36 research outputs found
Block-Based Development of Mobile Learning Experiences for the Internet of Things
The Internet of Things enables experts of given domains to create smart user experiences for interacting with the environment. However, development of such experiences requires strong programming skills, which are challenging to develop for non-technical users. This paper presents several extensions to the block-based programming language used in App Inventor to make the creation of mobile apps for smart learning experiences less challenging. Such apps are used to process and graphically represent data streams from sensors by applying map-reduce operations. A workshop with students without previous experience with Internet of Things (IoT) and mobile app programming was conducted to evaluate the propositions. As a result, students were able to create small IoT apps that ingest, process and visually represent data in a simpler form as using App Inventor's standard features. Besides, an experimental study was carried out in a mobile app development course with academics of diverse disciplines. Results showed it was faster and easier for novice programmers to develop the proposed app using new stream processing blocks.Spanish National Research Agency (AEI) - ERDF fund
Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions
Information Visualization (InfoVis) enjoys diverse adoption and applicability because of its strength in solving the problem of information overload inherent in institutional data. Policy and decision makers of higher education institutions (HEIs)
are also experiencing information overload while interacting with studentsโ data, because of its multidimensionality. This constraints decision making processes, and therefore requires a domain-specific InfoVis conceptual design framework which
will birth the domainโs InfoVis tool. This study therefore aims to design HEI Studentsโ data-focused InfoVis (HSDI) conceptual design framework which
addresses the content delivery techniques and the systematic processes in actualizing the domain specific InfoVis. The study involved four phases: 1) a usersโ study to investigate, elicit and prioritize the studentsโ data-related explicit knowledge preferences of HEI domain policy. The corresponding studentsโ data dimensions are then categorised, 2) exploratory study through content analysis of InfoVis design literatures, and subsequent mapping with findings from the usersโ study, to propose the appropriate visualization, interaction and distortion techniques for delivering the domainโs explicit knowledge preferences, 3) conceptual development of the design framework which integrates the techniquesโ model with its design processโas identified from adaptation of software engineering and InfoVis design models, 4) evaluation of the proposed framework through expert review, prototyping, heuristics evaluation, and usersโ experience evaluation. For an InfoVis that will appropriately
present and represent the domain explicit knowledge preferences, support the studentsโ data multidimensionality and the decision making processes, the study found that: 1) mouse-on, mouse-on-click, mouse on-drag, drop down menu, push
button, check boxes, and dynamics cursor hinting are the appropriate interaction techniques,
2) zooming, overview with details, scrolling, and exploration are the appropriate distortion techniques, and 3) line chart, scatter plot, map view, bar chart and pie chart are the appropriate visualization techniques. The theoretical support to the proposed framework suggests that dictates of preattentive processing theory, cognitive-fit theory, and normative and descriptive theories must be followed for InfoVis to aid perception, cognition and decision making respectively. This study contributes to the area of InfoVis, data-driven decision making process, and HEI studentsโ data usage process
Understanding Documentation Use Through Log Analysis: An Exploratory Case Study of Four Cloud Services
Almost no modern software system is written from scratch, and developers are
required to effectively learn to use third-party libraries or software
services. Thus, many practitioners and researchers have looked for ways to
create effective documentation that supports developers' learning. However, few
efforts have focused on how people actually use the documentation. In this
paper, we report on an exploratory, multi-phase, mixed methods empirical study
of documentation page-view logs from four cloud-based industrial services. By
analyzing page-view logs for over 100,000 users, we find diverse patterns of
documentation page visits. Moreover, we show statistically that which
documentation pages people visit often correlates with user characteristics
such as past experience with the specific product, on the one hand, and with
future adoption of the API on the other hand. We discuss the implications of
these results on documentation design and propose documentation page-view log
analysis as a feasible technique for design audits of documentation, from ones
