gerald F.; Moreland, Kenneth D.; Oldfield, Ron A.; Pedretti, Kevin P.; Rodrigues, Arun; Barrett, Richard F.; Ward, Harry L.; Vandyke, John P.; Vaughan, Courtenay T.; Wheeler, Kyle B.; Brandt, James M.; Brightwell, Ronald B.; Curry, Matthew L.; Fabian, Nathan D.; Ferreira, Kurt; Gentile, Ann C.; Hemmert, Karl S.

Abstract not provided.

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TYPE Presentation YEAR 2012
OSTI

Grandmaster: Interactive Text-Based Analytics of Social Media

Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

Fabian, Nathan D.; Davis, Warren L.; Raybourn, Elaine M.; Lakkaraju, Kiran L.; Whetzel, Jonathan H.

People use social media resources like Twitter, Facebook, forums etc. to shareand discuss various activities or topics. By aggregating topic trends acrossmany individuals using these services, we seek to construct a richer profileof a person's activities and interests as well as provide a broader context ofthose activities. This profile may then be used in a variety of ways tounderstand groups as a collection of interests and affinities and anindividual's participation in those groups. Our approach considers that muchof these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping ofindividuals with shared interests based on shared conversation, and not onexplicit social software linking them. In this paper, we discuss aproof-of-concept application called Grandmaster built to pull short sections oftext, a person's comments or Twitter posts, together by analysis andvisualization to allow a gestalt understanding of the full collection of allindividuals: how groups are similar and how they differ, based on theirtext inputs.

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TYPE Conference Poster YEAR 2016
ScopusOSTIDOI

Grandmaster: Interactive text-based analytics of social media

Fabian, Nathan D.; Davis, Warren L.; Raybourn, Elaine M.; Lakkaraju, Kiran L.; Whetzel, Jonathan H.

People use social media resources like Twitter, Facebook, forums etc. to share and discuss various activities or topics. By aggregating topic trends across many individuals using these services, we seek to construct a richer profile of a person’s activities and interests as well as provide a broader context of those activities. This profile may then be used in a variety of ways to understand groups as a collection of interests and affinities and an individual’s participation in those groups. Our approach considers that much of these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping of individuals with shared interests based on shared conversation, and not on explicit social software linking them. In this paper, we discuss a proof-of-concept application called Grandmaster built to pull short sections of text, a person’s comments or Twitter posts, together by analysis and visualization to allow a gestalt understanding of the full collection of all individuals: how groups are similar and how they differ, based on their text inputs.

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TYPE Conference Poster YEAR 2015
OSTIDOI
Results 1–25 of 34
Results 1–25 of 34