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: 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: One of the main objectives of higher education institutions is to provide a high-quality education to their students and reduce dropout rates. This can be achieved by predicting students’ academic achievement early using Educational Data Mining (EDM). This study aims to predict students’ final grades and identify honorary students at an early stage. Background: EDM research has emerged as an exciting research area, which can unfold valuable knowledge from educational databases for many purposes, such as identifying the dropouts and students who need special attention and discovering honorary students for allocating scholarships. Methodology: In this work, we have collected 300 undergraduate students’ records from three departments of a Computer and Information Science College at a university located in Saudi Arabia. We compared the performance of six data mining methods in predicting academic achievement. Those methods are C4.5, Simple CART, LADTree, Naïve Bayes, Bayes Net with ADTree, and Random Forest. Contribution: We tested the significance of correlation attribute predictors using four different methods. We found 9 out of 18 proposed features with a significant correlation for predicting students’ academic achievement after their 4th semester. Those features are student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses, i.e., database fundamentals, programming language (1), and computer network fundamentals. Findings: The empirical results show the following: (i) the main features that can predict students’ academic achievement are the student GPA during the first four semesters, the number of failed courses during the first four semesters, and the grades of three core courses; (ii) Naïve Bayes classifier performed better than Tree-based Models in predicting students’ academic achievement in general, however, Random Forest outperformed Naïve Bayes in predicting honorary students; (iii) English language skills do not play an essential role in students’ success at the college of Computer and Information Sciences; and (iv) studying an orientation year does not contribute to students’ success. Recommendations for Practitioners: We would recommend instructors to consider using EDM in predicting students’ academic achievement and benefit from that in customizing students’ learning experience based on their different needs. Recommendation for Researchers: We would highly endorse that researchers apply more EDM studies across various universities and compare between them. For example, future research could investigate the effects of offering tutoring sessions for students who fail core courses in their first semesters, examine the role of language skills in social science programs, and examine the role of the orientation year in other programs. Impact on Society: The prediction of academic performance can help both teachers and students in many ways. It also enables the early discovery of honorary students. Thus, well-deserved opportunities can be offered; for example, scholarships, internships, and workshops. It can also help identify students who require special attention to take an appropriate intervention at the earliest stage possible. Moreover, instructors can be aware of each student’s capability and customize the teaching tasks based on students’ needs. Future Research: For future work, the experiment can be repeated with a larger dataset. It could also be extended with more distinctive attributes to reach more accurate results that are useful for improving the students’ learning outcomes. Moreover, experiments could be done using other data mining algorithms to get a broader approach and more valuable and accurate outputs.

Aim/Purpose: The purpose of this paper is to develop and evaluate a debiasing-based approach to assessing the learning materials in problem-based learning (PBL) environments. Background: Research in cognitive debiasing suggests nine debiasing strategies improve decision-making. Given the large number of decisions made in semester-long, problem-based learning projects, multiple tools and techniques help students make decisions. However, instructors may struggle to identify the specific tools or techniques that could be modified to best improve students’ decision-making in the project. Furthermore, a structured approach for identifying these modifications is lacking. Such an approach would match the debiasing strategies with the tools and techniques. Methodology: This debiasing framework for the PBL environment is developed through a study of debiasing literature and applied within an e-commerce course using the Model for Improvement, continuous improvement process, as an illustrative case to show its potential. In addition, a survey of the students, archival information, and participant observation provided feedback on the debiasing framework and its ability to assess the tools and techniques within the PBL environment. Contribution: This paper demonstrates how debiasing theory can be used within a continuous improvement process for PBL courses. By focusing on a cognitive debiasing-based approach, this debiasing framework helps instructors 1) identify what tools and techniques to change in an PBL environment, and 2) assess which tools and techniques failed to debias the students adequately, providing potential changes for future cycles. Findings: Using the debiasing framework in an e-commerce course with significant PBL elements provides evidence that this framework can be used within IS courses and more broadly. In this particular case, the change identified in a prior cycle proved effective and additional issues were identified for improvement. Recommendations for Practitioners: With the growing usage of semester-long PBL projects in business schools, instructors need to ensure that their design of the projects incorporates techniques that improve student learning and decision making. This approach provides a means for assessing the quality of that design. Recommendation for Researchers: This study uses debiasing theory to improve course techniques. Researchers interested in assessment, course improvement, and program improvement should incorporate debiasing theory within PBL environments or other types of decision-making scenarios. Impact on Society: Increased awareness of cognitive biases can help instructors, students, and professionals make better decisions and recommendations. By developing a framework for evaluating cognitive debiasing strategies, we help instructors improve projects that prepare students for complex and multifaceted real-world projects. Future Research: The approach could be applied to multiple contexts, within other courses, and more widely within information systems to extend this research. The framework might also be refined to make it more concise, integrated with assessment, or usable in more contexts.

