huashen218/convxai-cia-dataset huggingface dataset:
Title: Designing CAST: A Computer-Assisted Shadowing Trainer for Self-Regulated Foreign Language Listening Practice
Authors: family: Reza
given: Mohi; family: Yoon
given: Dongwook
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: multimedia learning, audio and speech interfaces, self-regulated learning, computer-assisted language learning, speech shadowing

Abstract: Shadowing, i.e., listening to recorded native speech and simultaneously vocalizing the words, is a popular language-learning technique that is known to improve listening skills. However, despite strong evidence for its efficacy as a listening exercise, existing shadowing systems do not adequately support listening-focused practice, especially in self-regulated learning environments with no external feedback. To bridge this gap, we introduce Computer-Assisted Shadowing Trainer (CAST), a shadowing system that makes self-regulation easy and effective through four novel design elements — (i) in-the-moment highlights for tracking and visualizing progress, (ii) contextual blurring for inducing self-reflection on misheard words, (iii) self-listening comparators for post-practice self-evaluation, and (iv) adjustable pause-handles for self-paced practice. We base CAST on a formative user study (N=15) that provides fresh empirical grounds on the needs and challenges of shadowers. We validate our design through a summative evaluation (N=12) that shows learners can successfully self-regulate their shadowing practice with CAST while retaining focus on listening.
Title: Students’ Verbalized Metacognition During Computerized Learning
Authors: family: Bosch
given: Nigel; family: Zhang
given: Yingbin; family: Paquette
given: Luc; family: Baker
given: Ryan; family: Ocumpaugh
given: Jaclyn; family: Biswas
given: Gautam
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Affect, Confusion, Metacognition, Self-regulated learning

Abstract: Students in computerized learning environments often direct their own learning processes, which requires metacognitive awareness of what should be learned next. We investigated a novel method of measuring verbalized metacognition by applying natural language processing (NLP) to transcripts of interviews conducted in a classroom with 99 middle school students who were using a computerized learning environment. We iteratively adapted the NLP method for the linguistic characteristics of these interviews, then applied it to study three research questions regarding the relationships between verbalized metacognition and measures of 1) learning, 2) confusion, and 3) metacognitive problem-solving strategies. Verbalized metacognition was not directly related to learning, but was related to confusion and metacognitive problem-solving strategies. Results also suggested that interviews themselves may improve learning by encouraging metacognition. We discuss implications for designing computerized environments that support self-regulated learning through metacognition.
Title: Attitudes Surrounding an Imperfect AI Autograder
Authors: family: Hsu
given: Silas; family: Li
given: Tiffany Wenting; family: Zhang
given: Zhilin; family: Fowler
given: Max; family: Zilles
given: Craig; family: Karahalios
given: Karrie
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: code reading, autograder, folk theories, human-AI interaction, imperfect AI, EiPE, perception and acceptance of AI, ASAG, computer science education

Abstract: Deployment of AI assessment tools in education is widespread, but work on students’ interactions and attitudes towards imperfect autograders is comparatively lacking. This paper presents students’ perceptions surrounding a ∼ 90% accurate automated short-answer grader that determined homework and exam credit in a college-level computer science course. Using surveys and interviews, we investigated students’ knowledge about the autograder and their attitudes. We observed that misalignment between folk theories about how the autograder worked and how it actually worked could lead to suboptimal answer construction strategies. Students overestimated the autograder’s probability of marking correct answers as wrong, and estimates of this probability were associated with dissatisfaction and perceptions of unfairness. Many participants expressed a need for additional instruction on how to cater to the autograder. From these findings, we propose guidelines for incorporating imperfect short answer autograders into classroom in a manner that is considerate of students’ needs.
Title: A Visual Analytics Approach to Facilitate the Proctoring of Online Exams
Authors: family: Li
given: Haotian; family: Xu
given: Min; family: Wang
given: Yong; family: Wei
given: Huan; family: Qu
given: Huamin
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Online proctoring, visual analytics, mouse movement, head pose estimation

