Inquiet par le coronavirus ? Vous pouvez appeler le numï¿œro vert 0 800 130 000. C'est Emmanuel Macron lui-mï¿œme qui en a fait la promotion dans un tweet en avril 2020, en plein confinement. Ce 0 800 fait partie de la longue liste...

(Chronique d'avant-plage) Emmanuel Macron a écrit aux Français pour louer le parlementarisme et la stabilité institutionnelle. Il a pourtant contourné le Parlement pendant 7 ans et méprisé les Présidents des deux chambres en se contentant de les informer de la dissolution. Alors, avec un tel garant, que fait-on ?

With the rapid development and application of driverless technology, the number and location of vehicles, the channel and bandwidth of wireless network are time-varying, which leads to the increase of offloading delay and energy consumption of existing algorithms. To solve this problem, the vehicle terminal task offloading decision problem is modelled as a Markov decision process, and a task offloading algorithm based on DDQN is proposed. In order to guide agents to quickly select optimal strategies, this paper proposes an offloading mechanism based on task feature. In order to solve the problem that the processing delay of some edge server tasks exceeds the upper limit of their delay, a task scheduling mechanism based on buffer delay is proposed. Simulation results show that the proposed method has greater performance advantages in reducing delay and energy consumption compared with existing algorithms.

Smart cities research is growing all over the world seeking to understand the effect of smart cities from different angles, domains and countries. The aim of this study is to analyse how the smart city ICTs (e.g., big data analytics, AI, IoT, cloud computing, smart grids, wireless communication, intelligent transportation system, smart building, e-governance, smart health, smart education and cyber security) are related to government. services and society welfare from the perspective of China. This research confirmed a positive correlation of smart city ICTs to e-Govt. Services (e-GS). On the other hand, the research showed a positive influence of smart city ICTs on society's welfare. These findings about smart cities and ICTs inform us how the thought paradigm to smart technologies can cause the improvement of e-GS through economic development, job creation and social welfare. The study offers different applications of the theoretical perspectives and the management perspective which are significant to building a society during recent technologised era.

Access to quality healthcare is a fundamental right but unfortunately, India suffers from gender disparities in healthcare access. Telemedicine has the potential to improve access to healthcare services for women by eliminating traditional barriers. Therefore, our research aims to investigate the factors influencing the adoption of telemedicine among Indian women. This study has collected 442 responses and analysed them through structural equation modelling. The result indicates a strong and positive connection between the willingness to adopt telemedicine services and factors like performance expectancy, perceived benefits, e-health literacy, and perceived reliability. Notably, perceived reliability emerges as the most impactful predictor, closely followed by perceived benefits, while factors like effort expectancy and user resistance show no significant influence. This underscores the pivotal role of reliability and perceived benefits in shaping women's inclination toward adopting telemedicine. The study provides practical insights for telemedicine providers and policymakers to customise strategies and policies for effective promotion.

The video game industry (VGI) has evolved considerably, transitioning from a niche market to a substantial sector. The VGI's magnitude and the societal implications tied to video game consumption have naturally piqued the interest of scholars in marketing and consumer behaviour. This research serves a dual purpose: firstly, it consolidates existing VG literature by evaluating articles, concepts, and methodologies, systematically tracing their evolution; secondly, it outlines potential directions and implications for forthcoming research. Within this literature, a predominant focus lies on articles investigating purchase decisions concerning VGs, followed by those exploring the integration of video game consumption into broader social contexts. Notably, a limited number of articles delve into player-game interactions and experiences within gaming worlds. This imbalance can be attributed to the fact that such inquiries are often suited to psychology and multidisciplinary journals, while the marketing discipline has predominantly addressed the VGI from a marketing management standpoint.

In order to further improve the teaching quality of multimedia teaching in school daily teaching, a classroom facial expression emotion recognition model is proposed based on convolutional neural network. VGGNet and CliqueNet are used as the basic expression emotion recognition methods, and the two recognition models are fused while the attention module CBAM is added. Simulation results show that the designed classroom face expression emotion recognition model based on V-CNet has high recognition accuracy, and the recognition accuracy on the test set reaches 93.11%, which can be applied to actual teaching scenarios and improve the quality of classroom teaching.