Aim/Purpose: This study seeks to present a learning model of discrete mathematics elements, elucidate the content of teaching, and validate the effectiveness of this learning in a digital education context. Background: Teaching discrete mathematics in the realm of digital education poses challenges, particularly in crafting the optimal model, content, tools, and methods tailored for aspiring computer science teachers. The study draws from both a comprehensive review of relevant literature and the synthesis of the authors’ pedagogical experiences. Methodology: The research utilized a system-activity approach and aligned with the State Educational Standard. It further integrated the theory of continuous education as its psychological and pedagogical foundation. Contribution: A unique model for instructing discrete mathematics elements to future computer science educators has been proposed. This model is underpinned by informative, technological, and personal competencies, intertwined with the mathematical bedrock of computer science. Findings: The study revealed the importance of holistic teaching of discrete mathematics elements for computer science teacher aspirants in line with the Informatics educational programs. An elective course, “Elements of Discrete Mathematics in Computer Science”, comprising three modules, was outlined. Practical examples spotlighting elements of mathematical logic and graph theory of discrete mathematics in programming and computer science were showcased. Recommendations for Practitioners: Future computer science educators should deeply integrate discrete mathematics elements in their teaching methodologies, especially when aligning with professional disciplines of the Informatics educational program. Recommendation for Researchers: Further exploration is recommended on the seamless integration of discrete mathematics elements in diverse computer science curricula, optimizing for varied learning outcomes and student profiles. Impact on Society: Enhancing the quality of teaching discrete mathematics to future computer science teachers can lead to better-educated professionals, driving advancements in the tech industry and contributing to societal progress. Future Research: There is scope to explore the wider applications of the discrete mathematics elements model in varied computer science sub-disciplines, and its adaptability across different educational frameworks.

Aim/Purpose: To explore the effectiveness of utilizing the design thinking approach in developing digital self-directed learning environment to enhance digital literacy skills in Thai higher education. Background: To foster digital literacy skills in higher education, Thai students require more than access to technology. Emphasizing digital self-directed learning and incorporating Design Thinking approach, can empower students to learn and develop their digital skills effectively. This study explores the impact of digital self-directed learning environment, developed using a design thinking approach, on enhancing digital literacy skills among higher education students in Thailand. Methodology: The research methodology involves developing a digital self-directed learning environment, collecting and analyzing data, and using statistical analysis to compare the outcomes between different groups. The sample includes 60 undergraduate students from the School of Industrial Education and Technology at King Mongkut Institute of Technology, divided into a control group (n=30) and an experimental group (n=30). Data analysis involves mean, standard deviation, and one-way MANOVA. Contribution: This research contributes to the evidence supporting the use of Design Thinking in developing digital self-directed learning environment, demonstrating its effectiveness in meeting learners’ needs and improving learning outcomes in higher education. Findings: Key findings include: 1) the digital media and self-directed learning activities plan developed through the design thinking approach received high-quality ratings from experts, with mean scores of 4.87 and 4.93, respectively; and 2) post-lesson comparisons of learning outcome and digital literacy assessment scores revealed that the group utilizing digital media with self-directed learning activities had significantly higher mean scores than the traditional learning group, with a significance level of 0.001. Recommendations for Practitioners: Practitioners in higher education should use design thinking to develop digital self-directed learning environments that enhance digital literacy skills. This approach involves creating high-quality digital media and activities, promoting engagement and improved outcomes. Collaboration and stakeholder involvement are essential for effective implementation. Recommendation for Researchers: Researchers should continue to explore the effectiveness of design thinking approaches in the development of learning environments, as well as their influence on different educational aspects such as student engagement, satisfaction, and overall learning outcomes. Impact on Society: By enhancing digital literacy skills among higher education students, this study contributes to the development of a digitally skilled workforce, encourages lifelong learning, and aids individuals in effectively navigating the challenges of the digital era. Future Research: Future research could explore a broader range of student demographics and educational settings to validate the effectiveness of the Design Thinking approach in enhancing digital literacy. This could include integrating design thinking with alternative digital learning and teaching methods to further improve digital literacy.