Abstract: Online exams have become widely used to evaluate students’ performance in mastering knowledge in recent years, especially during the pandemic of COVID-19. However, it is challenging to conduct proctoring for online exams due to the lack of face-to-face interaction. Also, prior research has shown that online exams are more vulnerable to various cheating behaviors, which can damage their credibility. This paper presents a novel visual analytics approach to facilitate the proctoring of online exams by analyzing the exam video records and mouse movement data of each student. Specifically, we detect and visualize suspected head and mouse movements of students in three levels of detail, which provides course instructors and teachers with convenient, efficient and reliable proctoring for online exams. Our extensive evaluations, including usage scenarios, a carefully-designed user study and expert interviews, demonstrate the effectiveness and usability of our approach.
Title: ArgueTutor: An Adaptive Dialog-Based Learning System for Argumentation Skills
Authors: family: Wambsganss
given: Thiemo; family: Kueng
given: Tobias; family: Soellner
given: Matthias; family: Leimeister
given: Jan Marco
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: adaptive learning, educational applications, pedagogical conversational agents, argumentation learning

Abstract: Techniques from Natural-Language-Processing offer the opportunities to design new dialog-based forms of human-computer interaction as well as to analyze the argumentation quality of texts. This can be leveraged to provide students with adaptive tutoring when doing a persuasive writing exercise. To test if individual tutoring for students’ argumentation will help them to write more convincing texts, we developed ArgueTutor, a conversational agent that tutors students with adaptive argumentation feedback in their learning journey. We compared ArgueTutor with 55 students to a traditional writing tool. We found students using ArgueTutor wrote more convincing texts with a better quality of argumentation compared to the ones using the alternative approach. The measured level of enjoyment and ease of use provides promising results to use our tool in traditional learning settings. Our results indicate that dialog-based learning applications combined with NLP text feedback have a beneficial use to foster better writing skills of students.
Title: Fostering Equitable Help-Seeking for K-3 Students in Low Income and Rural Contexts
Authors: family: Odili Uchidiuno
given: Judith; family: Hammer
given: Jessica; family: Koedinger
given: Ken; family: Ogan
given: Amy
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Abstract: Adaptive Collaborative Learning Support (ACLS) systems improve collaboration and learning for students over individual work or collaboration with non-adaptive support. However, many ACLS systems are ill-suited for rural contexts where students often need multiple kinds of support to complete tasks, may speak languages unsupported by the system, and require more than pre-assigned tutor-tutee student pairs for more equitable learning. We designed an intervention that fosters more equitable help-seeking by automatically detecting student struggles and prompts them to seek help from specific peers that can help. We conducted a mixed-methods experimental study with 98 K-3 students in a rural village in Tanzania over a one-month period, evaluating how the system affects student interactions, system engagement, and student learning. Our intervention increased student interactions by almost 4 times compared to the control condition, increased domain knowledge interactions, and propelled students to engage in more cognitively challenging activities.
Title: To Disclose or Not to Disclose: Examining the Privacy Decision-Making Processes of Older vs. Younger Adults
Authors: family: Ghaiumy Anaraky
given: Reza; family: Byrne
given: Kaileigh Angela; family: Wisniewski
given: Pamela J.; family: Page
given: Xinru; family: Knijnenburg
given: Bart
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: affect heuristics, Information privacy, young adults, older adults, privacy calculus

Abstract: To understand the underlying process of users’ information disclosure decisions, scholars often use either the privacy calculus framework or refer to heuristic shortcuts. It is unclear whether the decision process varies by age. Therefore, using these common frameworks, we conducted a web-based experiment with 94 participants, who were younger (ages 19-22) or older (65+) adults, to understand how perceived app trust, sensitivity of the data, and benefits of disclosure influence users disclosure decisions. Younger adults were more likely to change their perception of data sensitivity based on trust, while older adults were more likely to disclose information based on perceived benefits of disclosure. These results suggest older adults made more rationally calculated decisions than younger adults, who made heuristic decisions based on app trust. Our findings negate the mainstream narrative that older adults are less privacy-conscious than younger adults; instead, older adults weigh the benefits and risks of information disclosure.
Title: “They See You’re a Girl if You Pick a Pink Robot with a Skirt”: A Qualitative Study of How Children Conceptualize Data Processing and Digital Privacy Risks
Authors: family: Sun
given: Kaiwen; family: Sugatan
given: Carlo; family: Afnan
given: Tanisha; family: Simon
given: Hayley; family: Gelman
given: Susan A.; family: Radesky
given: Jenny; family: Schaub
given: Florian
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Data Processing., Children, Digital Privacy

