In order to improve the evaluation effect of current university teaching reform, a new method for evaluating the quality of university course teaching reform is proposed based on data mining algorithms. Firstly, the optimal data clustering criterion was used to select evaluation indicators and a quality evaluation system for university curriculum teaching reform was established. Next, a reform quality evaluation model is constructed using BP neural network, and the training process is improved through genetic algorithm to obtain the model weight and threshold of the optimal solution. Finally, the calculated parameters are substituted into the model to achieve accurate evaluation of the quality of university curriculum teaching reform. Selecting evaluation accuracy and evaluation efficiency as evaluation indicators, the practicality of the proposed method was verified through experiments. The experimental results showed that the proposed method can mine teaching reform data and evaluate the quality of teaching reform. Its evaluation accuracy is higher than 96.3%, and the evaluation time is less than 10ms, which is much better than the comparison method, fully demonstrating the practicality of the method.

Research on the quality evaluation of teaching reforms plays an important role in promoting improvements in teaching quality. Therefore, an evaluation method of teaching reform quality in colleges and universities based on big data analysis is proposed. A multivariate logistic model is used to select the evaluation indicators for the quality evaluation of teaching reforms in universities. And clustering and cleaning of the evaluation indicator data are performed through big data analysis. The evaluation indicator data is used as input vectors, and the results of the teaching reform quality evaluation are used as output vectors. A support vector machine model based on the whale algorithm is built to obtain the relevant evaluation results. Experimental results show that the proposed method achieves a minimum recall rate of 98.7% for evaluation indicator data, the minimum data processing time of 96.3 ms, the accuracy rate consistently above 97.1%.

In order to improve the comprehensiveness of evaluation results and reduce errors, a teaching reform quality evaluation method based on fuzzy comprehensive evaluation is proposed. Firstly, on the premise of meeting the principles of indicator selection, factor analysis is used to construct an evaluation indicator system. Then, calculate the weights of various evaluation indicators through fuzzy entropy, establish a fuzzy evaluation matrix, and calculate the weight vector of evaluation indicators. Finally, the fuzzy cognitive mapping method is introduced to improve the fuzzy comprehensive evaluation method, obtaining the final weight of the evaluation indicators. The weight is multiplied by the fuzzy evaluation matrix to obtain the comprehensive evaluation result. The experimental results show that the maximum relative error of the proposed method's evaluation results is about 2.0, the average comprehensive evaluation result is 92.3, and the determination coefficient is closer to 1, verifying the application effect of this method.

In order to overcome the problems of low evaluation accuracy and long evaluation time in traditional teaching quality evaluation methods, a method of English distance information teaching quality evaluation based on decision tree classification algorithm is proposed. Firstly, construct teaching quality evaluation indicators under different roles. Secondly, the information gain theory in decision tree classification algorithm is used to divide the attributes of teaching resources. Finally, the rough set theory is used to calculate the index weight and establish the risk evaluation index factor set. The result of teaching quality evaluation is obtained through fuzzy comprehensive evaluation method. The experimental results show that the accuracy rate of the teaching quality evaluation of this method can reach 99.2%, the recall rate of the English information teaching quality evaluation is 99%, and the time used for the English distance information teaching quality evaluation of this method is only 8.9 seconds.

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.

This longitudinal, quasi-experimental study investigated students’ cognitive personality type using the Myers-Briggs personality Type Indicator (MBTI) in Internet-based Online and Face-to-Face (F2F) modalities. A total of 1154 students enrolled in 28 Online and 32 F2F sections taught concurrently over a period of fourteen years. The study measured whether the sample is similar to the national average percentage frequency of all 16 different personality types; whether specific personality type students preferred a specific modality of instructions and if this preference changed over time; whether learning occurred in both class modalities; and whether specific personality type students learned more from a specific modality. Data was analyzed using regression, t-test, frequency, and Chi-Squared. The study concluded that data used in the study was similar to the national statistics; that no major differences in preference occurred over time; and that learning did occur in all modalities, with more statistically significant learning found in the Online modality versus F2F for Sensing, Thinking, and Perceiving types. Finally, Sensing and Thinking (ST) and Sensing and Perceiving (SP) group types learned significantly more in Online modality versus F2F.

