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The aim of this paper is to show that online learning behaviors are dictated by both personal characteristics and regional differences.

Data were collected from 16,133 users in 25 regions of Taiwan. The paper examined usage behaviors by looking at 11 items of categorical variables about online learning. This study implemented a multi‐level latent class model to investigate online learning behavior patterns that exhibit regional differences.

The results showed that online learning patterns do exhibit regional differences, as the regional segments are dictated by the individual segments of different use patterns. For instance, the urban area segment comprised a higher proportion of members who are good at using the internet. The rural area segment made up a higher proportion of members who occasionally use the internet. Interestingly, rural users went online more often than urban users when in search of e‐learning or entertainment. On the other hand, the individual segments are dictated by users' personal characteristics. For instance, younger people are good at employing online learning and entertainment services. Moreover, those who use many types of online applications pay less respect to intellectual property rights than those who only use a few types of applications.

By using a massive amount of survey data to show regional differences in online learning behavior patterns, the findings herein will help internet service providers form an applicable guideline for developing service strategies of higher service satisfaction between products and users' needs.

Online learning is well-known by its flexibility of learning anytime and anywhere. However, how behavioral patterns tied to learning anytime and anywhere influence learning outcomes are still unknown.

This study proposed concepts of time and location entropy to depict students’ spatial-temporal patterns. A total of 5,221 students with 1,797,677 logs, including 485 on-the-job students and 4,736 full-time students, were analyzed to depict their spatial-temporal learning patterns, including the relationships between identified patterns and students’ learning performance.

Analysis results indicate on-the-job students took more advantage of anytime, anywhere than full-time students. Students with a higher tendency for learning anytime and a lower level of learning anywhere were more likely to have better outcomes. Gender did not show consistent findings on students’ spatial-temporal patterns, but partial findings could be supported by evidence in neural science or by cultural and geographical differences.

A more accurate approach for categorizing position and location might be considered. Some findings need more studies for further validation. Finally, future research can consider connections between other well-known performance predictors (such as financial situation, motivation, personality and major) and the type of learning patterns.

The findings gained from this study can help improve the understandings of students’ learning behavioral patterns and design as well as implement better online education programs.

This study proposed concepts of time and location entropy to identify successful spatial-temporal patterns of on-the-job and full-time students.

Informal learning networks are critical to response to calls for practitioners to reskill and upskill in off-site construction projects. With the transition to the coronavirus disease 2019 (COVID-19) pandemic, social media-enabled online knowledge communities play an increasingly important role in acquiring and disseminating off-site construction knowledge. Proximity has been identified as a key factor in facilitating interactive learning, yet which type of proximity is effective in promoting online and offline knowledge exchange remains unclear. This study takes a relational view to explore the proximity-related antecedents of online and offline learning networks in off-site construction projects, while also examining the subtle differences in the networks' structural patterns.

Five types of proximity (physical, organizational, social, cognitive and personal) between projects members are conceptualized in the theoretical model. Drawing on social foci theory and homophily theory, the research hypotheses are proposed. To test these hypotheses, empirical case studies were conducted on two off-site construction projects during the COVID-19 pandemic. Valid relational data provided by 99 and 145 project members were collected using semi-structured interviews and sociometric questionnaires. Subsequently, multivariate exponential random graph models were developed.

The results show a discrepancy arise in the structural patterns between online and offline learning networks. Offline learning is found to be more strongly influenced by proximity factors than online learning. Specifically, physical, organizational and social proximity are found to be significant predictors of offline knowledge exchange. Cognitive proximity has a negative relationship with offline knowledge exchange but is positively related to online knowledge exchange. Regarding personal proximity, the study found that the homophily effect of hierarchical status merely emerges in offline learning networks. Online knowledge communities amplify the receiver effect of tenure. Furthermore, there appears to be a complementary relationship between online and offline learning networks.

Proximity offers a novel relational perspective for understanding the formation of knowledge exchange connections. This study enriches the literature on informal learning within project teams by revealing how different types of proximity shape learning networks across different channels in off-site construction projects.

This paper aims to compare the nature of three different business and management education (BME) research streams (online/blended learning, entrepreneurship education and experiential learning), along with their citation sources to draw insights on their support and legitimacy bases, with lessons on improving such support and legitimacy for the streams and the wider BME research field.

The authors analyze the nature of three BME research streams and their citation sources through tests of differences across streams.

The three streams differ in research foci and approaches such as the use of managerial samples in experiential learning, quantitative studies in online/blended education and literature reviews in entrepreneurship education. They also differ in sources of legitimacy recognition and avenues for mobilization of support. The underlying literature development pattern of the experiential learning stream indicates a need for BME scholars to identify and build on each other’s work.

Identification of different research bases and key supporting literature in the different streams shows important core articles that are useful to build research in each stream.

Readers will understand the different research bases supporting the three research streams, along with their targeted audience and practice implications.

The discovery of different support bases for the three different streams helps identify the network of authors and relationships that have been built in each stream.

