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This paper used descriptive analysis and nonparametric test to explore the significant difference between Indian and Hungarian students' attitudes. A systematic data analysis was performed with primary samples (314) gathered from Indian and Hungarian universities. We proposed two significant hypotheses to achieve the main objective of the study. On the one hand, we did not find a significant difference for “Informative and quality based study” and “Confidence and motivation”, and another hand, both country students, have dissimilar thinking towards “Independent learning”, “Admission/job placement/examination”, “Future acceptance for 21st century” and “Rapid deliver and share content”. The descriptive analysis also proved that both country students have a positive attitude towards technology. Results of the paper also proved that Hungarian students think more positively as compared to Indian students. We proposed a comparative technique to be implemented as a real-time web module with the university website's integration. This method may overcome the traditional offline differential approaches.

Humanitarian logistics involve planning, acting and controlling situations in which vulnerable people are involved. Simulation techniques can successfully represent the dynamics of disasters, characterized by situations of complexity and uncertainties. This work aims to review papers that apply simulation techniques to solve problems that arise in disaster situations. To accomplish this, we analysed 33 papers published from 2010 to 2019, indexed in Scopus or Web of Science databases, that used simulation techniques to solve humanitarian problems. The descriptive analysis conducted found out that there is a small but increasing number of papers over time. Discrete events simulation and system dynamics are the most used techniques. From the content analysis we find that most research focuses on short term decisions and solving problems related to post-disaster response in unforeseen events. However, topics such as evacuation and transportation of victims are still unexplored.

The evaluation system of scientific research results in universities is an important facet of the field of humanities and social science. It is also an important index of measuring the scientific research level of universities and teachers’ scientific research competence. In the form of questionnaires, this paper thoroughly investigates the status quo of this evaluation system, including the evaluation of papers, works and application results, and also the advantages and shortages of new evaluation methods like the representative system, the third-party evaluation. The paper uses SPPS software to conduct descriptive analysis and inferential analysis on the questionnaire and find out the key factors of scientific research evaluation system. On the basis of the questionnaires, this paper also puts forward some suggestions on improving the evaluation system of scientific research results.

Bitcoin is a decentralized cryptocurrency that has led to a new trading model. It allows people to trade directly without going through financial institutions such as banks. This model results in many transactions that occur outside the law and beyond ethical constraints. In such an anonymous environment, the large number of entities using Bitcoin, and the huge scale of the Bitcoin trading network make it difficult for users to have a rough idea of the entire trading network before transaction. Thus, it is of great theoretical and practical significance to summarize the research problems, achievements and possible research trends based on Bitcoin data analysis. Therefore, in this paper we review the literatures about data analysis on Bitcoin transaction entities. Starting from the relevant conceptual framework of Bitcoin, this paper divides the existing research models into three categories, heuristic algorithm identification of entities, transaction descriptive statistics and network analysis, and visual system analysis. By analyzing the transaction entity, Bitcoin transaction data can be processed in a manner which is similar to an account, such as a bank or credit card, thereby achieving the purpose of in-depth analysis of all transaction activities related to the account entity. Finally, we summarize the data analysis results of Bitcoin transaction network and prospects of the future research directions.

As an economic organization, an economic globalization development company plays an important role in economic development. The company not only promotes the healthy development of the economy, but also eases the problem of excessive employment pressure in the process of participating in market competition. Funds play a vital role in the survival and development of enterprises. Therefore, the financial issue of listed companies is an important subject worthy of study. This article analyzes the financing methods of listed companies in the infrastructure construction industry and related influencing factors, and conducts actual data testing, and makes recommendations based on the analysis results. According to my country's current main financing methods, this paper collects 6 years of data from 27 companies in the infrastructure construction industry, and uses data analysis software such as SPSS to study and analyze the current financing forms and the correlation of influencing factors. This paper mainly uses descriptive statistics and multiple regression methods to study.

Class activities play an important role in both of student learning and building relationships among class participants. Many of construction management programs implement various class activities in their classes. This paper presents the quantitative analysis of various class activities in a statics class in a construction management from students' perspective. In this study, there are about 12 class activities which are performed in and outside of class by all class participants. Each activity was evaluated in terms of its impact on four areas of class environment, building relationships between an instructor and students, between students, and student learning. This paper addresses the most preferred activities in terms of four areas. Based on descriptive statistics, various class activities have a huge influence on four areas and any activities related to student learning and building relationships are highly preferred. With respect to students' final course grades, there are clear differences in preference of class activities between different groups of final grades. This information will be useful for the practitioners in managing and improving student learning and class environment through various class activities.