Aim/Purpose: Medical images are very sensitive data that can be transferred to medical laboratories, professionals, and specialist for referral cases or consultation. Strict security measures must be utilized to keep these data secured in computer networks when transferred to another party. On a daily basis, unauthorized users derive ways to gain access to sensitive patient medical information. Background: One of the best ways to which medical image could be kept secured is through the use of quantum cryptography Methodology : Applying the principles of quantum mechanics to cryptography has led to a remarkable new dimension in secured network communication infrastructure. This enables two legitimate users to produce a shared secret random bit string, which can be used as a key in cryptographic applications, such as message encryption and authentication. Contribution: This paper can make it possible for the healthcare and medical professions to construct cryptographic communication systems to keep patients’ transferred data safe and secured. Findings: This work has been able to provide a way for two authorized users who are in different locations to securely establish a secret network key and to detect if eavesdropping (a fraudulent or disruption in the network) has occurred Recommendations for Practitioners: This security mechanism is recommended for healthcare providers and practitioners to ensure the privacy of patients’ medical information. Recommendation for Researchers: This paper opens a new chapter in secured medical records Impact on Society Quantum key distribution promises network security based on the fundamental laws of quantum mechanics by solving the problems of secret-key cryptography . Future Research: The use of post-quantum cryptography can be further researched.

Aim/purpose The purpose of this article is to discuss the psychosocial and emotional outcomes of an introductory health science workshop designed to support and assist incoming health science students before starting their university study.   Background For the past two decades, a South Australian university offered an on-campus face to face workshop titled ‘Preparation for Health Sciences’ to incoming first-year students from eleven allied health programs such as Nursing, Physiotherapy and Medical Imaging. While many were locals, a good number came from regional and rural areas, and many were international students also. They consisted of both on-campus and off-campus students.   The workshop was created as a new learning environment that was available for students of diverse age groups, educational and cultural backgrounds to prepare them to study sciences. The content of the four-day workshop was developed in consultation with the program directors of the allied health programs. The objectives were to: introduce the assumed foundational science knowledge to undertake health sciences degree; gain confidence in approaching science subjects; experience lectures and laboratory activities; and become familiar with the University campus and its facilities. The workshop was delivered a week before the orientation week, before first-year formal teaching weeks. The topics covered were enhancing study skills, medical and anatomical terminology, body systems, basic chemistry and physics, laboratory activities, and assessment of learning.   Methodology In order to determine the outcomes of the workshop, a survey was used requiring participants to agree or disagree about statements concerning the preparatory course and answer open-ended questions relating to the most important information learned and the best aspects of the workshop. Several students piloted this questionnaire before use in order to ascertain the clarity of instructions, terminology and statements. The result of the 2015-2018 pre- and post-evaluation showed that the workshop raised confidence and enthusiasm in commencing university and that the majority considered the workshop useful overall. The findings of the survey are drawn upon to examine the psychosocial and emotional impacts of the workshop on participants. Using secondary qualitative analysis, the researchers identified the themes relating to the psychosocial and emotional issues conveyed by the participants.   Contribution The contributions of the article are in the areas of improving students’ confidence to complete their university degrees and increasing the likelihood of academic success. Findings Of the 285 students who participated in the workshops from 2015 to 2018, 166 completed the survey conducted at the conclusion of the initiative, representing a 58% response rate. The workshops achieved the objectives outlined at the outset. While there were many findings reported (Thalluri, 2016), the results highlighted in this paper relate to the psychosocial and emotional impacts of the workshop on students. Three themes emerged, and these were Increased preparedness and confidence; Networking and friendships that enhanced support, and Reduced anxiety to study sciences. Some drawbacks were also reported including the cost, time and travel involved. Recommendations for practitioners Students found the introductory workshop to be psychosocially and emotionally beneficial. It is recommended that the same approach be applied for teaching other challenging fields such as mathematics and physics within the university and in other contexts and institutions. Recommendations for researchers Improving and extending the workshop to provide greater accessibility and autonomy is recommended. A longitudinal study to follow up the durability of the workshop is also proposed. Impact on society The impacts in the broader community include: higher academic success for students; improved mental health due to social networking and friendship groups and reduced anxiety and fear; reduced dropout rate in their first year; greater potential to complete educational degrees; reduced wastage in human and financial resources; and increased human capital. Future research Addressing the limitations of cost, time and travel involved, and following-up with the participants’ academic and workplace performance are future directions for research.

