Recruiting, retaining, and educating students in computing is a frequent research topic in CHI. However, students’ sociotechnical experiences of registering for classes are understudied—especially those of socioeconomic-diverse students. These experiences matter: research shows that registration problems bring long-term consequences to student successes. We investigate students’ socioeconomic status (SES) impact on registration experiences through three studies: a case study with education professionals using an emerging analytic method, SocioeconomicMag (SESMag); interviews with faculty/staff/students from 8 universities; and observations of 14 SES-diverse students registering for classes. Results showed: (1) 5 SES-inclusivity bugs which arose 30 times, 72% more often by lower-SES students than by higher-SES students. (2) 6/7 lower-SES students (but only 2/7 higher-SES students) expected downstream problems from the registration issues. (3) The risk-to-negative-outcomes rate was 3 times higher for lower-SES students. (4) The issues generalized across 8 universities and potentially to >700 other universities who use the same registration portal.

In this panel, the presenters will discuss their collective experience of teaching software engineering courses and/or running software engineering projects that help students learn about and experience the impact of computing on society and the social good. While the benefits of practical experience in software engineering are generally indisputable, the logistics and management of such projects are often discouraging for faculty, leading many to exclude live clients from software engineering courses. The presenters will demystify and discuss the realities of running client-oriented classes and projects in the contexts of our institutions, which vary greatly in size and student demographics and represent both public and private colleges. In particular, we will discuss various approaches used to identify, design, create, and evaluate software engineering projects for societal and social impact. Project duration ranges from one semester to two or more, and participation in team projects is modeled as pre-professional training, complete with software tools, interpersonal dynamics, and evaluation methods.

Establishing productive research collaborations can be complex. As all universities and colleges are unique, the context in which potential collaborators work must be understood. Collaborations between MSI and non-MSI institutions are particularly challenging due to the diversity of MSI institutions, their missions, and the context in which MSI faculty work.

Inclusive design appears rarely, if at all, in most undergraduate computer science (CS) curricula. As a result, many CS students graduate without knowing how to apply inclusive design to the software they build, and go on to careers that perpetuate the prolif- eration of software that excludes communities of users. Our panel of CS faculty will explain how we have been working to address this problem. For the past several years, we have been integrating bits of inclusive design in multiple courses in CS undergraduate programs, which has had very positive impacts on students' ratings of their instructors, students' ratings of the education climate, and students' retention. The panel's content will be mostly concrete examples of how we are doing this, so that attendees can leave with an in-the-trenches understanding of what this looks like for CS faculty across specialization areas and classes. We also show how it can be used in a department's BPC Plan and point to resources on the CRA's BPCnet Activity Library and on OERcommons, to enable interested faculty to go forward with this approach in their own classes and departments.

Abstract Software producers are now recognizing the importance of improving their products’ suitability for diverse populations, but little attention has been given to measurements to shed light on products’ suitability to individuals below the median s ocio e conomic s tatus (SES)—who, by definition, make up half the population. To enable software practitioners to attend to both lower- and higher-SES individuals, this paper provides two new surveys that together can facilitate measuring how well a software product serves socioeconomically diverse populations. The first survey (SES-Subjective) is who-oriented: it measures who their potential or current users are in terms of their subjective SES (perceptions of their SES). The second survey (SES-Facets) is why-oriented: it collects individuals’ values for an evidence-based set of facet values (individual traits) that (1) statistically differ by SES and (2) affect how an individual works and problem-solves with software products. The surveys’ design goal is worldwide applicability, but as a first step, here we empirically validated both these surveys with deployments at University A and University B (464 and 522 responses, respectively), which showed reliability of both the surveys in a US context. Our results also statistically agree with both ground truth data on respondents’ socioeconomic statuses and with predictions from foundational literature. Finally, we explain how the pair of surveys can be uniquely actionable by software practitioners, such as in requirements gathering, debugging, quality assurance activities, maintenance activities, and fulfilling legal reporting requirements such as those being drafted by various governments for AI-powered software.

