Search

Computational thinking is one barrier to enculturating as a professional engineer. We created the Engineering Computational Thinking Diagnostic (ECTD) as an instructional tool that can identify at-risk first-year engineering students. The purpose of this study is to provide construct validity, internal consistency reliability, item characteristics, and criterion validity evidence for this diagnostic. From fall 2020 to fall 2021, 469 students from three institutions in the United States took the diagnostic. The data from 152 students at one institution was used to provide evidence of predictive validity. Exploratory and confirmatory factor analyses resulted in 20 items loading onto one factor in a good model fit range, with the internal consistency reliability coefficient, Cronbach $\alpha $ of 0.86. From item analyses based on classical test theory, the diagnostic items on average tended to be slightly easy but had sufficient discrimination power. The correlation matrix for criterion validity evidence indicated that the diagnostic functions well to differentiate students’ computational thinking ability by prior computer science course experience as well as by first-generation status. Predictive validity evidence from regression analyses revealed the statistically significant effect of students’ diagnostic scores assessed at the beginning of the first semester on predicting their end of semester course grades. The ECTD can have a broad impact because it provides a tool to gauge the entry-level skills of students, enabling early curriculum interventions to help retention and persistence to graduation. We make the case that the ECTD could contribute to the development of a more diverse workforce in engineering.

Student attrition is a long-standing problem in Computer Science (CS), as in many other disciplines, and it has gained momentum in the academic sphere. This study employs bibliometric analysis to shed light on the research stream of student attrition within CS. Bibliometric analysis is a popular technique for evaluating published scientific articles when empirical contributions are producing voluminous research streams. We collected 1310 articles from the Web of Science and Scopus databases, published over a period of 22 years from 2000 to 2022, to analyze the most relevant publication venues in the study of attrition in CS. Further analysis revealed the most cited institutions, countries, key themes, and other conceptual information. Keywords, such as “retention,” “computer science education,” “gender,” “introductory programming,” and “student success” emerged as dominant themes in attrition studies. As researchers work intensively to reduce attrition within CS, these thematic areas may continue to shape the future direction of attrition studies. Our study provides a comprehensive overview of research hotspots, thematic areas, and future directions for attrition studies in CS. This outcome could be valuable for young and emerging scholars who are starting their careers and looking to identify research hotspots in this field of interest.

The Reinforcement Learning (RL) paradigm is showing promising results as a generic purpose framework for solving decision-making problems (e.g., robotics, games, finance). The aim of this work is to reduce the learning barriers and inspire young students, researchers and educators to use RL as an obvious tool to solve robotics problems. This paper provides an intelligible step-by-step RL problem formulation and the availability of an easy-to-use interactive simulator for students at various levels (e.g., undergraduate, bachelor, master, doctorate), researchers and educators. The interactive tool facilitates the familiarization with the key concepts of RL, its problem formulation and implementation. In this work, RL is used for solving a robotics 2D navigational problem where the robot needs to avoid collisions with obstacles while aiming to reach a goal point. A navigational problem is simple and convenient for educational purposes, since the outcome is unambiguous (e.g., the goal is reached or not, a collision happened or not). Due to a lack of open-source graphical interactive simulators concerning the field of RL, this paper combines theoretical exposition with an accessible practical tool to facilitate the apprehension. The results demonstrated are produced by a Python script that is released as open-source to reduce the learning barriers in such innovative research topic in robotics.

As an interdisciplinary course, Machine Vision combines AI and digital image processing methods. This paper develops a comprehensive experiment on forest wildfire detection that organically integrates digital image processing, machine learning and deep learning technologies. Although the research on wildfire detection has made great progress, many experiments are not suitable for students to operate. Also, the detection with high accuracy is still a big challenge. In this paper, we divide the task of forest wildfire detection into two modules, which are wildfire image classification and wildfire region detection. We propose a novel wildfire image classification algorithm based on Reduce-VGGnet, and a wildfire region detection algorithm based on the optimized CNN with the combination of spatial and temporal features. The experimental results show that the proposed Reduce-VGGNet model can reach 91.20% in accuracy, and the optimized CNN model with the combination of spatial and temporal features can reach 97.35% in accuracy. Our framework is a novel way to combine research and teaching. It can achieve good detection performance and can be used as a comprehensive experiment for Machine Vision course, which can provide the support for talent cultivation in machine vision area.

This paper presents our experience implementing Collaborative Working Spheres (CWS) in the context of an international undergraduate course on Global Software Development (GSD) during the COVID-19 pandemic. Many universities around the world increased their efforts in creating educational alternatives for adequately addressing the educational challenges that this pandemic has introduced. A particular case is the training of software skilled graduates to work in globally distributed environments because learning this topic requires a lot of practical work when student motivation could have been negatively affected by the COVID-19 pandemic. However, providing highly practical GSD courses during a pandemic is a challenging task for many of these universities. It is against this backdrop that we have developed an educational tool to provide CWS, enabling undergraduate students to acquire practical experience in GSD and improve their communication and teamworking skills, even during the COVID-19 pandemic. An international empirical evaluation was conducted involving students and teachers from seven universities in different countries around the world. The obtained results showed that our approach can make a significant contribution to the development of practical projects on undergraduate GSD courses with students developing their knowledge and social skills associated with this topic. The data collected on the teachers’ perceptions suggested that our approach could also be useful in introducing the GSD approach at undergraduate level when social distancing is in place.