According to the authors’ knowledge, this paper is the first to uncover differences in nature and citation sources of the three continuously growing BME research streams with recommendations on ways to improve the support of the three streams.

This study aims to propose a learning pattern analysis method which can improve a predictive model’s performance, as well as discover hidden insights into micro-level learning pattern. Analyzing student’s learning patterns can help instructors understand how their course design or activities shape learning behaviors; depict students’ beliefs about learning and their motivation; and predict learning performance by analyzing individual students’ learning patterns. Although time-series analysis is one of the most feasible predictive methods for learning pattern analysis, literature-indicated current approaches cannot provide holistic insights about learning patterns for personalized intervention. This study identified at-risk students by micro-level learning pattern analysis and detected pattern types, especially at-risk patterns that existed in the case study. The connections among students’ learning patterns, corresponding self-regulated learning (SRL) strategies and learning performance were finally revealed.

The method used long short-term memory (LSTM)-encoder to process micro-level behavioral patterns for feature extraction and compression, thus the students’ behavior pattern information were saved into encoded series. The encoded time-series data were then used for pattern analysis and performance prediction. Time series clustering were performed to interpret the unique strength of proposed method.

Successful students showed consistent participation levels and balanced behavioral frequency distributions. The successful students also adjusted learning behaviors to meet with course requirements accordingly. The three at-risk patten types showed the low-engagement (R1) the low-interaction (R2) and the non-persistent characteristics (R3). Successful students showed more complete SRL strategies than failed students. Political Science had higher at-risk chances in all three at-risk types. Computer Science, Earth Science and Economics showed higher chances of having R3 students.

The study identified multiple learning patterns which can lead to the at-risk situation. However, more studies are needed to validate whether the same at-risk types can be found in other educational settings. In addition, this case study found the distributions of at-risk types were vary in different subjects. The relationship between subjects and at-risk types is worth further investigation.

This study found the proposed method can effectively extract micro-level behavioral information to generate better prediction outcomes and depict student’s SRL learning strategies in online learning. The authors confirm that the research in their work is original, and that all the data given in the paper are real and authentic. The study has not been submitted to peer review and not has been accepted for publishing in another journal.

Emergence of coronavirus disease 2019 (COVID-19) forced the world-wide education system to adopt online mode immediately. There are two main objectives of the paper: the first one is to know the acceptability of online mode of examination and learning amongst students by analysing the various aspects like difficulty, mental pressure, study pattern, etc. and the second one is to know the role of gender in adopting online education.

An online survey is conducted amongst the students of Graphic Era Hill University, Dehradun, India. Stratified sampling method has been used to select the students. First, a simple statistical analysis of the responses is conducted, and then chi-square test of independence has been used to know the dependency of various aspects on gender.

The two main findings of the present study are as follows: first, the online examinations were accepted with ease and low pressure in comparison to regular examination and second, the gender has a significant role in adopting online education with the observations that female students were more adoptable with online education in terms of assignments, study patterns and comfort. The present work also focuses on the challenges of online education like Internet connectivity, class interactions, etc.

The present work was carried out during the initial time of pandemic in India when the focus was to continue the academic process by utilizing all the available resources in the absence of well-defined standards of online education.

The findings of the paper can be used for making strategies for online education across the world.

The findings of the paper have shown that gender plays a significant role in adoptability of online education in Indian context.

The present work is conducted amid the environment of COVID-19. It reflects the analysis of students' responses towards the acceptability of online education under the difficult conditions developed due to the pandemic and subsequent lockdown.

Nowadays, the breakout of the COVID-19 pandemic has caused an important change in teaching models. The emotional experience of this change has an important impact on online teaching. This paper aims to explore its time evolution characteristics and provide reference for the development of online teaching in the post epidemic era.

The article firstly crawls the online teaching-related comment text data on Zhihu platform and performs emotional calculation to obtain a one-dimensional time series of daily average emotional values. Then, by using non-linear time-series analysis, this paper reconstructs the daily average emotion value time series in high-dimensional phase space, calculates the maximum Lyapunov exponent and correlation dimension and finally, explores the feature patterns through recurrence plot and recurrence quantification analysis.

It was found that the sequence has typical non-linear chaotic characteristics; its correlation dimension indicates that it contains obvious fractal characteristics; the public emotional evolution shows a cyclical rise and fall. By text mining and temporal evolution analysis, this paper explores the evolution law over chronically of the daily average emotion value time series, provides feasible strategies to improve students' online learning experience and quality and continuously optimizes this new teaching model in the era of pandemic.

Based on social knowledge sharing platform of Q&A, this paper models and analyzes users interaction data under online teaching-related topics. This paper explores the evolution law over a long time period of the daily average emotion value time series using text mining and temporal evolution analysis. It then offers workable solutions to enhance the quality and experience of students' online learning, and it continuously improves this new teaching model in the age of pandemics.

The general goal of this paper is to help educators understand the importance of MOOC training to school teachers and their hypothetical value for predicting the use of teaching strategies in the face-to face-classroom teaching. With this purpose, the study is guided by two research questions: (1) Are there different patterns of preferences in teaching strategies among school teachers when they participate in MOOC training? (2) To what extent the attributes selected from the data set to visualize patterns are suitable for the formation of models?

