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IntroductionPublic attitudes toward consensual same-sex relations are crucial to lesbians’ and gay men’s rights and society’s well-being, but research addressing this topic in China is limited. We comprehensively explored the current status and predictors of Weibo users’ attitudes toward individuals who are lesbian or gay (IWLG) at the provincial level in the Chinese mainland.MethodsNatural language processing and machine learning techniques were incorporated to analyze 1,934,008 Weibo posts from January 1, 2010, to December 31, 2020, to evaluate Weibo users’ expressed attitudes toward IWLG in 31 provinces in the Chinese mainland guided by the ABC Model of attitude.ResultsAlthough the general attitudes, feelings, and support for the rights of Weibo users toward IWLG among different provinces were relatively positive, knowledge about IWLG was noticeably inaccurate. Economic development and educational level positively predicted certain aspects of attitudes at the provincial level.ConclusionWeibo users from different provinces are generally supportive and accepting of people who are gay and the rights of the gay community. However, considerable misconceptions and inaccurate knowledge of IWLG surfaced in Weibo users’ posts. Economic development and educational level were important predictors of specific attitudes toward IWLG at the provincial level. Increased efforts to address the unbalanced and insufficient development between different provinces could help reduce the public’s prejudice, stigma, and discrimination toward IWLG. Policies that facilitate greater implementation of Comprehensive Sexuality Education sequentially and effectively are suggested as well.

Abstract With the rapid development of information technology, e-books have become convenient for students to improve their learning performance, especially when learning complicated concepts. However, research showed that acceptance of e-books by teachers is fragmented, due to several factors including the e-book design. Therefore, this study combined the potential positive impacts of openness and interaction on learning to design an open and interactive e-book for teaching K-12 students AI. It then applied a mixed method to investigate the factors that affect teachers’ acceptance of this open and interactive e-book based on the technology acceptance model (TAM) and interviews. The obtained results showed that teachers’ intention to continue using this e-book is significantly influenced by their perceived usefulness and attitude towards this e-book. Additionally, both the interactive and openness features were very helpful for teachers in using this e-book in their teaching plans. However, some of them raised several concerns like the interactive coding platform should be personalized based on students’ age. The findings of this study could help different stakeholders (e.g., instructional designers, teachers, policymakers) in facilitating the design and adoption of open and interactive e-books.

Group discussion is a common and important form of learning. The effectiveness of group discussion could be facilitated by the adaptive support of virtual agent. Argumentative knowledge construction is beneficial to learners’ acquisition of knowledge, but the effectiveness of argumentative scaffolding is not consistent in existing studies. In this study, a total of 64 college students (32 groups, two participants and one computer agent in each group) participated in the experiment and they were assigned to the experimental condition (16 groups) and the control condition (16 groups). In the control condition, the computer agent would give an idea from semantically different categories according to the automatic categorization of the current discussion. In the experimental condition, the computer agent provided argumentative scaffolding after giving diverse ideas to support participants’ deep processing. The argumentative scaffolding included two prompt questions, “do you agree with me?” and “could you give the reasons to support your viewpoint?.” The dependent variables were the interaction quality, network centrality, the breadth and depth of discussion, the self-reported of discussion effectiveness and the degree of change before and after the discussion. Findings revealed that compared with the control condition, the participants were more likely to discuss the keywords provided by the virtual agent and reported more comprehensive understanding of the discussion topic, but surveyed less ideas and interactions during the discussion under the argumentative condition. This study suggests that the argumentative scaffolding may have both positive and negative effect on the group discussion and it’s necessary to make a choice.

The present research conducted two experiments with an intelligent tutoring system to investigate the overall and dynamic impact of emotional support from a pedagogical agent (PA). In Experiment 1, a single factor intergroup design was used to explore the impact of PA's emotional support (supportive vs. non-supportive) on learners' emotions, intrinsic motivation, and learning gain. Sixty participants were recruited and randomly assigned to one of the two conditions. Experiment 2 also conducted a single factor between-subjects design to investigated the dynamic patterns between emotions and learning strategies among 30 participants using lag sequential analysis. Results showed that: Compared with the non-supportive pedagogical agent, the supportive pedagogical agent reduced frustration and improved learning gain, but did not increase intrinsic motivation. In addition, learners with the supportive pedagogical agent used more appropriate strategies after frustration and surprise, and use less ineffective strategies after confusion and enjoyment to avoid reaching a wrong answer. If learners did not receive emotional support in such cases, learning strategies following these emotions were more likely lead to negative cognitive results, or negative emotions tended to appear repeatedly. Instructors or PAs should identify learners' emotions in time and provide the appropriate emotional support according to learners' emotions.

The controversy in the relationship between item memory and source memory is a focus of episodic memory. Some studies show the trade-off between item memory and source memory, some show the consistency between them, and others show the independence between them. This review attempts to point out the connection-strength model, implying the different types and strengths of the important role of the item–source connections in the relationship between item memory and source memory, which is based on the same essence in the unified framework. The logic of the model is that when item memory and source memory share the same or relevant connection between item and source, they positively connect, or they are independently or negatively connected. This review integrates empirical evidence from the domains of cognition, cognitive neuroscience, and mathematical modeling to validate our hypothesis.