Aim/Purpose: This paper explores the potential value of critical thinking in computer science education and discusses strategies for its integration across the curriculum. Background: As technology rapidly evolves and becomes increasingly integrated into society, there is a growing need for computer science graduates who can think critically about the ethical, societal, and technical implications of their work. Methodology: This study employs a conceptual analysis approach, reviewing existing literature on critical thinking in computer science education and synthesising insights from various sources. The analysis focuses on identifying challenges in implementing critical thinking instruction and proposing practical solutions. Contribution: This paper provides an overview of the current discourse on integrating critical thinking into computer science curricula. It explores the distinction between critical thinking and computational thinking, discusses various pedagogical approaches, and offers insights into potential challenges of implementation. Findings: The paper identifies six key challenges in embedding critical thinking into computer science education. It proposes initial steps to address these challenges, including curriculum redesign, innovative assessment methods, and faculty development strategies. Recommendations for Practitioners: Educators should adopt a balanced approach that complements technical education with critical thinking exercises, using diverse teaching methods such as dialogue-based teaching and authentic instruction. Recommendation for Researchers: Future research should focus on empirical studies to assess the effectiveness of the proposed solutions and develop standardised tools for evaluating critical thinking skills in computer science. Impact on Society: By enhancing critical thinking skills in computer science education, we can produce graduates who are better equipped to address complex technological challenges and their societal implications. Future Research: Further investigation is needed into the most effective pedagogical approaches for teaching critical thinking in computer science, with a focus on multidisciplinary perspectives.

Aim/Purpose: This paper is part of a multi-case study that aims to test whether generative AI makes an effective coding assistant. Particularly, this work evaluates the ability of two AI chatbots (ChatGPT and Bing Chat) to generate concise computer code, considers ethical issues related to generative AI, and offers suggestions for how to improve the technology. Background: Since the release of ChatGPT in 2022, generative artificial intelligence has steadily gained wide use in software development. However, there is conflicting information on the extent to which AI helps developers be more productive in the long term. Also, whether using generated code violates copyright restrictions is a matter of debate. Methodology: ChatGPT and Bing Chat were asked the same question, their responses were recorded, and the percentage of each chatbot’s code that was extraneous was calculated. Also examined were qualitative factors, such as how often the generated code required modifications before it would run. Contribution: This paper adds to the limited body of research on how effective generative AI is at aiding software developers and how to practically address its shortcomings. Findings: Results of AI testing observed that 0.7% of lines and 1.4% of characters in ChatGPT’s responses were extraneous, while 0.7% of lines and 1.1% of characters in Bing Chat’s responses were extraneous. This was well below the 2% threshold, meaning both chatbots can generate concise code. However, code from both chatbots frequently had to be modified before it would work; ChatGPT’s code needed major modifications 30% of the time and minor ones 50% of the time, while Bing Chat’s code needed major modifications 10% of the time and minor ones 70% of the time. Recommendations for Practitioners: Companies building generative AI solutions are encouraged to use this study’s findings to improve their models, specifically by decreasing error rates, adding more training data for programming languages with less public documentation, and implementing a mechanism that checks code for syntactical errors. Developers can use the findings to increase their productivity, learning how to reap generative AI’s full potential while being aware of its limitations. Recommendation for Researchers: Researchers are encouraged to continue where this paper left off, exploring more programming languages and prompting styles than the scope of this study allowed. Impact on Society: As artificial intelligence touches more areas of society than ever, it is crucial to make AI models as accurate and dependable as possible. If practitioners and researchers use the findings of this paper to improve coders’ experience with generative AI, it will make millions of developers more productive, saving their companies money and time. Future Research: The results of this study can be strengthened (or refuted) by a future study with a large, diverse dataset that more fully represents the programming languages and prompting styles developers tend to use. Moreover, further research can examine the reasons generative AI fails to deliver working code, which will yield valuable insights into improving these models.