Aiming at the problem of aggravation of traffic congestion caused by unstable signal control of traffic signal control system, the Multi-Agent Deep Deterministic Policy Gradient-based Traffic Cyclic Signal (MADDPG-TCS) control algorithm is used to control the time and data dimensions of the signal control scheme. The results show that the maximum vehicle delay time and vehicle queue length of the proposed algorithm are 11.33 s and 27.18 m, which are lower than those of the traditional control methods. Therefore, this method can effectively reduce the delay of traffic signal control and improve the stability of signal control.

Along with amazing advancements in the field of digital payments, this article seeks to provide a summary of research undertaken over the last four decades and to suggest areas in need of additional study. This study employs a two-pronged technique for analysing its data. The first is concerned with performance analysis, and the second with science mapping. The study uses the apps VOS viewer and R-studio to do bibliometric data analysis. From 1982 until May 2022, the most trustworthy database, Scopus, is used to compile a database of 923 publications The findings of this study identify the scope of current research interest, which is explored with critical contributions from a variety of authors, journals, countries, affiliations, keyword analysis, citation analysis, co-citation analysis, and bibliometric coupling, as well as a potential research direction for further investigation in this emerging field.

The intelligent financial sharing platform in the online realm is capable of collecting, storing, processing, analysing and sharing financial data through the integration of AI and big data processing technologies. However, as data volume grows exponentially, the cost of financial data storage and processing increases, and the asset accounting and financial profit data sharing analysis structure in financial sharing platforms is inadequate. To address the issue of data security sharing in the intelligent financial digital sharing platform, this paper proposes a data-sharing framework based on blockchain and edge computing. Building upon this framework, a non-separable task distribution algorithm based on data sharing is developed, which employs multiple nodes for cooperative data storage, reducing the pressure on the central server for data storage and solving the problem of non-separable task distribution. Multiple sets of comparative experiments confirm the proposed scheme has good feasibility in improving algorithm performance and reducing energy consumption and latency.

In the context of smart city development, the managed pressure drilling (MPD) drilling process faces many uncertainties, but the characteristics of the process are complex and require accurate wellbore pressure control. However, this process runs the risk of introducing un-modelled dynamics into the system. To this problem, this paper employs neural network control techniques to construct a dual-network system for throttle pressure control, the design encompasses both the controller and identifier components. The radial basis function (RBF) network and proportional features are connected in parallel in the controller structure, and the RBF network learning algorithm is used to train the identifier structure. The simulation results show that the actual wellbore pressure can quickly track the reference pressure value when the pressure setpoint changes. In addition, the controller based on neural network realises effective control, which enables the system to track the input target quickly and achieve stable convergence.

To explore the computer-aided translation technology based on the intelligent algorithm of edge computing. This paper presents the research on computer-aided translation technology based on edge computing intelligent algorithm. In the K-means computer edge algorithm, it analyses the traditional way of average resource allocation and the way of virtual machine allocation. In the process of online solution, we have a more detailed understanding of the data information at the edge, and also avoid the connection relationship between network users and the platform, which has a certain impact on the internal operation efficiency of the system. The network user group is divided into several different types of existence through K-means computer algorithm, and various information resources are counted according to their own characteristics. Computer-aided translation technology can significantly improve the quality of translation, improve the translation efficiency, and reduce the translation cost.

As IOT develops drastically these years, the application of cloud computing in many fields has become possible. In this paper, we take low-voltage current transformers in power systems as the research object and propose a TCN-BI-GRU power measurement method that incorporates the signal characteristics based on the transformer input and output. Firstly, the basic signal enhancement extraction of input and output is completed by using EMD and correlation coefficients. Secondly, multi-dimensional feature extraction is completed to improve the data performance according to the established TCN network. Finally, the power prediction is completed by using BI-GRU, and the results show that the RMSE of this framework is 5.69 significantly lower than other methods. In the laboratory test, the device after being subjected to strong disturbance, its correlation coefficient feature has a large impact, leading to a large deviation in the prediction, which provides a new idea for future intelligent prediction.

The application of computer image digitisation has realised the transformation of people's production and lifestyle, and also promoted the development of the construction industry. This article aims to realise the research on architectural decoration design and production under computer network environment and promote the ecological development of indoor and outdoor design in the construction industry. This article proposes to use virtual reality technology in image digitisation to guide architectural decoration design research. In the comparative analysis of the weight of architectural decoration elements, among the calculated weights of secondary elements, the spatial function has the largest weight, which is 0.2155, and the landscape has the smallest weight, which is 0.0113. Among the three-level unit weights, the service area has the largest weight, which is 0.0976, and the fence frame has the smallest weight, which is 0.0119.