Computer engineering is a rapidly evolving discipline. How should we teach it to our students? [ABSTRACT FROM AUTHOR]

Computerisation and digitalisation are shaping the world in fundamental and unpredictable ways, which highlights the importance of computing education research (CER). As part of understanding the roots of CER, it is crucial to investigate the evolution of CER as a research discipline. In this paper we present a case study of a Finnish CER conference called Koli Calling, which was launched in 2001, and which has become a central publication venue of CER. We use data from 2001 to 2020, and investigate the evolution of Koli Calling’s scholarly communities and zoom in on it’s publication habits and internalisation process. We explore the narrative of the development and scholarly agenda behind changes in the conference submission categories from the perspective of some of the conference chairs over the years. We then take a qualitative perspective, analysing the conference publications based on a comprehensive bibliometric analysis. The outcomes include classification of important research clusters of authors in the community of conference contributors. Interestingly, we find traces of important events in the historical development of CER. In particular, we find clusters emerging from specific research capacity building initiatives and we can trace how these connect research spanning the world CER community from Finland to Sweden and then further to the USA, Australia and New Zealand. This paper makes a strategic contribution to the evolution of CER as a research discipline, from the perspective of one central event and publication venue, providing a broad perspective on the role of the conference in connecting research clusters and establishing an international research community. This work contributes insights to researchers in one specific CER community and how they shape the future of computing education

Contribution: An intensive three-month educational program can be used for rapid integration of foreign-born people into the IT industry. A novel method for integrating industrial needs with the practical parts of a bachelor’s Computer Science program. Background: The program was motivated by (1) the societal need to increase the meaningful integration of immigrants into the workforce, and (2) the demand for IT specialists in the IT labor market. Intended outcomes: An effective intensive software developer program with a high level of industrial integration and a working matching model for employment. Application design: The program consists of three different phases; recruitment of participants, training and job matching. The training is divided into six modules using five different teaching methods. An evaluation model, based on passive and active data, is implemented with fast learning loops for teachers and participants. Findings: The program has been run seven times with 263 unemployed participants of different nationalities. On average 82.6 percent of the participants found employment in the IT industry within 5 months of the course ending. Female participants are in the majority and are more successful in securing employment. The findings suggest that it was possible to rapidly prototype and deliver an advanced reskilling program within a university setting and use it as a positive method to support newcomers find meaningful work that has a direct benefit for the local IT industry, as well as for the wider society.

The need for organized computing education efforts dates back to the 1950s. Since then, computing education research (CER) has evolved and matured from its early initiatives and separation from mathematics education into a respectable research specialization of its own. In recent years, a number of meta-research papers, reviews, and scientometric studies have built overviews of CER from various perspectives. This paper continues that approach by offering new perspectives on the past and present state of CER: analyses of influential papers throughout the years, of the theoretical backgrounds of CER, of the institutions and authors who create CER, and finally of the top publication venues and their citation practices. The results reveal influential contributions from early curriculum guidelines to rigorous empirical research of today, the prominence of computer programming as a topic of research, evolving patterns of learning-theory usage, the dominance of high-income countries and a cluster of 52 elite institutions, and issues regarding citation practices within the central venues of dissemination.

Software modeling is a creative activity in which software components and their relationships are identified based on customer requirements. Based on the literature, object-oriented software modeling is based on four fundamental pillars which are abstraction, encapsulation, decomposition, and inheritance. However, despite the existence of guidelines and recommendations for implementing the object-oriented approach, novice software designers do not make good design decisions, leading to inefficient designs that cannot be modifiable, understandable, or user-friendly distribute at the development level. The literature reveals that the most common difficulties faced by software designers is a lack of understanding and confusion of concepts related to the object-oriented approach, as well as difficulties in creating Unified Modeling Language diagrams, especially class diagrams. The work presented in this article uses a qualitative and quantitative approach to determine, in a group of university students, what are the most recurrent difficulties and their persistence during the time. The qualitative case study is the method that allowed to generate the documents: diagnostic and evaluation tests. Additionally, a thematic analysis was used to identify, analyze and report patterns within the data. In order to know the occurrences of the problems in the case study, as part of our quantitative approach, a comparative study was applied to compare the results obtained between the diagnostic and evaluation tests and thus establish the similarities and differences among the cases observed, through the hierarchical clustering technique. The findings of this study show us 16 difficulties identified after the qualitative analysis, while the quantitative analysis shows us the number of occurrences and their persistence over time. The difficulties reported in both analyzes focus on these three difficulties: a) Definition of attributes that could be a class, b) Classes with inadequate or insufficient behavior and, c) Incorrect use of multiplicity between classes. Each of these difficulties is analyzed in depth in this study.

Project moveSMART uses a web-based platform to integrate physical education with computer science (CS) and computational thinking (CT). This article describes a series of tutorials to introduce elementary students to CS/CT by making connections to physical activity and grade-level curricula in other subjects. [ABSTRACT FROM AUTHOR]

Contribution: Students do not naturally give unbiased, accurate peer assessments (PAs). Hence, giving teamwork guidelines to students can improve their cooperation, understanding of one another, PA attitude, and PA accuracy. Background: During collaborative learning, PA can improve students’ autonomy, evaluation, and communication, which often enhances their task performance and learning outcomes. However, student bias, low effort, or imprecision can bias PA. Hypothesis: This study hypothesized that teamwork guidelines can improve students’ collaboration and hence their: 1) PA accuracy and 2) attitudes toward PA. Design/Method: This comparison study examined the 447 PAs and 143 survey responses of 143 university students studying computer science in 49 teams. 58 students received teamwork guidelines and 85 students did not. PAs and teacher assessments (TAs) were analyzed with a multilevel, structural equation model, and survey responses were analyzed with a multivariate outcome, multilevel, and ordered logit regression. Findings: PAs often exceeded TAs of the same student (positive bias), especialy for low-performing students. Compared to other students, students receiving teamwork guidelines gave PAs that resembled TAs more but had a similar positive bias. The relative and absolute differences in peer and TAs (PA – TA, |PA – TA|) mostly differed across teams rather than students. Also, students receiving teamwork guidelines viewed PA as more supportive of their learning, compared to other students without the teamwork guidelines. [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]

