In this research paper, we conducted a comparative study to measure the effectiveness of the provided technical support in computing project-based learning (PjBL) courses. Students learn much better by solving authentic real-world problems through PjBL. PjBL in computing education has proven to boost student motivation and engagement while enhancing academic performance. Crucial to PjBL in computing is the technical support that the instructors can provide to students, which is required for sustained, successful learning during project tasks. Without adequate support, PjBL will fall short of accomplishing its goals, leading to a rise in student frustration, a loss of motivation and engagement, and compromised learning outcomes. Measuring the impacts and effectiveness of the provided support is imperative for fostering continuous improvement, informed decision-making, and student success. It enables instructors to assess the impacts of their strategies, improve their approaches, and utilize their resources more effectively. To measure the impacts of technical support on students during PjBL, we performed a comparative study on two undergraduate computing courses in Spring 2024, Fundamentals of Software Engineering and Database Systems. In both courses, students work on two assigned projects, one with little and inadequate support and the other with adequate support. We administered a post-survey after each project was completed. We analyzed students' selfreflection responses across four sub-scales, support satisfaction, motivation, self-efficacy, and project satisfaction. The results show a statistically significant increase in the supported project in the Fundamentals of Software Engineering course and no difference in the Database Systems course. This finding is likely due to other differences between the two projects for the Database Systems course beyond support, such as project scale. Qualitative analysis of students' responses also indicates the need for support by students in the less supported projects. Based on our experience, we reflect on the question of what would constitute a good design for studies that seek to compare two different student learning experiences.

In this research paper, drawing from our own and other computing instructors' experiences, we highlight common technical challenges faced by students in software engineering project-based learning (PjBL) and discuss ways in which instructors can support students in overcoming them so that motivation is summoned and sustained. Through the use of practical hands-on experiences, PjBL has been shown to be an effective educational approach. However, unless projects are intentionally designed and supported in a way that summons and sustains student motivation, PjBL is likely to fail to accomplish its goals. Several factors influence student motivation, including their perception of the project's value and how confident they are in their ability to complete it. In particular, challenges that students perceive as insurmountable during the project can significantly weaken their motivation. On the other hand, supporting students to overcome such hurdles can be troublesome, especially in large classes as well as classes with diversity in student backgrounds. To generalize from our own experience, we designed a questionnaire targeted at PjBL computing instructors that contained closed and open questions on technical challenges faced by students, support instructors provided to overcome such challenges, and lessons learned by instructors on the effectiveness of their support. A total of 47 responses were collected from instructors with diverse backgrounds in terms of courses taught, students' years, and class sizes. We categorized the technical challenges into three main categories, namely (a) challenges in installing and configuring software packaged tools, (b) lack of prerequisite knowledge, and (c) challenges while completing project tasks. In this paper, we present the survey results from the three categories of technical challenges, their frequencies, importance, and effective support strategies instructors use to alleviate them.

An ensemble data-learning approach based on proper orthogonal decomposition (POD) and Galerkin projection (EnPOD-GP) is proposed for thermal simulations of multi-core CPUs to improve training efficiency and the model accuracy for a previously developed global POD-GP method (GPOD-GP). GPOD-GP generates one set of basis functions (or POD modes) to account for thermal behavior in response to variations in dynamic power maps (PMs) in the entire chip, which is computationally intensive to cover possible variations of all power sources. EnPOD-GP however acquires multiple sets of POD modes to significantly improve training efficiency and effectiveness, and its simulation accuracy is independent of any dynamic PM. Compared to finite element simulation , both GPOD-GP and EnPOD-GP offer a computational speedup over 3 orders of magnitude. For a processor with a small number of cores, GPOD-GP provides a more efficient approach. When high accuracy is desired and/or a processor with more cores is involved, EnPOD-GP is more preferable in terms of training effort and simulation accuracy and efficiency. Additionally, the error resulting from EnPOD-GP can be precisely predicted for any random spatiotemporal power excitation.

In this workshop, we introduce participants to the accomplishments and lessons learned from our ongoing NSF IUSE education research project, which is focused on supporting undergraduate project-based learning in computing education by developing and piloting a set of scaffolded course projects. The workshop has two main goals. One is to facilitate exchange of experiences on project-based learning among workshop participants. The other is to encourage adoption of the developed course projects by the broader computing education community.

Teaching students complex problem-solving skills using large-scale, real-world problems is challenging for both students and teachers alike. As a result, most courses use small, well-specified, toy-like problems, which are not representative of what students will encounter in the workforce. One approach that allows teachers to use large-scale problems in class is by introducing scaffolding. Scaffolding breaks a larger problem into smaller steps, which students can solve independently, while deemphasizing tangential concepts such as the complex configuration files needed to compile open-source software systems. Strong scaffolding supports student learning, preventing them from getting bogged down with unnecessary tasks or overwhelmed by complexity. This work investigates a scaffolded problem-based-learning module for computing courses, using a realistically-sized project with characteristics representative of the industry. The project was implemented in a computer science course with roughly 100 students, and the results speak to the importance of scaffolding for student success. In fact, there were two student assignments that lacked sufficient scaffolding, compared with other tasks, and the reduction in student scoring and persistence shows that project scaffolding is necessary when implementing these types of assignments. Most students felt the project helped prepare them for a job in their chosen field.

