Moderating effect of learning styles on a learning management system’s success

This study uses the DeLone and McLean model to determine the moderating impact of learning styles on the success of learning management systems from a student’s point of view. The main objectives of this research are: (1) to evaluate the Delone and McLean model of information system success in the context of learning management systems and, (2) to determine the effect of the learning styles of students on this model. An in-person survey of 258 engineering students was used to evaluate the research model. The analysis is based on structural equation modelling, specifically partial least squares. The results indicate that the research model explains use, user satisfaction, and perceived benefits of a learning management system. In addition, the Felder-Silverman learning styles (sensing-intuitive, visual-verbal; active-reflective; sequential-global) modify the strength of the relationships between the variables of the success model.     

KG-based memory recommendation algorithm for learning path

In intelligent education, most student-oriented learning path recommendation algorithms are based on either collaborative filtering methods or a 0-1 scoring cognitive diagnosis model. Unfortunately, they fail to provide a detailed report about the students’ mastery of knowledge and skill and explain the recommendation results. In addition, they are unable to offer realistic learning path recommendations based on students’ learning progress. Knowledge graph based memory recommendation algorithm(K...     

How a company’s gamification strategy influences corporate learning: A study based on gamified MSLP (Mobile social learning platform)

Advances in personal devices and communication technologies have changed the learning landscape. Gamification is the use of technology to promote and induce the user's internal motivation by exploiting the game's characteristics. Recently, mobile learning applications introduce various gamification strategies to provoke users' voluntary participation. However, empirical evidence on how to attract users and influence sustained usage is limited. This study establishes a theoretical basis for designing learning applications and discusses its impact on business. A series of gamification strategies such as Competition, Challenge, Compensation, Relationship, and Usability, were applied to a company's Mobile Social Learning Platform (MSLP). A survey result of 293 users from South Korea was used for the advanced mediation model analysis. The result showed that Challenge, Relationship, and Usability had affected Flow and Continuous usage intention. This paper argues that the user's usage intention will have a positive effect on voluntary learning. It also provides a significant implication for organizational effectiveness through the development and application of mobile learning platform.     

煤矿井下基于Q—learning算法的移动机器人路径规划

机器人的路径规划一直是机器人研究领域的难点问题。针对煤矿井下环境的不确定性,环境的复杂使机器人很难得到好的规划结果。采用强化学习算法中的Q-learning算法实现井下移动机器人的局部路径规划,并对Q函数中的即时回报进行加权修正,使算法更有效地利用环境特征信息,进一步提高了避障能力。最后通过VC 进行仿真和模拟。仿真实验说明该方法的有效性和可行性。     

Can book covers help predict bestsellers using machine learning approaches?

As the book publishing market changes from offline to online, readers tend to purchase books while paying more attention to book covers and metadata rather than the actual book contents. We examine whether publishers can know users’ satisfaction with books in advance, and both metadata and book covers help predict this satisfaction. Exploring effects of metadata and book covers on the satisfaction is not only necessary for publishers’ perspectives, but also for librarians’ perceptions. However, the majority of prior research on user preference-based book recommendation systems in both book industry and library system employed review comments, ratings, or book loan records. Thus, we open up the potentiality of other factors, which implicitly affect the satisfaction with books. We collected book titles, authors, publishers, reviews, ratings, and covers from the “Literature and Fiction” genre in the Amazon bookstore and conducted an experiment to predict readers’ satisfaction ratings based on book reviews, metadata, and book covers. Several deep learning classifiers (CNN, ResNet, LSTM, BiLSTM, GRU, BiGRU) were employed. Reviews alone can reach a certain level of prediction performance, but adding metadata, cover images, and cover objects to a review-based predictive model slightly improves that performance. Based on these results, we confirmed that both metadata and book covers improve predicting readers’ perceived satisfaction. This study is a pilot exploration of the idea that multimodal approaches can improve the prediction of the perceived satisfaction of book readers. Moreover, we have publicly released both source codes and data samples employed in this study.     

Transformer-based global–local feature learning model for occluded person re-identification

Most recent occluded person re-identification (re-ID) methods usually learn global features directly from pedestrian images, or use additional pose estimation and semantic analysis model to learn local features, while ignoring the relationship between global and local features, thus incorrectly retrieving different pedestrians with similar attributes as the same pedestrian. Moreover, learning local features using auxiliary models brings additional computational cost. In this work, we propose a Transformer-based dual-branch feature learning model for occluded person re-ID. Firstly, we propose a global–local feature interaction module to learn the relationship between global and local features, thus enhancing the richness of information in pedestrian features. Secondly, we randomly erase local areas in the input image to simulate the real occlusion situation, thereby improving the model’s adaptability to the occlusion scene. Finally, a spilt group module is introduced to explore the local distinguishing features of pedestrian. Numerous experiments validate the effectiveness of our proposed method.     