Aim/Purpose: This review’s main objective was to examine the existing literature on the use of 3D printers in primary education, covering students aged six to twelve across general, special, and inclusive educational environments. Background: A review of the literature indicated a significant oversight – prior reviews insufficiently distinguish the application of 3D printing in primary education from its utilization at higher educational tiers or focused on particular subject areas and learning domains. Considering the distinct nature and critical role of primary education in developing young students’ cognitive abilities and skills, it is essential to concentrate on this specific educational stage. Methodology: The scoping review was selected as the preferred research method. The methodological robustness was augmented through the utilization of the backward snowballing technique. Consequently, a total of 50 papers were identified and subjected to thorough analysis. Contribution: This review has methodically compiled and analyzed the literature on 3D printing use among elementary students, offering a substantial addition to academic conversations. It consolidated and organized research on 3D printers’ educational uses, applying robust and credible criteria. Findings: Many studies featured small sample sizes and limited research on inclusive and special education. The analysis revealed 82 distinct research goals and 13 educational fields, with STEM being the predominant focus. Scholars showed considerable interest in how 3D printers influence skills like creativity and problem-solving, as well as emotions such as engagement and motivation. The majority of studies indicated positive outcomes, enhancing academic achievement, engagement, collaboration, creativity, interest, and motivation. Nonetheless, challenges were noted, highlighting the necessity for teacher training, the expense of equipment, technical difficulties, and the complexities of blending new methods with traditional curricula. Recommendations for Practitioners: To capitalize on the benefits that 3D printers bring, curriculum planners are urged to weave them into their programs, ensuring alignment with educational standards and skill development. The critical role educators play in the effective implementation of this technology necessitates targeted professional development programs to equip them with the expertise for successful integration. Moreover, 3D printing presents a unique opportunity to advance inclusive education for students with disabilities, offering tailored learning experiences and aiding in creating assistive technologies. In recognizing the disparities in access to 3D printing, educational leaders must address the financial and logistical barriers highlighted in the literature. Strategic initiatives are essential to democratize 3D printing access, ensuring all students benefit from this educational tool. Recommendation for Researchers: Comparative studies are critical to elucidate the specific advantages and limitations of 3D printing technology due to the scarcity of research contrasting it with other tools. The variability in reporting durations of interventions and research environments underscores the necessity for uniform methodologies and benchmarks. Because research has predominantly focused on STEM/STEAM education, expanding into different educational areas could provide a comprehensive understanding of 3D printing’s capabilities. The existence of neutral and negative findings signals an opportunity for further investigation. Exploring the factors that impede the successful integration of 3D printing will inform the creation of superior pedagogical approaches and technological refinements. Future Research: As the review confirmed the significant promise of 3D printing technology in enriching education, especially in the context of primary education, the imperative for continued research to refine its application in primary education settings is highlighted.

Aim/Purpose: To conduct a needs assessment and subsequently create micro-storytelling media aimed at enhancing the Digital Intelligence Quotient (DQ) skills of young individuals. Background: In today's digital society, DQ has emerged as a vital skill that elevates individuals in all aspects of life, from daily living to education. To empower Thai youth, this study seeks to innovate DQ content by adapting it into a digital format known as micro-storytelling. This unique approach combines the art of storytelling with digital elements, creating engaging and effective micro-learning media Methodology: The methodology comprises three phases: 1) assessing the need for digital micro-storytelling development; 2) developing digital micro-storytelling; and 3) evaluating the DQ skills among young individuals. The sample group consisted of 55 higher education learners for needs assessment and 30 learners in the experiment group. Data analysis involves PNI modified, mean, and standard deviation. Contribution: This research contributes by addressing the urgent need for DQ skills in the digital era and by providing a practical solution in the form of digital micro-storytelling, tailored to the preferences and needs of Thai youth. It serves as a valuable resource for educators and policymakers seeking to empower young learners with essential digital competencies. Findings: The findings demonstrate three significant outcomes: 1) The learners wanted to organize their own learning experience with self-paced learning in a digital landscape, and they preferred digital media in the form of video. They were most interested in developing DQ to enhance their understanding of digital safety, digital security, and digital literacy; 2) according to a consensus of experts, digital micro-storytelling has the greatest degree of quality in terms of its development, content, and utilization, with an overall average of 4.86; and 3) the overall findings of the assessment of DQ skills indicate a favorable level of proficiency. Recommendations for Practitioners: Align materials with micro-learning principles, keeping content concise for effective knowledge retention. Empower students to personalize their digital learning and promote self-paced exploration based on their interests. Recommendation for Researchers: Researchers should continuously assess and update digital learning materials to align with the evolving digital landscape and the changing needs of students and investigate the long-term effects of DQ improvement, especially in terms of online safety and digital literacy in students' future lives and careers. Impact on Society: This study's impact on society is centered around fostering a DQ, promoting innovative educational approaches, and elevating Thai youth with essential digital skills. It contributes to a safer, more informed, and digitally literate generation prepared for the challenges of the digital era. Future Research: Undertake comparative studies to analyze the effectiveness of different digital learning formats and methodologies. Comparing micro-storytelling with other approaches can help identify the most efficient and engaging methods for enhancing DQ.