This qualitative research performs a thematic analysis of the learning objectives in existing project-based undergraduate software engineering courses to align them with the competency model defined in the Computing Curricula 2020 reports (CC2020). This study identifies the trends, strengths, and gaps in how the reviewed course learning objectives cover the knowledge, skill, and disposition components of the CC2020 competency model. The learning objectives were categorized according to knowledge elements, skills, and dispositions as defined in the CC2020 competency model. Our analysis shows that 54% of knowledge elements from the reviewed learning objectives do not have any skill level specified and overall, only two out of the eleven dispositions in CC2020 are specified (“Collaboration” and “Professional”). We also find that technical knowledge elements from the software development category (e.g., software process, software design, and software quality, verification & validation) and systems modeling category (e.g., systems analysis & design, and requirements analysis and specification), probably unsurprisingly, are covered the most often. Similarly, collaboration & teamwork, and oral & written communication are unsurprisingly the most common professional & foundational knowledge elements in the reviewed course's learning objectives as they are essential to project-based learning. Although they are essential for the completion of a successful software project, knowledge elements such as time management, security technology & implementation, and user experience design are rarely mentioned. We discuss the implications of our findings on course design.

A thermal simulation methodology derived from the proper orthogonal decomposition (POD) and the Galerkin projection (GP), hereafter referred to as PODTherm-GP, is evaluated in terms of its efficiency and accuracy in a multi-core CPU. The GP projects the heat transfer equation onto a mathematical space whose basis functions are generated from thermal data enabled by the POD learning algorithm. The thermal solution data are collected from FEniCS using the finite element method (FEM) accounting for appropriate parametric variations. The GP incorporates physical principles of heat transfer in the methodology to reach high accuracy and efficiency. The dynamic power map for the CPU in FEM thermal simulation is generated from gem5 and McPACT, together with the SPLASH-2 benchmarks as the simulation workload. It is shown that PODTherm-GP offers an accurate thermal prediction of the CPU with a resolution as fine as the FEM. It is also demonstrated that PODTherm-GP is capable of predicting the dynamic thermal profile of the chip with a good accuracy beyond the training conditions. Additionally, the approach offers a reduction in degrees of freedom by more than 5 orders of magnitude and a speedup of 4 orders, compared to the FEM.

The need for full-chip dynamic thermal simulation for effective runtime thermal management of multicore processors has been growing in recent years due to the rising demand for high-performance computing. In addition to simulation efficiency and accuracy, a high resolution is desirable in order to accurately predict crucial hot spots in the chip. This work investigates a simulation technique derived from proper orthogonal decomposition (POD) for full-chip dynamic thermal simulation of a multicore processor. The POD projects a heat transfer problem onto a mathematical space constituted by a finite set of basis functions (or POD modes) that are generated (ortrained) by thermal solution data collected from direct numerical simulation (DNS). Accuracy and efficiency of the POD simulation technique influenced by the quality of thermal data are examined thoroughly, especially in the areas with high thermal gradients. The results show that if the POD modes are trained by good-quality data, the POD simulation offers an accurate prediction of the dynamic thermal distribution in the multicore processor with an extremely small degree of freedom (DoF). A reduction in computational time over four orders of magnitude, compared to the DNS, can be achieved for full-chip dynamic thermal simulation with a resolution as fine as the DNS. The study has also demonstrated that the POD approach can be used to rigorously verify the accuracy of solutions offered by DNS tools. A practical approach is proposed to further enhance the accuracy and efficiency of the proposed full-chip thermal simulation technique.

In this work, an accurate and efficient thermal simulation approach based on proper orthogonal decomposition (POD) is applied to predict the temperature profile in an Intel Xeon E5-2699v3 CPU consisting of 18 cores. Using the POD method, the thermal problem is projected from a physical domain onto a functional space represented by a finite set of basis functions (or POD modes) that need to be trained by a large amount of thermal data. To generate the static and dynamic power consumption in space for the Xeon E5-2699v3 CPU as the heat source for thermal simulations in the training and validation, the cycle-level system simulator, gem5, and the power simulator, McPAT, are used. Gem5 is used to simulate the architectural characteristics of the CPU and gather performance counters. These statistics are gathered using a subset of the widely used SPLASH2 benchmark suite as the simulated workload. These performance statistics contain information regarding architectural events such as functional unit usage, cache accesses. With the generated power trace, thermal data is collected using FEniCS, an open-source platform that supports finite element method (FEM)-based simulation, subjected to a range of power variations in space and time. It has been demonstrated that the proposed approach is able to offer an accurate thermal prediction of the CPU with a reduction in degrees of freedom (DoF) by more than 5 orders of magnitude and a speedup of 3 orders of magnitude, compared to FEM.

In modern multicore processor architectures, caches are widely used to shorten the speed gap between the processor and the memory. However, caches are time unpredictable, especially the shared L2 cache used by different cores in a multicore processor. This paper studies several time-predictable scratchpad memory (SPM) based architectures for multicore processors. We propose the dynamic memory objects allocation-based partition, the static allocation-based partition, and the static allocation-based priority L2 SPM strategy to retain the characteristic of time predictability of SPMs while trying to maximize the performance and energy efficiency. Our experimental results indicate the strengths and weaknesses of each proposed architecture and allocation method, which offers interesting memory design options to enable real-time multicore computing.

With the advance of computer and communication technologies, wireless sensor networks (WSNs) are increasingly used in many aspects of our daily life. However, since the battery lifetime of WSN nodes is restricted, the WSN lifetime is also limited. Therefore, it is crucial to determine this limited lifetime in advance for preventing service interruptions in critical applications. This paper proposes a feasible static analysis approach to estimate the worst-case lifetime of a WSN. Assuming known routes with a given sensor network topology and S-MAC as the underlying MAC protocol, we statically estimate the lifetime of each sensor node with a fixed initial energy budget. These estimations are then compared with the results obtained through simulation which run with the same energy budget on each node. Experimental results of our research on TinyOS applications indicate that our approach can safely and accurately estimate the worst-case lifetime of WSNs. To the best of our knowledge, our work is the first one to estimate the worst-case lifetime of WSNs through static analysis method.