Can COVID-19 pandemic influence experience response in mobile learning?

The mass spreading of COVID-19 has changed the paradigm of the education industry. In China and many other nations, universities have introduced compulsory remote education programs such as mobile learning (m-learning) to prevent public health hazards caused by the pandemic. However, so far, there is still a lack of understanding of student’s learning experience responses in compulsory m-learning programs. As such, there is a necessity to explore the factors and mechanisms which drives students’ experience. This paper evaluates the influence of both pedagogy and technology on learner’s compulsory m-learning experience response (ER) by extending the mobile technology acceptance model (MTAM) during the COVID-19 pandemic. An online self-administered questionnaire was used to collect the data, which was then analysed through SmartPLS 3.2.9. Importance-performance matrix analysis was applied as a post-hoc procedure to gauge the importance and performance of the exogenous constructs. The results revealed that perceptions of m-learning’s learning content quality, user interface, and system’s connectivity affect the perceived mobile usefulness and easiness which in turn affects ER. This paper validates MTAM in the field of education by integrating MTAM with pedagogy and technology attributes under a social emergency setting such as the COVID-19 pandemic. In addition, the current research explains users' ER rather than behaviour intention which is commonly adopted in past studies.     

Autonomous driving in the uncertain traffic—— a deep reinforcement learning approach

Driving in the complex traffic safely and efficiently is a difficult task for autonomous vehicle because of the stochastic characteristics of engaged human drivers. Deep reinforcement learning (DRL), which combines the abstract representation capability of deep learning (DL) and the optimal decision making and control capability of reinforcement learning (RL), is a good approach to address this problem. Traffic environment is built up by combining intelligent driver model (IDM) and lane-change model as behavioral model for vehicles. To increase the stochastic of the established traffic environment, tricks such as defining a speed distribution with cutoff for traffic cars and using various politeness factors to represent distinguished lane-change style, are taken. For training an artificial agent to achieve successful strategies that lead to the greatest long-term rewards and sophisticated maneuver, deep deterministic policy gradient (DDPG) algorithm is deployed for learning. Reward function is designed to get a trade-off between the vehicle speed, stability and driving safety. Results show that the proposed approach can achieve good autonomous maneuvering in a scenario of complex traffic behavior through interaction with the environment.     

L2,1-norm robust regularized extreme learning machine for regression using CCCP method

As a way of training a single hidden layer feedforward network(SLFN),extreme learning machine(ELM) is rapidly becoming popular due to its efficiency. However, ELM tends to overfitting, which makes the model sensitive to noise and outliers. To solve this problem, L_2,1-norm is introduced to ELM and an L_2,1-norm robust regularized ELM(L_2,1-RRELM) was proposed. L_2,1-RRELM gives constant penalties to outliers to reduce their adverse effects by replacing lea...     

Deep learning and RGB-D based human action,human–human and human–object interaction recognition: A survey

Human activity recognition is one of the most studied topics in the field of computer vision. In recent years, with the availability of RGB-D sensors and powerful deep learning techniques, research on human activity recognition has gained momentum. From simple human atomic actions, the research has advanced towards recognizing more complex human activities using RGB-D data. This paper presents a comprehensive survey of the advanced deep learning based recognition methods and categorizes them in human atomic action, human–human interaction, human–object interaction. The reviewed methods are further classified based on the individual modality used for recognition i.e. RGB based, depth based, skeleton based, and hybrid. We also review and categorize recent challenging RGB-D datasets for the same. In addition, the paper also briefly reviews RGB-D datasets and methods for online activity recognition. The paper concludes with a discussion on limitations, challenges, and recent trends for promising future directions.     

Business undergraduates’ perceived use outcomes of Moodle in a blended learning environment: The roles of usability factors and external support

This study was designed to explore the effects or roles of usability factors (i.e., perceived ease of use, perceived usefulness, and satisfaction) and external support (i.e., teacher and peer support) on undergraduates’ use outcomes of Moodle in a blended learning environment. The research hypotheses derived from relevant constructs taken from the technology acceptance model, information systems continuance model, and the theory of reasoned action. The study’s dependent variable is use outcomes, which was conceptualized with factors such as academic performance, perceived learning assistance, and perceived impacts on learning. We conducted a cross-sectional survey and collected data from 126 undergraduate students attending a university in the Maritime region of Canada. The partial least squares technique was used to test the hypothesized relationships in the proposed research model. We found that usability factors have positive effects on students’ use outcomes; contrarily to predictions teacher and peer support did not. The findings of the study offer useful insights that can help HE administrators gain an understanding of antecedent factors likely to enhance students’ use outcomes of Moodle.