written for software developers to ones designed to support end users (e.g.,
Adobe Photoshop)
Sketchography - Automatic Grading of Map Sketches for Geography Education
Geography is a vital classroom subject that teaches students about the physical features of the planet we live on. Despite the importance of geographic knowledge, almost 75% of 8th graders scored below proficient in geography on the 2014 National Assessment of Educational Progress. Sketchography is a pen-based intelligent tutoring system that provides real-time feedback to students learning the locations, directions, and topography of rivers around the world. Sketchography uses sketch recognition and artificial intelligence to understand the userโs sketched intentions. As sketches are inherently messy, and even the most expert geographer will draw only a close approximation of the riverโs flow, data has been gathered from both novice and expert sketchers. This data, in combination with professorsโ grading rubrics and statistically driving AI-algorithms, provide real-time automatic grading that is similar to a human graderโs score. Results show the system to be 94.64% accurate compared to human grading
Languages of games and play: A systematic mapping study
Digital games are a powerful means for creating enticing, beautiful, educational, and often highly addictive interactive experiences that impact the lives of billions of players worldwide. We explore what informs the design and construction of good games to learn how to speed-up game development. In particular, we study to what extent languages, notations, patterns, and tools, can offer experts theoretical foundations, systematic techniques, and practical solutions they need to raise their productivity and improve the quality of games and play. Despite the growing number of publications on this topic there is currently no overview describing the state-of-the-art that relates research areas, goals, and applications. As a result, efforts and successes are often one-off, lessons learned go overlooked, language reuse remains minimal, and opportunities for collaboration and synergy are lost. We present a systematic map that identifies relevant publications and gives an overview of research areas and publication venues. In addition, we categorize research perspectives along common objectives, techniques, and approaches, illustrated by summaries of selected languages. Finally, we distill challenges and opportunities for future research and development
ํ์ฅ ๋ฐ์ดํฐ ์์ง ๋ฅ๋ ฅ์ ํ์ฅํ๊ธฐ ์ํ ์์ ๋ ๋์ ์ ํ ํธ๋ํน ๊ธฐ์ ์ ๋์์ธ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ, 2019. 2. ์์ง์ฑ.Collecting and tracking data in everyday contexts is a common practice for both individual self-trackers and researchers. The increase in wearable and mobile technologies for self-tracking encourages people to gain personal insights from the data about themselves. Also, researchers exploit self-tracking to gather data in situ or to foster behavioral change.
Despite a diverse set of available tracking tools, however, it is still challenging to find ones that suit unique tracking needs, preferences, and commitments. Individual self-tracking practices are constrained by the tracking tools' initial design, because it is difficult to modify, extend, or mash up existing tools. Limited tool support also impedes researchers' efforts to conduct in situ data collection studies. Many researchers still build their own study instruments due to the mismatch between their research goals and the capabilities of existing toolkits.
The goal of this dissertation is to design flexible self-tracking technologies that are generative and adaptive to cover diverse tracking contexts, ranging from personal tracking to research contexts. Specifically, this dissertation proposes OmniTrack, a flexible self-tracking approach leveraging a semi-automated tracking concept that combines manual and automated tracking methods to generate an arbitrary tracker design.
OmniTrack was implemented as a mobile app for individuals. The OmniTrack app enables self-trackers to construct their own trackers and customize tracking items to meet their individual needs. A usability study and a field development study were conducted with the goal of assessing how people adopt and adapt OmniTrack to fulfill their needs. The studies revealed that participants actively used OmniTrack to create, revise, and appropriate trackers, ranging from a simple mood tracker to a sophisticated daily activity tracker with multiple fields.
Furthermore, OmniTrack was extended to cover research contexts that enclose manifold personal tracking contexts. As part of the research, this dissertation presents OmniTrack Research Kit, a research platform that allows researchers without programming expertise to configure and conduct in situ data collection studies by deploying the OmniTrack app on participants' smartphones. A case study in deploying the research kit for conducting a diary study demonstrated how OmniTrack Research Kit could support researchers who manage study participants' self-tracking process.