E-government serves as a vital channel for citizen interactions with the public sector, where user enjoyment is of paramount importance. To date, few studies have comprehensively examined the determinants of citizen enjoyment in e-government. To address this research gap, we administered a survey and gathered data from 363 Australian residents using myGov for tax filing. Our analysis revealed a pronounced discrepancy between reported enjoyment and the intention to continue using the services. Although users demonstrated a strong intent to use e-government services, this intent did not uniformly align with enjoyment. Additionally, informed by self-determination theory, we developed and tested an e-government service enjoyment model to study the impacts of effort expectancy, technophilia, technology humanness, and engagement in fostering user enjoyment. Unexpectedly, the results showed that information privacy concerns, commonly seen as a deterrent in e-government adoption, did not significantly affect enjoyment. Our findings advance the discourse on e-government service improvement.

The purpose of this research is to investigate how genuine leaders impact the creativity and innovative behaviour (IWB) of information technology (IT) employees. It also examines the impact of perceived organisational support as a mediator in the correlations between authentic leadership as well as innovative behaviours. This study explores the influence of authentic leadership via the employee's IWB using aspects from social exchange theory as well as social cognitive theory. The data was collected from a sample of 487 employees of the IT sector in India. The partial least square method is applied to test the structural relationship of the research framework. Findings reveal that authentic leadership positively impact innovative work behaviour and perceived organisation support mediates authentic leadership and IWB. Additionally, when organisations and leaders support the employees and value their creative thinking then the employee replicates IWB in the organisation. The practical and theoretical implications are discussed.

The study investigated the influence of ebullient supervision on employee commitment in a Ghanaian public university through the mediating role of employee engagement. The simple random sampling technique was used to draw 302 administrative staff of the university to respond to the self-administered questionnaire on the constructs. Furthermore, the partial least square structural equation technique was deployed to test the research hypotheses in the study. The results showed that ebullient supervision had a significant positive relationship with employee commitment and employee engagement. The findings further revealed that employee engagement positively correlated with employee commitment. Finally, the study's findings established that employee engagement partially mediated the link between ebullient supervision and employee commitment. The study emphasised that various supervisors in a university's administration should create an environment that favours fun where subordinates can form ties with one another.

Pandemic has compelled the entire world to change their way of life and work. To control the infection rate, academic institutes deliver education online similarly. At least one smartphone is available in every home, and students use their smartphones to attend class. The study investigates the link between smartphone usage (SU) and academic performance (AP) during the pandemic. 490 data were obtained from various institutions and undergraduate students using stratified random sampling. These components were identified using factor analysis and descriptive methods, while the relationship of SU and AP based on gender classification was tested using Smart-PLS-SEM. The findings show that SU has a substantial relationship with academic success, whether done in class or outside of it. Even yet, the study found that SU and AP significantly impact both male and female students. Furthermore, the research focuses on SU outside and within the classroom to improve students' AP.

Work-life balance (WLB) is a widely explored topic in the academic discourse. The researchers are trying to find strategies to effectively balance their work and home responsibilities for women in management. This study aims to analyse how gender roles and inequalities shape the strategies of female academics in higher education institutions. Eighteen faculty members participated in the semi-structured interviews. The trustworthiness of qualitative inquiry was ascertained by using triangulation, thick descriptions, and peer reviews. Three major themes emerged from the analysis: emotional, religious and social strategies. Despite available support, faculty noted challenges in managing work and family roles and fighting with gender stereotypes. This research adds to the emerging concept of WLB literature from the developed countries' viewpoint. It also shows how WLB discourse varies from Western sensibilities and collaborates with the previously established strategies that female academics formulate in WLB.

In this paper we discuss the use of automated assessment in a variety of computer science courses that have been taught at Israel Academic College by the authors. The course assignments were assessed entirely automatically using Checkpoint, a web-based automated assessment framework. The assignments all used free-text questions (where the students type in their own answers). Students were allowed to correct errors based on feedback provided by the system and resubmit their answers. A total of 141 students were surveyed to assess their opinions of this approach, and we analysed their responses. Analysis of the questionnaire showed a low correlation between questions, indicating the statistical independence of the individual questions. As a whole, student feedback on using Checkpoint was very positive, emphasizing the benefits of multiple attempts, impartial marking, and a quick turnaround time for submissions. Many students said that Checkpoint gave them confidence in learning and motivation to practise. Students also said that the detailed feedback that Checkpoint generated when their programs failed helped them understand their mistakes and how to correct them.

