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Programming is becoming a new literacy, and programming education is being implemented in various settings. One of the issues faced by novice programmers is a lack of problem-solving skills. They have difficulty planning abstract solutions and expressing them in a program. This study proposes a programming learning system that supports novice programmers’ thinking processes when solving programming tasks. The system presents candidates for solutions in natural language and allows the user to solve the problem by choosing from a limited number of options. The method allows users to repeat quick trial-and-error solutions without being interrupted by coding-induced problems. We implemented the proposed system as a web application for use on a smartphone and applied it in a university’s introductory programming class. After applying the proposed system to 37 students for four weeks, we found a correlation between the number of times a user selected an option using the proposed system and the class task score. We confirmed the possibility that using the proposed system can improve problem-solving skills for programming tasks.

The log sequence is often referred to as a language in automated log analysis. The natural consequence of this is that the log sequence should have a structure consisting of words and sentences. However, the word definitions in the log sequence are not uniform in the literature. The first approach splits line-by-line, and the second retrieves word-like structures from the log sequence. The main challenge in the second approach is the measurement of results. There are approaches for constructing unsupervised metrics; however, we found them to be inconsistent. Other methods rely on manually prepared golden standards; however, a benchmark for golden segmentation is not available for any set of logs. To overcome this problem, we created a benchmark of preprocessed log event IDs gathered from the open-source CloudStack log and commercial Nokia software execution. We created a gold segmentation standard with the help of a human expert, and made it publicly available. We then tested known unsupervised segmentation methods used for log sequence segmentation and adapted the Nested Pitman-Yor Language Model. We found that the results of log segmentation performed by these methods vary significantly between the natural language domain and the log domain. VotingExperts achieved the best F-score, recording 97.3% for CloudStack and 44.1% for Nokia logs. The results are related to the uni-gram entropy of the log sequence, which differs across software platforms.

Technology helps to solve problems, but it may also lead to unintended consequences. For example, biofuels may help to overcome the disadvantages of fossil fuels, but their production might compete with food production leading to higher food prices and hunger. Therefore, in recent decades, the societal impact of technology has come to the center of attention. To deal with potential ethical issues related to technology, many scholars have emphasized the importance of addressing values already during the design phase of new technology. Values are understood as beliefs about what is good or desirable, like human autonomy, safety, sustainability, or privacy. Researchers in ethics and philosophy of technology have developed a variety of approaches, like value-sensitive design (VSD) and responsible research and innovation, to help engineers embed values in technological design (e.g.,). [ABSTRACT FROM AUTHOR]

Our biology affects how we interact with the world, including how we learn new knowledge and respond to challenges. This article explores the impact of neurochemicals in our brain on learning and explains how to leverage our biology to improve education and problem solving, focusing on computing education. Within this context, the article particularly examines the role of failure while learning. Learning, especially in technical fields, includes making errors on the path to success. Although these errors trigger the necessary neurochemical conditions for rapid learning, these failures can also be demotivating. To gain the benefits of failure while mitigating its negative consequences, this article recommends evidence-based behavioral strategies for making the best out of failing while learning and designing for failure in learning environments. [ABSTRACT FROM AUTHOR]

According to previous studies, the workforce competencies of engineers in manufacturing industries generally comprise technical, generic, and knowledge competencies. However, these categories of workforce competencies are insufficient for firms whose manufacturing processes are very easily interrupted by anomalies and whose core competency is highly related to production yield. In addition to drawing attention to the importance of identifying engineers’ problem-solving competencies, this article proposes a procedure to identify and rate workforce competencies for firms in the manufacturing industry. An IC packaging process engineer was used as an example to illustrate the proposed competency identification and rating procedure. In contrast to previous studies, this article suggests that problem-solving competencies can be identified from engineers’ responses to the anomalies commonly encountered in each step of the manufacturing process and that operational competency can be identified from the knowledge, techniques, or capabilities required to fulfill a task in each step of the manufacturing process. For the IC packaging and testing industry, this article can help human resources not only evaluate the competency of job candidates but also design training courses according to the job tenure of engineers. Furthermore, this article results can be used to design teaching material for apprenticeship to help engineers develop competencies in a structured manner and enhance their sensitivities to production-line problems. [ABSTRACT FROM AUTHOR]

Given the high uncertainty in R&D work, a key concern of R&D managers is to understand how to fail faster by managing time and cost efficiently among their R&D projects. This study investigates how using a variety of Six Sigma tools can differentially affect R&D project time and cost performance. Our data are collected from 35 R&D projects in an industrial R&D center, and a total of 30 different Six Sigma tools were found to be used in varying frequencies. We analyze this problem in two phases. First, by analyzing the frequencies of tool use in the 35 projects through factor analysis, we find the application of Six Sigma tools can be guided by two distinct patterns of tool use: design-oriented tools (DT) and non-design-oriented tools (non-DT). DT include quality function deployment and failure modes and effects analysis (FMEA) for defining and understanding of the focal problems, while non-DT include Pareto chart and design of experiment for generating and testing solutions for the defined problems. Second, based on the two categories of tool use, we further analyze the effects of the two patterns of tool use on project duration and project cost. Results show that using a variety of DT is associated with faster project completion and non-DT lower project cost. In addition, the use of Six Sigma tools (both DT and non-DT) is associated with shorter durations and higher costs for complex projects but lower costs and longer durations for less complex projects. That is, different modes of trade-ff between time and cost performance can occur when project complexity is considered as a moderating factor. Overall, our findings demonstrate new insights into how using a variety of problem-solving tools affects R&D project cost and duration simultaneously. [ABSTRACT FROM AUTHOR]