Abstract: As children become frequent digital technology users, concerns about their digital privacy are increasing. To better understand how young children conceptualize data processing and digital privacy risks, we interviewed 26 children, 4 to 10 years old, from families with higher educational attainment recruited in a college town. Our child participants construed apps’ and services’ data collection and storage practices in terms of their benefits, both to themselves and for user safety, and characterized both data tracking and privacy violations as interpersonal rather than considering automated processes or companies as privacy threats. We identify four factors shaping these mental models and privacy risk perceptions: (1) surface-level visual cues, (2) past digital interactions involving data collection, (3) age and cognitive development, and (4) privacy-related experiences in non-digital contexts. We discuss our findings’ design, educational, and public policy implications toward better supporting children in identifying and reasoning about digital privacy risks.
Title: “They think it’s totally fine to talk to somebody on the internet they don’t know”: Teachers’ perceptions and mitigation strategies of tweens’ online risks
Authors: family: Maqsood
given: Sana; family: Chiasson
given: Sonia
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: teachers, children, security and privacy risks, digital literacy

Abstract: Teachers play a key role in educating children about digital security and privacy. They are often at the forefront, witnessing incidents, dealing with the consequences, and helping children handle the technology-related risks. However, little is reported about teachers’ lived classroom experiences and their challenges in this regard. We conducted semi-structured interviews with 21 Canadian elementary school teachers to understand the risks teachers witness children aged 10–13 facing on digital media, teachers’ mitigation strategies, and how prepared teachers are to help children. Our results show that teachers regularly help children deal with digital risks outside of teaching official curriculum, ranging from minor privacy violations to severe cases of cyberbullying. Most issues reported by teachers were the result of typical behaviours which became risky because they took place over digital media. We use the results to highlight implications for how elementary schools address digital security and privacy.
Title: LociMotion: Towards Learning a Strong Authentication Secret in a Single Session
Authors: family: Doolani
given: Jayesh; family: Wright
given: Matthew; family: Setty
given: Rajesh; family: Haque
given: S M Taiabul
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: system-assigned passwords, Authentication, usable security, memorability

Abstract: In this work, we design and evaluate LociMotion, a training interface to learn a strong authentication secret in a single session. LociMotion automatically takes a random password with twelve lowercase letters (56-bit entropy) to generate the training interface. It first leverages users’ spatial and visual (declarative) memory by showing them a video clip based on the method of loci, and then consolidates the learning process by having them play a computer game that leverages their motor (procedural) memory. The results of a memorability study with 300 participants showed that LociMotion had a significantly higher recall success rate than a control condition. A second study with 200 participants demonstrated the effectiveness of LociMotion over a period of time (99%, 96%, and 81% recall success rates after 1, 4, and 18 days, respectively). LociMotion offers an alternative to the spaced repetition technique, as it does not require dozens of training sessions.
Title: On Smartphone Users’ Difficulty with Understanding Implicit Authentication
Authors: family: Koushki
given: Masoud Mehrabi; family: Obada-Obieh
given: Borke; family: Huh
given: Jun Ho; family: Beznosov
given: Konstantin
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Smartphone Unlocking, Implicit Authentication, Mental Models, Smart Lock, Active Authentication

Abstract: Implicit authentication (IA) has recently become a popular approach for providing physical security on smartphones. It relies on behavioral traits (e.g., gait patterns) for user identification, instead of biometric data or knowledge of a PIN. However, it is not yet known whether users can understand the semantics of this technology well enough to use it properly. We bridge this knowledge gap by evaluating how Android’s Smart Lock (SL), which is the first widely deployed IA solution on smartphones, is understood by its users. We conducted a qualitative user study (N=26) and an online survey (N=331). The results suggest that users often have difficulty understanding SL semantics, leaving them unable to judge when their phone would be (un)locked. We found that various aspects of SL, such as its capabilities and its authentication factors, are confusing for the users. We also found that depth of smartphone adoption is a significant antecedent of SL comprehension.
Title: Security Notifications in Static Analysis Tools: Developers’ Attitudes, Comprehension, and Ability to Act on Them
Authors: family: Tahaei
given: Mohammad; family: Vaniea
given: Kami; family: Beznosov
given: Konstantin (Kosta); family: Wolters
given: Maria K
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: security notifications, usable security, static analysis tools, software developers