Forensic document forgery investigations have elevated the need for source identification for printed documents during the past few years. It is necessary to create a reliable and acceptable safety testing instrument to determine the credibility of printed materials. The proposed system in this study uses a neural network to detect the original printer used in forensic document forgery investigations. The study uses a deep neural network method, which relies on the quality, texture, and accuracy of images printed by various models of Canon and HP printers. The datasets were trained and tested to predict the accuracy using logical function, with the goal of creating a reliable and acceptable safety testing instrument for determining the credibility of printed materials. The technique classified the model with 95.1% accuracy. The proposed method for identifying the source of the printer is a non-destructive technique.

The purpose of this study is to explore the effect of electronic service quality subdimensions upon the behavioural and praising intentions of users engaged in internet banking. Using the survey method, 203 responses were collected from users of online banking in Turkey. A partial least square structural equation model was constructed to test both the reliability and validity of the measurement, as well as the structural model. The results indicated that emotional benefits, ease of use, and control subdimensions, which are influenced through graphical quality and layout clarity, have a significant and positive impact upon the behavioural and praising intentions of users of online banking. The study did not find support for the direct effect of layout clarity upon behavioural and praising intentions.

The present study attempts to find out the determinants of FinTech adoption for financial inclusion by a microfinance institution in India. The factors such as efficiency, consistency, convenience, reliability are taken as predictors of organisational attitude. Similarly, organisational attitude, ease of use, and perceived benefits are considered as antecedents of organisational adoption intention of FinTech in microfinance institutions of India. The purposive sampling technique was used to get a filled survey instrument by target samples. The results indicate that convenience and consistency in the use of FinTech applications build a favourable attitude to adopt it. Furthermore, perceived benefits are the most important antecedents of the adoption intention of FinTech in the microfinance institution in India. Additionally, the reliability of the application has a positive but insignificant impact on organisational attitude to adopt FinTech. The implications of the present study are discussed.

Nostalgia has an intrinsic association with consumer behaviour. Retrieval of memories drives emotions among consumers and reinforces experience-led buying decisions. Despite nostalgia, and consumption being a common practice at various times in life, issues regarding the nostalgia stimuli on customers' perceptions and buying decisions remain less explored. This article aims at exploring the consumption pattern of adult consumers by analysing the influence of nostalgic behaviour referring to the autobiographic memories and social motivations. It describes the purchase intentions and consumption pattern among adult consumers in the context of self-reference criteria based on nostalgic memories and social motivations. This article offers constructive understanding on establishing relationship between nostalgic memories and consumption pattern over the temporal framework and establishing the brand loyalty and hedonic satisfaction. It contributes to the existing literature by critically examining the theoretical concepts and empirical findings of previous studies on perceptions of consumers on nostalgic emotions and their role in making buying decisions.

The world is transitioning towards the digitalisation of everyday tasks significantly. The impact of digital literacy on technological usage is immense. The awareness and utilisation of the digital India schemes are needed to determine unorganised retailers' overall satisfaction with digitalisation and technological usage. The chief motive of this research is to assess and analyse digital literacy in terms of technology usage and the awareness cum utilisation level of the various digital India schemes proposed by the Government of India for unorganised retailers. The conceptual framework consists of the factors such as digital literacy and digital India schemes that determine the overall satisfaction of retailers with technology usage. The corresponding results of the study synthesised the impact of digital literacy, digital India schemes, and the awareness cum utilisation level of technology among unorganised retailers based on recommendations to enhance the performance of the unorganised retail sector.