Contribution: Programming is a complex cognitive activity that involves both conceptual understanding and procedural skills, which is challenging for novices. To develop both program comprehension and implementation competency, this study proposed a live-coding-based instruction. Experts’ live coding with think-aloud was recorded. Students then learn algorithmic planning and coding skills by observing experts’ thinking and coding processes from the videos. To deploy this pedagogical strategy, a learning platform was developed to present the videos to students who could also annotate their reflection about the videos in the system to deepen their understanding of syntax and concepts of computer programming. Background: Traditional lecture-based programming instruction focuses more on the explanation of syntax and concepts but lacks revealing the dynamic and nonlinear thinking and coding process. Research Questions: This study is to explore the effects of live-coding-based instruction on students’ programming knowledge, including declarative program knowledge (program comprehension) and procedural program knowledge (coding skills), and whether the instruction changes their attitude toward programming learning or not. Methodology: An empirical study was conducted with 33 high-school students who were novice programmers in one semester to explore the effectiveness of the live-coding-based instruction and the use of the learning platform. Findings: The experiment results show that watching flowcharting ($r=0.369, p \mathbf { < }0.05$) or coding processes ($r=0.409, p \mathbf { < }0.05$) of experts improves coding skills. This implies that explicit depiction of algorithmic planning and coding processes are essential for building procedural programming knowledge. In addition, reflection on syntactic content of experts’ programming plays an important role in programming ($r=0.511,p \mathbf { < }0.01$). The research findings suggest that programming instruction could focus more on developing students’ problem-solving abilities by demonstrating the dynamic and nonlinear programming processes and providing opportunities for students to reflect on how syntactic knowledge could be applied to programming. [ABSTRACT FROM AUTHOR]

Contributions: This article presents a large-scale study which investigates students’ reaction to game-based learning as part of programming courses. The study focuses on knowledge acquisition, learner experience, and game usability. Background: Despite the rapid growth of the information and communication technologies (ICTs) sector, the lack of engagement with science, technology, engineering, and mathematics (STEM) subjects and high dropout rates in computer science and engineering majors is linked directly to the large number of unfilled vacancies in the ICT employment market. To tackle one of the underlying causes for this crisis, (i.e., traditional teaching paradigms struggle to attract students to rather abstract and difficult STEM subjects such as programming), innovative technology-enhanced learning solutions are sought. Intended Outcomes: A set of serious games were proposed and designed to promote students’ understanding of programming concepts, improve their confidence, stimulate their interest in STEM and increase engagement with the courses through vivid and appealing scenarios. Application Design: Targeting undergraduate and postgraduate students, the games focused on several key programming topics. They were designed to visualize the programming concepts in illustrative and entertaining scenarios. A comprehensive assessment methodology which includes surveys, observations, and interviews was employed to investigate the impact of the games. Findings: The results show that by using the games in the teaching and learning process all the students have benefited, although differently based on their location, educational backgrounds, and game played. The impact of detailed demographic aspects, such as participants’ use of technology, their initial attitude toward school, and learning STEM on the results needs further study. [ABSTRACT FROM AUTHOR]

Computer science pedagogy, especially in the higher education and vocational training context, has long-favored the hands-on practice provided by programming tasks due to the belief that this leads to better performance on hands-on tasks at work. This assumption, however, has not been experimentally tested against other modes of engagement such as worked example-based reflection. While theory suggests that example-based reflection could be better for conceptual learning, the concern is that the lack of practice will leave students unable to implement the learned concepts in practice, thus leaving them unprepared for work. In this article, therefore, we experimentally contrast programming practice with example-based reflection to observe their differential impact on conceptual learning and performance on a hands-on task in the context of a collaborative programming project. The industry paradigm of Mob Programming, adapted for use in an online and instructional context, is used to structure the collaboration. Keeping with the prevailing view held in pedagogy, we hypothesize that example-based reflection will lead to better conceptual learning but will be detrimental to hands-on task performance. Results support that reflection leads to conceptual learning. Additionally, however, reflection does not pose an impediment to hands-on task performance. We discuss possible explanations for this effect, thus providing an improved understanding of prior theory in this new computer science education context. We also discuss implications for the pedagogy of software engineering education, in light of this new evidence, that impacts student learning as well as work performance in the future. [ABSTRACT FROM AUTHOR]

There are many problems that procedural algorithms can solve efficiently. However, these algorithms are sometimes too slow to abide by the time available for performing the solution; other times, it is impossible to get a solution using procedural algorithms. A heuristic method is a practical approach that can reach an approximation of an efficient solution where the optimum is not guaranteed. Heuristic techniques are applied in many real-world problems, including crowd management; using heuristic-based models helped to comprehend crowd behavior better and increase simulation reliability. This paper reviews many heuristic-related articles to gather the aspects of the topic in one place and clear the fuzziness to make it easy to comprehend. The paper covers some of the previous works with similar approaches and presents state-of-the-art heuristic solutions for real-world problems. These techniques are discussed under three classifications: simple heuristics, meta-heuristics, and hyper-heuristics. Most importantly, the paper explores the heuristic role in crowd field problems concluding that heuristics are primarily applied in modeling when it comes to the Crowd field. It investigates different heuristics for crowd management. The main intent of this review is to establish a comprehensive understanding of heuristics-related operations in the crowd management field. Moreover, it aims to support other researchers’ future work and fill research gaps by highlighting the absence of crowd problems from heuristics literature and the limitations of each heuristics approach.

Scholars have suggested that design thinking (DT) should form an integral part of the new product development (NPD) process. However, the contribution of DT to NPD over the past years is yet to be synthesized. In this article, we develop a conceptual framework categorizing how DT contributes to NPD, and present a map to visualize how research in the area of DT in NPD has evolved over the past 20 years. A systematic analysis of 89 journal papers published between 2000 and 2020 helped to generate a conceptual framework, which provides a typology for discussing the growing state of the art of DT in NPD. The study enhances the academic debate by providing a holistic perspective on how DT contributes to the NPD ecosystem and offers directions for future research for the next 20 years. [ABSTRACT FROM AUTHOR]

Lean leadership might be among the most desirable goals for the development of successful managers. The most prominent organization exhibiting this style is Toyota. This article provides a comprehensive examination of leadership styles and their relation to the two lean principles, respect for people, and continuous improvement. The results shed light on critical leadership development for lean initiatives. Implications for theory and practice are discussed, and suggestions for future research are proposed. Managerial Relevance Statement-Lean management creates organizations which are financially sound and ethical. Major obstacles to create such lean management driven organizations seem to be soft factors of human behavior driven by leadership. This paper undertakes an effort to analyze the desirable leadership style(s) to enable managers and leaders to sustain or introduce lean management in their organization effectively. [ABSTRACT FROM AUTHOR]