This work makes artifacts contributions to the fields of human-computer interaction and ubiquitous computing, as well as expanding empirical understanding of how flexible self-tracking tools can enhance the practices of individual self-trackers and researchers. Moreover, this dissertation discusses design challenges for flexible self-tracking technologies, opportunities for further improving the proposed systems, and future research agenda for reaching the audiences not covered in this research.์ผ์์ ๋งฅ๋ฝ์์ ๋ฐ์ดํฐ๋ฅผ ๋ชจ์ผ๋ ํ๋์ธ ์
ํ ํธ๋ํน(self-tracking)์ ๊ฐ์ธ๊ณผ ์ฐ๊ตฌ์ ์์ญ์์ ํ๋ฐํ ํ์ฉ๋๊ณ ์๋ค. ์จ์ด๋ฌ๋ธ ๋๋ฐ์ด์ค์ ๋ชจ๋ฐ์ผ ๊ธฐ์ ์ ๋ฐ๋ฌ๋ก ์ธํด ์ฌ๋๋ค์ ๊ฐ์์ ์ถ์ ๋ํด ๋งํด์ฃผ๋ ๋ฐ์ดํฐ๋ฅผ ๋ ์ฝ๊ฒ ์์งํ๊ณ , ํต์ฐฐํ ์ ์๊ฒ ๋์๋ค. ๋ํ, ์ฐ๊ตฌ์๋ค์ ํ์ฅ(in situ) ๋ฐ์ดํฐ๋ฅผ ์์งํ๊ฑฐ๋ ์ฌ๋๋ค์๊ฒ ํ๋ ๋ณํ๋ฅผ ์ผ์ผํค๋ ๋ฐ์ ์
ํ ํธ๋ํน์ ํ์ฉํ๋ค.
๋น๋ก ์
ํ ํธ๋ํน์ ์ํ ๋ค์ํ ๋๊ตฌ๋ค์ด ์กด์ฌํ์ง๋ง, ํธ๋ํน์ ๋ํด ๋ค์ํ๋ ์๊ตฌ์ ์ทจํฅ์ ์๋ฒฝํ ์ถฉ์กฑํ๋ ๊ฒ๋ค์ ์ฐพ๋ ๊ฒ์ ์ฝ์ง ์๋ค. ๋๋ถ๋ถ์ ์
ํ ํธ๋ํน ๋๊ตฌ๋ ์ด๋ฏธ ์ค๊ณ๋ ๋ถ๋ถ์ ์์ ํ๊ฑฐ๋ ํ์ฅํ๊ธฐ์ ์ ํ์ ์ด๋ค. ๊ทธ๋ ๊ธฐ ๋๋ฌธ์ ์ฌ๋๋ค์ ์
ํ ํธ๋ํน์ ๋ํ ์์ ๋๋ ๊ธฐ์กด ๋๊ตฌ๋ค์ ๋์์ธ ๊ณต๊ฐ์ ์ํด ์ ์ฝ์ ๋ฐ์ ์๋ฐ์ ์๋ค. ๋ง์ฐฌ๊ฐ์ง๋ก, ํ์ฅ ๋ฐ์ดํฐ๋ฅผ ์์งํ๋ ์ฐ๊ตฌ์๋ค๋ ์ด๋ฌํ ๋๊ตฌ์ ํ๊ณ๋ก ์ธํด ์ฌ๋ฌ ๋ฌธ์ ์ ๋ด์ฐฉํ๋ค. ์ฐ๊ตฌ์๋ค์ด ๋ฐ์ดํฐ๋ฅผ ํตํด ๋ตํ๊ณ ์ ํ๋ ์ฐ๊ตฌ ์ง๋ฌธ(research question)์ ๋ถ์ผ๊ฐ ๋ฐ์ ํ ์๋ก ์ธ๋ถ๋๊ณ , ์น๋ฐํด์ง๊ธฐ ๋๋ฌธ์ ์ด๋ฅผ ์ํด์๋ ๋ณต์กํ๊ณ ๊ณ ์ ํ ์คํ ์ค๊ณ๊ฐ ํ์ํ๋ค. ํ์ง๋ง ํ์กดํ๋ ์ฐ๊ตฌ์ฉ ์
ํ ํธ๋ํน ํ๋ซํผ๋ค์ ์ด์ ๋ถํฉํ๋ ์์ ๋๋ฅผ ๋ฐํํ์ง ๋ชปํ๋ค. ์ด๋ฌํ ๊ฐ๊ทน์ผ๋ก ์ธํด ๋ง์ ์ฐ๊ตฌ์๋ค์ด ๊ฐ์์ ํ์ฅ ๋ฐ์ดํฐ ์์ง ์ฐ๊ตฌ์ ํ์ํ ๋์งํธ ๋๊ตฌ๋ค์ ์ง์ ๊ตฌํํ๊ณ ์๋ค.