Aim/Purpose. The problem statement in the proposed study focuses on that, despite the growing recognition that teenagers need to undergo security awareness training, little is known about the impacts security training experts believe implementing a mandatory gamified security awareness training curriculum in public middle schools will have on the long-term security behavior of students in Texas. Background. This study was guided by the research question: What are the impacts security training experts believe implementing a mandatory gamified security aware-ness training curriculum in public middle schools will have on the long-term security behaviors of students in Texas? The study gathers opinions from experts on the impacts of security awareness training on students. Methodology. Our research used semi-structured interviews with twelve experts chosen through the use of purposive sampling. The population for the study consisted of experts in the fields of security awareness training for and teaching middle school-aged children. Candidates were recruited through the Cyber-Texas Foundation and snowball sampling techniques. Contribution. The research contributed to the body of knowledge by using interviews to explore the impacts of security awareness training on middle school students based on the opinions and views of the teachers and instructors who work with middle school students. Findings. The findings of this study demonstrate that middle school is an ideal time to provide cybersecurity training and will impact student behaviors by making them more conscious of cyber threats and preparing them to be more tech-savvy professionals. The research also showed that well-designed cybersecurity games with real-world application combined with traditional teaching techniques can help students develop positive habits. The research also suggests that teachers possess the skills to teach cybersecurity classes and the classes can be integrated into the current school day without the need for any significant changes to existing daily schedules. Recommendations for Practitioners. A well-design gamification-based curriculum implemented in Texas Middle Schools, combined with traditional teaching techniques and repeated over an extended time period, will impact students’ behaviors by making them more able to recognize and respond to cyber risks and will transform them into more secure and tech-savvy members of society. Recommendations for Researchers. The research shows middle school instructors and technology experts believe the implementation of a security awareness training program in middle schools is both possible and practical, while also beneficial to the students. The recommendation is to encourage researchers to explore ways to build curricula and games capable of appealing to students and implementing the instruction into school programs. Impact on Society. Demonstrating that training provided in middle school will make lasting impacts and improvements to student behaviors benefits children and their families in the short-term and workplaces in the long-term. The development of a more security-conscious workforce can reduce the significant number of data breaches and cyber attacks resulting from the poor security habits of companies’ users. Future Research. Future research that will add significant value to the body of knowledge includes testing the effectiveness of habit-shaping games to determine whether existing long-term games maintain student interest. Qualitative studies could interview parents of teenagers using habit-shaping games to determine the effectiveness of the applications. Another qualitative study could interview teachers to determine how teachers’ ages affect their comfort level teaching technology classes. Both studies could provide valuable insights into how to implement security awareness training in schools.

Collaboration technologies enable people to communicate and use information to make organizational decisions. The United States Navy refers to this concept as information dominance. Various collaboration technologies are used by the Navy to achieve this mission. This qualitative descriptive study objectively examined how a matrix oriented Navy activity perceived an implemented collaboration technology. These insights were used to determine whether a specific collaboration technology achieved a mission of information dominance. The study used six collaboration themes as a foundation to include: (a) Cultural intelligence, (b) Communication, (c) Capability, (d) Coordination, (e) Cooperation, and (f) Convergence. It was concluded that collaboration technology was mostly perceived well and helped to achieve some levels of information dominance. Collaboration technology improvement areas included bringing greater awareness to the collaboration technology, revamping the look and feel of the user interface, centrally paying for user and storage fees, incorporating more process management tools, strategically considering a Continuity of Operations, and incorporating additional industry best practices for data structures. Emerging themes of collaboration were collected to examine common patterns identified in the collected data. Emerging themes included acceptance, awareness, search, scope, content, value, tools, system performance, implementation, training, support, usage, structure, complexity, approach, governance/configuration management/policy, and resourcing.