In the wake of the so-called fourth industrial revolution, computer programming has become a foundational competency across engineering disciplines. Yet engineering students often resist the notion that computer programming is a skill relevant to their future profession. Here are presented two activities aimed at supporting the early development of engineering students’ attitudes and abilities regarding programming in a first-year engineering course. Both activities offer students insights into the way programs are constructed, which have been identified as a source of confusion that may negatively affect acceptance. In the first activity, a structured, language-independent way to approach programming problems through guided questions was introduced, which has previously been used successfully in introductory computer science courses. The team hypothesized that guiding students through a structured reflection on how they construct programs for their class assignments might help reveal an understandable structure to them. Results showed that students in the intervention group scored nearly a full letter grade higher on the unit’s final programming assessment than those in the control condition. The second activity aimed to help students recognize how their experience with MATLAB might help them interpret code in other programming languages. In the intervention group, students were asked to review and provide comments for code written in a variety of programming languages. A qualitative analysis of their reflections examined what skills students reported they used and, specifically, how prior MATLAB experience may have aided their ability to read and comment on the unfamiliar code. Overall, the ability to understand and recognize syntactic constructs was an essential skill in making sense of code written in unfamiliar programming languages. Syntactic constructs, lexical elements, and patterns were all recognized as essential landmarks used by students interpreting code they did not write, especially in new languages. Developing an understanding of the static structure and dynamic flow required of programs was also an essential skill which helped the students. Together, the results from the first activity and the insights gained from the second activity suggest that guided questions to build skills in reading code may help mitigate confusion about program construction, thereby better preparing engineering students for computing-intensive careers. [ABSTRACT FROM AUTHOR]

Introduction: Design thinking has gained popularity over the last few decades due to its promise for social innovation and user-centered solutions for technical communication practices and pedagogy. Yet, our increasingly complex sociotechnical climate calls for the historical examination of the decades-old problem-solving model and re-envisioning of the prospect of design thinking in academia and industry. Research questions:1. What prominent historical narratives have informed design-thinking values and practices as we know them today? 2. What could be the future of design thinking in the technical communication profession? Research methodology: This article interrogates the historiography of design thinking by mapping its dominant narratives and constructs antenarrative futures by weaving adjuvant accounts into new trajectories for technical communication purposes and aspirations. Results: Based on the mapping of historical traces of design-thinking narratives, this article presents two root accounts of design-thinking development—the efficiency narrative and the participatory narrative—with key identifiers and examples. Retracing the stories to highlight stances of nondominant sources, the findings indicate the importance of social advocacy through two main antenarratives—inclusion and social justice. Conclusion and future research: Taking into account the antenaratives of design thinking, future applications should center inclusion and social justice advocacy in academic as well as industry settings. Future studies may investigate this approach to implementing design thinking and examine the corresponding outcomes. [ABSTRACT FROM AUTHOR]

Data-oriented design is a growing software development process for games that has not been well studied in academia. It seeks to subtract complicated design methods from problem solving and leverage the simplicity of what computer architecture is designed to do: input, transform, and output data. [ABSTRACT FROM AUTHOR]

Contributions: The study contributes to the social cognitive career theory (SCCT) by explaining high-school students’ career choices and finding possible relations between self-efficacy, interpersonal skills, what inspires them to choose a career, and their actual choices. The practical contribution of this research lies in understanding the impact of the For Inspiration and Recognition of Science and Technology (FIRST) program on its participants and graduates. Background: The FIRST program incorporates project-based learning that fosters the design and production of innovative robotics by teams of students who compete annually. Research Questions: 1) Do the FIRST program activities increase STEM exposure and encourage STEM career choices, and if so, how? 2) What are the factors that affect these choices? Are there correlations between those factors? If so, what are they? 3) Is the effect of the FIRST program on FIRST high-school students’ exposure and career choices different from that effect on FIRST graduates? If so, what are the differences and does gender play a role in these differences? Methodology: The research participants included 119 FIRST high-school students and 297 FIRST graduates. The research applied a convergent parallel mixed-methods approach, with data collected both qualitatively via interviews and quantitatively via questionnaires. Findings: Analysis of the data showed that the FIRST program increased participants’ STEM exposure and career choice in STEM domains. A significant, positive, strong correlation was found between interpersonal skills, STEM exposure, career choice, family and school support, and external motivation. [ABSTRACT FROM AUTHOR]

The growing interest of educational researchers in computational thinking (CT) has led to an expanding literature on assessments of CT skills and attitudes. However, few studies have examined whether CT attitudes influence CT skills. The present study examines the relationship between CT attitudes and CT skills for preservice teachers (PSTs). The Callysto CT test (CCTt) for Teachers was administered to $n\,\,=$ 105 PSTs to measure their CT attitudes and skills. Structural equation modeling was used to examine the relationship of participants’ CT and problem-solving skills with their attitudes toward CT, technology, coding, and data. Findings revealed that CT attitudes predicted CT skills and provided the first step in exploring the validity and reliability of the CCTt instrument. [ABSTRACT FROM AUTHOR]

Important Lessons Can be learned from the COVID-19 pandemic, which after more than two years of worldwide suffering is still among us. First, we now better understand that global health concerns cannot be tackled and solved individually and verify that the dream for universal health care is far from being fulfilled. Besides, biomedical technologies and medical devices, despite their transformative potential, cannot always reach those urgently needing them, due to centralized production, supply chain issues, intellectual property restrictions, and lack of raw materials and resources close to the point of care, which calls for a renovation of the biomedical industry aimed at sustainability and equity. To make matters worse, unethical behaviors of governments, companies, and citizens, from which the ongoing pandemic has provided plenty of examples, also endanger the already challenging progress toward the Sustainable Development Goals (SDGs), including “Goal 3” on “Good Health and Well Being.” [ABSTRACT FROM AUTHOR]

Although it is too soon to say we have reached the end of the COVID-19 pandemic, scientists, politicians, and ordinary citizens are looking to the future and asking the same question—will there be another one? The simple answer is yes. [ABSTRACT FROM AUTHOR]