Evaluation of research artefacts (such as models, frameworks and methodologies) is essential to determine their quality and demonstrate worth. However, in the information quality (IQ) research domain there is no existing standard set of criteria available for researchers to use to evaluate their IQ artefacts. This paper therefore describes our experience of selecting and synthesising a set of evaluation criteria used in three related research areas of information systems (IS), software products (SP) and conceptual models (CM), and analysing their relevance to different types of IQ research artefact. We selected and used a subset of these criteria in an actual evaluation of an IQ artefact to test whether they provide any benefit over a standard evaluation. The results show that at least a subset of the criteria from the other domains of IS, SP and CM are relevant for IQ artefact evaluations, and the resulting set of criteria, most importantly, enabled a more rigorous and systematic selection of what to evaluate.

More than a half billion people use the internet in China, and the environment in which these users work, study, and play using the internet is a rapidly changing one. User perceptions of the quality of information accessed through the internet and through more traditional sources of information may shift over time as the underlying social, cultural, and political environment changes. This study reports the results of a longitudinal survey study of perceptions of information quality of young adults using the internet in China. Results suggest that perceptions of the information quality of internet-based information have shifted more from 2007 to 2012 than perceptions of traditional text sources of information. Implications of the findings for researchers, educators, and information providers are discussed.

To better explore the application of unsupervised learning neural networks in psychological interventions for college students, this study investigates the relationships among latent psychological variables from the perspective of neural networks. Firstly, college students' psychological crisis and intervention systems are analysed, identifying several shortcomings in traditional psychological interventions, such as a lack of knowledge dissemination and imperfect management systems. Secondly, employing the Human-Computer Interaction (HCI) approach, a structural equation model is constructed for unsupervised learning neural networks. Finally, this study further confirms the effectiveness of unsupervised learning neural networks in psychological interventions for college students. The results indicate that in psychological intervention for college students. Additionally, the weightings of the indicators at the criterion level are calculated to be 0.35, 0.27, 0.19, 0.11 and 0.1. Based on the results of HCI, an emergency response system for college students' psychological crises is established, and several intervention measures are proposed.

Human behaviour, which is led by the human, emotional and occasionally fallible brain, is highly influenced by the environment in which choices are presented. This research paper explores the synergistic potential of the Internet of Behaviours (IoB) and digital nudges in the financial sector as new avenues for intervention while shedding light on the IoB benefits and the digital nudges' added value in these financial settings. Afterward, it proposes an IoB-Nudges conceptual model to explain how these two concepts would be incorporated and investigates their complementary relationship and benefits for this sector. Finally, the paper also discusses key challenges to be addressed by the IoB framework.

With the rapid development of computer information technology, the work of accountants has gradually evolved into an electronic trend and the management of accounting files has also undergone great changes. Combining with the current development trend of informatisation, this paper discusses the electronic management mode of enterprise accounting files under the condition of informatisation. Combined with the latest information technology, an enterprise electronic accounting file system is established and the research and development system is compared with the traditional paper accounting file management. The results have shown that the retrieval and query time of traditional paper accounting files is close to 2 hours. After the implementation of the electronic accounting file system, the retrieval and query time of files can be completed in only 2 minutes, and the query efficiency of files has been increased by nearly 60 times.

With the rapid development of Artificial Intelligence (AI) technology, significant breakthroughs have been made in its application in many fields. Especially, in the field of enterprise human resource management and employee performance evaluation, AI has demonstrated its powerful ability to optimise and improve performance. This study explores the application of AI in enterprise human resource management and how to use AI to evaluate employee performance. The research includes analysing and comparing existing AI-driven human resource management models, evaluating how AI can help improve employee performance and leadership styles, and designing and developing human resource management computer systems for enterprise employees. Through empirical research and case analysis, this study proposes a new AI-optimised employee performance evaluation model and explores its application and effect in practice. In general, the application of AI can improve the efficiency and accuracy of enterprise human resource management, and provide new possibilities for employee performance evaluation. At present, artificial intelligence technology has been widely used in various fields of daily life, especially in corporate human resource management, providing better support for the development of enterprises.

In the perspective of institutional theory, business education is an institutional field, in which two major institutional forces are accreditations and rankings. In this context, French business schools (BS) have adopted an isomorphic response by starting to engage in research and publishing in academic journals. Studies have discussed their research as a new institutional trajectory. However, what remains unknown is how they differ from each other in such research dynamics. To bring new insights to the discussion, this quantitative study examines, over the period of 2008-2018, the evolution of research of French BS by systematically comparing the 'best' schools with other schools in all analyses. The results indicate a strong isomorphism in terms of publication quantity and productivity, scale of research collaboration and the internationalisation of research. However, these schools are heterogeneous in terms research quality and scale of international research collaboration, reflecting the diversity in their research strategy.