Despite efforts to raise awareness of societal and ethical issues in CS education, research shows students often do not act upon their new awareness (Problem 1). One such issue, well-established by HCI research, is that much of technology contains barriers impacting numerous populations—such as minoritized genders, races, ethnicities, and more. HCI has inclusive design methods that help—but these skills are rarely taught, even in HCI classes (Problem 2). To address Problems 1 and 2, we created the Matchmaker Curriculum to pair CS faculty—including non-HCI faculty—with inclusive design elements to allow for inclusive design skill-building throughout their CS program. We present the curriculum and a field study, in which we followed 18 faculty along their journey. The results show how the Matchmaker Curriculum equipped 88% of these faculty with enough inclusive design teaching knowledge to successfully embed actionable inclusive design skill-building into 13 CS courses.

This work-in-progress (WIP) innovative practice paper focuses on skills developed by student instructional team members working in peer instruction in computing. Research has shown that peer instruction models such as supplemental instruction (SI) support computer science student learning and peer community building while extending the reach of the instructional team. SI participants achieve higher grades and experience greater retention rates in the STEM majors than their non-participating peers, an impact that can extend beyond the initial supplemental instruction semester. While benefits for Supplemental Instruction learners are well documented, the program's effect on the leaders is not well documented. Supplemental Instruction leaders receive training, mentoring, and peer guidance in developing teaching practices that meet students' needs. Unlike lecture-based instruction, supplemental instruction at Kean University involves modeling, guided problem-solving, and interaction with learners in small groups. Students who attend supplemental instruction may arrive with specific questions or academic needs that must be addressed to support further learning of the subject matter, making SI sessions spaces for improvisation and adaptive teaching. The authors contend that leading supplemental instruction sessions for computer science first and second-semester coursework allows leaders to develop flexible communication practices and career adaptability skills, which are vital” soft skills” for career development. Research indicates that career adaptability leads to stronger employment outcomes, such as wages, job satisfaction, and workplace engagement. This work-in-progress paper draws on career development, entrepreneurial mindset research, and social science research on improvisation. The paper makes inferences regarding the apprenticeship and guidance practices that support adaptive thinking and improvisation in SI at Kean University, and develops recommendations for faculty and staff who lead peer instruction efforts in engineering and/or computing departments. Innovations in this paper include considering how SI leaders develop career adaptability in their work, which can support leader recruitment and lead to new mechanisms for improving SI leader performance.

Motivation: Many university CS programs have begun teaching various types of CS-related societal issues using approaches such as ethics, Responsible CS, inclusive design, and more. However, some recent research suggests that, although these programs have been able to teach awareness, students often fail to act upon this awareness. To address this problem, University X's CS program tried an unusual approach—integrating hands-on inclusive design skills in small ways across all four years of the CS major. But did it work? That is, did the students who experienced this change across the major actually build more inclusive technology than the students who did not experience it?

This study builds on prior research in the field of jailbreaking, focusing on the vulnerabilities of state-of-the-art Large Language Models (LLMs) and their associated chatbots. The primary objective is to evaluate these vulnerabilities through a multilingual lens, extending the scope of previous monolingual studies. The main chatbots under examination include ChatGPT (both legacy ChatGPT -3.5 and the latest ChatGPT -40 model), Gemini, Microsoft Copilot, and Perplexity. Researchers conducted multilingual adversarial attacks, facilitating cross-language and cross-model comparisons, and explored different modalities, such as text versus speech inputs. The findings reveal significant weaknesses in these major models, particularly in their susceptibility to adversarial attacks conducted in languages such as Spanish, Russian, and Traditional Chinese. Given the global proliferation and accessibility of these models, it is imperative to rigorously assess the robustness of LLMs against adversarial inputs across multiple languages and modalities.

This paper addresses the significant challenge of selecting suitable foundation shades, particularly for darker skin tones, which have been inadequately represented and catered to in the beauty industry. It introduces an innovative system designed to offer individualized makeup recommendations, significantly diminishing the time and effort traditionally demanded of consumers when searching for appropriate products. Utilizing machine learning methodologies and computer vision techniques, the system analyzes image data to precisely identify a range of skin tones, enabling it to propose compatible foundation shades automatically. Unlike sophisticated Large Language Models, such as ChatGPT, Gemini, Microsoft Copilot, or Claude, which are not equipped to undertake such visually driven tasks due to ethical guidelines that prohibit them from processing personal images without explicit consent and the absence of face image processing capabilities, this technological development represents a step forward in fostering inclusivity, illustrating the transformative potential of AI in accommodating the unique beauty preferences of all individuals.