Immigrant students face a multitude of problems, among which are poor social adaptation and school integration. On the other hand, although digital narrations are widely used in education, they are rarely used for aiding students or for the resolution of complex problems. This study exploits the potential of digital narrations towards this end, by examining how the development and presentation of an autobiographical digital narration can assist immigrant students in overcoming their adaptation difficulties. For that matter, a female student presenting substantial problems was selected as the study’s subject. Data was collected from all the participating parties (subject, teacher, classmates) using a variety of tools, before, during, and after the intervention. It was found that through the digital narration she was able to externalize her thoughts and feelings and this, in turn, helped her in achieving a smoother integration in the school environment. In addition, the attitudes and perceptions of the other students for their foreign classmate were positively influenced. The intervention was short in duration and it did not require special settings. Hence, it can be easily applied and educators can consider using similar interventions. On the other hand, further research is recommended to establish the generalizability of the study’s findings.

Aim/Purpose: Over the past few years, new approaches to introducing young children to computational thinking have grown in popularity. This paper examines the role that user interfaces have on children’s mastery of computational thinking concepts and positive interpersonal behaviors. Background: There is a growing pressure to begin teaching computational thinking at a young age. This study explores the affordances of two very different programming interfaces for teaching computational thinking: a graphical coding application on the iPad (ScratchJr) and tangible programmable robotics kit (KIBO). Methodology : This study used a mixed-method approach to explore the learning experiences that young children have with tangible and graphical coding interfaces. A sample of children ages four to seven (N = 28) participated. Findings: Results suggest that type of user interface does have an impact on children’s learning, but is only one of many factors that affect positive academic and socio-emotional experiences. Tangible and graphical interfaces each have qualities that foster different types of learning

Aim/Purpose: The aims of this study were to investigate the feasibility of automatic assessment of programming tasks and to compare manual assessment with automatic assessment in terms of the effect of the different assessment methods on the marks of the students. Background: Manual assessment of programs written by students can be tedious. The assistance of automatic assessment methods might possibly assist in reducing the assessment burden, but there may be drawbacks diminishing the benefits of applying automatic assessment. The paper reports on the experience of a lecturer trying to introduce automated grading. Students’ solutions to a practical Java programming test were assessed both manually and automatically and the lecturer tied the experience to the unified theory of acceptance and use of technology (UTAUT). Methodology: The participants were 226 first-year students registered for a Java programming course. Of the tests the participants submitted, 214 were assessed both manually and automatically. Various statistical methods were used to compare the manual assessment of student’s solutions with the automatic assessment of the same solutions. A detailed investigation of reasons for differences was also carried out. A further data collection method was the lecturer’s reflection on the feasibility of automatic assessment of programming tasks based on the UTAUT. Contribution: This study enhances the knowledge regarding benefits and drawbacks of automatic assessment of students’ programming tasks. The research contributes to the UTAUT by applying it in a context where it has hardly been used. Furthermore, the study is a confirmation of previous work stating that automatic assessment may be less reliable for students with lower marks, but more trustworthy for the high achieving students. Findings: An automatic assessment tool verifying functional correctness might be feasible for assessment of programs written during practical lab sessions but could be less useful for practical tests and exams where functional, conceptual and structural correctness should be evaluated. In addition, the researchers found that automatic assessment seemed to be more suitable for assessing high achieving students. Recommendations for Practitioners: This paper makes it clear that lecturers should know what assessment goals they want to achieve. The appropriate method of assessment should be chosen wisely. In addition, practitioners should be aware of the drawbacks of automatic assessment before choosing it. Recommendation for Researchers: This work serves as an example of how researchers can apply the UTAUT theory when conducting qualitative research in different contexts. Impact on Society: The study would be of interest to lecturers considering automated assessment. The two assessments used in the study are typical of the way grading takes place in practice and may help lecturers understand what could happen if they switch from manual to automatic assessment. Future Research: Investigate the feasibility of automatic assessment of students’ programming tasks in a practical lab environment while accounting for structural, functional and conceptual assessment goals.