Aiming to improve the accuracy, recall, and F1 value of data classification, this paper proposes an applied university innovation and entrepreneurship data classification method based on the nearest neighbour criterion. Firstly, the decision tree algorithm is used to mine innovation and entrepreneurship data from applied universities. Then, dynamic weight is introduced to improve the similarity calculation method based on edit distance, and the improved method is used to realise data de-duplication to avoid data over fitting. Finally, the nearest neighbour criterion method is used to classify applied university innovation and entrepreneurship data, and cosine similarity is used to calculate the similarity between the samples to be classified and each sample in the training data, achieving data classification. The experimental results demonstrate that the proposed method achieves a maximum accuracy of 96.5% and an average F1 score of 0.91. These findings indicate a high level of accuracy, recall, and F1 value for data classification using the proposed method.

In order to improve recommendation coverage, a personalised recommendation method for English online learning resources based on improved collaborative filtering algorithm is studied to enhance the comprehensiveness of personalised recommendation for learning resources. Use matrix decomposition to decompose the user English online learning resource rating matrix. Cluster low dimensional English online learning resources by improving the K-means clustering algorithm. Based on the clustering results, calculate the backfill value of English online learning resources and backfill the information matrix of low dimensional English online learning resources. Using an improved collaborative filtering algorithm to calculate the predicted score of learning resources, personalised recommendation of English online learning resources for users based on the predicted score. Experimental results have shown that this method can effectively backfill English online learning resources, and the resource backfilling effect is excellent, and it has a high recommendation coverage rate.

Due to the problems of low clustering accuracy and efficiency in traditional similar resource clustering methods, this paper studies an English MOOC similar resource clustering method based on grey correlation. Principal component analysis was used to extract similar resource features of English MOOC, and feature selection methods was used to pre-process similar resource features of English MOOC. On this basis, based on the grey correlation method, the pre-processed English MOOC similar resource features are standardised, and the correlation degree between different English MOOC similar resource features is calculated. The English MOOC similar resource correlation matrix is constructed to achieve English MOOC similar resource clustering. The experimental results show that the contour coefficient of the proposed method is closer to one, and the clustering accuracy of similar resources in English MOOC is as high as 94.2%, with a clustering time of only 22.3 ms.

The value and utility of traditional knowledge in conserving and commercialising genetic resources are increasingly becoming apparent due to advances in biotechnology and bioprospecting. However, the absence of an international legally binding instrument within the WIPO system means that traditional knowledge associated with genetic resources is not sufficiently protected like other forms of intellectual property. This means that indigenous peoples and local communities (IPLCs) do not benefit from the commercial exploitation of these resources. The efficacy of domestic tools to protect traditional knowledge and in balancing the rights of IPLCs and intellectual property rights (IPRs) is still debated. This paper employs a doctrinal research methodology based on desktop research of international and regional law instruments and the Constitution of the Republic of South Africa, 1996, to determine the basis for balancing the protection of genetic resources and associated traditional knowledge with competing interests of IPLCs and IPRs in South Africa.

In order to overcome the problems of large error, low evaluation accuracy and long evaluation time in traditional evaluation methods of ideological and political education, this paper designs a quantitative evaluation method of ideological and political education achievements based on collaborative filtering algorithm. First, the evaluation index system is constructed to divide the teaching achievement evaluation index data in a small scale; then, the quantised dataset is determined and the quantised index weight is calculated; finally, the collaborative filtering algorithm is used to generate a set with high similarity, construct a target index recommendation list, construct a quantitative evaluation function and solve the function value to complete the quantitative evaluation of teaching achievements. The results show that the evaluation error of this method is only 1.75%, the accuracy can reach 98%, and the time consumption is only 2.0 s, which shows that this method can improve the quantitative evaluation effect.

In order to improve the speedup and recognition accuracy of the recognition process, this paper designs a data mining method based on label mapping for long-term and short-term browsing behaviour of network users. First, after removing the noise information in the behaviour sequence, calculate the similarity of behaviour characteristics. Then, multi-source behaviour data is mapped to the same dimension, and a behaviour label mapping layer and a behaviour data mining layer are established. Finally, the similarity of the tag matrix is calculated based on the similarity calculation results, and the mining results are output using SVM binary classification process. Experimental results show that the acceleration ratio of this method exceeds 0.9; area under curve receiver operating characteristic curve (AUC-ROC) value increases rapidly in a short time, and the maximum value can reach 0.95, indicating that the mining precision of this method is high.