Irrefutable evidence shows that greater openness toward external partners enhances a firm's ability to solve innovation-related problems. To manage open innovation (OI) projects, firms use a variety of governance modes, including market-based contracts, platform intermediaries, and equity and non-equity partnerships. While innovation projects can be very diverse and characterized by various attributes, such as complexity and knowledge hiddenness, only a few conceptual studies have hitherto considered project attributes as drivers of OI governance mode selection. Using a sample of 85 OI projects and a set of illustrative cases, this article explores empirically how project attributes influence OI governance mode selection. This empirical study advances previous conceptual work on OI governance. By accounting for project-level heterogeneity, we explore the microfoundations of OI and provide more stable and fundamental insights into OI governance than previous industry- and firm-level analyses did. In addition, we suggest that effective OI management depends on matching project attributes with the benefits and costs of specific governance modes. Finally, we argue that this study enhances understanding and conceptualization of the relationship between project complexity and decomposability in the context of OI. [ABSTRACT FROM AUTHOR]

Innovation communities are being deployed increasingly by firms to gather ideas from users to sustain their competitive advantage. As one of the representative characteristics of innovation communities, effective interaction has always been perceived as a prerequisite for new product development. To investigate the inherent creative output logic, this article proposes a theoretical model to delineate the relationships among users’ online interactions [peer-to-peer (P2P) and peer-to-firm (P2F)], community self-government management (CSM, which refers to a style of management that admits users not only as participators but also as governors in the community), and creative output quantity (/quality) of new products. This model is validated empirically using data of 2000 sample users crawled from 20 firm-hosted innovation communities and 80 survey data from community administrators. The findings reveal the significant effects of users’ P2P and P2F interactions on the creative output quality and quantity as well as the moderating role of CSM. These findings suggest implications for how firms manage innovation communities and cultivate user interactions with peers and firms to capture the wisdom of the crowd. [ABSTRACT FROM AUTHOR]

This article illustrates that the task design and problem selection are of characteristic influence to evoke sense-reason-act programming (SRA) among primary school pupils when programming robots. Research shows that the task design influences the development of computational thinking (CT). The literature provides evidence that the context, the problem space, and the representation of the problem to apply SRA-programming require the programming task to be embedded in a dynamic context in which a programmable robot must use sensory information to anticipate changes in the environment. In order to ascertain whether the problem space and the task design influence the evocation of SRA-thinking, this article uses a research design comparing the differences between two programming conditions (static/dynamic). In these conditions, pupils use Lego EV-3 robots and Mindstorms software to solve programming problems. As a post-measurement, a Lego challenge is applied. In this article, it is shown that the integration of a dynamic task design to solve a programming problem is essential for a deeper understanding of CT skills. Furthermore, when pupils can immediately test the consequences of their program in a dynamic environment and, thus, the learning environment provides an appropriate problem, they gain a deeper understanding of the added value of sensors and will be better able to reason about complex problems. It is found that programming in a dynamic problem environment almost naturally evokes SRA-thinking, as opposed to programming in a static environment. The influence of SRA-programming as demonstrated identifies characteristics of CT. [ABSTRACT FROM AUTHOR]

For technology (like serious games) that aims to deliver interactive learning, it is important to address relevant mental experiences such as reflective thinking during problem solving. To facilitate research in this direction, we present the weDraw-1 Movement Dataset of body movement sensor data and reflective thinking labels for 26 children solving mathematical problems in unconstrained settings where the body (full or parts) was required to explore these problems. Further, we provide qualitative analysis of behaviours that observers used in identifying reflective thinking moments in these sessions. The body movement cues from our compilation informed features that led to average F1 score of 0.73 for binary classification of problem-solving episodes by reflective thinking based on Long Short-Term Memory neural networks. We further obtained 0.79 average F1 score for end-to-end classification, i.e., based on raw sensor data. Finally, the algorithms resulted in 0.64 average F1 score for subsegments of these episodes as short as 4 seconds. Overall, our results show the possibility of detecting reflective thinking moments from body movement behaviours of a child exploring mathematical concepts bodily, such as within serious game play. [ABSTRACT FROM AUTHOR]

Contribution: This study identifies the attributes of complexity that can be incorporated into engineering design problems at first-year undergraduate engineering projects. The findings are compiled as a set of guidelines to aid engineering educators in crafting design problems. Background: Engineering professionals solve complex problems like design problems, troubleshooting problems, system-analysis problems, and decision-making problems. To develop proficiency in solving such problems, accreditation bodies for engineering education mandate the development of knowledge and skills for solving complex problems. The literature presents several attributes of complexity, but it does not clearly state which of these attributes are relevant for first-year engineering problem-solving and incorporate them into the engineering curriculum. Research Question: This study qualitatively investigates the research question, “How to craft engineering design problems with known attributes of complexity for first-year undergraduate engineering education?” Methodology: The study is situated in a project-based learning course offered at first-year engineering at a university in India. It identifies the attributes of complexity in interdisciplinary (mechatronic) design projects (prototypes) through thematic analysis of faculty interviews. Further, this was validated by analyzing the prototypes. Findings: Several themes of complexity emerged; this article presents only two themes: 1) knowledge and 2) skills related to solving the engineering design problem and the types of relationships between the prototype’s functions. [ABSTRACT FROM AUTHOR]

Contribution: This article explores the impact of the combination of various pedagogical approaches to deliver high-quality learning experiences for online-based engineering students. Though existing research extensively studied online education approaches, limited research has investigated how to bridge the gap between online cloud-based and campus-based students more effectively for obtaining hands-on engineering skills. Background: Given that engineers aim to solve real-world problems, engineering graduates need to obtain relevant experiences for employability. However, it is challenging to deliver such experiences in online education, which motivates innovative ways to integrate practical experiments in Web-based resources. Intended Outcomes: Practical and industry-relevant skills with flexibility in terms of time and pace of learning are intended to be achieved in the teaching framework which is expected to improve learning experiences for online students. Application Design: The adopted mixed pedagogical approach revolves around real-life problem-based learning delivered in the online mode using recorded experiments on energy-efficient design for three cohorts of the fourth-year engineering students, two of which are completely cloud-based students and the other one has a mix of on-campus and cloud-based students. Findings: The effectiveness of the adopted approach is measured through quantitative and qualitative evaluation tools. The evaluation demonstrated that cloud student engagement and motivation improved substantially by integrating explicit, analytical, as well as embodied learnings while enabling them to perform equally well as compared to the campus-based students. [ABSTRACT FROM AUTHOR]

