Image annotation tactics: transitions, strategies and efficiency

Chen, Jing; Wang, Dan; Xie, Iris; Lu, Quan

doi:10.1016/j.ipm.2018.06.009

Cited by 11 publications

(6 citation statements)

References 37 publications

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“…Third, for the 208 samples collected, the average number of apps used per search experience was 3.23, so the tactic transition sequence was averaged into three phases appropriate based on previous experience (Xie and Joo, 2010b;Chen et al, 2018). The first inapp search session is the beginning phase, the last in-app search session is the ending phase, and the middle phase is the other in-app search sessions except for the first and last apps.…”

Section: Data Processing and Analysis Methodsmentioning

confidence: 99%

Transitions in daily search tactics: during the cross-app interaction search process

Chen

2023

ITP

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PurposeCross-app interactive search has become the new normal, but the characteristics of their tactic transitions are still unclear. This study investigated the transitions of daily search tactics during the cross-app interaction search process.Design/methodology/approachIn total, 204 young participants' impressive cross-app search experiences in real daily situations were collected. The search tactics and tactic transition sequences in their search process were obtained by open coding. Statistical analysis and sequence analysis were used to analyze the frequently applied tactics, the frequency and probability of tactic transitions and the tactic transition sequences representing characteristics of tactic transitions occurring at the beginning, middle and ending phases. FindingsCreating the search statement (Creat), evaluating search results (EvalR), evaluating an individual item (EvalI) and keeping a record (Rec) were the most frequently applied tactics. The frequency and probability of transitions differed significantly between different tactic types. “Creat? EvalR? EvalI? Rec” is the typical path; Initiate the search in various ways and modifying the search statement were highlighted at the beginning phase; iteratively creating the search statement is highlighted in the middle phase; Moreover, utilization and feedback of information are highlighted at the ending phase. Originality/valueThe present study shed new light on tactic transitions in the cross-app interactive environment to explore information search behaviour. The findings of this work provide targeted suggestions for optimizing APP query, browsing and monitoring systems.

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Section: Data Processing and Analysis Methodsmentioning

confidence: 99%

Transitions in daily search tactics: during the cross-app interaction search process

Chen

2023

ITP

Self Cite

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“…In the process of improving the efficiency of university English reading teaching strategy, all of the above are satisfied, and students will gain a sense of reading happiness in reading, and the reading effect will far exceed passive and coping reading. With the help of psycholinguistics, literature [21] proposes a brand-new reading theory. Reading, he believes, is a complex psycholinguistic activity that is the result of the author's interaction with language information and readers.…”

Section: Related Workmentioning

confidence: 99%

Improving College English Reading Teaching Efficiency Based on Student Behavior Data Mining and Mobile Edge Computing

Wan

2022

Wireless Communications and Mobile Computing

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The reading scope of university English reading is constantly expanding, and the teaching content is gradually increasing. Students are unable to grasp the article’s ideas while reading, resulting in an incorrect understanding of the article and the selection of incorrect topics. In English reading, teachers should begin by broadening students’ knowledge, increasing the number of new words they learn on a regular basis, gradually building reading experience, and improving reading efficiency. Data mining (abbreviated as DM) is a method of extracting hidden, unknown, but potentially useful information and knowledge from a large amount of incomplete, noisy, fuzzy, and random practical application data. Based on student behavior DM and mobile edge computing, this paper investigates strategies to improve the efficiency of university English reading instruction. Teachers and students can interact more easily with the help of university English reading teaching based on student behavior DM, and good interpersonal interaction can help students better understand and master the language. It is also beneficial for teachers to provide more tailored guidance for students’ individual university English reading teaching levels and learning abilities, as well as to assist them in developing personalized efficiency improvement strategies. The goal of DM student behavior is to discover knowledge, mine information, and apply rules without making any assumptions, with unknown, useful, and effective results.

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“…To curb the effect of bias on persona creation, researchers have proposed personas based on largescale quantitative data [2,27,31,58,10,65]. Overall, personas can be classified into three categories according to their usage of data: (a) personas based solely on data, (b) personas based on data but with considerable fictive elements, and (c) entirely fictive personas created without data [39].…”

Section: Data-driven Personas and The Problem Of Transparencymentioning

confidence: 99%

“…thinking (e.g., regarding race, gender), as image processing is a complex cognitive task [10]. On the other hand, previous research quite strongly postulates that there is no known method to avoid bias in the interpretation of personas [24,59] and that stereotyping is an essential part of the subjective persona experience [38].…”

Section: Limitations and Future Workmentioning

confidence: 99%

Persona Transparency: Analyzing the Impact of Explanations on Perceptions of Data-Driven Personas

Salminen

Santos

Jung

et al. 2019

International Journal of Human–Computer Interaction

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Computational techniques are becoming more common in persona development. However, users of personas may question the information in persona profiles because they are unsure of how it was created. This problem is especially vexing for data-driven personas because their creation is an opaque algorithmic process. In this research, we analyze the effect of increased transparencyi.e., explanations of how the information in data-driven personas was producedon user perceptions. We find that higher transparency through these explanations increases the perceived completeness and clarity of the personas. Contrary to our hypothesis, the perceived credibility of the personas decreases with the increased transparency, possibly due to the technical complexity of the persona profiles disrupting the facade of the personas being real people. This finding suggests that explaining the algorithmic process of data-driven persona creation involves a "transparency trade-off". We also find that the gender of the persona affects the perceptions, with transparency increasing perceived completeness and empathy of the female persona, but not for the male persona. Therefore, transparency may specifically assist in the acceptance of female personas. We provide practical implication for persona creators regarding transparency in persona profiles.

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Image annotation tactics: transitions, strategies and efficiency

Cited by 11 publications

References 37 publications

Transitions in daily search tactics: during the cross-app interaction search process

Transitions in daily search tactics: during the cross-app interaction search process

Improving College English Reading Teaching Efficiency Based on Student Behavior Data Mining and Mobile Edge Computing

Persona Transparency: Analyzing the Impact of Explanations on Perceptions of Data-Driven Personas

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