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This study used the ARCS approach to investigate the effects of university students' motivation, including attention, relevance, confidence, and satisfaction, to use the Programming Teaching Assistant (PTA) on their Programming Problem-Solving Skills (PPSS). Previous studies have shown that PTA features enhance learners' programming performance, but relevant studies have not empirically evaluated the factors influencing learners' problem-solving skills in PTA-based programming language practice. Thus, the current study used the ARCS model to investigate the effects of its core factors, Attention (A), Relevance (R), Confidence (C), and Satisfaction (S), on the learners' PPSS. A total of 99 students major in computer science and engineering (CSE) participated in this study. Multiple linear regression analysis was conducted and the results indicated that these motivational factors influenced learners' PPSS in PTA-based programming language practice. The PTA has proven to be a motivating platform for programming language practice. Finally, relevant implications are offered.

The purpose of this study is to review journal papers on educational big data research published from 2010 to 2018. A total of 143 papers were selected. The papers were characterized based on three dimensions: (a) educational goals; (b) educational problems addressed; and (c) big data analytical techniques used. A qualitative content analysis approach was conducted to develop a coding scheme for analyzing the selected papers. The results identified four types of educational goals, with a clear predominance of quality assurance. The identification of the most mentioned educational problems resulted in four main concerns: the lack of detecting student behavior modeling and waste of resources; inappropriate curricula and teaching strategies; oversights of quality assurance; and privacy and ethical issues. Concerning the most mentioned big data analytical techniques, the coding scheme revealed that the majority of the papers focused on the educational data mining technique followed by the learning analytics technique. The visual analytics technique was mentioned in a few papers. The results also indicated that the educational data mining technique is the most suitable technique to use for quality assurance and to provide potential solutions for the lack of detecting student behavior modeling and the waste of resources in institutions.

As the use of online learning has continued to expand, there has been a growing demand for advanced software or social sites that can enhance learning outcomes in online settings. It is important to explore how learner-learner, learner-teacher and learner-content interactions affect on learning outcomes while using the social sites such as Facebook, Twitter, WeChat, Blogs, etc. Although some prior research highlight that there is a demand for interaction in online courses, specific design tactics and empirical evaluation of the effects of interaction on learning outcomes in blog-based courses are lacking. Thus, a blog learning platform named "Learner's Digest Blog" (LDB) was developed to facilitate the three types of interactions and enhance learning outcomes. A total of 26 learners participated in the blog-based interactive learning environment. The study addressed two research questions, and regression analysis was used to analyze the data. The findings indicated a significant influence between learner-learner interaction, learner-teacher interaction and learner-content interaction on subjective learning outcome. In contrast, there was no significant influence of learner-teacher interaction on objective learning outcomes, but learner-learner and learner-content did significantly affect objective learning outcomes. A significant relationship was also found between students' subjective and objective learning outcomes.

Due to the emergency educational consequences of the COVID-19 epidemic, it is necessary to find an effective and alternative way of learning. Moreover, during this epidemic, the Chinese Ministry of Education announced the "Disrupted Classes, Undisrupted Learning" initiative, offering flexible online learning. With the aim of facilitating a sound learning environment when using online learning systems, various kinds of tools can be applied. Tencent Meeting is one tool that offers various learning features. This study aimed to investigate how students perceived the effect of the Tencent Meeting system features on the two main determinants of the TAM model, namely perceived ease of use (PEOU) and perceived usefulness (PU). The study further analyzed the effects of these two determinants on learning performance. Data were analyzed using regression analysis. The study found the Tencent Meeting system features could account for 55.1% of PEOU and 40% of PU. Another finding shows that PEOU and PU had a significant effect on the students' learning performance. Finally, a discussions and implications are presented.

