Understanding Player Patterns by Combining Knowledge-Based Data Abstraction with Interactive Visualization

Javvaji, Nithesh; Harteveld, Casper; El-Nasr, Magy Seif

doi:10.1145/3410404.3414257

Cited by 11 publications

(4 citation statements)

References 38 publications

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“…First, the stakeholder must identify an expert trace. An expert trace (Sawyer et al, 2018;Javvaji et al, 2020) is the sequence an expert would generate given the educational environment. Such a trace can be created by having an expert interact with an educational environment and then visualizing their sequence.…”

Section: Step 3: Visualizationmentioning

confidence: 99%

“…While we demonstrate the method in a single case study, we have been able to apply the same approach to several other digital and learning environments, such as E-sports (Ahmad et al, 2019), alternative reality games (Javvaji et al, 2020), and May's Journey, a puzzle game that teaches programming (Jemmali et al, 2020). In our experience working with this approach, data abstraction is key to ensuring that the data remains interpretable even at large scales.…”

Section: Generalizability Of the Approachmentioning

confidence: 99%

“…In our experience working with this approach, data abstraction is key to ensuring that the data remains interpretable even at large scales. While our case study used the technique to examine only 13 students, the visualization methods we leverage have been demonstrated in previous work with significantly larger numbers (such as hundreds and even thousands of users or sessions) (Javvaji et al, 2020;Ahmad et al, 2019;Nguyen et al, 2015). In all cases, interpretability was maintained through an abstraction scheme that generalized lower-level actions into fewer higher-level actions and ensured that the visualized traces were manageable for a human stakeholder.…”

Section: Generalizability Of the Approachmentioning

confidence: 99%

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Analyzing Students’ Problem-Solving Sequences

et al. 2022

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Educational technology is shifting toward facilitating personalized learning. Such personalization, however, requires a detailed understanding of students’ problem-solving processes. Sequence analysis (SA) is a promising approach to gaining granular insights into student problem solving; however, existing techniques are difficult to interpret because they offer little room for human input in the analysis process. Ultimately, in a learning context, a human stakeholder makes the decisions, so they should be able to drive the analysis process. In this paper, we present a human-in-the-loop approach to SA that uses visualization to allow a stakeholder to better understand both the data and the algorithm. We illustrate the method with a case study in the context of a learning game called Parallel. Results reveal six groups of students organized based on their problem-solving patterns and highlight individual differences within each group. We compare the results to a state-of-the-art method run with the same data and discuss the benefits of our method and the implications of this work.

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Section: Step 3: Visualizationmentioning

confidence: 99%

Section: Generalizability Of the Approachmentioning

confidence: 99%

Section: Generalizability Of the Approachmentioning

confidence: 99%

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Analyzing Students’ Problem-Solving Sequences

et al. 2022

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“…In other domains, review of process has been valuable for optimizing human workflows [40,41] and learning [27,28]. In games, graph-based process visualizations are used for game analytics and user experience research [2,3,9,[16][17][18]20,30,45]. Player-facing process visualizations, in contrast, are typically designed as timelines depicting the ordering of actions taken over the course of a game [1,22,24,32].…”

Section: Related Workmentioning

confidence: 99%

Towards an Understanding of How Players Make Meaning from Post-Play Process Visualizations

Kleinman

Villareale

Shergadwala

et al. 2022

Lecture Notes in Computer Science

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Player-facing, retrospective gameplay visualizations help players track progress and learn from others. Visualizations of a user's step by step process, may be able to advance retrospective visualization. However, we currently do not know how players make meaning from process visualizations of game data. In this work, we take a first step towards addressing this gap by examining how players make meaning from process visualizations of other players' gameplay. We identify two interpretation methods comprised of six techniques and discuss what these results mean for future use of player-facing process visualizations.

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Echoing the Gameplay: Analyzing Gameplay Sessions across Genres by Reconstructing Them from Recorded Data

MacCormick

Zaman

2022

International Journal of Human–Computer Interaction

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Understanding Player Patterns by Combining Knowledge-Based Data Abstraction with Interactive Visualization

Cited by 11 publications

References 38 publications

Analyzing Students’ Problem-Solving Sequences

Analyzing Students’ Problem-Solving Sequences

Towards an Understanding of How Players Make Meaning from Post-Play Process Visualizations

Echoing the Gameplay: Analyzing Gameplay Sessions across Genres by Reconstructing Them from Recorded Data

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