Contribution: Understanding pupils’ conceptualization of robots and programming can help teachers to avoid a disconfirmation experience by selecting more appropriate educational tools, robot designers in improving the robot design, and researchers in further improvement of the field. Background: Human–robot interaction (HRI) is affected by the actual but also expected robot’s appearance and capabilities. Multiple factors, such as age, gender, media exposure, ICT exposure, or culture, influence mental models regarding robots; therefore, it is important to investigate those for a specific cohort in the designated geographical area. Research Questions: The mental models regarding robots and programming, and the way they are possibly biased by popular culture, exposure to ICT or parental influence were studied. Research questions concerned cognitive elements of mental models, namely, definitions and knowledge of robots, and programming and how those progress in time. Also, the research studied a figurative aspect of mental models regarding robots, with a focus on anthropomorphic features. Methodology: To research the influence of the short-term HRI, four classes of eight to nine years old elementary school pupils were included in a workshop where pre and postquestionnaires were used as research instruments. Besides pupils, later in this two-phase longitudinal research, after a year of formal education in Informatics, research instruments also included teachers and parents, to investigate their influence on children’s mental models. Findings: The change of mental models under the influence of the one-time workshop was not permanent. However, a combination of maturation with informal and formal intervention supports the conceptualization of programming. [ABSTRACT FROM AUTHOR]

Contribution: This article features a systematic literature review with the objective of presenting a study that reflects the current scenario of research on the cognitive load theory (CLT) in the domain of teaching and learning computer programming. Background: Computer programming is a highly cognitive skill, requiring mastering multiple competencies, and recognized as being difficult to learn, for this reason, the cognitive load (CL) in the learner’s working memory emerged as an influential concept, making CLT one of the most common theories in computing education research. Research Questions: What are the approaches that relate CLT to teaching and learning computer programming? What CLT-related concepts are covered? What evidence is reported with respect to this context? Methodology: Following a formal protocol, a survey was performed for papers linking CLT to teaching and learning programming published between 2010 and 2020. The selection of papers was based on a set of criteria established to drive the selection process, including alignment with the research questions and paper quality assessment. Findings: The approaches applied in the papers are based on measuring the CL; through instructional design based on the development or use of resources or tools, a range of different pedagogy strategies and the CLT concepts. With respect to the concepts, the subjective measurement technique and worked example effect are the most commonly deployed. As far as the evidence reported, the better part is related to the worked example effect and measuring CLs. [ABSTRACT FROM AUTHOR]

Contribution: This study presents the results of a systematic mapping on the classification and organization of open educational resources (OERs) focused on Computer Science Education (CSEd) in digital sources. Background: The number of open resources (e.g., images, videos, and websites) available on the Internet for the teaching of Computer Science has increased in recent years. As a consequence, several authors have proposed different ways of classifying them, which include lists of disciplines, classification algorithms, and semantic enrichment. However, such studies are dispersed and fragmented in the literature, making the current state-of-the-art unclear. Research Questions: 1) What digital sources have been used to classify OERs for CSEd? 2) What CSEd domains have been explored for the classification of OERs? and 3) What approaches have been adopted for the classification of OERs for CSEd? Methodology: Three search strategies were performed: 1) automatic search in the five major digital libraries of CSEd; 2) manual search in the major CSEd conferences and journals; and 3) snowballing on the references and citations. As a result, 22 relevant studies were selected. Findings: The main findings were systematization of the main OER sources for CSEd, identification of 64 CSEd domains covered by OERs organized in a taxonomy, the immaturity level of investigations and tools, and lack of mechanisms that organize OERs for CSEd. [ABSTRACT FROM AUTHOR]

Object-oriented programming (OOP) is not only an integral part of computing degrees but also a requirement in non-computing majors such as engineering. Understanding OOP concepts can be difficult for novice programmers, and often leads to the development of misconceptions. This is exacerbated when the discipline requires students to learn a technical low-level language such as C++, as is the case in many engineering disciplines. We propose a block-based programming language extension, Blockly-OOP, to help students learn core OOP concepts without the technical complexities associated with traditional textual languages. The Blockly-OOP Learning Environment was developed by integrating Blockly-OOP with learning activities that guide students through programming exercises that target popular OOP misconceptions. An evaluation (n = 238) in a second-year programming course (CS2) showed that a block-based programming language helps students improve their understanding of object-oriented concepts, warranting further research in this area. [ABSTRACT FROM AUTHOR]

