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This paper aims to develop a curriculum-based virtual and physical mobile (CVPM) learning model, which integrates related physical and e-learning resources associated with elementary school curricula and supports elementary education.

This model provides a highly participative and well-monitored environment with fruitful interactions among students, teachers and museum staff. A practical system was implemented in the National Museum of Natural Science in Taiwan.

The results show that the proposed model can effectively extend related learning resources to satisfy the requirements of elementary education. In addition, this model can engage students’ self-directed explorations through problem-solving learning content during the learning process.

This learning model was implemented in a one-day theme-based learning activity. Generic learning outcomes and qualitative analysis were used to examine the students’ perceptions and the teachers’ feedback of this learning model, respectively.

The results show that both students and teachers highly appreciated this learning model. The model not only strengthens the connection between museum and elementary curricula but also enhances students’ perceptions and positive attitudes toward natural science courses. Finally, this study offers suggestions for future research.

This study develops a CVPM learning model which integrates elementary school courses to satisfy the requirements of elementary education and meet museums’ educational strategies for schools. The practical learning system and services have been successfully implemented and are widely available to elementary schools in Taiwan.

Until relatively recently, science teaching was based on conveying theoretical concepts. Nevertheless, in the last few years we have gradually understood the importance of building mental models that represent scientific reality. Model-based science teaching has been used at a school level with satisfactory results. However, only a few studies have been published so far on science modelling in higher education.

The present thematic review analyses the concept of model in science and the works published in recent years on models in physics education.

Throughout these years, special importance has been given to the acquisition of student learning models. These models can either be introduced in the teaching process or acquired by students in their learning process using specific teaching tools. As a conclusion of this review, the authors say that although such strategies are increasingly used in the teaching of science at a school level, few works delve into the importance of acquiring models in higher education. More specifically, there are few research works published in the context of teaching physics in university courses.

This study review and analyses works published on this issue and aims to provide knowledge as a starting point for future research.

The purpose of this paper is to establish the key predictors of Master of Business Administration (MBA) students' performance, considering the interaction between personality type, learning approaches and educational achievement. A structural equation model is formulated to check out the relationship perfection between the construct variables.

The study subjects were 208 MBA students at Zhejiang University. A questionnaire encompassing three sections was presented to the respondents. The first section is the revised two‐factor version of the study process questionnaire (R‐SPQ‐2F) developed by Biggs et al. The second section consists of 20 items developed based on a review of the International Personality Item Pool (IPIP) Five‐Factor Personality Inventory (Buchanan, 2001). The third section is about the respondents' background and personal information such as age, gender and their performance represented by their scores in the National Admission Examination System and their major course grade achieved during the first term of their academic year. A structural equation model was designed to examine the relationship between the study variables. Then covariance structural analyses of collected data were conducted for testing the model.

The main findings are significant correlations between the three personality traits, namely, extraversion, conscientiousness and openness to experience with the deep approach (DA) to learning predicting high MBA students' performance.

Personality type and students' approaches to learning constitute determinant factors impacting upon the education of management graduate students. Thus, institutions providing MBA programs must allocate more interest to examine their students' characteristics, in correlation with their performance rating. In this regard, the present study is designed to provide business education stakeholders with a modest model to depict the key predictors of MBA students' performance, particularly, part‐time MBA program whose participants are, in most cases, fully employed students, struggling to cope with their professional, educational and social duties.

This study empirically investigates the relationship between personality type and students' approaches to learning to find out their influence on MBA students' performance. The model results evidently demonstrate, on the one hand a significant correlation between these factors, and on the other hand their influence on the participants' performance reflecting high rating in pertinence with personality traits of extraversion, conscientiousness and openness to experience and the DA to learning.

The purpose of this paper is to suggest that the information ethics (IE) of students appear to improve more through adoption of the technology mediated learning (TML) platform rather than face-to-face (FTF) approach. In addition, it shows the pattern changes in each scenario resulting from the ethics training and analyses them from the Confucian ethics perspective, indicating that researchers should consider this aspect in future models.

This study employed non-parametric methods to test the outcome of the “information ethics course” of two kinds of training platforms. FTF training: 193 students; TML training: 185 students.

The TML platform produces a more significant improvement in the students’ respect for rules, privacy, accessibility, and intellectual property (IP) cognition, rather than the FTF method. Based on the findings, two propositions (eight sub-propositions) are formulated and revised two sub-propositions.

However, this study has a few limitations that can be enhanced by further research in the future: first, the data were only collected from one university (National Pingtung University), thus, the external validity is not satisfactory for all Chinese context students. Second, it is necessary to collect both of scenario-based and qualitative data from different cultural context students (such as Mainland China, the USA, Europe, Arabia, etc.) and then compare their results, thereby making further contributions to the current study. Third, the study was intentionally used as the measure of progress in ethical understanding without highlighting the difference between intentionality and actual behavior.