๋ณธ ์ฐ๊ตฌ์ ๋ชฉํ๋ ์์ ๋ ๋์---์ฐ๊ตฌ์ ๋งฅ๋ฝ๊ณผ ๊ฐ์ธ์ ๋งฅ๋ฝ์ ์์ฐ๋ฅด๋ ๋ค์ํ ์ํฉ์ ํ์ฉํ ์ ์๋---์
ํ ํธ๋ํน ๊ธฐ์ ์ ๋์์ธํ๋ ๊ฒ์ด๋ค. ์ด๋ฅผ ์ํด ๋ณธ๊ณ ์์๋ ์ด๋ํธ๋(OmniTrack)์ด๋ผ๋ ๋์์ธ ์ ๊ทผ๋ฒ์ ์ ์ํ๋ค. ์ด๋ํธ๋์ ์์ ๋ ๋์ ์
ํ ํธ๋ํน์ ์ํ ๋ฐฉ๋ฒ๋ก ์ด๋ฉฐ, ๋ฐ์๋ ํธ๋ํน(semi-automated tracking)์ด๋ผ๋ ์ปจ์
์ ๋ฐํ์ผ๋ก ์๋ ๋ฐฉ์๊ณผ ์๋ ๋ฐฉ์์ ์กฐํฉ์ ํตํด ์์์ ํธ๋์ปค๋ฅผ ํํํ ์ ์๋ค.
๋จผ์ ์ด๋ํธ๋์ ๊ฐ์ธ์ ์ํ ๋ชจ๋ฐ์ผ ์ฑ ํํ๋ก ๊ตฌํํ์๋ค. ์ด๋ํธ๋ ์ฑ์ ๊ฐ๊ฐ์ธ์ด ์์ ์ ํธ๋ํน ๋์ฆ์ ๋ง๋ ํธ๋์ปค๋ฅผ ์ปค์คํฐ๋ง์ด์งํ์ฌ ํ์ฉํ ์ ์๋๋ก ๊ตฌ์ฑ๋์ด ์๋ค. ๋ณธ๊ณ ์์๋ ์ฌ๋๋ค์ด ์ด๋ป๊ฒ ์ด๋ํธ๋์ ์์ ์ ๋์ฆ์ ๋ง๊ฒ ํ์ฉํ๋์ง ์์๋ณด๊ณ ์ ์ฌ์ฉ์ฑ ํ
์คํธ(usability testing)์ ํ๋ ๋ฐฐํฌ ์ฐ๊ตฌ(field deployment study)๋ฅผ ์ํํ์๋ค. ์ฐธ๊ฐ์๋ค์ ์ด๋ํธ๋์ ํ๋ฐํ ์ด์ฉํด ๋ค์ํ ๋์์ธ์ ํธ๋์ปคโ์์ฃผ ๋จ์ํ ๊ฐ์ ํธ๋์ปค๋ถํฐ ์ฌ๋ฌ ๊ฐ์ ํ๋๋ฅผ ๊ฐ์ง ๋ณต์กํ ์ผ์ผ ํ๋ ํธ๋์ปค๊น์งโ๋ค์ ์์ฑํ๊ณ , ์์ ํ๊ณ , ํ์ฉํ์๋ค.