Contribution: This article discusses the use of manufacturing simulation games to study collaborative problem-solving skills in engineering students. The simulation represents the mass production paradigm in which large quantities of identical products are produced. Empirical data is collected from the simulation to evaluate the skills engineering students used in solving the problem and their group effectiveness. Background: The use of simulation games to teach problem solving in design and manufacturing is an effective approach to convey concepts to students. Simulation games engage students in experiential and collaborative learning with fun elements. Research Questions: How does hands-on simulation engage students in collaborative problem solving? How does participation in collaborative problem solving affect group effectiveness? Methodology: This work presents a study of 37 university-level engineering students in the United States. Participants worked in groups completing the simulation game and responded to surveys on their various skills used. Findings: Participants utilized analytical, metacognitive, and thinking skills in their engagement, reported that the simulation games enhanced their understanding of manufacturing concepts and active collaboration improved problem-solving effectiveness. [ABSTRACT FROM AUTHOR]

Quality improvement teams engage in multiple learning opportunities during an improvement project. These learning opportunities consist of both induced opportunities, such as formal training efforts, and incidental opportunities due to surprises (i.e., events that violate expectations) encountered during problem solving activities. In this paper, we propose that these two types of team learning opportunities can result in method-driven learning (MDL) at the team level. Our data are collected using a unique longitudinal survey design spanning 31 weeks that involved 33 participants in five design for Six Sigma projects conducted in different behavioral healthcare organizations, yielding a total of 562 weekly survey responses. We examine two modes of surprises. Those occurring spontaneously (measured based on self-rated and coder-rated variables) and reflective surprises (measured as externally-induced treatments). Our results show that: 1) the proportion of surprises occurring spontaneously during problem solving activities is positively correlated with team learning (i.e., MDL); and 2) collective reflections on surprises also facilitate team learning. Furthermore, we find evidence that surprises occurring spontaneously can negatively moderate the effect of induced learning opportunities (measured as efforts expended on training) on team learning. However, we find no evidence of such a negative moderation effect for surprises from reflection. Overall, our paper develops a nuanced understanding about the direct and moderating effects of surprises on team learning in the behavioral healthcare context, thereby contributing not only to the quality management literature but also quality improvement practices in behavioral healthcare. [ABSTRACT FROM AUTHOR]

Background: This study focuses on how group communication affects group productivity. The specific scope of the study is pair programming. The study aims to discover whether intra-pair communication in pair programming has a significant impact on the pair programming process and output. Literature review: Many of the pair programming communication studies are descriptive and qualitative studies whose foci lay more on communication contents and alternative message deliveries. As a result, more research that focuses on analyzing the effectiveness of a person's communication skill level while performing a demanding task is needed. Research question: Does the communication competency level significantly impact pair programming output? Methodology: A pool of novice university programming students was deployed for the experiment. The Conversational Skills Rating Scale (CSRS) was used to categorize them into three cohorts—"high-high,” “high-low,” and “low-low.” The confounding variables were controlled. Results: No significant difference was found among the three cohorts in terms of their pair programming code output. Additionally, the post-experiment questionnaire responses revealed no significant difference in compatibility and confidence levels, but did show a significant difference in communication level. Conclusion: With all things being equal, a programmer's high communication skill level doesn't play a significant role in the programming output in a pair programming setting. [ABSTRACT FROM AUTHOR]

Projects are becoming increasingly more complex at work. This applies to the information technology (IT) field as well. In this Technology Manager Notebook article, we provide a brief review and comparison of management principles from the latest editions of two commonly referenced frameworks in the field of IT: the IT Infrastructure Library and Project Management Book of Knowledge. This is followed by sharing some practical observations and considerations to better manage projects from an IT project management perspective. [ABSTRACT FROM AUTHOR]

This article describes an example of data mining techniques applied to an open educational environment. These novel assessment methods in the educational robotics (ER) field provide empirical evidence of problem-solving styles behind the key tasks of proposed activities within real operative scenarios. A supervised, mixed machine learning (ML) approach was applied to data from seven Italian secondary schools (197 students), and four ML techniques [logistic regression (LR), support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF)] were explored to predict students’ success. [ABSTRACT FROM AUTHOR]

Visualization, interactive computer graphics, and related topics have been a particularly dynamic area of computer science, producing advances that impact society. Working at times in a research laboratory and at times for two science funding agencies, I held positions at the level of an individual researcher, Director of a VR Laboratory, and funding agency Program Director. This article will discuss some of my experiences, including insight into how science programs in funding agencies are initiated. [ABSTRACT FROM AUTHOR]

Computational thinking (CT) can be considered a 21st century core skill and, accordingly, it should be taught to students at an early age. Nevertheless, the implementation of CT in school curricula is still in an experimental stage, given that different performance metrics remain unclear, including the appropriate age for learning each skill, the significant and achievable computational concepts for each school year, teaching strategies, learning effectiveness, and strategies for assessing development. Based upon constructivist problem-solving learning strategies and supported by a three-dimensional framework, a game-based environment with both individual and collaborative playing modes has been developed as a learning and an assessment tool via learning analytics. Moreover, an exploratory case study has been carried out involving 176 primary school students. Results suggest that this environment is suitable as a learning and assessment tool for CT skills in primary school, providing enduring learning, particularly when it engages the collaborative playing mode. It seems to be better adapted to early primary school stage students, who showed greater improvements and who were able to assimilate more computational concepts than expected. Likewise, special needs or low percentile students benefit even more greatly from the learning tool and especially from the collaborative playing mode. The combination of different assessment methodologies—including CT pre- and posttests, data-driven analytics, direct observation, and questionnaires—provides assessment for each of the framework computational dimensions. [ABSTRACT FROM AUTHOR]

