Procrastination is a major issue faced by students which can lead to negative impacts on their academic performance and mental health. Productivity tools aim to help individuals to alleviate this
behavior by providing self-regulatory support. However, the processes of how these applications help students conquer academic procrastination are under-explored. Particularly, it is essential to understand what aspects of these applications help which kinds of students in accomplishing their academic tasks. In this paper, we address this gap by presenting an academic planning and time
management app (Proccoli) and a study designed to understand the association between student procrastination modeling, in-app behaviors, and perceived performance with app evaluation. As the
core of our study, we analyze student perceptions of Proccoli and its impact on their study tasks and time management skills. Then, we model student procrastination behaviors by Hawkes process
mining, assess student in-app behaviors by specifying planning and performance-related measures and evaluate the relationship between student behaviors and the evaluation survey results. Our
study shows a need for personalized self-regulation support in Proccoli, as students with different in-app studying behaviors are found to have different perceptions of the app functionalities and the
association between the prompts for social accountability students received by using Proccoli and their procrastination behavior is significant.
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Planning Habit: Daily Planning Prompts with Alexa
The widespread adoption of intelligent voice assistants (IVAs), like Amazon’s Alexa or Google’s Assistant, presents new opportunities for designers of persuasive technologies to explore how to support people’s behavior change goals and habits with voice technology. In this work, we explore how to use planning prompts, a technique from behavior science to make specific and effective plans, with IVAs. We design and conduct usability testing (N = 13) on a voice app called Planning Habit that encourages users to formulate daily plans out loud. We identify strategies that make it possible to successfully adapt planning prompts to voice format. We then conduct a week-long online deployment (N = 40) of the voice app in the context of daily productivity. Overall, we find that traditional forms of planning prompts can be adapted to and enhanced by IVA technology.
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- Award ID(s):
- 1700832
- NSF-PAR ID:
- 10252037
- Editor(s):
- Ali, Raian; Lugrin, Birgit; Charles, Fred
- Date Published:
- Journal Name:
- Persuasive Technology (PERSUASIVE 2021)
- Volume:
- 12684
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Mobile and web apps are increasingly relying on the data generated or provided by users such as from their uploaded documents and images. Unfortunately, those apps may raise significant user privacy concerns. Specifically, to train or adapt their models for accurately processing huge amounts of data continuously collected from millions of app users, app or service providers have widely adopted the approach of crowdsourcing for recruiting crowd workers to manually annotate or transcribe the sampled ever-changing user data. However, when users' data are uploaded through apps and then become widely accessible to hundreds of thousands of anonymous crowd workers, many human-in-the-loop related privacy questions arise concerning both the app user community and the crowd worker community. In this paper, we propose to investigate the privacy risks brought by this significant trend of large-scale crowd-powered processing of app users' data generated in their daily activities. We consider the representative case of receipt scanning apps that have millions of users, and focus on the corresponding receipt transcription tasks that appear popularly on crowdsourcing platforms. We design and conduct an app user survey study (n=108) to explore how app users perceive privacy in the context of using receipt scanning apps. We also design and conduct a crowd worker survey study (n=102) to explore crowd workers' experiences on receipt and other types of transcription tasks as well as their attitudes towards such tasks. Overall, we found that most app users and crowd workers expressed strong concerns about the potential privacy risks to receipt owners, and they also had a very high level of agreement with the need for protecting receipt owners' privacy. Our work provides insights on app users' potential privacy risks in crowdsourcing, and highlights the need and challenges for protecting third party users' privacy on crowdsourcing platforms. We have responsibly disclosed our findings to the related crowdsourcing platform and app providers.