๋ค์์ผ๋ก, ์ด๋ํธ๋์ ํ์ฅ ๋ฐ์ดํฐ ์์ง ์ฐ๊ตฌ์ ํ์ฉํ ์ ์๋๋ก ์ฐ๊ตฌ ํ๋ซํผ ํํ์ '์ด๋ํธ๋ ๋ฆฌ์์น ํท(OmniTrack Research Kit)'์ผ๋ก ํ์ฅํ์๋ค. ์ด๋ํธ๋ ๋ฆฌ์์น ํท์ ์ฐ๊ตฌ์๋ค์ด ํ๋ก๊ทธ๋๋ฐ ์ธ์ด ์์ด ์ํ๋ ์คํ์ ์ค๊ณํ๊ณ ์ด๋ํธ๋ ์ฑ์ ์ฐธ๊ฐ์๋ค์ ์ค๋งํธํฐ์ผ๋ก ๋ฐฐํฌํ ์ ์๋๋ก ๋์์ธ๋์๋ค. ๊ทธ๋ฆฌ๊ณ ์ด๋ํธ๋ ๋ฆฌ์์น ํท์ ์ด์ฉํด ์ผ์ง๊ธฐ๋ก ์ฐ๊ตฌ(diary study)๋ฅผ ์ง์ ์ํํ์๊ณ , ์ด๋ฅผ ํตํด ์ด๋ํธ๋ ์ ๊ทผ๋ฒ์ด ์ด๋ป๊ฒ ์ฐ๊ตฌ์๋ค์ ์ฐ๊ตฌ ๋ชฉ์ ์ ์ด๋ฃจ๋ ๋ฐ์ ๋์์ ์ค ์ ์๋์ง ์ง์ ํ์ธํ์๋ค.
๋ณธ ์ฐ๊ตฌ๋ ํด๋จผ-์ปดํจํฐ ์ธํฐ๋์
(Human-Computer Interaction) ๋ฐ ์ ๋น์ฟผํฐ์ค ์ปดํจํ
(Ubiquitous Computing) ๋ถ์ผ์ ๊ธฐ์ ์ ์ฐ์ถ๋ฌผ๋ก์จ ๊ธฐ์ฌํ๋ฉฐ, ์์ ๋ ๋์ ์
ํ ํธ๋ํน ๋๊ตฌ๊ฐ ์ด๋ป๊ฒ ๊ฐ์ธ๊ณผ ์ฐ๊ตฌ์๋ค์ ๋์ธ ์ ์๋์ง ์ค์ฆ์ ์ธ ์ดํด๋ฅผ ์ฆ์งํ๋ค. ๋ํ, ์์ ๋ ๋์ ์
ํํธ๋ํน ๊ธฐ์ ์ ๋ํ ๋์์ธ์ ๋์ , ์ฐ๊ตฌ์์ ์ ์ํ ์์คํ
์ ๋ํ ๊ฐ์ ๋ฐฉ์, ๋ง์ง๋ง์ผ๋ก ๋ณธ ์ฐ๊ตฌ์์ ๋ค๋ฃจ์ง ๋ชปํ ๋ค๋ฅธ ์ง๋จ์ ์ง์ํ๊ธฐ ์ํ ํฅํ ์ฐ๊ตฌ ๋
ผ์ ์ ๋ํ์ฌ ๋
ผ์ํ๋ค.Abstract
CHAPTER 1. Introduction
1.1 Background and Motivation
1.2 Research Questions and Approaches
1.2.1 Designing a Flexible Self-Tracking Approach Leveraging Semiautomated
Tracking
1.2.2 Design and Evaluation of OmniTrack in Individual Tracking Contexts
1.2.3 Designing a Research Platform for In Situ Data Collection Studies
Leveraging OmniTrack
1.2.4 A Case Study of Conducting an In Situ Data Collection Study
using the Research Platform
1.3 Contributions
1.4 Structure of this Dissertation
CHAPTER 2. Related Work
2.1 Background on Self-Tracking
2.1.1 Self-Tracking in Personal Tracking Contexts
2.1.2 Utilization of Self-Tracking in Other Contexts
2.2 Barriers Caused by Limited Tool Support
2.2.1 Limited Tools and Siloed Data in Personal Tracking
2.2.2 Challenges of the Instrumentation for In Situ Data Collection
2.3 Flexible Self-Tracking Approaches
2.3.1 Appropriation of Generic Tools
2.3.2 Universal Tracking Systems for Individuals
2.3.3 Research Frameworks for In Situ Data Collection
2.4 Grounding Design Approach: Semi-Automated Tracking
2.5 Summary of Related Work
CHAPTER3
DesigningOmniTrack: a Flexible Self-Tracking Approach
3.1 Design Goals and Rationales
3.2 System Design and User Interfaces
3.2.1 Trackers: Enabling Flexible Data Inputs
3.2.2 Services: Integrating External Trackers and Other Services
3.2.3 Triggers: Retrieving Values Automatically
3.2.4 Streamlining Tracking and Lowering the User Burden
3.2.5 Visualization and Feedback
3.3 OmniTrack Use Cases
3.3.1 Tracker 1: Beer Tracker
3.3.2 Tracker 2: SleepTight++
3.3.3 Tracker 3: Comparison of Automated Trackers
3.4 Summary
CHAPTER 4. Understanding HowIndividuals Adopt and Adapt OmniTrack
4.1 Usability Study
4.1.1 Participants
4.1.2 Procedure and Study Setup
4.1.3 Tasks
4.1.4 Results and Discussion
4.1.5 Improvements A_er the Usability Study
4.2 Field Deployment Study