Today's digital society has turned the development of students' computational thinking capabilities into a critical factor for their future success. As higher education institutions, we need to take responsibility for this development in every degree course we offer, and provide students with the kind of subjects and activities that best contribute to this aim. In this paper, we study the impact of following an introductory programming course on the development of the computational thinking capabilities of university students. In order to achieve this aim, a concurrent cohort observational study was carried out in which we measured both the subjective and objective computational thinking capabilities of 104 participants (50 first year students enrolled on a Bachelor's degree course in Psychology at the Catholic University of Murcia (UCAM), and 54 first year students enrolled on a Bachelor's degree course in Health Information Systems at the University of Alicante (UA)). The statistical procedures applied to test our hypotheses were a two-way mixed ANOVA, a paired-sample T-test and an independent-sample T-test. The data shows that the group at UA had an initial higher subjective perception of their computational capabilities than the group at UCAM. This perception was supported by their objective scores, which were also significantly higher. However, the subjective assessment of computational capability of the UA group diminished after exposure to the programming course, contrasting with the fact that their objective computational capabilities improved significantly. In the UCAM group, both subjective and objective capabilities remained constant over time. Based on these results, we can conclude that computational thinking capabilities are not developed naturally, but need to be trained. Providing such training to all our students, and not only to those enrolled on undergraduate degrees in engineering, is of paramount importance to allow them to face the challenges of their future professions. This paper empirically demonstrates the extent to which exposing subjects to a programming course may contribute to this aim.

Predicting students' line of actions helps educators give adequate guidance to students, but this remains a challenge in science, technology, engineering, and mathematics (STEM) education. Given this, there is a scarcity of related research that will help improve teachers' prediction capabilities on students' line of actions when tackling ill-defined problems (IDPs), as well as how emerging data mining techniques could contribute to such prediction. The present study aims to fill the gap by measuring the quality of teachers' predictions (labeled expert prediction), where 43 elementary teachers predict students' step-by-step actions when solving an IDP through the light path task (LPT), and then comparing its quality with that of machine prediction, executed via sequential pattern mining techniques. Data on students' lines of action were collected from 501 5th- and 6th-grade students, aged 11-12. The results showed the significantly lower accuracy of expert prediction compared to machine prediction, which highlights the advantages of using data mining in predicting students' actions and shows its possible application as a recommendation system to provide adaptive guidance in future STEM education.

Norwegian high-tech industries face a rapidly changing market need. Staying ahead of competitors and developing significant innovative solutions are essential for business value. Systems engineering has proven to be an effective approach for developing technical (hard) systems. People, organizations, and technical functionality contribute to an increasing complexity in today's high-tech systems. This makes the traditional systems engineering approach insufficient for innovation in a socio-technical context. This article looks toward systems architecting, systems oriented design, and participatory design for collaborative and creative ways of working to support systems engineers in developing significant innovations. We explore a rich toolbox and the outline of a new methodology for such co-creative problem solving. First, we identify industry needs for the new methodology and derive success criteria for the toolbox embodied in the new methodology. Through ten industry cases within Norwegian high-tech industries, we analyze and discuss the toolbox composed of methods and tools for early exploration, validation, and knowledge transfer in the concept phase. Finally, we provide examples on how the toolbox supports the industry needs and outline the new methodology. [ABSTRACT FROM AUTHOR]

Aiming at time-variant problems solving, a special type of recurrent neural networks, termed zeroing neural network (ZNN), has been proposed, developed, and validated since 2001. Although equality-constrained time-variant quadratic programming (TVQP) has been well solved using the ZNN approach, TVQP problems with inequality constraints involved have not been satisfactorily handled by the existing ZNN models. To overcome this issue, this paper designs a ZNN model with exponential convergence for solving equality- and inequality-constrained TVQP problems. Considering a fast convergence is preferred in some time-critical applications in practice, a predefined-time stabilizer is for the first time utilized to endow the ZNN model with predefined-time convergence, leading to a predefined-time convergent ZNN (PTCZNN) model that exhibits an antecedently- and explicitly-defined convergence time. Theoretical analysis is performed with the convergence of the two ZNN models including the predefined-time convergence of the PTCZNN model rigorously proved. Validations are comparatively conducted to verify the effectiveness and superiority of the PTCZNN model in terms of convergence performance. To demonstrate the potential applications, the PTCZNN model is applied to image fusion and kinematic control of two robotic arms with joint limits considered. The efficacy and applicability of the PTCZNN model are validated by the illustrative examples. This is the first time to develop a ZNN model working as a quadratic programming solver that is applicable to kinematic control of robotic arms with joint constraints handled since the emergence of ZNNs. [ABSTRACT FROM AUTHOR]

Three-way decision model (3WDM) with decision-theoretical rough sets (DTRSs) always addresses precise cost parameters and precise attribute values in uncertain problem solving. However, intuitionistic fuzzy sets (IFSs), as extensions of fuzzy sets, are described by dual parameters, namely the membership and nonmembership degrees. Therefore, under intuitionistic fuzzy environments, the 3WDM confronted great challenges when intuitionistic fuzzy number (IFN) cost parameters and IFN attribute values arise together. In this paper, by considering the impacts of both the IFN cost parameters and the IFN attribute values, new 3WDMs with IFNs are constructed from the membership degree and nonmembership degree perspectives, respectively. Then optimistic and pessimistic 3WDMs with IFNs are, respectively, established based on different risk preferences for more accurate decisions. Subsequently, by introducing a sequential strategy, the presented models are applied to address attribute increments in practical problems. Finally, we present a numerical example and some experiments to validate the efficiency of sequential 3WDMs. [ABSTRACT FROM AUTHOR]

The growth of the field of visual analytics reflects a sustained, long-term commitment to applying convergent research principles to computer science research. Charting the future research directions that the field of visual analytics will evolve toward requires an understanding of the field's rich, productive history of collaboration and a discussion of current challenges. [ABSTRACT FROM AUTHOR]