Abstract: Static analysis tools (SATs) have the potential to assist developers in finding and fixing vulnerabilities in the early stages of software development, requiring them to be able to understand and act on tools’ notifications. To understand how helpful such SAT guidance is to developers, we ran an online experiment (N=132) where participants were shown four vulnerable code samples (SQL injection, hard-coded credentials, encryption, and logging sensitive data) along with SAT guidance, and asked to indicate the appropriate fix. Participants had a positive attitude towards both SAT notifications and particularly liked the example solutions and vulnerable code. Seeing SAT notifications also led to more detailed open-ended answers and slightly improved code correction answers. Still, most SAT (SpotBugs 67%, SonarQube 86%) and Control (96%) participants answered at least one code-correction question incorrectly. Prior software development experience, perceived vulnerability severity, and answer confidence all positively impacted answer accuracy.
Title: Bits Under the Mattress: Understanding Different Risk Perceptions and Security Behaviors of Crypto-Asset Users
Authors: family: Abramova
given: Svetlana; family: Voskobojnikov
given: Artemij; family: Beznosov
given: Konstantin; family: Böhme
given: Rainer
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: security, cluster analysis, user behavior, Crypto-asset

Abstract: Crypto-assets are unique in tying financial wealth to the secrecy of private keys. Prior empirical work has attempted to study end-user security from both technical and organizational perspectives. However, the link between individuals’ risk perceptions and security behavior was often obscured by the heterogeneity of the subjects in small samples. This paper contributes quantitative results from a survey of 395 crypto-asset users recruited by a novel combination of deep and broad sampling. The analysis accounts for heterogeneity with a new typology that partitions the sample in three robust clusters – cypherpunks, hodlers, and rookies – using five psychometric constructs. The constructs originate from established behavioral theories with items purposefully adapted to the domain. We demonstrate the utility of this typology in better understanding users’ characteristics and security behaviors. These insights inform the design of crypto-asset solutions, guide risk communication, and suggest directions for future digital currencies.
Title: Privacy Champions in Software Teams: Understanding Their Motivations, Strategies, and Challenges
Authors: family: Tahaei
given: Mohammad; family: Frik
given: Alisa; family: Vaniea
given: Kami
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: software development, privacy champions, user privacy

Abstract: Software development teams are responsible for making and implementing software design decisions that directly impact end-user privacy, a challenging task to do well. Privacy Champions—people who strongly care about advocating privacy—play a useful role in supporting privacy-respecting development cultures. To understand their motivations, challenges, and strategies for protecting end-user privacy, we conducted 12 interviews with Privacy Champions in software development teams. We find that common barriers to implementing privacy in software design include: negative privacy culture, internal prioritisation tensions, limited tool support, unclear evaluation metrics, and technical complexity. To promote privacy, Privacy Champions regularly use informal discussions, management support, communication among stakeholders, and documentation and guidelines. They perceive code reviews and practical training as more instructive than general privacy awareness and on-boarding training. Our study is a first step towards understanding how Privacy Champions work to improve their organisation’s privacy approaches and improve the privacy of end-user products.
Title: Exploring User-Centered Security Design for Usable Authentication Ceremonies
Authors: family: Fassl
given: Matthias; family: Gröber
given: Lea Theresa; family: Krombholz
given: Katharina
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Authentication, Usability, Man-in-the-Middle (MitM), Instant Messaging, User-Centered Design

Abstract: Security technology often follows a systems design approach that focuses on components instead of users. As a result, the users’ needs and values are not sufficiently addressed, which has implications on security usability. In this paper, we report our lessons learned from applying a user-centered security design process to a well-understood security usability challenge, namely key authentication in secure instant messaging. Users rarely perform these key authentication ceremonies, which makes their end-to-end encrypted communication vulnerable. Our approach includes collaborative design workshops, an expert evaluation, iterative storyboard prototyping, and an online evaluation. While we could not demonstrate that our design approach resulted in improved usability or user experience, we found that user-centered prototypes can increase the users’ comprehension of security implications. Hence, prototypes based on users’ intuitions, needs, and values are useful starting points for approaching long-standing security challenges. Applying complementary design approaches may improve usability and user experience further.
Title: I Don’t Need an Expert! Making URL Phishing Features Human Comprehensible
Authors: family: Althobaiti
given: Kholoud; family: Meng
given: Nicole; family: Vaniea
given: Kami
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: real-time learning, phishing awareness, Phishing, decision support, security education, URL reading, usable privacy and security

Abstract: Judging the safety of a URL is something that even security experts struggle to do accurately without additional information. In this work, we aim to make experts’ tools accessible to non-experts and assist general users in judging the safety of URLs by providing them with a usable report based on the information professionals use. We designed the report by iterating with 8 focus groups made up of end users, HCI experts, and security experts to ensure that the report was usable as well as accurately interpreted the information. We also conducted an online evaluation with 153 participants to compare different report-length options. We find that the longer comprehensive report allows users to accurately judge URL safety (93% accurate) and that summaries still provide benefit (83% accurate) compared to domain highlighting (65% accurate).