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Open-ended tasks can be both beneficial and challenging to students learning to program. Such tasks allow students to be more creative and feel ownership over their work, but some students struggle with unstructured tasks and, without proper scaffolds, this can lead to negative learning experiences. Scratch is a widely used coding platform to teach computer science in classrooms and is designed to support learner creativity and expression. With its open-ended nature, Scratch can be used in various ways in the classroom to meet the needs of schools and districts. One challenge of using Scratch in classrooms is supporting learners in exploring their interests and fostering creativity while still meeting the instructional goals of a lesson and ensuring all students are engaged with, and understand, focal concepts and practices. In this paper, we investigate the use of planning sheets to fa- cilitate novice programmers designing and implementing Scratch programs based on open-ended prompts. To evaluate the plan- ning sheets, we look at how closely students’ implemented Scratch projects match their plans and whether the implemented Scratch projects met the technical requirements for the given lesson. We analyzed 303 Scratch projects from 155 middle grade students (ages 10-14) who were introduced to programming via the Scratch Encore Curriculum. Completed Scratch projects that used planning sheets (202) were qualitatively coded to evaluate how closely they matched the initial plan, and Scratch programs (303) were analyzed with an automated grader to check if technical project requirements were met. Our results reveal that students that used planning sheets met significantly more technical project requirements and had more complex structures than those that did not have planning sheets. Results differ based on teacher and type of planning sheet used (physical vs. virtual). This work suggests that planning sheets are a helpful tool for young learners when completing open-ended coding projects.more » « less
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Abstract The planning, design, and maintenance of stormwater infrastructure must be informed by changing rainfall patterns due to climate change. However, there is little consensus on how future climate information should be used, or how uncertainties introduced by use of different methods and datasets should be characterized or managed. These uncertainties exacerbate existing challenges to using climate information on local or municipal scales. Here we analyze major cities in the U.S., 48 of which developed climate adaptation and resilience plans. Given the prevalence of depth duration frequency (DDF) curves for planning infrastructure for rainfall, we then assessed the underlying climate information used in these 48 plans to show how DDF curves used for resilience planning and the resulting outcomes can be affected by stakeholders’ methodological choices and datasets. For rainfall extremes, many resilience plans varied by trend detection method, data preprocessing steps, and size of study area, and all used only one of the available downscaled climate projection datasets. We evaluate the implications of uncertainties across five available climate datasets and show the level of climate resilience to extreme rainfall depends on the dataset selected for each city. We produce risk matrices for a broader set of 77 U.S. cities to highlight how local resilience strategies and decisions are sensitive to the climate projection dataset used in local adaptation plans. To help overcome barriers to using climate information, we provide an open dataset of future daily rainfall values for 2-, 5-, 10-, 25-, 50-, and 100 years annual recurrence intervals for 77 cities and compare resilience outcomes across available climate datasets that each city can use for comparison and for robust resilience planning. Because of uncertainty in climate projections, our results highlight the importance of no-regret and flexible resilience strategies that can be adjusted with new climate information.
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Frank, Brian W. ; Jones, Dyan L. ; Ryan, Qing X. (Ed.)Many of the activities and cognitive processes that physicists use while solving problems are "invisible" to students, which can hinder their acquisition of important expert-like skills. Whereas the detailed calculations performed by researchers are often published in journals and textbooks, other activities such as those undertaken while planning how to approach a problem are rarely discussed in published research. Hence, these activities are especially hidden from students. To better understand how physicists solve problems in their professional research, we leveraged the framework of cognitive task analysis to conduct semi-structured interviews with theoretical physicists (N=11). Here we elucidate the role of planning and preliminary analysis in theorists' work. Theorists described using a variety of activities in order to decide if their project was doable while also generating possible solution paths. These actions included doing cursory calculations, reflecting on previous knowledge, gaining intuition and understanding by studying prior work, and reproducing previous results. We found that theorists typically did not pursue projects unless they had a clear idea of what the outcome of their project would be, or at least knew that they would be able to make progress on the problem. Thus, this preliminary design and analysis phase was highly important for theorists despite being largely hidden from students. We conclude by suggesting potential ways to incorporate our findings into the classroom to give students more numerous opportunities to engage in these expert-like practices.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-79460-6_7