This study aimed to gain insights into the differences in perceptions of blog writing of two types of writers (i.e., digital natives [DNs] vs. digital immigrants [DIs]). The study focused on the generational literature and Web 2.0 as an online writing platform, investigating the generational differences in DN and DI writers' perceptions on a blog-based writing platform. The "WritingGen" blog was developed for this study to provide a web-based writing place to facilitate writers' writing and editing practices. An empirical study was conducted involving 34 Taiwanese blog writers with five hypotheses to be verified. Data were analyzed using independent samples t tests and logistic regression. The results revealed that the DN writers have significantly more positive attitudes toward blog writing, higher frequencies in blog-based writing behavior, perceived higher satisfaction, and higher knowledge acquisition than the DI writers. Based on these findings, pedagogical implications are provided.

Despite the growing interest in digital game-based learning (DGBL), there has been a lack of attention paid to the effects of individual differences, such as gaming flow experience and gender differences, in a reward-based achievement system. To this end, this study developed an achievement system with a reward mechanism to facilitate English learning. This study investigated how individuals' gaming flow experience levels affected their levels of learning motivation, and whether any gender differences existed in gaming flow experience and learning motivation while engaging in the achievement system. The results showed that gaming flow experience significantly predicted learning motivation, whereby the students with high gaming flow experience were six times more likely to have high learning motivation than those with low gaming flow experience. Subsequent analysis showed that the female students had significantly higher gaming flow than the male students, but the male and female students showed similar learning motivation. Furthermore, the results indicated that the male students achieved more interactive rewards than the female students, but no significant differences were found in the male and female students' achievement of other types of rewards. Based on these findings, the authors contribute to the literature by developing a framework which can be applied to support designers to accommodate individual differences in DGBL.

The development of online social environments has changed the manner of social interaction and communication, which are driven by individual human actions. Thus temporal variations in interaction networks are deeply impacted by the temporal dimension of human activity. In this paper, we address this issue through a detailed analysis on the retweets and comments of 550,000 Twitter users. We propose a temporal network model to represent the interaction network on Twitter, in which each node contains an activity window and the emergence of the edges between nodes are dependent on it. Specifically, the activity window is defined as the backtracking length from the message flow posted by the user’s friend, which represents the user’s social ability. It complies with a power-law distribution with an exponential cut-off. The interaction network is sparser and more clustered than the followee-follower network, in which the interaction stability and burstiness fluctuate with the activity window or with the degree to which the two users are involved in the communication. Finally, the effect of activity window on the aggregating degrees of the interaction network is examined.

A growing number of studies have been conducted on digital game-based learning (DGBL). However, there has been a lack of attention paid to individuals' self-efficacy and learning performance in the implementation of DGBL. This study therefore investigated how the badge mechanism in DGBL enhanced users' self-efficacy in the subject domain of English as a foreign language. In the study, a digital game-based English learning environment was designed with a badge mechanism including digital badges, leaderboard ranking, and learning practice with star icons. A quasi-experimental design was implemented. A total of 50 third-grade elementary school students participated in this study. Hypotheses were tested, and data were analyzed using paired sample "t" tests, multiple regression analyses, linear regression analyses, and logistic regression analyses. The results show that the badge mechanism had a significant positive influence on the learners' self-efficacy and English learning performance. The study also investigated how self-efficacy could affect English learning performance, with the results revealing that it had a significant positive influence on learning performance. In addition, a subsequent analysis showed that those students with higher self-efficacy performed better than those with lower self-efficacy.

This study investigated how individuals' reading and writing habit levels contributed to their levels of learning effectiveness while participating in a learning blog. A survey was conducted to understand the relationship between individuals' reading and writing habit levels (i.e., heavy or light) in terms of their attitudes and behavioral patterns, on the one hand, and the level of learning effectiveness (i.e., high or low) in a learning blog. Toward this end, a total of 73 readers and 56 writers participated in a blog, "Learner's Digest", developed for the purpose of the study with informative blog posts to facilitate meaningful learning. Hypotheses were tested and data were analyzed using logistic regression analysis. The results revealed significant relationships between heavy reading and writing habits and learning effectiveness. Discussion of the results and the implications of this study are also presented.

