A Supporting Tool for Enhancing User’s Mental Model Elicitation and Decision-Making in User Experience Research

Martín, Marina; Macías, José A.

doi:10.1080/10447318.2022.2041885

Cited by 9 publications

(4 citation statements)

References 79 publications

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“…Future research can study other types of interaction in which thought bubbles can be leveraged, perhaps with some adaptions. For example, researchers can investigate the use of thought bubbles for diegetic elicitation through non-verbal queries such as card sorting [63], or diagrammatic representations [77]. In addition, recent advances in AI have inspired many researchers to study human-AI interaction [1,86] and investigate mental models of AI [40,92].…”

Section: Discussionmentioning

confidence: 99%

Thought Bubbles: A Proxy into Players' Mental Model Development

Mohaddesi,

Chicoine,

Gong

et al. 2023

Preprint

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Studying mental models has recently received more attention, aiming to understand the cognitive aspects of human-computer interaction. However, there is not enough research on the elicitation of mental models in complex dynamic systems. We present Thought Bubbles as an approach for eliciting mental models and an avenue for understanding players' mental model development in interactive virtual environments. We demonstrate the use of Thought Bubbles in two experimental studies involving 250 participants playing a supply chain game. In our analyses, we rely on Situation Awareness (SA) levels, including perception, comprehension, and projection, and show how experimental manipulations such as disruptions and information sharing shape players' mental models and drive their decisions depending on their behavioral profile. Our results provide evidence for the use of thought bubbles in uncovering cognitive aspects of behavior by indicating how disruption location and availability of information affect people's mental model development and influence their decisions.CCS Concepts: • Human-centered computing → Empirical studies in HCI; User studies.

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Section: Discussionmentioning

confidence: 99%

Thought Bubbles: A Proxy into Players' Mental Model Development

Mohaddesi,

Chicoine,

Gong

et al. 2023

Preprint

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“…To further improve the user experience, designers should stay abreast of new technologies and their potential effects on HCI (Martín & Macías, 2023).…”

Section: Discussionmentioning

confidence: 99%

Human-Computer Interaction: Enhancing User Experience in Interactive Systems

Kheder

2023

kje

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In this research, we investigate how human-computer interaction (HCI) can be used to improve the user experience (UX) of interactive systems. Studies in cognitive psychology, information processing, and human factors are examined as they relate to the development of HCI. It highlights how HCI has shifted its focus from functionality to user-friendliness, teaching ability, efficiency, enjoyment, and emotion. To better understand the current state of HCI and UX research, practice, and theory, a systematic literature study is performed. Focusing on users' goals wants, and characteristics at every stage of the design process is central to user-centered design (UCD) ideas and approaches, which are discussed at length in this article. We investigate usability testing as a crucial technique for bettering HCI, focusing on its advantages in pinpointing usability problems, boosting system efficacy, and boosting user pleasure. Methods for creating tests, finding participants, collecting data, and analyzing results are discussed. The importance of prototype methods in HCI and user-centric design is also emphasized in the study. This article delves into the practice of creating prototypes to collect user feedback, iterate designs, and perfect interactive systems. Techniques covered include paper prototyping, interactive wireframes, and high-fidelity prototypes. We propose interaction design frameworks like the User-Centered Design Process (UCDP) and the Double Diamond model to help designers prioritize users when developing interactive systems. The study also delves into how technologies like augmented reality, virtual reality, natural language processing, machine learning, and gesture-based interfaces have revolutionized HCI in recent years. The paper defends user-centric design's place in HCI, pointing out how UX affects user happiness, participation, and output. Researchers and practitioners in HCI and software engineering can greatly benefit from this paper's findings.

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“…The two main challenges in constructing a decision tree include facilitating the growth of the tree to ensure accurate classification of the training dataset and the pruning phase, which involves the elimination of redundant nodes and branches to improve classification accuracy [13]. In this model, each node represents an object, each forked path represents a potential attribute value, and each terminal node corresponds to the value of the object, which is determined by the path from the root node to a specific leaf node [14]. The most classical algorithm for decision trees is the ID3 algorithm, which is based on the use of information gain methods as a selection criterion at all levels of the decision tree to help determine the appropriate attribute to be used at each node.…”

Section: Decision Treementioning

confidence: 99%

Customer Segmentation Based on Machine Learning Methods

Wang

2024

HSET

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In recent years, as times change, consumer behavior continues to change rapidly, and their preferences and consumer attitudes change with age and experience. In the generalization of the mass market, it is difficult to identify the needs and desires of customers through various promotional tools. Therefore, customer segmentation can be an option for marketers to offer preferential goods or services to customers. Segmentation can help the company to quickly identify the preferences of the customers and provide them with the desired goods. However, there are significant differences between customers, making it difficult for merchants to segment customers through simple attribute filtering. Fortunately, with the development of machine learning, machine learning-based customer segmentation methods have received a lot of attention from researchers. However, different machine learning methods have different characteristics and there are some differences in commercial applications. Therefore, this paper analyses the principles and performance of the algorithms to provide reference for researchers in related fields. Firstly, this paper introduces several common machines learning methods, including Logistic Regression, Decision Tree, Random Forest and AdaBoost, and then compares the effectiveness of these algorithms through experiments. Finally, this paper looks forward to future research directions.

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A Supporting Tool for Enhancing User’s Mental Model Elicitation and Decision-Making in User Experience Research

Cited by 9 publications

References 79 publications

Thought Bubbles: A Proxy into Players' Mental Model Development

Thought Bubbles: A Proxy into Players' Mental Model Development

Human-Computer Interaction: Enhancing User Experience in Interactive Systems

Customer Segmentation Based on Machine Learning Methods

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