Aim/Purpose: The aim of this article is to develop a tool to detect plagiarism in real time amongst students being evaluated for learning in a computer-based assessment setting. Background: Cheating or copying all or part of source code of a program is a serious concern to academic institutions. Many academic institutions apply a combination of policy driven and plagiarism detection approaches. These mechanisms are either proactive or reactive and focus on identifying, catching, and punishing those found to have cheated or plagiarized. To be more effective against plagiarism, mechanisms that detect cheating or colluding in real-time are desirable. Methodology: In the development of a tool for real-time plagiarism prevention, literature review and prototyping was used. The prototype was implemented in Delphi programming language using Indy components. Contribution: A real-time plagiarism detection tool suitable for use in a computer-based assessment setting is developed. This tool can be used to complement other existing mechanisms. Findings: The developed tool was tested in an environment with 55 personal computers and found to be effective in detecting unauthorized access to internet, intranet, and USB ports on the personal computers. Recommendations for Practitioners: The developed tool is suitable for use in any environment where computer-based evaluation may be conducted. Recommendation for Researchers: This work provides a set of criteria for developing a real-time plagiarism prevention tool for use in a computer-based assessment. Impact on Society: The developed tool prevents academic dishonesty during an assessment process, consequently, inculcating confidence in the assessment processes and respectability of the education system in the society. Future Research: As future work, we propose a comparison between our tool and other such tools for its performance and its features. In addition, we want to extend our work to include testing for scalability of the tool to larger settings.

Aim/Purpose: This research aims to describe and demonstrate the results of an intervention through educational robotics to improve the computational thinking of student teachers. Background: Educational robotics has been increasing in school classrooms for the development of computational thinking and digital competence. However, there is a lack of research on how to prepare future teachers of Kindergarten and Elementary School in the didactic use of computational thinking, as part of their necessary digital teaching competence. Methodology: Following the Design-Based Research methodology, we designed an intervention with educational robots that includes unplugged, playing, making and remixing activities. Participating in this study were 114 Spanish university students of education. Contribution: This research helps to improve the initial training of student teachers, especially in the field of educational robotics. Findings: The student teachers consider themselves digital competent, especially in the dimensions related to social and multimedia aspects, and to a lesser extent in the technological dimension. The results obtained also confirm the effectiveness of the intervention through educational robotics in the development of computational thinking of these students, especially among male students. Recommendations for Practitioners: Teacher trainers could introduce robotics following these steps: (1) initiation and unplugged activities, (2) gamified activities of initiation to the programming and test of the robots, (3) initiation activities to Scratch, and (4) design and resolution of a challenge. Recommendation for Researchers: Researchers could examine how interventions with educational robots helps to improve the computational thinking of student teachers, and thoroughly analyze gender-differences. Impact on Society: Computational thinking and robotics are one of the emerging educational trends. Despite the rise of this issue, there are still few investigations that systematize and collect evidence in this regard. This study allows to visualize an educational intervention that favors the development of the computational thinking of student teachers. Future Research: Researchers could evaluate not only the computational thinking of student teachers, but also their didactics, their ability to teach or create didactic activities to develop computational thinking in their future students.

Aim/Purpose: This paper aims to explore whether having state Computer Science standards in place will increase young children’s exposure to coding and powerful ideas from computer science in the early years. Background: Computer science education in the K-2 educational segment is receiving a growing amount of attention as national and state educational frameworks are emerging. By focusing on the app ScratchJr, the most popular free introductory block-based programming language for early childhood, this paper explores if there is a relationship between the presence of state frameworks and ScratchJr’s frequency of use. Methodology: This paper analyzes quantitative non-identifying data from Google Analytics on users of the ScratchJr programming app. Google Analytics is a free tool that allows access to user activity as it happens in real time on the app, as well as audience demographics and behavior. An analysis of trends by state, time of year, type of in-app activities completed, and more are analyzed with a specific focus on comparing states with K-12 Computer Science in place versus those without. Contribution: Results demonstrate the importance of having state standards in place to increase young children’s exposure to coding and powerful ideas from computer science in the early years. Moreover, we see preliminary evidence that states with Computer Science standards in place support skills like perseverance and debugging through ScratchJr. Findings: Findings show that in the case of ScratchJr, app usage decreases during the summer months and on weekends, which may indicate that coding with ScratchJr is more often happening in school than at home. Results also show that states with Computer Science standards have more ScratchJr users on average and have more total sessions with the app on average. Results also show preliminary evidence that states with Computer Science standards in place have longer average session duration as well as a higher average number of users returning to edit an existing project. Recommendations for Practitioners: Successful early childhood computer science education programs must teach powerful ideas from the discipline of computer science in a developmentally appropriate way, provide means for self-expression, prompt debugging and problem solving, and offer a low-floor/high-ceiling interface for both novices and experts. Practitioners should be aware in drops in computer science learning during the summer months when school is not in session. Recommendation for Researchers: Researchers should consider the impact of state and national frameworks on computer science learning and skills mastered during the early childhood years. Researchers should look for ways to continue engaging students in computer science education during times when school is not in session. Impact on Society: Results demonstrate the importance of having state CS standards in place to increase young children’s exposure to coding and powerful ideas from computer science in the early years. Moreover, we see preliminary evidence that states with Computer Science standards in place support skills like perseverance and debugging through ScratchJr. Future Research: Future research should continue collecting Google Analytics from the ScratchJr app and track changes in usage. Future research should also collect analytics from a wide range of programming applications for young children to see if the trends identified here are consistent across different apps.