The assessment of student learning is essential, with instructors using end-of-course assessments against Course Objectives and Student Learning Objectives (SLOs), typically through assignments and exams against a predetermined benchmark. However, the reliance on outdated paper-based systems can delay crucial information access and lead to errors and inconsistencies in assessments. Therefore, a pilot study is underway to explore the benefits of transitioning from paper-based to electronic-based assessment systems. This shift aims to facilitate easier monitoring and more consistent tracking of course objectives, potentially enhancing the quality of courses and curriculum. All faculty-submitted assessment data is securely stored on a server, which supports the creation of an interactive data visualization tool. This tool is primarily designed to aid faculty in making informed decisions to better engage, motivate, and improve the learning experience for students. Moreover, the electronic system promises significant efficiency improvements by streamlining information retrieval and standardizing annual assessment processes.

Computer science (CS) and information technology (IT) curricula are grounded in theoretical and technical skills. Topics like equity and inclusive design are rarely found in mainstream student studies. This results in graduates with outdated practices and limitations in software development. A research project was conducted to educate the faculty to integrate inclusive software design into the CS undergraduate curriculum. The objective is to produce graduates with the ability to develop inclusive software.

Utilizing the Affinity Research Group (ARG) model, the Computing Alliance of Hispanic Serving Institutions (CAHSI) has provided training for faculty and student research experiences for decades. ARG, a CAHSI signature practice, focuses on deliberate, structured faculty and student research, with accompanying technical, communication, and professional skills development. In the latest iterations that have spanned the pandemic and its recovery, CAHSI has iterated on a virtual training and support network for faculty and students interested in broadening the participation of Hispanic undergraduate students in computer science, to increase the number of Hispanics who move on to graduate studies in the field. This work-in-progress paper analyzes shifting support structures during a multi-year effort to promote undergraduate research development using the Affinity Research Group (ARG) model. As CAHSI grows to include research-intensive universities that have recently reached the 25% Hispanic enrollment threshold, the faculty mentor training has evolved to emphasize growth mindset and asset-based frameworks for working with undergraduate students in research, particularly important in computing departments which graduate students are more commonly engaged in research. The paper describes areas of need as the populations of faculty and students shift. It addresses the questions: R1) How do faculty engaged in the LREU shift perspectives regarding a) student selection for research, b) pedagogical purposes of research for student development, and c) their ability to implement ARG? R2) To what extent do designed elements of the LREU professional development inform faculty practice and faculty perspectives regarding undergraduate research?

Involving diverse individuals who bring different perspectives, experiences, and disciplinary knowledge in solving problems is critical in our nation's ability to innovate and compete in a global economy. Unfortunately, the trends in the number of graduates with advanced degrees, in particular ethnically and racially diverse citizens and permanent residents, are insufficient to meet current and future national needs. This is exacerbated in computing, which is one of the least diverse fields. Despite the growth in numbers of Hispanics nationally and their representation in undergraduate studies, the number of Hispanic citizens and permanent residents who enter and complete graduate computing studies is disturbingly low. Studies report that Hispanic graduate students across all fields of study feel isolated and alienated, face lack of support, experience low expectations from faculty, and a negative racial/ethnic climate. Students often encounter a STEM culture centered on competition and selectivity, and this must be addressed to increase pathways to the doctorate to support our nation's economic and national security goals. This paper describes a collective effort of institutions with high enrollments of Hispanic students that have built partnerships among non-doctoral-granting and doctoral-granting institutions to increase representation of Hispanics in graduate studies. Led by NSF's Eddie Bernice Johnson Computing Alliance of Hispanic-Serving Institutions (CAHSI), the collective employs evidence-based practices grounded in the Hispanic-servingness literature to address the root causes.