The online car-hailing service enriches people’s travel options. The analysis on factors influencing online car-hailing demand could help improve the operation efficiency and service level. In this paper, we conduct an in-depth analysis for the data of SSV (Shenzhou Special Vehicle) from both perspectives of qualitative and quantitative. In the qualitative side, we present a descriptive analysis to depict customer behaviors through the characteristics of travel demand, distance, and weather conditions. In the other quantitative side, we propose two multiple linear regression models to analyze the factors influencing online car-hailing demand. After that, we further provide several management insights for operation management of online car-hailing service. Although this paper is a specific case study, it provides several effective suggestions and modeling references for the similar problems.

Based on the Shared mind model, this paper tries to find out and verify the factors that influence the teamwork ability of college sports associations. Based on some theoretical research, this paper proposes three factors that may affect teamwork ability: communication, trust and the use of information technology. This paper collects data through questionnaire survey, and USES SPSS statistical analysis software to carry out descriptive statistics and hypothesis testing on the data. The results show that communication, trust and the use of information technology have positive effects on the teamwork ability of college sports associations. To effectively improve team collaboration, team members should actively use information technology, such as chat sharing software, to assist the team in completing tasks. At the same time, team members should also enhance the level of trust and form good communication.

In this paper we present a bibliometrics analysis of the 10 editions of the Global Engineering Education Conference (EDUCON). EDUCON is a conference of the IEEE Education Society for region 8. It started in 2010 and since then it is organized annually. With this study we intend to analyze the growth of the conference, namely through the evolution of the number of submissions/publications, and the number of citations per article. We also intend to analyze the number of references per article. A descriptive statistics of these indicators is done along the paper. With an initial set of 10 editions, it will be possible to investigate the impact of future editions, by performing a sliding-frame analysis, i.e. replacing the oldest by the newest edition and analyzing how this impacts the presented charts.

After the comprehensive attention, organizations have turned out to be progressively informed of the real worth of knowledge management. Surely, organizational learning and knowledge assets are recognized as an efficacious instrument for its competitiveness. However, another significant factor in organizational knowledge which needs recognition in this modern debate that continuous learning is not always the case for being competitiveness, it is also about unlearning the organizational knowledge at the right times. Despite the continuous expansion of organization unlearning (OL) studies, there is still a lack of a clear notion of the organizational Unlearning. The study aims to investigate how has the research work in the concept of organizational unlearning evolved in the knowledge management (KM) area? What factor influence organizations to unlearn their knowledge? A systematic literature review is employed on the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Providing the Inclusion/Exclusion criteria, 26 papers are determined from the Scopus database for the data synthesis. Research finds that roughly 85% of the selected papers are published within ten years. By following all the explanation, common understanding of organizational unlearning is the creation of the ground to facilitate the new knowledge and its application. Unlearning can be unintentional or deliberate, harmful or profitable to the organization, but in both circumstances, it has a direct impact on organizational competitiveness. The study identifies that individual role is considered as an important enabler of organizational unlearning and their role such as manager is vital to manage both unintentional & intentional unlearning. The descriptive analysis also shows a lack of empirical studies in the organizational unlearning field.

Currently, due to the globalization phenomenon and technological evolution, we are facing a new challenging set of paradigms encompassing social, industrial, financial and cultural issues. Hence, it is a difficult task to anticipate the new demands of the market concerning the most appreciated skills in the forthcoming workforce. This paper intends to report the sets of skills that comprise the desirable profile of the quality professional in the 21stcentury. To meet this purpose, a worldwide online survey was held online throughout the first quarter of 2018, assessing (adopting a 5-point Likert scale) the importance of 27 skills identified in relevant literature. A total of 319 valid answers, originated from 61 different countries, were collected and summarized through descriptive statistics. The results suggest that seven sets of skills (appreciated skills to forthcoming quality professionals) comprise the profile of the quality professional in the 21st century. Thus, professionals aiming the quality leader role may now tailor their skills based on the information provided in this paper. In addition, companies can use these results to specify the dimensions that their human resources should develop.