Aim/Purpose: In this study, the researchers investigated whether the out-of-class learning approach could help the students to attain any valuable learning outcomes for cybersecurity learning and could enhance the perceived value of cybersecurity education among the students. Background: Cybersecurity learning poses challenges for its students to learn a complicated subject matter and the students may be intimidated by the challenging courses in cybersecurity programs. Therefore, it is essential for the faculty members to devise some mechanisms to promote cybersecurity learning to increase its student retention. The mechanism suggested by this study was the out-of-class learning approach. Methodology: The researchers in this study employed a content analysis and adopted a semiotic method to analyze qualitative data. The researchers also conducted crosstabulation analyses using chi-square tests to detect the significant differences in the emerging learning outcomes from the two different out-of-class learning activities investigated in this study. Contribution: This study addressed the difficulty of cybersecurity education and proposed a viable mechanism to promote the student learning in such a complicated subject matter. Findings: For cybersecurity education, the out-of-class learning approach is a viable pedagogical mechanism that could lead the students to several learning outcomes, including connecting them to the real-life scenarios related to the cybersecurity profession, guiding them to their career choices and development, stimulating their intellectual growth, creating their justification of learning, and raising their cybersecurity awareness. Recommendations for Practitioners: The instructors of any cybersecurity programs should incorporate some out-of-class learning activities into the courses in their programs, especially the introductory-level courses. Additionally, it is important to coordinate the out-of-class learning activities with the in-class lessons to enable the students to justify what they have learned in their classrooms and motivate them to learn more. Recommendation for Researchers: Researchers could look beyond in-class learning and laboratory learning to investigate the impacts of out-of-class learning activities on cybersecurity education to help the students to attain better learning outcomes. Impact on Society: By promoting cybersecurity education, universities and colleges could attain a higher retention rate of the students in their cybersecurity programs. The higher retention rate of the students in cybersecurity programs would help to ease the critical shortage of cybersecurity talent. Future Research: Future research could explore the impacts of other out-of-class learning activities on cybersecurity learning; for example: job shadowing, attending cybersecurity conferences, internship, developing cybersecurity systems or tools for actual customers, working on cybersecurity research with faculty members. Additionally, future studies could investigate the effects of the out-of-class learning approach on promoting other academic programs that are characterized by intensely complex and technical nature, similar to cybersecurity programs.