4.2.1 Study Setup
4.2.2 Participants
4.2.3 Data Analysis and Results
4.2.4 Reflections on the Deployment Study
4.3 Discussion
4.3.1 Expanding the Design Space for Self-Tracking
4.3.2 Leveraging Other Building Blocks of Self-Tracking
4.3.3 Sharing Trackers with Other People
4.3.4 Studying with a Broader Audience
4.4 Summary
CHAPTER 5. Extending OmniTrack for Supporting In Situ Data Collection
Studies
5.1 Design Space of Study Instrumentation for In-Situ Data Collection
5.1.1 Experiment-Level Dimensions
5.1.2 Condition-Level Dimensions
5.1.3 Tracker-Level Dimensions
5.1.4 Reminder/Trigger-Level Dimensions
5.1.5 Extending OmniTrack to Cover the Design Space
5.2 Design Goals and Rationales
5.3 System Design and User Interfaces
5.3.1 Experiment Management and Collaboration
5.3.2 Experiment-level Configurations
5.3.3 A Participants Protocol for Joining the Experiment
5.3.4 Implementation
5.4 Replicated Study Examples
5.4.1 Example A: Revisiting the Deployment Study of OmniTrack
5.4.2 Example B: Exploring the Clinical Applicability of a Mobile Food
Logger
5.4.3 Example C: Understanding the Effect of Cues and Positive Reinforcement
on Habit Formation
5.4.4 Example D: Collecting Stress and Activity Data for Building a
Prediction Model
5.5 Discussion
5.5.1 Supporting Multiphase Experimental Design
5.5.2 Serving as Testbeds for Self-Tracking Interventions
5.5.3 Exploiting the Interaction Logs
5.6 Summary
CHAPTER 6. Using the OmniTrack Research Kit: A Case Study
6.1 Study Background and Motivation
6.2 OmniTrack Configuration for Study Instruments
6.3 Participants
6.4 Study Procedure
6.5 Dataset and Analysis
6.6 Study Result
6.6.1 Diary Entries
6.6.2 Aspects of Productivity Evaluation
6.6.3 Productive Activities
6.7 Experimenter Experience of OmniTrack
6.8 Participant Experience of OmniTrack
6.9 Implications
6.9.1 Visualization Support for Progressive, Preliminary Analysis of
Collected Data
6.9.2 Inspection to Prevent Misconfiguration
6.9.3 Providing More Alternative Methods to Capture Data
6.10 Summary
CHAPTER 7. Discussion
7.1 Lessons Learned
7.2 Design Challenges and Implications
7.2.1 Making the Flexibility Learnable
7.2.2 Additive vs. Subtractive Design for Flexibility
7.3 Future Opportunities for Improvement
7.3.1 Utilizing External Information and Contexts
7.3.2 Providing Flexible Visual Feedback
7.4 Expanding Audiences of OmniTrack
7.4.1 Supporting Clinical Contexts
7.4.2 Supporting Self-Experimenters
7.5 Limitations
CHAPTER 8. Conclusion
8.1 Summary of the Approaches
8.2 Summary of Contributions
8.2.1 Artifact Contributions
8.2.2 Empirical Research Contributions
8.3 Future Work
8.3.1 Understanding the Long-term E_ect of OmniTrack