This innovative practice work-in-progress paper presents our approach of using data analytics as an alternative solution to eliminate grading bias. Effective grading involves maintaining consistency among all students, irrespective of gender, race, ethnic background, and prior performance. Related work in this area has shown that prior work submitted by a student influences future scores given. Some of the popular methods used to eliminate grading bias involves grading rubrics, anonymous or blind grading, and/or computerized auto-graders. In spite of all these methods, some types of grading such as essays and projects still require subjective grading, which opens the door to conscious or unconscious bias.Given the student data available regarding performance, colleges and universities are turning to analytic solutions to extract meaning from huge volumes of student data to help improve retention, graduation, and student performance rates. While looking at all the analytic options can be a daunting task, these analytic options can be categorized at a high level into three distinct types: (a) Descriptive Analytics, which use data aggregation and data mining to provide insight into the past and answer “What has happened?”; (b) Predictive Analytics, which use statistical models and forecasts techniques to understand the future and answer “What could happen?”; and (c) Prescriptive Analytics, which use optimization and simulation algorithms to advise on possible outcomes and answer “What should we do?” In this paper, we use Prescriptive Analytics to provide students with advice on what action to take, based on a tool which predicts each student’s performance.

This work-in-progress research paper stems from a larger project where we are developing and gathering validity evidence for an instrument to measure undergraduate students’ perceptions of support in science, technology, engineering, and mathematics (STEM). The refinement of our instrument functions to extend, operationalize, and empirically test the model of co-curricular support (MCCS). The MCCS is a conceptual framework of student support that explains how a student’s interactions with the professional, academic and social systems within a college could influence their success more broadly in an undergraduate STEM degree program. Our goal is to create an instrument that functions diagnostically to help colleges effectively allocate resources for the various financial, physical, and human capital support provided to undergraduate students in STEM. While testing the validity of our newly developed instrument, an analysis of the data revealed differences in perceived support among College of Engineering (COE) and College of Science (COS) students. In this work-in-progress paper, we examine these differences at one institution using descriptive statistics and Welch’s t-tests to identify trends and patterns of support among different student groups.

The government of Saudi Arabia has adopted MGovernment for the effective delivery of services. One advantage that it offers is unique opportunities for real-time and personalized access to government information and services. However, a low adoption rate of m-Government services by citizens is a common problem in Arab countries, including Saudi Arabia, despite the best efforts of the Saudi government. Therefore, this paper explores the determinants of citizens’ intention to adopt and use m-Government services, in order to increase the adoption rate. This study was based on the Mobile Government Adoption and Utilization Model (MGAUM) that was developed for the purpose. Data was collected, and the final sample consisted of 1,286 valid responses. The descriptive analysis presented in this paper indicates that all the proposed factors in our MGAUM model were statistically significant in influencing citizens’ intention to adopt and use m-Government services.

Large number of fields uses augmented reality in order to enhance the efficiency and effectiveness of processes. Similarly, augmented reality is an emerging trend in the supply chain management field. Growth and rapid progress in the use of augmented reality in supply chain and logistics industry are growing remarkable manner in during past few years. However, application of augmented reality technology is in infant stage in warehouse operations in the Sri Lankan context. Therefore, this paper aims to explore the possibilities of augmented reality applications and its dynamics in warehouse operations. Research paper first presents an integrated model of augmented reality applications arising from a comprehensive literature review and qualitative interviews. A questionnaire-based survey was then undertaken to test the possibilities of augmented reality applications. This mixed method research used 30 respondents. Data analysis were comprising of descriptive analysis, reliability and validity test. Finally, data were tested using Henry Garret Ranking method. Research result indicates that even though the technology is not mature enough at the moment, the potential benefits are enormous that make a promising future. Future directions for further research are discussed. This research provides a starting point for researching the use augmented reality in warehouse operations in the Sri Lankan context.

Through studying the highly diverse profile of household and dwelling energy consumption in EU countries, this paper descriptively analyzes the role of electrified heating in the so called fourth generation district heating systems. Then, the paper studies the critical technologies to realize such systems, based on our review on commercial projects and researches in the German market.