Aim/Purpose: This research aims to evaluate models from meta-learning techniques, such as Riemannian Model Agnostic Meta-Learning (RMAML), Model-Agnostic Meta-Learning (MAML), and Reptile meta-learning, to obtain high-quality metadata. The goal is to utilize this metadata to increase accuracy and efficiency in identifying chili varieties in smart agriculture. Background: The identification of chili varieties in smart agriculture is a complex process that requires a multi-faceted approach. One challenge in chili variety identification is the lack of a large and diverse dataset. This can be addressed using meta-learning techniques, which allow the model to leverage knowledge learned from other related tasks or artificially expand the dataset by applying transformations to existing data. Another challenge is the variation in growing conditions, which can affect the appearance of chili varieties. Meta-learning techniques can help address this challenge by allowing the model to adapt to variations in growing conditions with task-specific embeddings and optimizations. With the help of meta-learning techniques, such as data augmentation, data characterization, selection of datasets, and performance estimation, quality metadata for accurate identification of chili varieties can be achieved even in the presence of limited data and variations in growing conditions. Furthermore, the use of meta-learning techniques in chili variety identification can also assist in addressing challenges related to the computational complexity of the task. Methodology: The research approach employed is quantitative, specifically comparing three models from meta-learning techniques to determine which model is most suitable for our dataset. Data was collected from the variety assembly garden in the form of images of chili leaves using a mobile device. The research successfully gathered 1,974 images of chili leaves, with 697 images of large red chilies, 649 images of curly red chilies, and 628 images of cayenne peppers. These chili leaf images were then processed using augmentation techniques. The results of image data augmentation were categorized based on leaf characteristics (such as oval, lancet, elliptical, serrated leaf edges, and flat leaf edges). Subsequently, training and validation utilized three models from meta-learning techniques. The final stage involved model evaluation using 2-way and 3-way classification, as well as 5-shot and 10-shot learning scenarios to select the dataset with the best performance. Contribution: Improving classification accuracy, with a focus on ensuring high-quality data, allows for more precise identification and classification of chili varieties. Enhancing model training through an emphasis on data quality ensures that the models receive reliable and representative input, leading to improved generalization and performance in identifying chili varieties. Findings: With small collections of datasets, the authors have used data augmentation and meta-learning techniques to overcome the challenges of limited data and variations in growing conditions. Recommendations for Practitioners: By leveraging the knowledge and adaptability gained from meta-learning, accurate identification of chili varieties can be achieved even with limited data and variations in growing conditions. The use of meta-learning techniques in chili variety identification can greatly improve the accuracy and reliability of the identification process. Recommendation for Researchers: Using meta-learning techniques, such as transfer learning and parameter optimization, researchers can overcome challenges related to limited data and variations in growing conditions in chili variety identification. Impact on Society: The findings from this research can help identify superior chili seeds, thereby motivating farmers to cultivate high-quality chilies and achieve bountiful harvests. Future Research: We intend to verify our approach on a more extensive array of datasets and explore the implementation of more resilient regularization techniques, going beyond image augmentation, within the meta-learning techniques. Furthermore, our goal is to expand our research to encompass the automatic learning of parameters during training and tackle issues associated with noisy labels. Building on the insights gained from our observed outcomes, a future objective is to enhance the refinement of model-agnostic meta-learning techniques that can effectively adapt to intricate task distributions with substantial domain gaps between tasks. To realize this aim, our proposal involves devising model-agnostic meta-learning techniques specifically designed for multi-modal scenarios.

In recent years, schools (as well as universities) have added cyber security to their computer science curricula. This topic is still new for most of the current teachers, who would normally have a standard computer science background. Therefore the teachers are trained and then teaching their students what they have just learned. In order to explore differences in both populations’ learning, we compared measures of software quality and security between high-school teachers and students. We collected 109 source files, written in Python by 18 teachers and 31 students, and engineered 32 features, based on common standards for software quality (PEP 8) and security (derived from CERT Secure Coding Standards). We use a multi-view, data-driven approach, by (a) using hierarchical clustering to bottom-up partition the population into groups based on their code-related features and (b) building a decision tree model that predicts whether a student or a teacher wrote a given code (resulting with a LOOCV kappa of 0.751). Overall, our findings suggest that the teachers’ codes have a better quality than the students’ – with a sub-group of the teachers, mostly males, demonstrate better coding than their peers and the students – and that the students’ codes are slightly better secured than the teachers’ codes (although both populations show very low security levels). The findings imply that teachers might benefit from their prior knowledge and experience, but also emphasize the lack of continuous involvement of some of the teachers with code-writing. Therefore, findings shed light on computer science teachers as lifelong learners. Findings also highlight the difference between quality and security in today’s programming paradigms. Implications for these findings are discussed.

