285 research outputs found
Exploring Metacognition, Multitasking and Test Performance in a Lecture Context
Multitasking has become more prevalent with recent advancements in technology (Judd, 2014; Junco & Cotten, 2012). Many self-report studies, and the few available experimental manipulations, consistently indicate that media multitasking is related to decrements in learning. The present study extends the current literature by explicitly documenting studentsâ responses to media-based interruptions to learning. The current study also documents other behaviours students engage in that may or may not be related to multitasking when technology is available during lectures. In addition, the study explores the role of metacognition as a contributor to learning in a media-rich educational setting. In total, 118 Introductory Psychology students attended a 40-minute lecture and were assigned to one of three conditions: Facebook multitasking, multitasking choice, and no-technology control. Prior to participating, they completed a measure of metacognitive awareness and perceptions toward technology. After the lecture, they were tested for content knowledge, metacognitive awareness, and perceptions toward multitasking. A subsample of students in the technology conditions was video recorded and asked to identify their actions and thoughts at key times during the lecture. Qualitative coding of these interviews yielded seven overall themes dealing with multitasking behaviours and seven themes specific to learning behaviours. Overall, there was a trend towards increasing metacognition over time, with some aspects such as monitoring appearing in both the traditional measure of metacognitive awareness and in the studentsâ thematic summaries. Student performance was lower for content where prompts/messages were sent to the learners, suggesting that prompts and messages are problematic distractions for learning. Overall, the present study documents what multitasking looks like in todayâs students, and identifies factors that do or do not influence multitasking behaviours and outcomes
An Inclusive Report on Robust Malware Detection and Analysis for Cross-Version Binary Code Optimizations
Numerous practices exist for binary code similarity detection (BCSD), such as Control Flow Graph, Semantics Scrutiny, Code Obfuscation, Malware Detection and Analysis, vulnerability search, etc. On the basis of professional knowledge, existing solutions often compare particular syntactic aspects retrieved from binary code. They either have substantial performance overheads or have inaccurate detection. Furthermore, there aren't many tools available for comparing cross-version binaries, which may differ not only in programming with proper syntax but also marginally in semantics. This Binary code similarity detection is existing for past 10 years, but this research area is not yet systematically analysed. The paper presents a comprehensive analysis on existing Cross-version Binary Code Optimization techniques on four characteristics: 1. Structural analysis, 2. Semantic Analysis, 3. Syntactic Analysis, 4. Validation Metrics. It helps the researchers to best select the suitable tool for their necessary implementation on binary code analysis. Furthermore, this paper presents scope of the area along with future directions of the research
Exploring the Potential of Large Language Models in Artistic Creation: Collaboration and Reflection on Creative Programming
Recently, the potential of large language models (LLMs) has been widely used
in assisting programming. However, current research does not explore the artist
potential of LLMs in creative coding within artist and AI collaboration. Our
work probes the reflection type of artists in the creation process with such
collaboration. We compare two common collaboration approaches: invoking the
entire program and multiple subtasks. Our findings exhibit artists' different
stimulated reflections in two different methods. Our finding also shows the
correlation of reflection type with user performance, user satisfaction, and
subjective experience in two collaborations through conducting two methods,
including experimental data and qualitative interviews. In this sense, our work
reveals the artistic potential of LLM in creative coding. Meanwhile, we provide
a critical lens of human-AI collaboration from the artists' perspective and
expound design suggestions for future work of AI-assisted creative tasks.Comment: 15 pages, 4 figure
Large Language Models for Software Engineering: A Systematic Literature Review
Large Language Models (LLMs) have significantly impacted numerous domains,
notably including Software Engineering (SE). Nevertheless, a well-rounded
understanding of the application, effects, and possible limitations of LLMs
within SE is still in its early stages. To bridge this gap, our systematic
literature review takes a deep dive into the intersection of LLMs and SE, with
a particular focus on understanding how LLMs can be exploited in SE to optimize