The researchers introduce the AssureAIDoctor (AAID) App - a pioneering application that aims to revolutionize healthcare by integrating the latest artificial intelligence (AI) features into a mobile-native product. The app leverages the OpenAI API to simulate virtual doctor-patient interactions, offering users potential remedies for their symptoms. The distinguishing feature of AAID is its use of DALL-E, an advanced image generator, and the forthcoming OpenAI's Code interpreter. This allows users to enhance their interactions with the AI by uploading images, thereby personalizing their healthcare experience. The app's user interface is designed to support this advanced functionality. Preliminary tests show promising results, with AAID accurately responding to various symptom inputs. While scalability is a key focus, the app addresses potential challenges such as increased operational costs associated with Microsoft Azure AI cloud and OpenAI API services. Despite these challenges, AAID is committed to making healthcare accessible for underrepresented and uninsured individuals. The app embodies the potential of AI in healthcare, promising to make healthcare more equitable and accessible.

K-12 school counselors, along with parents and teachers, are uniquely positioned to support and encourage student educational and career choices. From elementary and middle school, school counselors inform and guide the class and future career choices of the students they work with, establishing access and success for high school courses, such as AP CS courses. With new statewide K-12 CS standards being implemented, a one-year study was undertaken to increase the awareness of computing careers and computer science education by learning first how school counselors perceived computer science, determining how school counselors could increase their understanding of computing careers and finally, identifying the best approaches to reach school counselors with professional learning opportunities designed to increase awareness and understanding of computer science. The professional learning workshops for school counselors were designed as coaching calls, supporting school counselors and aligning with important benchmarks in the K-12 academic year, such as student registration for classes in the next academic year. Qualitative and quantitative methods used included pre- and post-surveys, meeting attendance and interviews with the school counselors. The experience report presented here shows significant increases in school counselor awareness and understanding of computing careers. In particular, the results indicate that multi-event professional learning over time has more impact than shorter (1-2 day) events. The percentage of participants who changed their counseling more than doubled for many of the advising practices. Future work will build on this foundation, while providing more resources for school counselors, and structured to boost attendance and engagement.

Computer science (CS) students' curricula is heavily focused on technical skills, and CS ethics, usability, equity, and people/society considerations are not well-integrated into the CS curriculum. If these topics are introduced, they are disconnected from the core courses. As a result, students do not learn to incorporate inclusive practices into their software designs. Thus, students create software through the perspective of a computer scientist - when the important perspective is that of the intended users. As a result, students entering the workforce are inclined to design software that is non-inclusive. We propose the integration of inclusive design in the undergraduate curriculum will result in students creating inclusive software. This research, based on the foundations of inclusive design methods, investigates a new approach to teaching CS. Inclusive software design is embedded into computing courses for all four years of the undergraduate CS curriculum. This new approach is "minimally invasive", occupying very little classroom time, instead it is integrated into the course work that is already assigned. With this work, we hope to answer the following questions: (1) Will this new approach improve students' ability to design inclusive software? (2) Will this approach create an inclusive climate among peers? (3) Will it affect student's success or lack thereof? (4) How and to what extent is this embedded inclusive design curriculum feasible to use?

What if “regular” Computer Science (CS) faculty each taught elements of inclusive design in “regular” CS courses across an undergraduate curriculum? Would it affect the CS program's climate and inclusiveness to diverse students? Would it improve retention? Would students learn less CS? Would they actually learn any inclusive design? To answer these questions, we conducted a year-long Action Research investigation, in which 13 CS faculty integrated elements of inclusive design into 44 CS/IT offerings across a 4-year curriculum. The 613 affected students’ educational work products, grades, and/or climate questionnaire responses revealed significant improvements in students’ course outcomes (higher course grades and fewer course fails/incompletes/withdrawals), especially for marginalized groups; revealed that most students did learn and apply inclusive design concepts to their CS activities; and revealed that inclusion and teamwork in the courses significantly improved. These results suggest a new pathway for significantly improving students’ retention, their knowledge and usage of inclusive design, and their experiences across CS education—for marginalized groups and for all students.

Motivation: Although asynchronous online CS courses have enabled more diverse populations to access CS higher education, research shows that online CS-ed is far from inclusive, with women and other underrepresented groups continuing to face inclusion gaps. Worse, diversity/inclusion research in CS-ed has largely overlooked the online courseware—the web pages and course materials that populate the online learning platforms—that constitute asynchronous online CS-ed’s only mechanism of course delivery.