Recent student protests in South Africa have called for a decolonized curriculum and for free higher education. As student protests intensified for fees to fall, the management of many universities mandated that online courses must rise in order to negate some of the negative consequences of these protests. However, many university academics started out as specialists within their own field, having little or no instructional design skills which are required to successfully develop such online courses. The purpose of this paper is to present academic participant perspectives of a solution, or intervention, that uses the rapid prototyping instructional design model to develop online courses in a quick and efficient manner. The intervention addresses three skillsets which are discussed in the paper, namely instructional design, online facilitation and educational technology. An exploratory design was implemented with descriptive statistics involving quantitative analysis of the collected data to determine the participants' perspective on their skills gained. An online survey tool was used to collect the data. Most of the participants felt that after completing the intervention they were able to systematically develop an online course (67%), were confident that they could formulate learning outcomes (75%) and communicate with students online (83%).

Real estate systems hold a lot of information that can be mined for useful patterns. This paper aims to build a Key Performance Indicators (KPIs) to evaluate the best feature for classification of real estate data. ANalysis Of VAriance (ANOVA) and correlation coefficients are used in this paper to evaluate the key features in the database. The main objective of KPIs is to select the most relevant quantitative factors and finding the various key features such as Length, Breadth, Area, Collector rate and Commission per sale with House price index attribute. Extensive analysis have been done to evaluate a set of metrics that enable the performance management of a real estate system. It has been observed that the selected features rapidly increases with the house price index, therefore are more useful to improve the accuracy of machine learning models.

The recent diverge in Data Science research studies towards Geoinformatics is well justified. Geographical dimensions are constantly captured by data collection engines in different shapes and forms. Data mining methods however, expect highly structured and organized datasets. Organizing data is especially challenging for geographical forms. Another critical form of data that is difficult to structure is text. Subsequently, the continuous rise in content through social media and the Internet of Things (IoT) has made Text Mining more relevant than ever before. Geographical data are mostly presented (if longitudes and latitudes data are not present) through text (Country, State, City). This paper is focused on mining through bodies of text that have a direct relevance to a geographical location, and extracting knowledge from that text for a better understanding of a certain chosen political/social/economical topic. In the novel method presented in this manuscript, geographical data is coupled with textual data to enable insights and correlations that are not possible otherwise. Additionally, the method mines for sentiments and assigns quantifiable sentiment scores. In geo-enabled mining of text, issues such as Emojis and stop-words (to, from, the, and) pose a technical challenge; the method presented in this paper addresses that as well. Besides the mentioned descriptive data insights (user's location, tweet test, age, language, gender, and number of tweets), this paper introduces a method that describes geographical patterns within text (using cosine distance measures and hierarchical clustering). Finally, experimental work is presented, results are recorded, evaluated, and used to define conclusions and future directions.

The following paper offers an insight about a pilot study concerning the readiness level of companies in terms of Industry 4.0. The pilot study was conducted in Germany and France with the aim of evaluating the possible gap in development between industries in both countries. This paper focuses on the results in Germany. The methodology was designed according to the research onion and an inductive approach was selected. A maturity scoring model was created to evaluate the current level of readiness of Industry 4.0. The companies were classified according to enterprise size from small to big and rated in different dimensions from level zero to six (outsider to top performer). A survey method was chosen and realized through personal interviews or conference calls. The evaluation was carried out using the method of descriptive statistics as the data was only a sample out of a larger population. For the study 24 German enterprises were interviewed focusing on six different dimensions named as product development process, steering and control, manufacturing and operations, smart services, process organization and big data. As an overall result it can be determined that level two (intermediate) was the highest average level which has been achieved relating to the enterprise size. The highest rating was reached in the field of big data. However, it has to be noted that there is large potential in German companies to reach a higher readiness level in terms of Industry 4.0.

International audience; This paper proposes an assessment of the relationships between freight trip generation (FTG) rates and accessibility. First, the paper introduces the context and motivates the proposed framework. Second, the framework is presented, which combines an FTG model, two accessibility indicators and a linear regression to relate them. The FTG model is presented and calibrated on the basis of surveyed data. The two accessibility indicators proposed here follow a potential and an exponential mathematical relationship, respectively. Third, the framework is applied to the urban area of Lyon, were no surveys are available. The FTG rates are estimated and the accessibility indexes calculated, then both descriptive statistics and linear regression analyses are performed. The potential accessibility index seems to have a better correlation to FTG and could be a good decision support indicator. This work, which remains exploratory, finishes by proposing practical implications and further development areas.