Because of the problems of low mining precision and slow mining speed in traditional enterprise marketing investment data mining methods, a fast mining method for enterprise marketing investment databased on improved association rules is proposed. First, the enterprise marketing investment data is collected through the crawler framework, and then the collected data is cleaned. Then, the cleaned data features are extracted, and the correlation degree between features is calculated. Finally, according to the calculation results, all data items are used as constraints to reduce the number of frequent itemsets. A pruning strategy is designed in advance. Combined with the constraints, the Apriori algorithm of association rules is improved, and the improved algorithm is used to calculate all frequent itemsets, Obtain fast mining results of enterprise marketing investment data. The experimental results show that the proposed method is fast and accurate in data mining of enterprise marketing investment.

In order to overcome the problems of low efficiency, low user satisfaction and poor customer growth rate under the traditional marketing strategy, this paper studies the innovative strategy of mobile payment business marketing strategy under the internet environment. First of all, study the status quo of mobile payment business marketing in the internet environment, obtain mobile payment business data through questionnaire survey, and analyse the problems in mobile payment business marketing. Secondly, build a user profile of mobile payment business marketing, and classify user attributes, consumption characteristics and user activity through K-means clustering method; Finally, the marketing strategy is innovated from three aspects: product marketing, pricing marketing and channel marketing. The results show that the marketing benefit after the application of this strategy is 19.52 million yuan, the user satisfaction can reach 98.9%, and the customer growth rate can reach 21.3%, improving the marketing benefit of mobile payment business.

This paper presents a review of the literature on the challenges of national information and communication technology (ICT) policies in the context of African countries. National ICT policies have been aligned with socio-development agendas of African countries. However, the policies have not delivered the expected outcomes due to many challenges. Studies have been conducted in isolation to highlight the challenges in the policy process. The study used grounded theory informed literature review to holistically analyse the problems in the context of African countries. The results were categorised in the typology of the policy process to understand the challenges from a broad perspective. The problems were categorised into agenda setting, policy formulation, legal frameworks, implementation and evaluation. In addition, there were constraints related to policy monitoring in the policy phases and imbalance of power among the policy stakeholders. The review suggests areas of further research.

Using information criteria is a common method for making a decision about which model to use for forecasting. There are many different methods for evaluating forecasting models, such as MAE, RMSE, MAPE, and Theil-U, among others. After the creation of AIC, AICc, HQ, BIC, and BICc, the two criteria that have become the most popular and commonly utilised are Bayesian IC and Akaike's IC. In this investigation, we are innovative in our use of exponential weighting to get the log-likelihood of the information criteria for model selection, which means that we propose assigning greater weight to more recent data in order to reflect their increased precision. All research data is from the major stock markets' daily observations, which include the USA (GSPC, DJI), Europe (FTSE 100, AEX, and FCHI), and Asia (Nikkei).

As academia in general, and research funders in particular, place ever greater importance on data as an output of research, so the value of good research data management practices becomes ever more apparent. In response to this, the Innovative Design and Manufacturing Research Centre (IdMRC) at the University of Bath, UK, with funding from the JISC, ran a project to draw up a data management planning regime. In carrying out this task, the ERIM (Engineering Research Information Management) Project devised a visual method of mapping out the data records produced in the course of research, along with the associations between them. This method, called Research Activity Information Development (RAID) Modelling, is based on the Unified Modelling Language (UML) for portability. It is offered to the wider research community as an intuitive way for researchers both to keep track of their own data and to communicate this understanding to others who may wish to validate the findings or re-use the data.

This paper describes the FISHNet project, which developed a repository environment for the curation and sharing of data relating to freshwater science, a discipline whose research community is distributed thinly across a variety of institutions, and usually works in relative isolation as individual researchers or within small groups. As in other “small sciences”, these datasets tend to be small and “hand-crafted”, created to address particular research questions rather than with a view to reuse, so they are rarely curated effectively, and the potential for sharing and reusing them is limited. The paper addresses a variety of issues and concerns raised by freshwater researchers as regards data sharing, describes our approach to developing a repository environment that addresses these concerns, and identifies the potential impact within the research community of the system.