Artificial Intelligence (AI) bots receive much attention and usage in industry manufacturing and even store cashier applications. Our research is to train AI bots to be software engineering assistants, specifically to detect biases and errors inside AI software applications. An example application is an AI machine learning system that sorts and classifies people according to various attributes, such as the algorithms involved in criminal sentencing, hiring, and admission practices. Biases, unfair decisions, and flaws in terms of the diversity, equity, and inclusion (DEI), in such systems could have severe consequences. As a Hispanic-Serving Institution, we are concerned about underrepresented groups and devoted an extended amount of our time to implementing “An Assure AI” (AAAI) Bot to detect biases and errors in AI applications. Our state-of-the-art AI Bot was developed based on our previous accumulated research in AI and Deep Learning (DL). The key differentiator is that we are taking a unique approach: instead of cleaning the input data, filtering it out and minimizing its biases, we trained our deep Neural Networks (NN) to detect and mitigate biases of existing AI models. The backend of our bot uses the Detection Transformer (DETR) framework, developed by Facebook, to monitor and detect the deep learning model’s internal biases.

While computer science programs are available in many US K-12 schools, students often do not enroll for various reasons such as lack of awareness or encouragement. School counselors, positioned to have an important impact on students' access to educational and career opportunities, often lack information on computer science content, curriculum, and careers. To address this problem, school counselors from underserved school districts are receiving professional learning (PL) on computer careers, the need for additional K-12 student enrollment in computer science courses, and strategies to discuss computer careers with all students in the school. Preliminary data showed that fewer than half the participants had information about CS curriculum and career options before the PL, but gained information about CS and changed their advising practices after a few sessions. The objective is to assess if the PL for the K-12 school counselor community measurably affects the interactions and equity awareness of K-12 school counselors.

Within computer science education, a growth mindset is encouraged. However, faculty development on the use of growth mindset in the classroom is rare and resources to support the use of a growth mindset are limited. A framework for a computer science growth mindset classroom, which includes faculty development, lesson plans, and vocabulary for use with students, has been developed. The objective is to determine if faculty development in growth mindset and active use of the growth mindset cues in the CS0 and CS1 classroom result in superior academic outcomes. Comparative study results are presented for two semesters of virtual classroom environments: one semester without Growth Mindset, and one semester with Growth Mindset. Female students demonstrated the most growth, as measured by academic grades, in CS0, and maintained that growth in CS1. Males demonstrated growth as well, with both males and females converging at the same high point of accomplishment at the end of CS1. Race and ethnicity gaps between students were reduced, improving academic equity.

A year-long proof-of-concept research project explored the impact of utilizing pedagogical strategies from teaching English as a Second Language and Bilingual education to improve the outcomes of Hispanics in Advanced Placement Computer Science A (AP CSA) and AP Computer Science Principles (AP CSP). The project was conducted in the US during the 2019-2020 academic year with twenty-two AP Computer Science teachers. The intervention was evaluated by comparing the College Board's 2020 national and state AP CSA and AP CSP exam statistics with the aggregate scores of the students in the classroom of the project's teacher cohort. Teacher surveys were also used to evaluate the impact of the study on teacher's perception and teaching practices. Results from this research show that Hispanic students in the research cohort passed the AP CSP test at the same rate as all students passing the AP CSP test in the cohort. This contrasts favorably with the national and state Hispanic passing AP CSP test takers which are reported as 10 to 13% below all students AP CSP passing test takers. The pedagogical interventions used here are one approach to promote equity in computer science education. This paper presents a description of the project, a comparison of the project's data to the 2020 national and state AP CSA and AP CSP exam statistics, a summary of the teacher survey data and recommendations for continued research in this area.