Contribution: In this article, the authors present findings and insights on the efficacy of using an educational block-based programming (BBP) environment—Blocks4DS, to teach the binary search tree (BST). Background: For a decade, BBP environments have been a hot topic in the computer science education (CSEd) community to promote interactive active learning of programming. However, little attention has been paid to BBP environments’ efficacy on nonintroductory courses like data structures and algorithms (DS&A). DS&A courses are challenging to students due to levels of abstraction that could be reduced by syntax-free features existing in BBP interfaces. Research Questions: 1) Can undergraduate computing-major students learn about the BST using Blocks4DS? 2) Do undergraduate computing-major students understand better BSTs when learning with a BBP environment? and 3) How do undergraduate computing-major students perceive Blocks4DS for nonintroductory computer science (CS) topics? Methodology: A mixed-method study was designed, using a control and intervention group with 83 first and second-year CS students, to evaluate the efficacy of Blocks4DS compared to traditional instructional methods (e.g., whiteboard and pseudocode). The authors evaluated its efficacy based on students’ conceptual understanding and perceptions. Findings: It was found that, regardless of prior experience with text-based programming languages and instructional approaches, students introduced to the BST with Blocks4DS gained significant conceptual understanding, and performed as well as peers instructed with pseudocode. Also, 83.3% of students found the tool engaging and 72.3% found it useful to learn DS&A. This suggests that Blocks4DS can be used to teach DS&A. [ABSTRACT FROM AUTHOR]

Delivery of core programming principles to novices is a challenging task and many introductory programming languages and platforms have been designed to support this process. Educational programming languages generally focus on alleviating the syntax overhead enforced on novice learners by designing languages with simple and concise keywords. Furthermore, only the most basic programming concepts and principles are incorporated and many languages follow unique methods to provide more simplified learning environments. However, considering the way programs are authored using these platforms, two common contrasting approaches to program representation are identified as text-based and block-based representations. Additionally, a hybrid approach of dual-modality interfaces, which combines the best of both techniques has gained traction as a current trend in the development of educational programming platforms. However, despite these extensive features, not all introductory programming languages can cater to the exact requirements of novice learners and a dearth of comprehensive studies and literature reviews have been conducted to investigate this context. This paper explores and presents a comprehensive review of how different elements of educational programming languages and platforms contribute towards learning by novices under the Technology Acceptance Model (TAM). The review is conducted under two main constructs of TAM as (1) Perceived Usefulness (PU) and (2) Perceived Ease of Use (PEOU) and external factors regarding the programming environment, language design, included programming concepts and supporting features such as the target audience group, language extensibility, and availability of learning materials are thoroughly investigated considering the typical behavioral patterns of novices concerning computer programming education.

This study attempts to explore cognition during program comprehension through physiological evidence by recording and comparing electroencephalogram (EEG) activities in different frequency bands and the eye movements of the participants with high or low programming abilities. An experiment was conducted with thirty-three undergraduate students majoring in Computer Science. We recorded their EEG activities when they were reading two programs with three types of program constructs. At the same time, the participants’ eye movements were recorded by an eye tracker to further understand the relationship between the program comprehension process and EEG activities. Experimental results show that the high-performance participants displayed higher performance for working memory (theta power), attention resource allocation (lower alpha power), and interaction between working memory and semantic memory (upper alpha power) in program comprehension tasks of complex constructs, which proves related theories proposed in the existing research on programming and cognition. The results of this study not only offer objective evidence of the roles cognition plays in program comprehension but also provide educators with suggestions for designing suitable pedagogical strategies.

Supporting teaching and learning programming with learning analytics is an active area of inquiry. Most data used for learning analytics research comes from learning management systems. However, such systems were not developed to support learning programming. Therefore, educators have to resort to other systems that support the programming process, which can pose a challenge when it comes to understanding students’ learning since it takes place in different contexts. Methods that support the combination of different data sources are needed. Such methods would ideally account for the time-ordered sequence of students’ learning actions. In this article, we use a novel method (multi-channel sequence mining with Hidden Markov Models, HMMs) that allows the combination of multiple data sources, accounts for the temporal nature of students’ learning actions, and maps the transitions between different learning tactics. Our study included 291 students enrolled in a higher education programming course. Students’ trace-log data were collected from the learning management system and from a programming automated assessment tool. Data were analyzed using multi-channel sequence mining and HMM. The results reveal different patterns of students’ approaches to learning programming. High achievers start earlier to work on the programming assignments, use more independent strategies and consume learning resources more frequently, while the low achievers procrastinate early in the course and rely on help forums. Our findings demonstrate the potentials of multi-channel sequence mining and how this method can be analyzed using HMM. Furthermore, the results obtained can be of use for educators to understand students’ strategies when learning programming.

Existing literature has provided strong evidence that educational escape rooms are engaging and effective learning activities when they are properly conducted in the classroom. However, no prior research has determined whether the positive effects of these novel educational activities on students’ perceptions and learning persist when conducted remotely. This article performs, for the first time, a comparative study of the effectiveness of face-to-face and remote educational escape rooms. For this purpose, two versions of the same educational escape room were conducted: one in-class and one remotely. Both experiences were evaluated by means of three different instruments: (1) a pre-test and a post-test for measuring learning gains, (2) a questionnaire for assessing students’ perceptions, and (3) a web platform for recording student interaction data during the activities. The results obtained suggest that, although remote educational escape rooms for learning programming can be as engaging as their face-to-face counterparts, their learning effectiveness is somewhat lower.