Aim/Purpose: This paper reports on a case study project which had three goals; to develop a suite of original interactive digital skills e-tutorials to be embedded in undergraduate and postgraduate courses; to evaluate the students’ experience and engagement with the e-tutorials over one semester; and to explore their general attitudes towards online and blended learning. Background: Online and blended learning modes continue to grow in popularity in higher education, with the aim of streamlining and enhancing student learning, supporting collaboration and creativity, and equipping students with the skills they will require to work and live in an increasingly digitized world. This practice-based case study highlights factors which positively and negatively affect user engagement with digital learning objects and explores students’ perceptions of the role of online learning within their academic programs. Methodology: A suite of nine interactive e-tutorials, addressing essential digital literacy skills for university students, was developed through instructor and student peer collaboration using Articulate software, informed by best practice. The e-tutorials were embedded in the institutional Learning Management System for three undergraduate and postgraduate courses, in which digital literacy formed the core learning content, to complement classroom-based learning. Students in these courses were surveyed via SurveyMonkey about their specific experience of using the e-tutorials, as well as their general perceptions of digital literacy and online learning. Eighty-six students in total completed the questionnaire, which consisted of twenty-three closed- and open-ended questions. Contribution: Through highlighting both the positive and the challenging aspects of the students’ reported experience of online learning, this case study contributes useful insights to the body of literature on user engagement with digital learning objects in higher education, as well as students’ perceptions and experience of blended learning. Findings: The e-tutorials were perceived as valuable in reinforcing classroom learning, allowing respondents to revise concepts and materials covered in face-to-face classes, at their own pace and in their own time. Survey responses showed that the accessibility, ease-of-use, design and duration of the e-tutorials were deemed effective in terms of user engagement; however, several technological challenges were identified, such as browser incompatibility, uneven sound quality and general Internet connection issues, which disrupted their learning. Overall, students expressed enjoyment of the learning facilitated by the e-tutorials; however, rather than favoring online learning alone, they expressed a preference for a blended learning environment, with a combination of complementary learning approaches; survey respondents did not generally wish to forego face-to-face classes entirely. Recommendations for Practitioners: Instructors should seek to strategically embed interactive digital learning objects in their courses at defined points of need in a logical structure, e.g., to reinforce classroom-based learning, or to support specific skill development. Potential disruption to learning should be minimized by following best practice guidelines to ensure ease of access, a seamless user experience, and timely feedback, as well as providing adequate support for rapid resolution of technical glitches. Recommendation for Researchers: E-tutorials offer a useful means of exploring ways in which students acquire learning in the digital environment. A wider, collaborative exploration is needed to provide comparative studies which move beyond case studies. Impact on Society: Online learning mechanisms, such as e-tutorials, offer students different means of acquiring essential literacy skills and different ways to interact with content. E-tutorials constitute reusable learning objects, which can be accessed as just-in-time delivery modes, when students perceive they need to review particular skills or reinforce learning material. Future Research: This research is now expanding into different types of reusable learning objects. E-tutorials may be developed in multiple ways, and comparative research around e-tutorial models will deepen our understanding of how students interact with content in formal learning contexts. As the digital educational landscape continues to expand alongside traditional face-to-face and analogue learning modes, a key research focus will be student and instructor perceptions and experience of blended learning in different contexts.

Aim/Purpose: Although many computer science measures have been proposed, visualizing individual students’ capabilities is difficult, as those measures often rely on specific tools and methods or are not graded. To solve these problems, we propose a rubric for measuring and visualizing the effects of learning computer programming for elementary school students enrolled in computer science education (CSE), which is independent of the programming language being used. Background: In this research, we proposed a rubric based on existing CSE standards and criteria having a programming education-learning goal. We then applied this rubric to actual lessons to visualize the educational effects. Methodology: The proposed new rubric for teaching computer programming, based on existing standards and criteria, was applied to fourth- and sixth-grade students in Japan. We assessed which skills were cultivated through quizzes before and after the teaching. Contribution: This paper contributes on how to make and utilize a rubric for programming education in computer science. We evaluated and visualized the proposed rubric’s learning effects on children and found that our proposed rubrics are independent of any particular method or tool. Findings: The results of this survey are twofold: (1) we proposed a rubric of programming education in computer science, independent of the programming tools used and (2) we succeeded in visualizing students’ learning stages by applying the proposed rubric to programming education conducted in a Japanese elementary school. Recommendations for Practitioners: Evaluating educational effects in CSE is important. In particular, graded assessments of learner abilities can reveal individual characteristics. This research is useful for assessing CSE because it focuses specifically on programming education. Recommendation for Researchers: The rubric’s suggestions and quality improvements in CSE help learners assess their learning progress and will clarify the cultivated computer science skills. Impact on Society: This research evaluates CSE based on a rubric in the programming education field. Future Research: Future work is needed to improve the proposed rubric’s quality and relevance. Also, this rubric should be applied to many classes to increase the number of evaluations and analyses.