Personality indicates the behavioural and emotional characteristics of an individual and this paper helps to analyse whether the students' academic performance is strongly influenced by their personality based characteristics by conducting survey among the engineering students of different branch of study in Thanjavur District. The major study objectives are to identify the factors that are significant and to which extent the factors of personality and academic performance measures are correlated. Here, in this paper independent factors of personality which is considered to identify the relationship among demographic factors, independent factors and academic performance of students are Extroversion, Agreeableness, Conscientiousness, Emotional stability and Openness. Data has been collected from engineering students among different branches with a sample size of 75 and can be examined with the help of statistical tools like descriptive analysis, regression & Chi-square test. The results reveal that the factors found that, independent factors of personality like conscientiousness, agreeableness and openness influences the academic performance of student.

Big data analytics assist the organizations and business managers to make decision more efficient by analyzing given data and applying some algorithms upon it. Data analytics is the process in which, we find patterns and draw conclusions about the information with the help of raw data. Mainly, three kinds of analytical approaches are existent for analysis of dataset, including descriptive analytics, predictive analytics and prescriptive analytics. In this paper, authors' focused on comparative analysis of descriptive analytical algorithms, along with their different approaches and applications in different areas. In addition to this, various types of data analytics have also been elaborated, in this paper.

This paper explores the opinions and the level of awareness of undergraduate students on environmental issues, renewable energy and climate change. With the aim of getting a closer insight, a survey was conducted among the students who studied renewable energy as one of the core courses offered to all freshmen enrolled in various disciplines at Habib University. This paper is an attempt to comprehend the effectiveness of this course in terms of behavior change, basic understanding of scientific phenomena related to energy and to develop a sense of responsibility. The methodology used was questionnaire based qualitative analysis using descriptive statistics. The results of survey indicate that renewable energy education enhances the students' perception and attitudes towards renewable energy as an alternative energy source to fossil fuels. It also enhances their level of understanding and willingness to adopt renewable and sustainable energy practices. The findings reveal that a student's awareness level towards the most challenging issues is increased up to approximately 80% after taking the energy course at Habib University. The majority of students (65%), agreed to opt for renewable energy despite higher implementation costs and 87 % showed an interest to install solar energy plans in their homes.

The adaptive tutoring system is one of the applications in teaching and learning processes, providing worldwide accessibility and all-day availability. Apparently, studies on user preference across gender for adaptive tutoring systems remain scarce among university users in Malaysia. With the aim of describing the differences between genders in user preferences for the method of gaining information, online games, online movies, online music, pictures, as well as colors of the adaptive tutoring system, this paper used quantitative methods through data gathering for finding the results of this study. Sixty-eight respondents were chosen from among academicians, administrative staff and students of a public university in Malaysia. Descriptive analysis of the data shows that there exists a pattern of different preferences for adaptive tutoring systems across gender. This paper provides data supporting the need to design different interfaces for male and female users.

Data Analytics is the trending domain that analyses data to observe patterns and predict future outcomes. The outcomes are based upon analysis of past and current trends and behaviors. Data analytics deals with both descriptive and predictive analyses of data. Descriptive Data Analytics summarizes the data, it's behavior and draws useful conclusion from it. Predictive Data Analytics is the branch of data analytics that predicts future outcomes based on the current and historical data. These future predictions are drawn by observing patterns followed for past data and outcomes for the past events for similar scenarios. In this paper, various branches of data analytics have been discussed. Big data analytics architecture gives an overview of the various tools and system structure involved in big data analytics. Big data analytics is closely related to data mining and hence, implements data mining algorithms. Latter part of the paper covers machine learning algorithms and neural networks for training the dataset to recognize patterns for the modeled data and predict outcomes based on the training and pattern recognition. Modeling of data using neural networks helps in generating accurate and exhaustive outcomes.