8.3.2 Utilizing External Information and Contexts
8.3.3 Extending the Input Modality to Lower the Capture Burden
8.3.4 Customizable Visual Feedback
8.3.5 Community-Driven Tracker Sharing
8.3.6 Supporting Multiphase Study Design
8.4 Final Remarks
APPENDIX A. Study Material for Evaluations of the OmniTrack App
A.1 Task Instructions for Usability Study
A.2 The SUS (System Usability Scale) Questionnaire
A.3 Screening Questionnaire for Deployment Study
A.4 Exit Interview Guide for Deployment Study
A.5 Deployment Participant Information
APPENDIX B
Study Material for Productivity Diary Study
B.1 Recruitment Screening Questionnaire
B.2 Exit Interview Guide
Abstract (Korean)Docto
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Newcomersโ Barriers. . . Is That All? An Analysis of Mentorsโ and Newcomersโ Barriers in OSS Projects
Newcomersโ seamless onboarding is important for open collaboration communi- ties, particularly those that leverage outsidersโ contributions to remain sustainable. Nevertheless, previous work shows that OSS newcomers often face several barriers to contribute, which lead them to lose motivation and even give up on contributing. A well-known way to help newcomers overcome initial contribution barriers is men- toring. This strategy has proven effective in offline and online communities, and to some extent has been employed in OSS projects. Studying mentorsโ perspectives on the barriers that newcomers face play a vital role in improving onboarding pro- cesses; yet, OSS mentors face their own barriers, which hinder the effectiveness of the strategy. Since little is known about the barriers mentors face, in this thesis, we investigate the barriers that affect mentors and their newcomer mentees. We interviewed mentors from OSS projects and qualitatively analyzed their answers.
We found 44 barriers: 19 that affect mentors; and 34 that affect newcomers (9 af- fect both newcomers and mentors). Interestingly, most of the barriers we identified (66%) have a social nature. Additionally, we identified 10 strategies that mentors indicated to potentially alleviate some of the barriers. Since gender-related chal- lenges emerged in our analysis, we conducted nine follow-up structured interviews to further explore this perspective. The contributions of this thesis include: iden- tifying the barriers mentors face; bringing the unique perspective of mentors on barriers faced by newcomers; unveiling strategies that can be used by mentors to support newcomers; and investigating gender-specific challenges in OSS mentor- ship. Mentors, newcomers, online communities, and educators can leverage this knowledge to foster new contributors to OSS projects