Aim/Purpose: To encourage students’ engagement in peer assessments and provide students with better-quality feedback, this paper describes a technique for author-reviewer matching in peer assessment systems – a Balanced Allocation algorithm. Background: Peer assessment concerns evaluating the work of colleagues and providing feedback on their work. This process is widely applied as a learning method to involve students in the progress of their learning. However, as students have different ability levels, the efficacy of the peer feedback differs from case to case. Thus, peer assessment may not provide satisfactory results for students. In order to mitigate this issue, this paper explains and evaluates an algorithm that matches the author to a set of reviewers. The technique matches authors and reviewers based on how difficult the authors perceived the assignment to be, and the algorithm then matches the selected author to a group of reviewers who may meet the author’s needs in regard to the selected assignment. Methodology: This study used the Multiple Criteria Decision-Making methodology (MCDM) to determine a set of reviewers from among the many available options. The weighted sum method was used because the data that have been collected in user profiles are expressed in the same unit. This study produced an experimental result, examining the algorithm with a real collected dataset and mock-up dataset. In total, there were 240 students in the real dataset, and it contained self-assessment scores, peer scores, and instructor scores for the same assignment. The mock-up dataset created 1000 records for self-assessment scores. The algorithm was evaluated using focus group discussions with 29 programming students and interviews with seven programming instructors. Contribution: This paper contributes to the field in the following two ways. First, an algorithm using a MCDM methodology was proposed to match authors and reviewers in order to facilitate the peer assessment process. In addition, the algorithm used self-assessment as an initial data source to match users, rather than randomly creating reviewer – author pairs. Findings: The findings show the accurate results of the algorithm in matching three reviewers for each author. Furthermore, the algorithm was evaluated based on students’ and instructors’ perspectives. The results are very promising, as they depict a high level of satisfaction for the Balanced Allocation algorithm. Recommendations for Practitioners: We recommend instructors to consider using the Balanced Allocation algorithm to match students in peer assessments, and consequently to benefit from personalizing peer assessment based on students' needs. Recommendation for Researchers: Several MCDM methods could be expanded upon, such as the analytic hierarchy process (AHP) if different attributes are collected, or the artificial neural network (ANN) if fuzzy data is available in the user profile. Each method is suitable for special cases depending on the data available for decision-making. Impact on Society: Suitable pairing in peer assessment would increase the credibility of the peer assessment process and encourage students’ engagement in peer assessments. Future Research: The Balanced Allocation algorithm could be applied using a single group, and a peer assessment with random matching with another group may also be conducted, followed by performing a t-test to determine the impact of matching on students’ performances in the peer assessment activity.

Aim/Purpose: This paper presents a machine learning approach for analyzing Course Learning Outcomes (CLOs). The aim of this study is to find a model that can check whether a CLO is well written or not. Background: The use of machine learning algorithms has been, since many years, a prominent solution to predict learner performance in Outcome Based Education. However, the CLOs definition is still presenting a big handicap for faculties. There is a lack of supported tools and models that permit to predict whether a CLO is well written or not. Consequently, educators need an expert in quality and education to validate the outcomes of their courses. Methodology: A novel method named CLOCML (Course Learning Outcome Classification using Machine Learning) is proposed in this paper to develop predictive models for CLOs paraphrasing. A new dataset entitled CLOC (Course Learning Outcomes Classes) for that purpose has been collected and then undergone a pre-processing phase. We compared the performance of 4 models for predicting a CLO classification. Those models are Support Vector Machine (SVM), Random Forest, Naive Bayes and XGBoost. Contribution: The application of CLOCML may help faculties to make well-defined CLOs and then correct CLOs' measures in order to improve the quality of education addressed to their students. Findings: The best classification model was SVM. It was able to detect the CLO class with an accuracy of 83%. Recommendations for Practitioners: We would recommend both faculties’ members and quality reviewers to make an informed decision about the nature of a given course outcome. Recommendation for Researchers: We would highly endorse that the researchers apply more machine learning models for CLOs of various disciplines and compare between them. We would also recommend that future studies investigate on the importance of the definition of CLOs and its impact on the credibility of Key Performance Indicators (KPIs) values during accreditation process. Impact on Society: The findings of this study confirm the results of several other researchers who use machine learning in outcome-based education. The definition of right CLOs will help the student to get an idea about the performances that will be measured at the end of a course. Moreover, each faculty can take appropriate actions and suggest suitable recommendations after right performance measures in order to improve the quality of his course. Future Research: Future research can be improved by using a larger dataset. It could also be improved with deep learning models to reach more accurate results. Indeed, a strategy for checking CLOs overlaps could be integrated.