Promoting creativity is considered an important goal of education, but creativity is notoriously hard to measure. In this article, we make the journey from defining a formal measure of creativity, that is, efficiently computable to applying the measure in a practical domain. The measure is general and relies on core theoretical concepts in creativity theory, namely fluency, flexibility, and originality, integrating with prior cognitive science literature. We adapted the general measure for projects in the popular visual programming language Scratch. We designed a machine learning model for predicting the creativity of Scratch projects, trained and evaluated on human expert creativity assessments in an extensive user study. Our results show that opinions about creativity in Scratch varied widely across experts. The automatic creativity assessment aligned with the assessment of the human experts more than the experts agreed with each other. This is a first step in providing computational models for measuring creativity that can be applied to educational technologies, and to scale up the benefit of creativity education in schools. [ABSTRACT FROM AUTHOR]

Automated Programming Assessment Systems (APAS) are used for overcoming problems associated with manually managed programming assignments, such as objective and efficient assessing in large classes and providing timely and helpful feedback. In this paper we survey the literature and software in this field and identify the set of necessary features that make APAS comprehensive - such that it can support all key stages in the assessment process. Put differently, comprehensive APAS is generic enough to meet the demands of “any” computer science course. Despite the vast number of publications, the choice of software turns out to be very limited. We contribute by developing Edgar, a comprehensive open-source APAS which, to the best of our knowledge, exceeds any other similar free and/or open-source tool. Edgar is the result of three years of development and usage in, for the time being, eight courses dealing with various programming languages and paradigms (C, Java, SQL, etc.). Edgar supports various text-based programming languages, multi-correct multiple-choice questions, provides rich exam logging and monitoring infrastructure to prevent potential fraudulent behaviour, and subsequent data analysis and visualization of students' scores, exams, question quality, etc. It can be deployed on all major operating systems and is written in a modular fashion so that it can be adjusted and scaled to a custom fit. We comment on the architecture and present data from real-world use-cases to support these claims. Edgar is in active use today (1000+ students per semester) and it is being constantly developed with new features.

The coronavirus pandemic hit the entire education sector hard. All students were sent home and lectures started to be delivered through video-conferencing systems. CrownLabs is an open-source project providing an answer to the problem of delivering remote computing laboratories. Simplicity is one of its main characteristics, requiring nothing but a simple web browser to interact with the system and being all heavyweight computations performed at the university premises. Cooperation and mentoring are also encouraged through parallel access to the same remote desktop. The entire system is built up using components from the Kubernetes ecosystem, to replicate a “cloud grade” infrastructure, coupled with custom software implementing the core business logic. To this end, most of the complexity has been delegated to the infrastructure, to speed up the development process and reduce the maintenance burden. An extensive evaluation has been performed in both real and simulated scenarios to validate the overall performance: the results are encouraging, as well as the feedback from the early adopters of the system.

Integrating hardware prototyping platforms such as Arduino, Raspberry Pi or BeagleBone Board in education is becoming more prevalent as the number of courses utilizing such platforms is continuously increasing. In this work, we conduct an analytical investigation on the plurality of courses that utilize or integrate hardware prototyping platforms. We examine curriculum and instructional material (e.g. course syllabi or outlines) through publicly available web informational resources (e.g. search engines). We use this data to determine the degree to which these platforms are used as effective learning technologies in existing courses. We further use this data to determine hardware platforms integration statistics and distribution based on the number of courses, types of platforms employed and institutions using them as learning technologies. This statistical data can be used to help determine the current status of the utilization and adoption rate of hardware prototyping platforms into courses. In this paper, we present evidence that hardware prototyping technologies are employed as tools for teaching and learning. Based on examining forty five universities worldwide, we determine that there are on average nine unique courses per university which utilize or integrate a variety of these platforms into courses. We also determine that 75% of these courses are Science, Technology, Engineering, and Math- (STEM-) based while 25% are Non-STEM. We further use our findings to provide insights on the extent to which educational institutions are utilizing these platforms as learning technologies and applying project-based or experiential learning approaches as part of their curriculum development.

With the rise of distance learning, new challenges have emerged for educators. Among these challenges, developing effective and motivating group activities for students in the remote classroom is one of the top priorities to be addressed. According to existing literature, educational escape rooms have proven to be engaging and effective learning activities when conducted face-to-face. However, no prior research has analyzed the instructional effectiveness of these activities when they are conducted remotely. Furthermore, none of the educational escape rooms reported in the literature has been designed for teaching software modeling. This article analyzes an educational escape room conducted remotely in a software engineering fundamentals course for teaching software modeling. A total of three evaluation instruments were used: a pre-test and a post-test to measure students' learning gains, a questionnaire to collect students' perceptions, and a web platform for automatically gathering data on students' interactions. The contribution of this article is two-fold. On the one hand, it provides, for the first time, evidence that remote educational escape rooms can be effective learning activities. On the other hand, it provides, also for the first time, proof that educational escape rooms are effective and engaging activities for teaching software modeling.

More than twenty years have passed since the term Flipped Classroom was coined. Despite of the increasing interest in this teaching methodology, fields like Computer Science still need more research in order to judge the impact of this methodology in the learning process. This work presents a comparative study between traditional lectures and a partial application of the Flipped Classroom methodology in the subject Language Processors of a Computer Science degree. The study involved 158 students during two weeks and focused on practical contents of the subject. The results show that students in the Flipped Classroom group were more responsible of their learning process, consequently they got significant better learning outcomes either during or at the end of the learning process. Students' satisfaction was highly positive as well. This work increases the amount of experiments about Flipped Classroom within the field of Computer Science. In addition, this work serves as an example of how to begin the transition from traditional lectures to the Flipped Classroom methodology.