Future quadratic programming (FQP) is an interesting and challenging topic due to its unknown future information and time-dependent feature. In this paper, a continuous-time zeroing neurodynamics (CTZN) model for quadratic programming is first obtained via zeroing neurodynamics (ZN) method. Then, a general four-step Zhang et al. discretization formula is presented and adopted to discretize the above CTZN model, and thus the general four-step discrete-time ZN (DTZN) algorithm for the FQP is developed. For comparison, a three-step DTZN algorithm and a one-step DTZN algorithm for the FQP are also presented. It is worth noting that there is an important parameter termed stepsize in the DTZN algorithms, which is closely related to their stability. If the value of stepsize is outside its effective interval, the DTZN algorithms are impossible to achieve convergence in terms of residual errors, which leads to failure of the FQP problem solving. By utilizing bilinear transformation and Routh stability criterion, the effective stepsize interval of the general four-step DTZN algorithm is confirmed via theoretical proof. Besides, numerical results substantiate the effectiveness and superiority of the general four-step DTZN algorithm as well as the accuracy of the effective stepsize interval. Finally, the general four-step DTZN algorithm is applied to fulfill the path-tracking control of different robot manipulators, with the effectiveness and superiority further validated. [ABSTRACT FROM AUTHOR]

With the broader and highly successful usage of machine learning (ML) in industry and the sciences, there has been a growing demand for explainable artificial intelligence (XAI). Interpretability and explanation methods for gaining a better understanding of the problem-solving abilities and strategies of nonlinear ML, in particular, deep neural networks, are, therefore, receiving increased attention. In this work, we aim to: 1) provide a timely overview of this active emerging field, with a focus on “post hoc” explanations, and explain its theoretical foundations; 2) put interpretability algorithms to a test both from a theory and comparative evaluation perspective using extensive simulations; 3) outline best practice aspects, i.e., how to best include interpretation methods into the standard usage of ML; and 4) demonstrate successful usage of XAI in a representative selection of application scenarios. Finally, we discuss challenges and possible future directions of this exciting foundational field of ML. [ABSTRACT FROM AUTHOR]

The chip side wall crack of semiconductor nanometer packaging process has always been an important technological problem that the global semiconductor packaging industry needs to overcome. This research has helped the world’s biggest semiconductor package factory to improve the chip side wall crack issue faced by nanometer wafer packaging process, thus enhancing its yield rate. After analyzing the abnormality of nanometer wafer packaging process, this research team found that the chip side wall crack problem was caused by a poor laser waveform during the laser cutting process, resulting in debris along the chip side wall. Subsequently, as the diamond cutter cuts into the chip in the following process, the cutter impacts the debris which then impacts the side wall resulting in a side wall crack. The Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ) analysis was used to deduce a suitable improvement method. After applying the TRIZ analysis, this research team confirmed that by modifying the laser equipment to create a more uniform laser waveform, the diamond cutter was able to achieve a clean cut of the chip without impacting debris and thus significantly decreased the chip side wall crack occurrences. The shipment yield rate was increased from 92.8% to 99.61% as a result of the team’s modifications. [ABSTRACT FROM AUTHOR]

Solving mathematical (math) word problems (MWP) automatically is a challenging research problem in natural language processing, machine learning, and education (learning) technology domains, which has gained momentum in the recent years. Applications of solving varieties of MWPs can increase the efficacy of teaching–learning systems, such as e-learning systems, intelligent tutoring systems, etc., to help improve learning (or teaching) to solve word problems by providing interactive computer support for peer math tutoring. This article is specifically intended to benefit such teaching–learning systems on arithmetic word problems solving by adding an interactive and intelligent word problem solver to assess an individual's learning outcome. This article presents arithmetic mathematical word problems solver (AMWPS), an educational software application for solving arithmetic word problems involving single equation with single operation. This article is based on a combination of a machine learning based (classification) approach and a rule-based approach. We start with classification of arithmetic word problems into four categories (Change, Compare, Combine, and Division–Multiplication) along with their subcategories, followed by the classification of operations (+, –, *, and /) related to different subcategories. Our system processes an input arithmetic word problem, predicts the category and subcategory, predicts the operation, identifies and retrieves the relevant quantities within the problem with respect to answer generation, and formulates and evaluates the mathematical expression to generate the final answer. AMWPS outperformed similar systems on the standard AddSub and SingleOp datasets and produced new state-of-the-art result (94.22% accuracy). [ABSTRACT FROM AUTHOR]

Today, in the era of robotics, different types of educational robots have been used extensively in school classrooms to facilitate teaching activities related to a variety of computer science concepts. Numerous studies have been performed that attempt to examine the effects of using tangible interfaces to enhance collaborative learning experiences. In most of these studies, feedback, which is a vital function for a successful game activity, is mainly provided by the trainers. However, this kind of feedback can be considered as static and general, while each trainee seeks clear, consistent, and even personalized feedback. This article proposes an interactive learning tool for introducing algorithmic thinking and sequencing using educational robots suitable for elementary and intermediate students. In more detail, in this article, we leverage a fuzzy-rule-based system and computer vision techniques to provide immediate, personalized feedback and recommendations to young students while they perform a series of activities using tangible robots. These activities relate to teaching programming skills and improve the algorithmic thinking of students. Experimental results revealed that participants were able to increase their algorithmic/programming thinking skills while developing a positive attitude toward programming. The interactive gaming factor that is embedded in the use of tangible robots, while participating in the activities, was proved to be a compelling and a rewarding experience. The article concludes that the use of the proposed feedback mechanism, when placed in a robot game environment, can lead to a positive and more effective learning process. [ABSTRACT FROM AUTHOR]

Efficient optimisation algorithms have become important tools for finding high-quality solutions to hard, real-world problems such as production scheduling, timetabling, or vehicle routing. These algorithms are typically “black boxes” that work on mathematical models of the problem to solve. However, many problems are difficult to fully specify, and require a “human in the loop” who collaborates with the algorithm by refining the model and guiding the search to produce acceptable solutions. Recently, the Problem-Solving Loop was introduced as a high-level model of such interactive optimisation. Here, we present and evaluate nine recommendations for the design of interactive visualisation tools supporting the Problem-Solving Loop. They range from the choice of visual representation for solutions and constraints to the use of a solution gallery to support exploration of alternate solutions. We first examined the applicability of the recommendations by investigating how well they had been supported in previous interactive optimisation tools. We then evaluated the recommendations in the context of the vehicle routing problem with time windows (VRPTW). To do so we built a sophisticated interactive visual system for solving VRPTW that was informed by the recommendations. Ten participants then used this system to solve a variety of routing problems. We report on participant comments and interaction patterns with the tool. These showed the tool was regarded as highly usable and the results generally supported the usefulness of the underlying recommendations. [ABSTRACT FROM AUTHOR]