As computer science programs experience booming enrollments, departmental priorities for educating all students are shifting. Nurturing a departmental culture that promotes inclusion when resources and space are scarce can be difficult. As a Birds of a Feather session, the facilitators will lead participants in a discussion of practices that support inclusion at the departmental and classroom levels. We focus on ways that faculty and departments can prepare and implement inclusive practices and policies that support the ideas that all students can learn and belong, and that ability is not fixed, but malleable. We will also discuss how departmental efforts can be documented and measured for effectiveness. While evaluation is always important, departments impacted by new BPC requirements piloted by the NSF to measure departmental changes to support equity (see https://www.nsf.gov/cise/bpc/White_Paper.pdf) may be particularly interested. Participants will form small guided discussion groups with one BOF facilitator in each group. Groups will be provided centering questions and artifacts to guide their discussion. Participants will reconvene to report out across groups. The BOF is appropriate for computing educators who are focusing on/or developing departmental practices that support the success of all learners, with an emphasis on underrepresented and underserved groups. It is also appropriate for educational researchers who emphasize department and classroom level research related to growth mindset, asset-based pedagogy, culturally relevant pedagogy, and inclusion.

A growth mindset encourages the development of intelligence, in contrast to a fixed mindset, which considers intelligence to be fixed and unable to be changed. Within computer science education, there is an awareness of a growth mindset, but resources to support the use of a growth mindset in the computer science classroom are limited, and faculty development in the use of growth mindset in the classroom is rare. Researchers have developed a framework for a computer science growth mindset classroom, which includes faculty development, lesson plans, and vocabulary for use with students. In addition, the faculty have formed a community of practice which identifies areas where growth mindset techniques can be used and has implemented these strategies in their classrooms. The framework and materials developed are presented here, with early results from this ongoing work. The objective is to determine if faculty development on growth mindset and active use of the framework for a growth mindset classroom results in superior academic outcomes in CS0 and CS1. Preliminary results are for faculty in both face-to-face and virtual classroom environments.

This lightning talk describes a proof of concept research project funded by a Google CS Education (CS-ER) Research grant. The project focuses on pedagogical interventions aimed at improving the outcomes of English Language Learners (ELLs) in Advanced Placement Computer Science. The research underway examines the use of Sheltered Instruction (SI), a model from English as a Second Language (ESL) and bilingual education, used in mainstream classrooms across other content areas, in the AP CSA and AP CSP classroom. Strategies and pedagogy from the Sheltered Instruction model are being infused into AP Computer Science curriculum and used in classrooms in participating districts. The districts have been selected to include a range of ELLs and native English speakers. The impact of this approach will be measured by comparing the AP CS exam scores of students in the participating districts with the national and state AP CS exam scores. This lightning talk will focus on the pedagogy development which has taken place and preliminary findings from two cohorts of AP CS and AP CSA teachers, in particular the impact and changes to the teacher's development of CS education lessons and in-class lesson delivery. The project is an interdisciplinary collaboration between faculty from the School of Computer Science and the School of Curriculum and Teaching.

Surging enrollments in computer science programs have necessitated the scaling-up of many programs, resulting in more curriculum offerings and larger courses enrollments to meet increasing student demand. To ensure that all students are receiving equitable information and mentoring, a pathway for computer science student success has been designed as a game, offering concrete steps for students to take in each of their four undergraduate years. Incentives are offered to students as they complete each step, culminating in badges. The use of program-wide gamification has been well-received by students, with student engagement and retention positively impacted. Lessons learned from this pilot include the importance of early introduction to the students, faculty reinforcement in the classroom, and the use of experience rewards over material rewards.

With the growth of Infrastructure as a Service (IaaS) in cloud computing, and the Internet of Things (IoT), network monitoring of the global infrastructure will continue to expand, as cybersecurity will become of primary importance. Current network monitoring approaches, particularly those that are agent-based, are proprietary, costly, and may not scale. This paper proposes a lightweight, scalable agentless system which can be configured, setup, and begin monitoring network health within minutes. The architecture utilizes a polling script to query Simple Network Monitoring Protocol (SNMP) demon for metrics, Prometheus Time Series Database instances for storage, and a Grafana Dashboard for metric presentation and alerting. The system design removes the learning curve necessary for most open source monitoring systems, while still providing similar metrics. The approach demonstrated here shows the ease and security with which specific site network monitoring can be deployed in a cloud configuration. Data from the network monitoring can be forwarded to a central analysis site for assessment and correlation. The proposed system is scalable, extensible for use in most cloud hosting environments, and provides network managers with direct control over the monitoring process and dashboard.