Contribution: This article provides evidence on the effectiveness of game-based learning (GBL) for computer science education when using educational video games created by teachers using authoring tools. Background: Although teacher-oriented authoring tools for creating educational video games can help overcome the main barriers hampering the use and uptake of GBL, there is a lack of studies examining the effectiveness of educational video games authored by teachers using these authoring tools by means of rigorous scientific methods. Research Questions: Is GBL using teacher-authored games more effective than traditional teaching in terms of motivation for computer science students? Is GBL using teacher-authored games more effective than traditional teaching in terms of knowledge acquisition for computer science students? Methodology: Two randomized control trials with pretest, post-test, and one questionnaire were conducted to compare the effectiveness of traditional teaching and GBL in two computer science courses. The sample of the two experiments was composed by 75 and 49 students, respectively. Half of the students attended a traditional lecture, while the other half learned solely by playing teacher-authored educational video games. Findings: The results show that GBL using teacher-authored games was practically as effective as traditional teaching in terms of knowledge acquisition, but that it was emphatically successful in increasing student motivation. Students who learned by playing educational video games found the experience more motivating and fun than their counterparts, and a vast majority of them preferred the GBL approach over traditional teaching. [ABSTRACT FROM AUTHOR]

We summarize integrating cybersecurity and artificial intelligence (AI) research in cybersecurity education and implementing a module in an existing noncybersecurity undergraduate engineering course. This initiative will drive the broader community to focus on the convergence of cybersecurity and AI education. [ABSTRACT FROM AUTHOR]

A method for programming reactive systems, called scenario-based algorithmics, can have several advantages, both in programming and in computer science education. We provide new examples, experiments, and perspectives. [ABSTRACT FROM AUTHOR]

Based on hundreds of thousands of hours of data about how students learn in massive open online courses, educational machine learning promises to help students who are learning to code. However, in most classrooms, students and assignments do not have enough historical data for feeding these data hungry algorithms. Previous work on predicting dropout is data hungry and, moreover, requires the code to be syntactically correct. As we deal with beginners’ code in a text-based language our models are trained on noisy student text; almost 40% of the code in our datasets contains parsing errors. In this article, we compare two machine learning models that predict whether students need help regardless of whether their code compiles or not. That is, we compare two methods for automatically predicting whether students will be able to solve a programming exercise on their own. The first model is a heavily feature-engineered approach that implements pedagogical theories of the relation between student interaction patterns and the probability of dropout; it requires a rich history of student interaction. The second method is based on a short program (that may contain errors) written by a student, together with a few hundred attempts by their classmates on the same exercise. This second method uses natural language processing techniques; it is based on the intuition that beginners’ code may be closer to a natural language than to a formal one. It is inspired by previous work on predicting people's fluency when learning a second natural language. [ABSTRACT FROM AUTHOR]

In addition to being a well-liked form of recreation, escape rooms have drawn the attention of educators due to their ability to foster teamwork, leadership, creative thinking, and communication in a way that is engaging for students. As a consequence, educational escape rooms are emerging as a new type of learning activity under the promise of enhancing students' learning through highly engaging experiences. These activities consist of escape rooms that incorporate course materials within their puzzles in such a way that students are required to master these materials in order to succeed. Although several studies have reported on the use of escape rooms in a wide range of disciplines, prior research falls short of addressing the use of educational escape rooms for teaching programming, one of the most valuable skills of the twenty-first century that students often have difficulties grasping. This paper reports on the use of an educational escape room in a programming course at a higher education institution and provide, for the first time, insights on the instructional effectiveness of using educational escape rooms for teaching programming. The results of this paper show that appropriate use of educational escape rooms can have significant positive impacts on student engagement and learning in programming courses. These results also suggest that students prefer these activities over traditional computer laboratory sessions. Finally, another novel contribution of this paper is a set of recommendations and proposals for educators in order to help them create effective educational escape rooms for teaching programming.

Computational thinking has become a required capability in the student learning process, and digital games as a teaching approach have presented promising educational results in the development of this competence. However, properly evaluating the effectiveness and, consequently, student progress in a course using games is still a challenge. One of the most widely implemented ways of evaluation is with an automated analysis of the code developed in the classes during the construction of digital games. Nevertheless, this topic has not yet been explored in aspects such as incremental learning, the model and teaching environment and the influences of acquiring skills and competencies of computational thinking. Motivated by this knowledge gap, this paper introduces a framework proposal to analyze the evolution of computational thinking skills in digital games classes. The framework is based on a data mining technique that aims to facilitate the discovery process of the patterns and behaviors that lead to the acquisition of computational thinking skills, by analyzing clusters with an unsupervised neural network of self-organizing maps (SOM) for this purpose. The framework is composed of a collection of processes and practices structured in data collection, data preprocessing, data analysis, and data visualization. A case study, using Scratch, was executed to validate this approach. The results point to the viability of the framework, highlighting the use of the visual exploratory data analysis, through the SOM maps, as an efficient tool to observe the acquisition of computational thinking skills by the student in an incremental course.

Unified modeling language (UML) is a modeling standard in the software industry. However, students face difficulties when learning how to model complete and correct UML diagrams, in part because the traditional teacher-centered approach still dominates in computer science. In an attempt to bridge this gap, and to improve the effectiveness of teaching and learning in computing courses, educators use strategies that encourage students' active participation in the learning process. The goal of this paper is to understand how active learning strategies influence the teaching and learning process of UML diagrams. We performed four case studies in undergraduate courses at two universities. We collected: (a) students' perceptions of each strategy through questionnaires and focus group sessions; and (b) instructors' perceptions of the challenges faced in using such strategies through semi-structured interviews. We found fifteen factors that influence the active learning of UML diagrams. Whereas the literature shows that students often positively respond to active learning strategies, our results show that some students have had negative responses to the new strategies. Additionally, we identified four challenges that instructors may face when employing active learning strategies. The contributions of this paper include: factors perceived by students about how active learning strategies influence the learning of UML diagrams, the challenges and difficulties faced by instructors in applying these strategies, and suggestions on how to circumvent challenges noted by instructors.