Teaching object-oriented programming to students in an in-classroom environment demands well-thought didactic and pedagogical strategies in order to guarantee a good level of apprenticeship. To teach it on a completely distance learning environment (e-learning) imposes possibly other strategies, besides those that the e-learning model of Open University of Portugal dictates. This article analyses the behavior of the students of the 1st cycle in Computer Science while interacting with the object-oriented programming (OOP) discipline available to them on the Moodle platform. Through the evaluation of the level of interaction achieved in a group of relevant selected actions by the students, it is possible to identify their relevancy to the success of the programming learning process. Data was extracted from Moodle, numerically analyzed, and, with the use of some charts, behavior patterns of students were identified. This paper points out potential new approaches to be considered in e-learning in order to enhance programming learning results, besides confirming a high level of drop-out and a low level of interaction, thus finding no clear correlation between students’ success and the number of online actions (especially in forums), which reveals a possible failure of the main pillar on which the e-learning model relies.

Web portals have been used as information products to deliver personalized, feature-rich, and flexible information needs to Internet users. However, all portals are not equal. Most of them have relatively a small number of visitors, while a few capture the majority of surfers. This study seeks to uncover the factors that contribute the perceived quality of a general portal. Based on 21 factors derived from an extensive literature review on Information Product Quality (IPQ), web usage, and media use, an experimental study was conducted to identify the factors that are perceived by web portal users as most relevant. The literature categorizes quality factors of an information product in three dimensions: information, physical, and service. This experiment suggests a different clustering of factors: Content relevancy, Communication interactiveness, Information currency, and Instant gratification. The findings in this study will help developers find a more customer-oriented approach to developing high-traffic portals.

Aim/Purpose: Professional companies selling persuasive-communication services via the World Wide Web need to be exemplars of effective informing practices. Their credibility is at risk if their websites do not excel in marketing message and use of medium. Their unique brands need to be expressed through website technology and content, or they cannot compete successfully. Background: Compares marketing communication consultants’ websites with expert criteria. Methodology: Content analysis of 40 advertising agency websites. Contribution: Links an evaluation of advertising agency compliance with expert website criteria to established branding constructs. Findings: Most small advertising agencies could improve their brand reputations through better compliance with experts’ recommended website design and content criteria. Recommendations for Practitioners: A hierarchy of recommendations for practitioners is offered, addressing ease and importance. Impact on Society: Clarity and credibility of message and medium improve our ability to practice effective informing. Future Research: Explore online communications of specialized populations such as digital marketing experts.

Aim/Purpose: The aim of this paper is to identify the possibilities for reducing the second and third levels of the digital divide (or inequality) through conscious application of digital technologies, especially through the promotion of digital means for information, enlightenment, and entertainment. Background: This article reviews studies carried out between 2000 and 2017, which investigate the social benefits of digital technology use for disadvantaged user groups and, especially, of their outcomes in terms of increasing digital skills and motivation to use information and communication technologies. Methodology: The literature review of the selected texts was carried out using thematic content analysis. The coding scheme was open but based on the theory of three levels of digital divide by van Dijk. Contribution: The results of the analysis show the difficulties related to the attempts of reducing the digital divide on the second and third level using only digital interventions, but also reveal the potential of these interventions. Findings: The literature review confirms the connection of different levels of digital divide with other relational and structural inequalities. It provides insights into the strengths and weaknesses of digital interventions aimed at the reduction of digital inequalities. Their success depends on the consideration of the context and participants needs as well as on carefully planned strategies. The paper summarizes and demonstrates the shortcomings and limitations of poorly designed interventions in reducing the digital divide but emphasizes the possibilities of raising the motivation and benefits for the participants of strategically planned and implemented projects. Recommendations for Practitioners: While planning a digital intervention with the aim of reducing digital inequalities, it is necessary to assess carefully the context and the needs of participants. Educational interventions should be based on suitable didactic and learning strategies. Recommendation for Researchers: More research is needed into the factors that increase the effectiveness of digital interventions aimed at reducing the digital divide. Future Research: We will apply the findings of this literature review in an intervention in the context of Lithuanian towns of different sizes.