Teachers should draw upon the principles of Ren, Yi, and Li, from the Confucian ethics perspective to encourage students to respect the IE for Chinese context students. In addition, emphasis should be placed on the ability of students to build their information ethics cognition through the cognitive information processing learning methods, which can enhance the “accessibility,” “accuracy,” “privacy,” and “IP” cognition of Chinese students in both the FTF and TML platform learning process. This will help to reduce students’ unethical behavior as they advance in their future careers.

The purpose of this paper is to study stated effects of independent variables of trust, social networks, and information and communication technologies (ICTs) on explicit and tacit knowledge. Further, it explained contributions of explicit and tacit knowledge to performance in academic environment.

This study is based on research model of hypothetical relationships of various criterion and predictor variables. Structural equation model was used in which research model was analyzed by using confirmatory factor analysis.

Some criterion variables were observed for predictor variables of explicit and tacit knowledge. It was observed that explicit knowledge contributed indirectly and tacit knowledge contributed directly to overall performance. Current research explained explicit and tacit knowledge as contributors for performance. It also explained effects and patterns of explicit and tacit knowledge toward performance.

This research highlighted that the effects of contributing factors for explicit and tacit knowledge have variations in response to socio-economic and geo-political circumstances. These variations can be expected from other issues like use and access of ICTs. But contributing pattern of knowledge for performance remains same.

Many academics are being asked to develop virtual learning environments as part of their workload. It could be suggested that they are ill‐prepared to do so and hence are opening themselves up to criticism from students. This paper examines the concept of service quality which is used in the business world and explores its relevance for assessing on‐line learning environments. A framework of issues is presented to highlight issues and provide suggestions for improving the perception of service quality. The intangibility of the service and the service delivery channel create obstacles in the design and support of virtual learning environments. However, if instructors clearly define the service they are offering, effectively manage student expectations and use a range of methods to improve service quality they can go a long way in delivering service quality in their courses.

This study aims to propose a blackboard approach using multistrategy machine learning student modeling techniques to learn the properties of students' inconsistent behaviors during their learning process.

These multistrategy machine learning student modeling techniques include inductive reasoning (similarity‐based learning), deductive reasoning (explanation‐based learning), and analogical reasoning (case‐based reasoning).

According to the properties of students' inconsistent behaviors, the ITS (intelligent tutoring system) may then adopt appropriate methods, such as intensifying teaching and practicing, to prevent their inconsistent behaviors from reoccurring.

This research sets the learning object on a single student. After the inferences are accumulated from a group of students, what kinds of students tend to have inconsistent behaviors or under what conditions the behaviors happened for most students can be learned.

This study investigates the main factors that lead to students’ satisfaction toward executive education blended learning.

Merging five theories (consumer satisfaction theory, self-determination theory, investment model, happy-productive student theory and service quality model), the author used quantitative research to develop a model, explaining executive education satisfaction taught in a blended learning approach. Administrated questionnaires were physically distributed in various business universities that offer education to mid-career executives in Egypt. Two hundred and seventy questionnaires were examined through structural equation modeling path coefficient analysis.

Results show that satisfaction with executive education in traditional classroom boundaries are due to three internal factors (personal/psychological): self-regulated learning, perceived future financial rewards and perceived ease of course; and two external factors: quality of instructor and course design. When courses are given online, satisfaction is developed due to two internal factors (personal/psychological): self-regulated learning and perceived future financial reward; and one external factor, course design.

Marketization signifies students as consumers of universities; student’s satisfaction is increasingly important for educational entities to attract and retain students. Thus, this study develops a coherent student’s satisfaction model to better manage and market executive education, leading to students’ contentment in theory and practice.

Student’s satisfaction has multiple facets that are stochastic as education evolves and develops. The contribution stems from the incorporation of various theories to explain student’s satisfaction of executive education taught in a blended approach: traditional classroom boundaries and digital platforms that offer access to online education. The research extracts significant set of reasons, showing executive education satisfaction is not entirely similar to other education programs; and satisfaction toward blended learning in executive education is not entirely similar to education offered solely online or physically.

The purpose of this paper is to discuss the theoretical and practical need for research into the learning conditions that influence a student’s self-assessment of their competences in management education. By means of a theoretical review, the paper introduces a model that integrates various learning conditions related to a student’s affective learning ability and the role of the teacher that may have an influence on a student’s self-assessment of their competences in the field of management.

The authors describe the analysis of data from questionnaires on the experience of undergraduate students from business administration at the University of Las Palmas de Gran Canaria.

The results reveal the importance of a student’s motivation for professional development, their self-efficacy as well as the important role of the teacher in a student’s self-assessment of their competences.

The paper fulfils an identified need to study the conditions that influence a student’s self-assessment of their learning.

For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis.

This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach.

The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students.

The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.