In recent years, educational escape rooms have started to gain momentum in the academic community. Prior research has reported on the use of educational escape rooms in several fields. However, earlier works have failed to assess the impact of this sort of activities for teaching programming in terms of learning effectiveness. This work fills the existing gap in the literature by examining an educational escape room for teaching programming in a higher education setting by means of three different instruments: (1) a pre-test and a post-test for measuring learning gains, (2) a survey for assessing students' perceptions, and (3) a web platform for recording student interaction data during the activity. The results of this work provide, for the first time, empirical evidence that educational escape rooms are an effective and engaging way of teaching programming.

Software engineering standards are essential sources of codified knowledge for all software engineers. Could the professors who are not teaching software engineering standards to software engineering students be accused of malpractice? [ABSTRACT FROM AUTHOR]

Manually determining concepts present in a group of questions is a challenging and time-consuming process. However, the process is an essential step while modeling a virtual learning environment since a mapping between concepts and questions using mastery level assessment and recommendation engines is required. In this article, we investigated unsupervised semantic models (known as topic modeling techniques) to assist computer science teachers in this task and propose a method to transform Computer Science 1 teacher-provided code solutions into representative text documents, including the code structure information. By applying nonnegative matrix factorization and latent Dirichlet allocation techniques, we extract the underlying relationship between questions and validate the results using an external dataset. We consider the interpretability of the learned concepts using 14 university professors’ data, and the results confirm six semantically coherent clusters using the current dataset. Moreover, the six topics comprise the main concepts present in the test dataset, achieving 0.75 in the normalized pointwise mutual information metric. The metric correlates with human ratings, making the proposed method useful and providing semantics for large amounts of unannotated code. [ABSTRACT FROM AUTHOR]

Operating Systems (OS) is an important area of knowledge included in virtually all undergraduate computing curricula and in some engineering curricula as well. Teaching and learning an OS undergraduate course have always been a challenge. Several different approaches have been used for OS teaching and learning. Nevertheless, it is not easy for a teacher to choose one of them. No guidelines are available on how to choose one of them to match the specific objectives of each OS course. The objective of this paper is to analyze the approaches that have been used to improve OS teaching and learning by applying a systematic map. In particular, we consider the following dimensions: learning objectives, assessment, empirical study, methodology, and mode (face-to-face, online, or blended). The systematic map devised in this paper is focused on the time span from 1995 to 2017 and considered six of the major publications on the Computer Science Education. We considered three journals (the Journal of Engineering Education, the IEEE TransactionS on Education, and the International Journal of Engineering Education) and three conferences (the ACM Technical Symposium on Computer Science Education-SIGCSE, the Conference on Computing Education Research-ITiCSE, and the International Conference on Computing Education Research-Koli). A total of 55 papers were included in the study after performing a search based on the inclusion/exclusion criteria. Nine approaches to improve OS teaching and learning were identified and analyzed. Furthermore, the implications for OS instructors and for research in this field are discussed.

Effective advisement can help to address the disproportionately lower self-efficacy, identity, and sense of belonging experienced by Black students in computing degree programs. Black social media influencers who produce video log (vlog) commentary content on the YouTube platform were investigated to determine the influence they have on improving computing identity for Black students. This exploratory study consists of three studies: first, a synthesis of vlog commentary college and career advisement videos, coding for the quality of advisement, usability, and user experience; second, an advisor effectiveness and user experience survey using a selected Black social media influencer who provides computing college and career advisement; and third, a user experience and interaction preference survey using a selected Black social media influencer. Findings suggest YouTube influencers could be effective, particularly for beginners in the computing field. Future studies intend to further explore Black computing advisement through social media over a long term and at varying levels of interaction. [ABSTRACT FROM AUTHOR]

This article explores the intersection of two central issues in computer science education (CSed)—what is meant by "equitable computer science education" and how attendant equity goals play out as school districts attempt to implement comprehensive K12 CSed initiatives. Based on analysis of qualitative data gathered over 15 months of one district's efforts in this area, our findings illustrate how varying conceptualizations of equitable CSed are at play during institutional change processes in a school district, and how negotiation and management of differing equity-related goals takes place within CSed systems change efforts. The implications of this work point to the need of district-wide CSed efforts to engage stakeholders from across levels in the system in the process of planning and implementation. Additionally, we see a potential need for deliberative routines where various conceptions and attendant goals around equity—both related and unrelated to CSed—can be understood and negotiated among district actors. [ABSTRACT FROM AUTHOR]

Black high school (HS) boys are an untapped source for initiatives in broadening participation in computer science (CS). This article describes a five week program that employs active and problem-based learning activities to teach tech innovation and entrepreneurship to local teens in the Washington, DC, USA area. Called #WatchMeCode, the program's objective is to build black HS boys' confidence in coding, communicating their programming skills, and rebuilding their thoughts about computer science. The program uses a constructivist pedagogical approach allowing students to build their understanding of computer science and technology through Internet-search-based history lessons, podcasts, and presentations by other black male tech entrepreneurs. Additionally, students ventured out into their community with hands-on design-thinking and customer discovery skills to observe their environment, its issues/problems, and developed new technology with their unique perspectives in mind. Results showed that participants became interested and confident in computer science by creating their own tech innovations. [ABSTRACT FROM AUTHOR]