Mobile technologies are widely used in education, both as standalone tools and in conjunction with other applications. The WeChat-based app Rain Classroom is one such application. Rain Classroom includes learning activities not only during class but also before and after class. Previous research has suggested strong demand for WeChat-based apps that can facilitate learning. However, relevant studies have not empirically evaluated the effect of Rain Classroom on English learning performance. This study examined the effects of using Rain Classroom for English learning. A total of 152 students were divided into control and experimental groups, each with 76 students. The experimental group had significantly better English reading, writing and listening scores than the control group did. The effects of the perceived features of Rain Classroom and learner satisfaction on learning performance were also investigated. Both factors had a significant positive influence on learning performance. The Rain Classroom has been proven to be an effective English learning tool. On the basis of the study findings, relevant implications are offered. [ABSTRACT FROM AUTHOR]

Early school years are an important period to lay out the foundation for learning a second language. In addition to mastering the basic language skills and keeping the learning process fun, promoting a lifelong learning habit should also be emphasised. Motivating elementary school students to learn English and avoiding misconceptions associated with the learning process are very important issues. This study attempted to explore the integration of book, computer and robot to create a novel and joyful English learning environment. Books are the most common learning media. If we can design a special mechanism linking books with digital learning content and a robot, students will be able to obtain supplementary learning content including motions performed by the robot to enhance learning outcomes. To implement this idea, a new learning system was designed and developed. Learning activities included vocabulary, single sentence read-along, full article read-along, conversation, sing-along and dancing. Cloze tests were implemented and the feasibility of the learning system was evaluated. Data collected from field observation and interviews were analysed. The results suggest that this system did enrich students' learning experience and enhance their motivation and engagement. (Contains 6 figures and 1 table.)

Previous studies have shown that trait emotional intelligence (EI) is an important factor affecting children's learning performance in physical instruction settings. However, there have been very few studies exploring the effects of children's high- or low-trait EI levels on both gaming performance and learning performance in digital game-based learning environments. Therefore, in this study, we developed a game-based English learning environment to investigate how different levels of trait EI would affect children's gaming performance and learning performance. A total of 51 third-grade elementary school students participated in this study. The students' trait EI was measured by a modified version of the Trait EI Questionnaire-Child Form. The students' gaming performance was evaluated by the achievement of badges, and their learning performance was assessed by a paper-based examination after playing the game. The collected data were analyzed using Pearson correlation analysis and independent samples t tests. The results showed that there were significant correlations between trait EI, gaming performance, and learning performance; this implies that trait EI had a significant relationship with both gaming performance and learning performance. Moreover, the results showed that there was a significant correlation between the gaming performance and learning performance for those students with low-trait EI. However, the results showed that gaming performance and learning performance were not significantly correlated for the high-trait EI students. A subsequent analysis showed that the high-trait EI students' gaming performance and learning performance were significantly better than those with low-trait EI. [ABSTRACT FROM AUTHOR]

The development of online social environments has changed the manner of social interaction and communication, which are driven by individual human actions. Thus temporal variations in interaction networks are deeply impacted by the temporal dimension of human activity. In this paper, we address this issue through a detailed analysis on the retweets and comments of 550,000 Twitter users. We propose a temporal network model to represent the interaction network on Twitter, in which each node contains an activity window and the emergence of the edges between nodes are dependent on it. Specifically, the activity window is defined as the backtracking length from the message flow posted by the user's friend, which represents the user's social ability. It complies with a power-law distribution with an exponential cut-off. The interaction network is sparser and more clustered than the followee-follower network, in which the interaction stability and burstiness fluctuate with the activity window or with the degree to which the two users are involved in the communication. Finally, the effect of activity window on the aggregating degrees of the interaction network is examined. [ABSTRACT FROM AUTHOR]