processes and outcomes. Through a comprehensive review approach, we collect and
analyze a total of 229 research papers from 2017 to 2023 to answer four key
research questions (RQs). In RQ1, we categorize and provide a comparative
analysis of different LLMs that have been employed in SE tasks, laying out
their distinctive features and uses. For RQ2, we detail the methods involved in
data collection, preprocessing, and application in this realm, shedding light
on the critical role of robust, well-curated datasets for successful LLM
implementation. RQ3 allows us to examine the specific SE tasks where LLMs have
shown remarkable success, illuminating their practical contributions to the
field. Finally, RQ4 investigates the strategies employed to optimize and
evaluate the performance of LLMs in SE, as well as the common techniques
related to prompt optimization. Armed with insights drawn from addressing the
aforementioned RQs, we sketch a picture of the current state-of-the-art,
pinpointing trends, identifying gaps in existing research, and flagging
promising areas for future study
Dagstuhl Reports : Volume 1, Issue 2, February 2011
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-HĂŒbner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro PezzĂ©, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn
Generative Artificial Intelligence for Software Engineering -- A Research Agenda
Generative Artificial Intelligence (GenAI) tools have become increasingly
prevalent in software development, offering assistance to various managerial
and technical project activities. Notable examples of these tools include
OpenAIs ChatGPT, GitHub Copilot, and Amazon CodeWhisperer. Although many recent
publications have explored and evaluated the application of GenAI, a
comprehensive understanding of the current development, applications,
limitations, and open challenges remains unclear to many. Particularly, we do
not have an overall picture of the current state of GenAI technology in
practical software engineering usage scenarios. We conducted a literature
review and focus groups for a duration of five months to develop a research
agenda on GenAI for Software Engineering. We identified 78 open Research
Questions (RQs) in 11 areas of Software Engineering. Our results show that it
is possible to explore the adoption of GenAI in partial automation and support
decision-making in all software development activities. While the current
literature is skewed toward software implementation, quality assurance and
software maintenance, other areas, such as requirements engineering, software
design, and software engineering education, would need further research
attention. Common considerations when implementing GenAI include industry-level
assessment, dependability and accuracy, data accessibility, transparency, and
sustainability aspects associated with the technology. GenAI is bringing
significant changes to the field of software engineering. Nevertheless, the
state of research on the topic still remains immature. We believe that this
research agenda holds significance and practical value for informing both
researchers and practitioners about current applications and guiding future
research
Exploring User Perspectives on ChatGPT: Applications, Perceptions, and Implications for AI-Integrated Education
Understanding user perspectives on Artificial Intelligence (AI) in education
is essential for creating pedagogically effective and ethically responsible
AI-integrated learning environments. In this paper, we conduct an extensive
qualitative content analysis of four major social media platforms (Twitter,
Reddit, YouTube, and LinkedIn) to explore the user experience (UX) and
perspectives of early adopters toward ChatGPT-an AI Chatbot technology-in
various education sectors. We investigate the primary applications of ChatGPT
in education (RQ1) and the various perceptions of the technology (RQ2). Our
findings indicate that ChatGPT is most popularly used in the contexts of higher
education (24.18%), K-12 education (22.09%), and practical-skills learning
(15.28%). On social media platforms, the most frequently discussed topics about
ChatGPT are productivity, efficiency, and ethics. While some early adopters
lean toward seeing ChatGPT as a revolutionary technology with the potential to
boost students' self-efficacy and motivation to learn, others express concern
that overreliance on the AI system may promote superficial learning habits and
erode students' social and critical thinking skills. Our study contributes to
the broader discourse on Human-AI Interaction and offers recommendations based
on crowd-sourced knowledge for educators and learners interested in
incorporating ChatGPT into their educational settings. Furthermore, we propose
a research agenda for future studies that sets the foundation for continued
investigation into the application of ChatGPT in education.Comment: Preprint versio
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