Contribution: A model of tiered in-lab, compulsory lab programming sessions suitable for use in flipped learning environments. This article also addresses the scarcity of research that explores the effects of in-class activity organization. Background: When facing a subject they perceive as dry or difficult, some students may feel discouraged from making an effort to learn. Previous research has shown that students have a positive attitude toward assignment tiering, as a form of scaffolding, in voluntary, take-home assignments. Related models either are designed for different settings or have obvious problems with applicability in flipped learning environments. Intended Outcomes: A lab environment that motivates students to learn and consequently to perform better on in-class evaluations and on exams. The approach should be positively received by students. Application Design: Drawing upon the previous teaching experience, the proposed approach is a modification of an assignment tiering model found in the academic literature. With two gated tiered assignments per class, the steepness of the learning curve is alleviated for struggling students, while the injection of deliberate errors, reflexive questions, and ad hoc modification requests keep experienced students engaged with challenging tasks. Findings: The results of the six-year study, performed on an assembly language programming subject, are encouraging: Test groups were better in terms of motivation and performance, and the great majority of students expressed a positive attitude toward the new model. [ABSTRACT FROM AUTHOR]

Modern computer graphics courses require students to complete assignments involving computer programming. The evaluation of student programs, either by the student (self-assessment) or by the instructors (grading) can take a considerable amount of time and does not scale well with large groups. Interactive judges giving a pass/fail verdict do constitute a scalable solution, but they only provide feedback on output correctness. In this article, we present a tool to provide extensive feedback on student submissions. The feedback is based both on checking the output against test sets, as well as on syntactic and semantic analysis of the code. These analyses are performed through a set of code features and instructor-defined rubrics. The tool is built with Python and supports shader programs written in GLSL. Our experiments demonstrate that the tool provides extensive feedback that can be useful to support self-assessment, facilitate grading, and identify frequent programming mistakes. [ABSTRACT FROM AUTHOR]

A recent growth area in computer science education is physical computing, which involves combining software and hardware to build interactive physical systems that sense and respond to the real world. This article provides an overview of physical computing and its value in the classroom, using the BBC micro:bit as an example. [ABSTRACT FROM AUTHOR]

Our educational project has three primary goals. First, we want to provide a robust vision of how hardware and software interplay, by integrating the design of an instruction set (through microprogramming) and using that instruction set for assembly programming. Second, we wish to offer a versatile and interactive tool where the previous integrated vision could be tested. The tool we have developed to achieve this is called WepSIM and it provides the view of an elemental processor together with a microprogrammed subset of the MIPS instruction set. In addition, WepSIM is flexible enough to be adapted to other instruction sets or hardware components (e.g., ARM or x86). Third, we want to extend the activities of our university courses, labs, and lectures (fixed hours in a fixed place), so that students may learn by using their mobile device at any location, and at any time during the day. This paper presents how WepSIM has improved the teaching of Computer Architecture courses by empowering students with a more dynamic and guided learning process. In this paper, we show the results obtained during the experience of using the simulator in the Computer Structure course of the Bachelor's Degree in Computer Science and Engineering, University Carlos III of Madrid. [ABSTRACT FROM AUTHOR]

Nowadays, remote collaborative learning tools for computer science education mostly emphasize providing learning resources and realizing virtual collaborative learning environment for students. Many people in this field tend to have their mind fixed on the process improvement of such a collaboration as a whole, while few notice that individuals may have different roles and impacts on this type of teamwork. There usually is a “supervisor” on the team, who offers support to all members in the collaborative learning environment. However, such support may not always be as accessible as students demand it to be. Therefore, this paper describes a cloud-based tool to support software engineering practice courses in collaboration with remote tutors. This system utilizes a cloud storage platform to provide sharing of multimedia study materials and a better management of project developing cycles. A remote collaborative component called the Virtual Debug Laboratory is designed to improve and share students' debugging experience in the same team. The most innovative feature of this system is that it amplifies the role of tutoring in remote collaborative learning environments so that tutors can, in real time, assist students in debugging during actual project developing and demonstrate step by step to the students the process of debugging. The results of the analyzed data regarding the use of this system indicate that the system can potentially enhance students' abilities in project developing and debugging in software engineering practice courses. It is our hope that these preliminary data can provide a future reference for the software education community.

Contribution: This paper describes a highly flexible design for a master’s-level server administration course that uses updated technologies, addresses the most important course design challenges and extracts useful feedback from student work. The main contribution of this design is eliminating the self-regulation challenge students encounter in similar highly flexible computer-related courses. This is attributed to the introduction of orientation lectures and a competitive element; these may have helped students to achieve their goals on time. Background: The Web server administration course on the master’s of the Web engineering program was intended to inculcate a series of competences, such as common administration techniques, procedures to increase security, efficient website deployment, and an introduction to clusters and infrastructure deployment automation. The course generally failed to transmit these competencies effectively; complaints were made about the difficulty of the course and the low applicability of its contents. Intended Outcomes: The design facilitates students finding assessment activities that better suit their profiles or previous experience, and enables them to customize their assessment, thus encouraging them and giving future value to their work. Application Design: The course design maximizes assessment flexibility by providing students with many optional activities from which to choose. It also promotes a healthy competitive spirit to improve student’s motivation and results. Findings: Complaints about the difficulty or lack of applicability were no longer made. The course is highly valued by the students, achieving a very high pass rate and average score. [ABSTRACT FROM AUTHOR]