A key issue in defining a product line is specifying the allowable set of products that will be produced using product line assets, i.e., the scope of the domain. This paper conducts an evaluation to compare two different approaches for defining domain scope, decision model as defined in the Family-oriented Abstraction, Specification, Translation (FAST) process and the feature model as defined in the Feature-Oriented Domain Analysis (FODA) process. The comparison is based on applying the approaches to two examples, one a textbook example and the other to a product line we maintain on an open source website, in order to identify guidelines for improving the identification and representation of a software family. Our conclusion is that decision model includes both commonality and variability definition at software architecture level and thus it is more suitable for larger product line with a significant number of commonality and variability.

A research study measuring perceptions of computing held by Hispanic high school students has been conducted using two visual programming interfaces. Undergraduates conducted regular weekend classes using both Alice and App Inventor lessons to provide computing instruction to the students enrolled in an enrichment program. The goal of this research was to identify and measure high school student perceptions of computing after being introduced computing using drag-drop programming interfaces. The results of this work demonstrates how student interest in computing increases once exposed to computing, but the interest increment is not enough for them to major in a computing area, highlighting the importance of ongoing engagement in computing throughout the high school years.

This paper presents a methodology for increasing undergraduate Computer Science (CS) major enrollment at comprehensive public universities, particularly those that have first generation college students or students from underrepresented populations in professional computer science in the United States. While there has been significant prior discussion of undergraduate recruiting to increase major enrollment in CS, this is the first identification of a systematic approach to recruiting distinct undergraduate student populations into the CS major. Nationally, highly selective and selective universities and colleges have worked within their student populations to increase major enrollments, while CS departments at comprehensive public universities primarily focused on regional recruiting have also worked to identify and increase CS major enrollments. The approach outlined here addresses regional recruiting for undergraduate majors in CS and has been used at two public universities. The resulting methodology serves as a template for any department or faculty member seeking to increase undergraduate enrollment in CS.

In this paper, we discuss one aspect of the measurement issue: how to measure end-to-end application response time (ART) relative to aggregated "tunable" network latency, or tunable latency. The goal is to enhance our understanding of the relationship between these two metrics for database access applications. Tunable latency is defined as follows: the sum of the "round trip" queuing delay and data transmission/insertion delay from beginning to end of the application transmission. Our problem space concentrates on developing a methodology to graphically characterize response time as a function of tunable latency for existing database access applications in a wired, single-threaded, multi-user, post-deployment client/server environment. A number of tools were used in developing this methodology which was not obvious from the tools' documentation. To test its feasibility before actual field use, we used an experimental setup to emulate the real user environment. In so doing, we now have two proposed methodologies: one for the experimental setup version (of the in-service scenario) and one for the "actual" in-service scenario. We present results obtained from the experimental method. The resulting graphs can be a consultation tool for network tuning and control, classification of user applications by priority class-of-service, SLA negotiation and manual QoS provisioning.

High performance monitoring and control of real-time environments over the Internet is of importance to many users. The general public, as well as professionals, desires the ability to remotely acquire real-time information at their fingertips. This paper presents a smart network design for real-time data acquisition. A streamlined web server designed specifically for embedded applications produces the real-time output for a dynamic environment. This technological enhancement is significant because it can be used in conjunction with any "smart" device. A unique characteristic of the design presented here is its ability to easily adapt to a variety of systems/applications. In order to achieve this type of information processing, the design approach used here was required to demonstrate the following abilities: (1) operate on a small platform without the use of an OS, (2) exchange and modify real-time data over the network, and (3) demonstrate the possibility that this type of system can be used for security purposes.

The delivery of applications with both real-time and non-real-time service expectations, such as multimedia education, using the WWW has led to a new model for service provisioning. The profile of a distance learning session, including video-on-demand, is unlike any other service delivered over the WWW or a dedicated intranet. After developing several WWW-based multimedia distribution systems for distance learning. A profile and service expectation has been developed. By including real-time feedback to the students and administrators, information about the effectiveness of the education being delivered can be made. A pre-fetch or reservation strategy, coupled with historical information about student behavior can significantly improve information delivery to the desktop. Presentation of this model, a discussion of the design, and analysis of the results found in actual use are provided.