Extensive research has been carried out for preserving the privacy of identifiers in dataset during Data Mining. Various dimensions based on Cryptographic principles, Perturbation and Secure Sum Computation have been studied to achieve privacy. Effective techniques to maximize privacy and minimize information loss have always been intriguing. The work in this paper presents a comparison based on experimental study of three fundamental perturbation techniques viz. — Additive, Multiplicative and Geometric Data Perturbation [GDP] for Privacy Preserving Data Mining [PPDM]. These techniques form the basis of many advanced Perturbation techniques as described later. The literature doesn't embark a clear cut comparison amongst the three techniques based on suitable metrics. We have identified various statistical metrics that must be considered for evaluating Perturbation techniques. The facet of research is independent in this context, and this paper will try to confer the applicability of perturbation techniques by descriptive statistics through experiments under one roof. A comparison amongst the perturbation based techniques is conferred at the end to exemplify the importance of this research.

Providing e-Government services to business sector is a fundamental mission of governmental agencies in Saudi Arabia. The adoption of e-Government systems is less than satisfactory in many countries, particularly in developing countries. One pertinent, unanswered question is what are the key factors that influence the adoption and utilisation level of users from business sector. This paper utilised e-Government Adoption and Utilisation Model (EGAUM) proposed in our previous work in order to analyse determinants of higher level of e-Government adoption and usage. The study involved 48 participating business entities from two major cities in Saudi Arabia, Riyadh and Jeddah. The descriptive analysis is presented in this paper and the results indicated that all the proposed factors have degree of influence on the adoption and utilisation level. Perceived Benefits, Functional Quality of Service, Previous Experience, Perceived Simplicity, Accessibility and Regulations & Policies factors were found to be the significant factors that are most likely to influence the adoption and usage level of users from business sector.

The strongest land falling cyclone, typhoon Haiyan, left in its wake a great need for the government to have a means to monitor damages and manage relief, recovery and rehabilitation efforts. The Intelligent Operations Center for Emergency Management (IOC-EM) was built to address the needs of the Philippine government in the response, recovery and rehabilitation of the affected areas not only of a typhoon such as Haiyan but other calamities as well. These calamities and emergencies may include earthquakes, volcano eruptions and landslides. This paper presents the IOC-EM that integrates and illustrates the data needed by decision makers to make timely and fact-based decisions in the advent of a natural calamity. The data is managed and presented through the use of descriptive analytics. The IOC-EM data warehouse stores historical data that can be used for building more complex predictive and prescriptive analytics models. In this paper, we present the design of the IOC-EM and its data warehouse.

The paper proposes an approach to perform clustering on time series data. The data is fetched from temperature sensors, that are located in a public building, which defines certain features of the data as an origin of it. We present preliminary descriptive statistics of the data, which shows that it cannot serve as a feature in our case. The approach addresses to the problem of sufficient subsequence length for a time series, which allows to form stable groups of specific data. We have considered several distance metrics. The method is the iterative process of construction a solution, during which the window in a time series is the subject to be changed. The results might be used to build more precise yet general prediction model for groups of similar objects.

The issue of plagiarism has only become a recent priority in Kosovo, some public universities have taken steps to create their anti-plagiarism system which is one of the methods that are being done to prevent plagiarism. The only public University that has implemented the anti-plagiarism system in Kosovo is the University “Ukshin Hoti” Prizren. The research is based on the dataset obtained from the University “Ukshin Hoti” Prizren. In this paper, the data extracted from the SAP anti-plagiarism software of the University “Ukshin Hoti” for the controlled documents from 17.11.2020 to 19.01.2021 are analyzed. Initially, we will examine the frequency distribution analyzes for faculties, levels of studies, the status of documents, and the type of documents were extracted from the dataset. Then, we show descriptive statistics for local similarity, URL similarity, character, and word count. After the descriptive statistics, we are going to analyze the One-Way ANOVA for similarity, local similarity, and URL Similarity by faculty. Also, by the T- Test, we analyzed differences in similarity rates by study level and document type. The results obtained show that there are significant differences between faculties in terms of plagiarism ratio. Also, there are differences of plagiarism according to the type of documents. However, no significant difference was found in plagiarism according to the level of studies.

