Purpose: The purpose of this paper is to treat WeChat moments as social media environments and applies the research model to explore the effect of social media environments on perceived interactivity from the perspective of environmental psychology. Design/methodology/approach: This paper proposes social media environments as effective stimuli for future participate in online social interactions. First, two cues of social media environments (user-to-system cues and user-to-user cues) can be important antecedents of users’ perception of interactivity. Second, users’ intention of future participates in online social interactions can be influenced by three dimensions of perceived interactivity (action control, connectedness and responsiveness). Using data from 334 users of WeChat moments, the authors conduct partial least squares analysis to validate the research model. Findings: The results indicate that both technological and social environments positively affect three dimensions of perceived interactivity, respectively, including action control, connectedness and responsiveness. Moreover, actual findings also suggest that higher perceived interactivity increases users’ intention of future participate in online social interactions. Originality/value: This work contributes to in-depth research on the relationships between social environments and perceived interactivity. Besides, this paper demonstrates that both technological and social cues of social media environments are significant elements in simulating users’ internal experience and behavioral intention. The main conclusions of this study can be valuable to social media developers and managers.

This pilot study examines how students’ performance has evolved in an Object-oriented (OO) programming course and contributes to the learning analytic framework for similar programming courses in university curriculum. First, we briefly introduce the research background, a novel OO teaching practice with consecutive and iterative assignments consisting of programming and testing assignments. We propose a planned quantitative method for assessing students’ gains in terms of programming performance and testing performance. Based on real data collected from students who engaged in our course, we use trend analysis to observe how students’ performance has improved over the whole semester. By using correlation analysis, we obtain some interesting findings on how students’ programming performance correlates with testing performance, which provides persuasive empirical evidence in integrating software testing practices into an Object-oriented programming curriculum. Then, we conduct an empirical study on how students’ design competencies are represented by their program code quality changes over consecutive assignments by analyzing their submitted source code in the course course system and the GitLab repository. Three different kinds of profiles are found in the students’ program quality in the OO design level. The group analysis results reveal several significant differences in their programming performance and testing performance. Moreover, we conduct systematical explanations on how students’ programming skill improvement can be attributed to their object-oriented design competency. By performing principal component analysis on software statistical data, a predictive OO metrics suite for both students’ programming performance and their testing performance is proposed. The results show that these quality factors can serve as useful predictors of students’ learning performance and can provide effective feedback to the instructors in the teaching practices.

Knowledge Tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive modeling on the sequence of former exercises has been proven effective for this task. One of the primary challenges in autoregressive modeling for Knowledge Tracing is effectively representing the anterior (pre-response) and posterior (post-response) states of learners across exercises. Existing methods often employ complex model architectures to update learner states using question and response records. In this study, we propose a novel perspective on knowledge tracing task by treating it as a generative process, consistent with the principles of autoregressive models. We demonstrate that knowledge states can be directly represented through autoregressive encodings on a question-response alternate sequence, where model generate the most probable representation in hidden state space by analyzing history interactions. This approach underpins our framework, termed Alternate Autoregressive Knowledge Tracing (AAKT). Additionally, we incorporate supplementary educational information, such as question-related skills, into our framework through an auxiliary task, and include extra exercise details, like response time, as additional inputs. Our proposed framework is implemented using advanced autoregressive technologies from Natural Language Generation (NLG) for both training and prediction. Empirical evaluations on four real-world KT datasets indicate that AAKT consistently outperforms all baseline models in terms of AUC, ACC, and RMSE. Furthermore, extensive ablation studies and visualized analysis validate the effectiveness of key components in AAKT.

Social networks are formed by individuals, in which personalities, utility functions, and interaction rules are made as close to reality as possible. Taking the competitive product-related information as a case, we proposed a game-theoretic model for competitive information dissemination in social networks. The model is presented to explain how human factors impact competitive information dissemination which is described as the dynamic of a coordination game and players’ payoff is defined by a utility function. Then we design a computational system that integrates the agent, the evolutionary game, and the social network. The approach can help to visualize the evolution of % of competitive information adoption and diffusion, grasp the dynamic evolution features in information adoption game over time, and explore microlevel interactions among users in different network structure under various scenarios. We discuss several scenarios to analyze the influence of several factors on the dissemination of competitive information, ranging from personality of individuals to structure of networks.