With the rapid development of online education in recent years, there has been an increasing number of learning platforms that provide students with multi-step questions to cultivate their problem-solving skills. To guarantee the high quality of such learning materials, question designers need to inspect how students' problem-solving processes unfold step by step to infer whether students' problem-solving logic matches their design intent. They also need to compare the behaviors of different groups (e.g., students from different grades) to distribute questions to students with the right level of knowledge. The availability of fine-grained interaction data, such as mouse movement trajectories from the online platforms, provides the opportunity to analyze problem-solving behaviors. However, it is still challenging to interpret, summarize, and compare the high dimensional problem-solving sequence data. In this paper, we present a visual analytics system, QLens, to help question designers inspect detailed problem-solving trajectories, compare different student groups, distill insights for design improvements. In particular, QLens models problem-solving behavior as a hybrid state transition graph and visualizes it through a novel glyph-embedded Sankey diagram, which reflects students' problem-solving logic, engagement, and encountered difficulties. We conduct three case studies and three expert interviews to demonstrate the usefulness of QLens on real-world datasets that consist of thousands of problem-solving traces. [ABSTRACT FROM AUTHOR]

Motivated by the practical demands for simplification of data toward being consistent with human thinking and problem-solving, as well as tolerance of uncertainty, information granules are becoming important entities in data processing at different levels of data abstraction. This article proposes a method to construct classifiers from multiresolution hierarchical granular representations using hyperbox fuzzy sets. The proposed approach forms a series of granular inferences hierarchically through many levels of abstraction. An attractive characteristic of our classifier is that it can maintain a high accuracy in comparison to other fuzzy min-max models at a low degree of granularity based on reusing the knowledge learned from lower levels of abstraction. In addition, our approach can reduce the data size significantly as well as handle the uncertainty and incompleteness associated with data in real-world applications. The construction process of the classifier consists of two phases. The first phase is to formulate the model at the greatest level of granularity, while the later stage aims to reduce the complexity of the constructed model and deduce it from data at higher abstraction levels. Experimental analyses conducted comprehensively on both synthetic and real datasets indicated the efficiency of our method in terms of training time and predictive performance in comparison to other types of fuzzy min-max neural networks and common machine learning algorithms. [ABSTRACT FROM AUTHOR]

This article discusses how leadership skills can be employed to inspire project team members to overachieve with unexpected results. The team voluntarily worked overtime, proactively anticipated customer's needs, and actively mitigated project risks. Integrity is a leadership currency for which you are guaranteed to yield marvels. [ABSTRACT FROM AUTHOR]

Research problem: The role that physical, temporal, and cultural distances play in global software development projects has been well researched. Culturally diverse teams separated by physical distances across multiple time zones face significant challenges in collaborating effectively with each other. This article examines a fourth dimension—cognitive distance—that relates to the problem-solving style of teams that can also have an impact on their ability to collaborate successfully. Research questions: 1. Does cognitive distance affect communication among global software development teams collaborating with each other? 2. How does cognitive distance affect the sentiment/emotion of global software development teams collaborating with each other? Literature review: Prior research shows that collaboration among teams on global software development projects is impacted by practices to manage collaboration; appropriate use of collaboration technologies; collaboration readiness that relates to individual characteristics such as personality traits, motivation, and trust; and shared understanding in group problem-solving. While shared understanding has looked at the effectiveness of the use of common language and knowledge sharing, it has not examined how differences in problem-solving styles of geographically dispersed teams impact their ability to collaborate successfully. Methodology: We examined project artifacts and email communication among geographically dispersed teams within a global software development project. From the project artifacts, we examined tasks allocated to different teams. From the emails, we established the communication network and volume of communication, and performed a sentiment analysis on email content. This analysis allowed us to observe not only the quality of communication among the teams but also the sentiment/emotion that reflected how well they were working together. Results and discussion: Managing teams that vastly differ in problem-solving styles and tasks requires that project managers be aware of these differences and introduce liaisons that reach across the teams to help bridge the cognitive divide. [ABSTRACT FROM AUTHOR]

Contribution: This article aims to explore learner variables that predict the effectiveness of university class using the Havruta method. Background: This article was conducted on 105 learners enrolled in the “General Physics and Experiments 2” class at K University in South Korea. Research Questions: Independent and dependent variables were selected from previous studies to predict the effectiveness of the Havruta method in a university class. Methodology: Descriptive statistics, such as frequency and averages, were used to analyze the general characteristics of the subjects, and Pearson’s $r$ was used to check for correlation among all variables. Variables that predicted learning satisfaction and flow were evaluated through the multiple regression analysis. Findings: The results indicate that the independent variable, “openness to learning opportunities” significantly explained learning satisfaction by 16.1%. Together with “initiative and independence in learning,” they were significant in explaining the learning flow by 32.2%. These findings have practical application in universities in integrating the Havruta method to their classes. [ABSTRACT FROM AUTHOR]

The rapid, global spread of COVID-19 has led to an unprecedented rise in enrollments in online learning experiences among learners of all ages. In this article, we explore the impact of the global pandemic on a massive open online course, Problem Solving Using Computational Thinking, with a particular focus on the topics learners chose for their final projects. The Computational Thinking MOOC was designed using a project-based learning approach and aims to provide learners with an introduction to the "big ideas" of computational thinking using a range of case studies that encompass topics such as airport surveillance, epidemiology, and human trafficking. Beyond observing a sharp increase in enrollment and engagement at the time the pandemic began, we discuss ways in which the course's project-based pedagogy allowed learners to bring their present experiences and concerns together with the course's subject matter in order to meet the learning objectives for the course. Many learners chose to address aspects of the pandemic in the course's final project and applied ideas about computational thinking to peer-graded assignments that conveyed an individualized sense of importance and urgency. We assert that this approach, along with the inclusion of a timely epidemiology case study, enabled learners to more deeply internalize the role that computational thinking can play in their own lives and in society as a whole. [ABSTRACT FROM AUTHOR]