Health awareness and management is the global concern. Many promotional and awareness campaigns are run to uplift the awareness regarding the health issues. Social advertising solves the purpose of creating awareness among masses through highlighting the social cause through a strategically crafted message to generate a positive action among homogenous audience. These awareness messages are disseminated among the through the different media platforms such as such as print media, electronic, radio broadcast and now digital media which is the new form of communication. As a result, exposure to such communications is usually passive. These campaigns often face competition from influences such as pervasive advertising ads, strong social expectations, and addictive- or habit-driven behaviors. There are various health issues which are addressed by such public service advertising (e.g., use of tobacco, malnutrition, personal hygiene, alcohol, sex-related behaviors, drugs, heart disease risk factors, cancer screening and prevention, effects of smoking, road safety, child survival, and organ or blood donation). In our opinion mass media efforts can affect significant improvements in health-related behaviors or avert undesirable changes in large communities. This research paper will explore the awareness level generated from social advertising regarding the health issues among the youth below the age of 30 and subsequently will analyze various challenges involved to more strengthen these social messages. The study based on which data tabulation has been made through SPSS (Statistical Package for Social Scientists). To analyze the data through descriptive statistics such as percentage and mean to adequately verify information collected for this study.

With the accelerated pace of aging in China, the elderly industry, known as the “silver economy”, is showing a trend of vigorous development. Because it has hundreds of millions of consumer groups, the elderly industry is also called “sunrise industry” by businessmen. First of all, this paper makes a systematic descriptive statistics according to the basic situation of the survey, ensures the reliability and stability of the survey scale through reliability analysis, and judges the correlation between variables through validity analysis. Cluster analysis is used to outline three types of consumer portraits to explore the willingness of different groups to integrate into e-commerce; LR model is used to explore the influence of gender, education, social interaction and other factors on the integration intention of e-commerce live streaming, and its influence degree under interaction is further analyzed by OR value. Through factor analysis to establish the factors affecting the purchase intention of middle-aged and elderly consumers, in order to analyze how to further develop the e-commerce market; to build a structural equation model to establish the interaction between indicators; the use of case analysis, in view of specific cases to discuss, to seek the future trend of e-commerce live shopping. The team spent nearly 3 months designing questionnaires, consulting literature, crawling data, conducting in-depth research, analyzing the survey results and writing reports. As this survey is during the epidemic period, most of the data come from online, so the data quality will decline to a certain extent.

The elaboration of didactic sequences based on a pedagogical-tecnological theoretical foundation makes it possible to enrich the quality of teaching proposals and didactic resources. This paper presents an adaptation of the Multidisciplinary Gradual Model [1] that incorporates Blum’s modeling cycle [2] for the elaboration of an educational resource that is embedded in a didactic sequence. The usefulness of the proposed model is the feasibility of implementing different development technologies and different positions in the pedagogical aspect, which allows the creators to diversify of resources. A case study is presented for the didactic sequence “Measures of central tendency” in a third year group of a technological career

Mechanics Dynamics is an important fundamental course in engineering education. However, this course experiences a high failure rate among engineering students. This may be due to visualization problems associated with static images, complex engineering models, and concept misunderstanding. This paper presents the preliminary research of student perception on the blended learning flipped classroom approach in overcoming their learning difficulties in Mechanics Dynamics course. This pilot study aims to collect the learning difficulties of students in the Mechanics Dynamics and students’ perception of blended learning using flipped classroom approach. A questionnaire has been designed and distributed through an online platform. The sample size is 30 which targets the engineering students in Malaysia’s university who already took the Mechanics Dynamics course in less than five years. The findings are analyzed using a descriptive statistics approach. The initial findings indicate that the visualization problem is the main concern among the students. Although the students show low awareness regarding the blended learning flipped classroom approach, they have a positive attitude towards the element of the blended learning approach to be implemented in their classroom.

There is a growing interest in blended learning models that combine online learning with traditional classroom teaching. Besides, the Computer Basic course is an essential course in the learning process of college students. However, the current situation is that the course content is heavy and few learning hours. Therefore, the blended learning is used for the course, and online learning is part of blended learning. In order to know the effectiveness of online learning, we analyzed the learning behavior on the Superstar online platform of a university in Yunnan. This paper uses descriptive statistics and correlation analysis method to analyze the relationship between online learning and the final exam score. It finds that online learning behavior has a no significant impact on the final exam score. Thus, we need to use more effective blended learning approach to improve the quality of online and offline learning and to promote personalized learning and interaction among learners.