An official website of the United States government
MCVT (Making Computing Visible and Tangible) Cards are a toolkit of paper-based computing cards intended for use in the codesign of inclusive computing education. Working with groups of teachers and students over multiple design sessions, we share our toolkit, design drivers and material considerations; and use cases drawn from a week-long codesign workshop where seven teachers made and adapted cards for their future classroom facilitation. Our findings suggest that teachers valued the MCVT toolkit as a resource for their own learning and perceived the cards to be useful for supporting new computational practices, specifically for learning through making and connecting to examples of everyday computing. Critically reviewed by teachers during codesign workshops, the toolkit however posed some implementation challenges and constraints for learning through making and troubleshooting circuitry. From teacher surveys, interviews, workshop video recordings, and teacher-constructed projects, we show how teachers codesigned new design prototypes and pedagogical activities while also adapting and extending paper-based computing materials so their students could take advantage of the unique technical and expressive affordances of MCVT Cards. Our design research contributes a new perspective on using interactive paper computing cards as a medium for instructional materials development to support more inclusive computing education.
more »
« less
From cloud computing to sky computing
We consider the future of cloud computing and ask how we might guide it towards a more coherent service we call sky computing. The barriers are more economic than technical, and we propose reciprocal peering as a key enabling step.
more »
« less
- PAR ID:
- 10310458
- Date Published:
- Journal Name:
- HotOS '21: Proceedings of the Workshop on Hot Topics in Operating Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Despite increasing demands for skilled workers within the technological domain, there is still a deficit in the number of graduates in computing fields (computer science, information technology, and computer engineering). Understanding the factors that contribute to students’ motivation and persistence is critical to helping educators, administrators, and industry professionals better focus efforts to improve academic outcomes and job placement. This article examines how experiences contribute to a student’s computing identity, which we define by their interest, recognition, sense of belonging, and competence/performance beliefs. In particular, we consider groups underrepresented in these disciplines, women and minoritized racial/ethnic groups (Black/African American and Hispanic/Latinx). To delve into these relationships, a survey of more than 1,600 students in computing fields was conducted at three metropolitan public universities in Florida. Regression was used to elucidate which experiences predict computing identity and how social identification (i.e., as female, Black/African American, and/or Hispanic/Latinx) may interact with these experiences. Our results suggest that several types of experiences positively predict a student’s computing identity, such as mentoring others, having a job, or having friends in computing. Moreover, certain experiences have a different effect on computing identity for female and Hispanic/Latinx students. More specifically, receiving academic advice from teaching assistants was more positive for female students, receiving advice from industry professionals was more negative for Hispanic/Latinx students, and receiving help on classwork from students in their class was more positive for Hispanic/Latinx students. Other experiences, while having the same effect on computing identity across students, were experienced at significantly different rates by females, Black/African American students, and Hispanic/Latinx students. The findings highlight experiential ways in which computing programs can foster computing identity development, particularly for underrepresented and marginalized groups in computing.more » « less
-
Despite increasing demands for skilled workers within the technological domain, there is still a deficit in the number of graduates in computing fields (computer science, information technology, and computer engineering). Understanding the factors that contribute to students’ motivation and persistence is critical to helping educators, administrators, and industry professionals better focus efforts to improve academic outcomes and job placement. This article examines how experiences contribute to a student’s computing identity, which we define by their interest, recognition, sense of belonging, and competence/performance beliefs. In particular, we consider groups underrepresented in these disciplines, women and minoritized racial/ethnic groups (Black/African American and Hispanic/Latinx). To delve into these relationships, a survey of more than 1,600 students in computing fields was conducted at three metropolitan public universities in Florida. Regression was used to elucidate which experiences predict computing identity and how social identification (i.e., as female, Black/African American, and/or Hispanic/Latinx) may interact with these experiences. Our results suggest that several types of experiences positively predict a student’s computing identity, such as mentoring others, having a job, or having friends in computing. Moreover, certain experiences have a different effect on computing identity for female and Hispanic/Latinx students. More specifically, receiving academic advice from teaching assistants was more positive for female students, receiving advice from industry professionals was more negative for Hispanic/Latinx students, and receiving help on classwork from students in their class was more positive for Hispanic/Latinx students. Other experiences, while having the same effect on computing identity across students, were experienced at significantly different rates by females, Black/African American students, and Hispanic/Latinx students. The findings highlight experiential ways in which computing programs can foster computing identity development, particularly for underrepresented and marginalized groups in computing.more » « less
-
Brain-inspired Hyper-dimensional(HD) computing is a novel and efficient computing paradigm. However, highly parallel architectures such as Processing-in-Memory(PIM) are bottle-necked by reduction operations required such as accumulation. To reduce this bottle-neck of HD computing in PIM, we present Stochastic-HD that combines the simplicity of operations in Stochastic Computing (SC) with the complex task solving capabilities of the latest HD computing algorithms. Stochastic-HD leverages deterministic SC, which enables all of HD operations to be done as highly parallel bitwise operations and removes all reduction operations, thus improving the throughput of PIM. To this end, we propose an in-memory hardware design for Stochastic-HD that exploits its high level of parallelism and robustness to approximation. Our hardware uses in-memory bitwise operations along with associative memory-like operations to enable a fast and energy-efficient implementation. With Stochastic-HD, we were able to reach a comparable accuracy with the Baseline-HD. Furthermore, by proposing an integrated Stochastic-HD retraining approach Stochastic-HD is able to reduce the accuracy loss to just 0.3%. We additionally accelerate the retraining process in our hardware design to create an end-to-end accelerator for Stochastic-HD. Finally, we also add support for HD Clustering to Stochastic-HD, which is the first to map the HD Clustering operations to the stochastic domain. As compared to the best PIM design for HD, Stochastic-HD is also 4.4% more accurate and 43.1× more energy-efficient.more » « less
-
Abstract Computer applications often leave traces or residues that enable forensic examiners to gain a detailed understanding of the actions a user performed on a computer. Such digital breadcrumbs are left by a large variety of applications, potentially (and indeed likely) unbeknownst to their users. This paper presents the concept of residue-free computing in which a user can operate any existing application installed on their computer in a mode that prevents trace data from being recorded to disk, thus frustrating the forensic process and enabling more privacy-preserving computing. In essence, residue-free computing provides an “incognito mode” for any application. We introduce our implementation of residue-free computing, R esidue F ree , and motivate R esidue F ree by inventorying the potentially sensitive and privacy-invasive residue left by popular applications. We demonstrate that R esidue F ree allows users to operate these applications without leaving trace data, while incurring modest performance overheads.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3458336.3465301