Peer instruction (PI) and think-pair-share (TPS) strategies might bring positive outcome during classroom teaching. When introduced properly to school teachers, these strategies help students see reason beyond the answers by sharing with other students their response and thus learning from each other. This study aims to use educational data mining (EDM) techniques to visualize patterns and propose models based on the teaching strategies training to be used in face-to-face classroom teaching. The data set includes five attributes extracted from school teachers' Massive Open Online Courses (MOOC) training interaction data. All analysis and visualization were performed using Python, and the models were evaluated using fivefold cross-validation. The modeling performance of three different algorithms (decision tree, random forest and K-means) was tested on the data set. The results of model accuracy were presented as a confusion matrix. The experimental results indicate that the random forest (RF) algorithm outperforms decision tree (DT) and K-means algorithms with an accuracy of 96.4%.

This visualization information on the grouping of school teachers based on the teaching strategies serves as an essential reference for school teachers choosing between the two types of strategies within their face-to-face classroom settings. Teachers may use the finding obtained for an initial understanding of which strategies will fit well on their classroom teaching based on their subject majors. Moreover, the classification accuracy rates of DT and RF algorithms were the highest and considered highly significant to allow developing predictive models for similar EDM cases and provide a positive effect on the learning environment.

This visualization information on the grouping of school teachers based on the teaching strategies serves as an essential reference for school teachers choosing between the two types of strategies within their face-to-face classroom settings. Teachers may use the finding obtained for an initial understanding of which strategies will fit well on their classroom teaching based on their subject majors. Unlike predicting different patterns of preferences in teaching strategies among school teachers when they participate in MOOC training, using visualization was found much more comfortable, less complicated and more time-efficient for small data sets. Moreover, the classification accuracy rates of decision tree and random forest algorithms were the highest and considered highly significant to allow developing predictive models for similar educational data mining cases and provide a positive effect on the learning environment.

DT classifier in this study ranks first before model optimization, but second after model optimization in terms of accuracy. Therefore, the goodness of the indicators needs to be further studied to devise a reasonable intervention.

A different group of school teachers attending training on teaching strategies in a different online platform is required in future research to cross-validate these study findings.

The authors declare that this submission is their own work and to the best of their knowledge it contains no materials previously published or written by another person, or substantial proportions of material that have been accepted for the award of any other degree at any other educational institution.

The purpose of this study is to theorize that computer-assisted language learning (CALL) can be integrated in English language learning with a focus on cultural learning of both home and target language.

The present study used a systematic methodology to conceive the language and home-culture integrated online learning (LHIOL) curriculum design based on relevant conceptual frameworks and gather qualitative data from focused group interviews of 30 teachers and 3,000 students’ open-ended questionnaires, along with learning artifacts to identify major themes.

CALL, used as cultural and linguistic material, helps students embrace their cultural identities, especially ethnic minorities, capitalize on their distinctive values, and appreciate and empathize with other languages and cultures. The instructors advocate for localizing intercultural communicative competence (ICC) educational content into Vietnamese culture, using real multimedia resources. However, the LHIOL curriculum faced systemic constraints regarding competitions between linguistic and cultural instruction, teachers’ refusal to recognize ICC’s importance and recognition of an explicit link between virtual cultural learning and their lives.

LHIOL is a preliminary practical effort to suggest how a cultural education from one’s native tongue can be integrated into a culture-focused English/Western language environment. By incorporating fundamental concepts that underpin the integration of language and culture as well as CALL, improving ICC offers a framework that can be applied to elucidate cultural learning.

This study aims to use eye-tracking technology to conduct an empirical study about online learning process analysis, thus aiming to understand the attentional preferences and learning paths in online learners.

With eye movement tracking and data analysing technology, the Tobii X120 eye-tracking instrument, Tobii studio and online learning platform are used to record and visualise data of eye moving and learning steps during the real task-based online learning processes of 14 online learners. According to Barbara A. Soloman’s learning style classification framework, these learners’ learning style was presented in four dimensions. Based on data of eye moving, leaning style and operation in online course, the correlation about learners’ preferences of learning content, online learning paths and learning style were analysed based on according data.

The paper provides empirical insights about how change is brought about during online learning. It is found that there is no significant difference in attention preference between the students with the difference on the learning style of visual-verbal, although each person has a different attention preference on the learning content.

The limitation of this study is that only one common type of video learning process is studied. The learning process of various types of instructional videos in online learning will be done in future research.

In this study, eye-movement tracking technology is used to understand students’ learning path and learning preference in the online learning process, which is helpful to optimise the online learning process and improve the efficiency of online learning.

This research findings have been approved by relevant experts and have won the first prize in the school-level competition of South China Normal University in China.

In this study, the technology of psychology (eye-tracking technology) is introduced into the study of real task-based online learning process in the subject of educational technology, realising the integration of multi-disciplinary research techniques and methods.