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The driving concept of students’ sports training involves a unique activity that is often tightly correlated to students’ efficiency and varies with the momentum of sports training. Supervised learning is one of the smart methods with positive results in the fields of classification techniques. Due to the excessive currency unit associated with sports, sports forecasting is a growing area that must be well predicted. Therefore, in this paper, sports training based on the supervised learning (STSLM) model has been proposed to evaluate and predict student sports efficiency. STSLM models are based on various variables, such as traditional student ratings, performance, and efficiency. The emphasis is on the efficiency of students predicting sports outcomes. STSLM defines evaluation methods, information sources, effective models for testing students’ sports training, and unique challenges to forecast sports outcomes. The experimental results have been performed. The suggested STSLM model enhances the efficiency ratio of 96.3%, injury prevention level of 98.2%, fitness level of 95.5%, evaluation ratio of 98.8%, and training optimization ratio of 97.2% compared to other existing approaches.

Group work can inspire students, encourage constructive learning, and improve essential critical thinking, communication, and decision-making in the present competitive world. The risk factors in group learning include students who prefer working alone and strongly despise dealing with things created by gathering in teams. Online learners often have problems locating lasting peace times for group therapy sessions are considered an essential factor. A predictive Group Learning Behavior Approach (PGLBA) has been proposed. Students who commute to college agree to group conferences and workgroup learning and Inquiry learning for higher education. The grounded Inquiry Learning Approach is invented to strengthen students’ enjoyment of active group learning, and the students find times for group meetings that are often mutually advantageous. The simulation analysis is performed based on performance, accuracy, and efficiency proves the proposed framework’s reliability. The experimental results show that the proposed PGLBA-IL model enhances the accuracy ratio of 81.2%, an efficiency ratio of the number of students 86.4%, and the overall performance analysis ratio of 85.1% compared to others existing approaches.

Education is a dynamic system by which students perceive the factors necessary to fit them into the society. Education is mainly intentional learning that grooms individuals to achieve success in their adult lives. Evaluation of teaching techniques, course management (CM), communication, and student monitoring are the main characteristics of today’s education system. The aim to plan the curriculum of education management in both schools and colleges leads to the implementation of an MS-BDA. The development process for evaluation of teaching techniques and CM includes the use of the sentiment analysis method, which assesses the emotional feelings of students studying the course by managing curriculum quality. The big data analysis with MNN is developed by considering the communication and student monitoring system. This system evaluates the monitoring model provided in MS-BDA for assessing student communication on merging the voice-over with the communication language processing system. The simulation analysis is performed based on accessibility, adaptability, and efficiency, proving the proposed framework’s reliability. Therefore, the system outputs an accuracy of 99.1% when compared to the existing methods.

Wearable devices are starting to invade our daily lives at a rapid pace. Smartwatches are the most popular among these devices, but because of their novelty and the market, manufacturers are trying to understand which is the best way to approach this market and the kind of characteristics these devices might or might not have.

Students are usually technology enthusiasts and often fall into the “early adopters” category when it comes to new technologies. However, there is not much research work in the literature concerning smartwatches and the type of characteristics that should be considered when buying one of these products.

The present study aims to assess different smartwatch models to determine which is the most suitable for Portland State University (PSU) students. The methodology used was the Hierarchical Decision Model (HDM), a Multi-Criteria Decision Making (MCDM) method that decomposes complex problems and situations in a hierarchical fashion, dividing it into smaller fractions to make the problem easier to approach and assess. The method relies on experts’ judgments in order to assess the components of the model. After the experts have done their assessments, mathematical routines are applied and the relationships between components, as well as the best alternative to solve the problem, are presented.

The criteria contained in the model were based on a survey and on a literature review. The alternatives were chosen to represent the diversity of the market, and the experts were students who are technology-oriented and well informed about the products and the market. After the application of the model, an analysis is made on the main results (alternatives scores) and also the importance of each criteria and subcriteria.

Cooperative learning is a common teaching application in curriculum planning and learning in many departments in recent years. With students from different backgrounds at the department of design and questionnaire surveys, three dimensions of “Learning Motivation and Attitude”, “Cooperative Skills and Peer Interaction” and “Teacher-Student Relationship” were investigated. The results of the t-test showed that genders affected significantly the teacher-student relationship. In an educational system, there were significant differences in the three dimensions between the students in full-time and the night school. Grade analysis and verification results from ANOVA showed the full-time juniors had the most active participation in the cooperative learning model, followed by the full-time seniors and night school seniors. The research results also revealed that the students at lower grades had low participation in cooperative learning. The results provide a reference for academic research in design education and teaching models.

This paper summarizes the methodology and conclusions used on a master thesis that had the research aim of exploring how Web 2.0 and social networks are having an effect on users' information behavior. The method used for the collection of data was a semi structured interview, containing questions constructed according to the issues of Web 2.0 and social networks identified on the literature, along with typical features or characteristics of social networks. Purposive sampling was used to select the interview participants. The method for analyzing data was discourse analysis and a framework of categories was created to present the data in a certain order. This study identified various trends and tendencies in users' information behavior and some future directions for research were proposed. Findings of this type of study provide insights to users' information behavior in information systems, they could contribute to a better understanding of the users and to the design of such systems; this is relevant when it is necessary to build information systems from the point of view of users needs and behaviors, that is, by taking a bottom-up approach.