Abstract Background The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics, electronics, and dynamical systems. After the teams shared their progress at the conclusion of an 18-month period, the ONR decided to fund a joint applied project in the Navy that integrated those systems on the subject matter of electronic circuits. The University of Memphis took the lead in integrating these systems in an intelligent tutoring system called ElectronixTutor. This article describes the architecture of ElectronixTutor, the learning resources that feed into it, and the empirical findings that support the effectiveness of its constituent ITS learning resources. Results A fully integrated ElectronixTutor was developed that included several intelligent learning resources (AutoTutor, Dragoon, LearnForm, ASSISTments, BEETLE-II) as well as texts and videos. The architecture includes a student model that has (a) a common set of knowledge components on electronic circuits to which individual learning resources contribute and (b) a record of student performance on the knowledge components as well as a set of cognitive and non-cognitive attributes. There is a recommender system that uses the student model to guide the student on a small set of sensible next steps in their training. The individual components of ElectronixTutor have shown learning gains in previous decades of research. Conclusions The ElectronixTutor system successfully combines multiple empirically based components into one system to teach a STEM topic (electronics) to students. A prototype of this intelligent tutoring system has been developed and is currently being tested. ElectronixTutor is unique in its assembling a group of well-tested intelligent tutoring systems into a single integrated learning environment.

Abstract Background This study investigated learning outcomes and user perceptions from interactions with a hybrid intelligent tutoring system created by combining the AutoTutor conversational tutoring system with the Assessment and Learning in Knowledge Spaces (ALEKS) adaptive learning system for mathematics. This hybrid intelligent tutoring system (ITS) uses a service-oriented architecture to combine these two web-based systems. Self-explanation tutoring dialogs were used to talk students through step-by-step worked examples to algebra problems. These worked examples presented an isomorphic problem to the preceding algebra problem that the student could not solve in the adaptive learning system. Results Due to crossover issues between conditions, experimental versus control condition assignment did not show significant differences in learning gains. However, strong dose-dependent learning gains were observed that could not be otherwise explained by either initial mastery or time-on-task. User perceptions of the dialog-based tutoring were mixed, and survey results indicate that this may be due to the pacing of dialog-based tutoring using voice, students judging the agents based on their own performance (i.e., the quality of their answers to agent questions), and the students’ expectations about mathematics pedagogy (i.e., expecting to solving problems rather than talking about concepts). Across all users, learning was most strongly influenced by time spent studying, which correlated with students’ self-reported tendencies toward effort avoidance, effective study habits, and beliefs about their ability to improve in mathematics with effort. Conclusions Integrating multiple adaptive tutoring systems with complementary strengths shows some potential to improve learning. However, managing learner expectations during transitions between systems remains an open research area. Finally, while personalized adaptation can improve learning efficiency, effort and time-on-task for learning remains a dominant factor that must be considered by interventions.

Background: Diagnosing with low-grade gliomas (LGGs) can be a very shocking and stressful experience, a traumatic event potentially leading to the development of posttraumatic stress symptoms (PTSS), and posttraumatic growth (PTG). Understanding how patients cognitively and behaviorally response to their diagnosing is also important to postoperative treatment. Thus, the current study explored the association between PTG and quality of life (QoL) of Chinese patients with LGGs. The moderation effects of coping strategies and PTSS on the relationship between PTG and QoL have been examined as well.Methods: Posttraumatic stress symptoms, Posttraumatic growth, coping strategies, and QoL were measured by using self-report surveys. Three hundred and thirty patients completed surveys approximately 1 month after surgery. We used three multiple regression models and added interaction terms in these models to test the moderation effects of PTSS and coping strategies on the relationship between PTG and QoL.Results: The results of hierarchical multiple regression suggested that PTG significantly predicted QoL, both PTSS and coping strategies moderated the association between PTG and QoL. Specifically, the association between PTG and QoL for patients who have non-significant PTSS is stronger than those who have significant PTSS. Furthermore, as the score of Avoidant Coping increases, the association between PTG and QoL becomes weaker.Conclusion: Posttraumatic growth may help to improve the QoL of LGGs patients, but PTSS and Avoidant Coping impeded the positive effect of PTG on QoL.

Background: Self-regulated learning refers to the monitoring and controlling of one's own cognitive performance before, during, and after a learning episode. Previous literature suggested that self-regulated learning had a significant relationship with academic achievement, but not all self-regulated learning strategies exerted the same influences. Using an invalid strategy may waste the limited psychological resources, which will cause the ego depletion effect. The present meta-analysis study intended to search for the best self-regulated learning strategies and inefficient strategies for Chinese students in elementary and secondary school, and analyzed the critical phases of self-regulated learning according to Zimmerman's theory. The moderating effects of gender, grade, and publication year were also analyzed.Methods: Empirical studies which conducted in real teaching situations of elementary and secondary education were systematically searched using Chinese academic databases. Studies focused on undergraduate students, students of special education, or online learning environments were excluded. Fifty-five cross-sectional studies and four intervention studies (which generated 264 independent samples) were included with a total sample size of 23,497 participants. Random effects model was chosen in the current meta-analysis, and publication bias was also examined.Results: The results indicated that the overall effect size of self-regulated learning on academic achievement was small for primary and secondary school students in China. The effect sizes of self-efficacy, task strategies, and self-evaluation were relatively higher than other strategies. Self-regulated learning strategies have the largest effect size on science disciplines (including mathematics and physics). Performance phase and self-reflection phase are key phases of self-regulated learning. From 1998 to 2016, the effect size between self-regulated learning and academic achievement was gradually decreasing.Conclusions: The main findings of the current study showed that self-efficacy, task strategies, and self-evaluation were key self-regulated learning strategies for Chinese students. Performance phase and self-reflection phase played significant roles in the process of self-regulated learning. Future studies need to include more intervention studies with rigorous treatment fidelity control and provide more empirical evidence from online learning, so as to compare the different effects of self-regulated learning between traditional education and online education.

This study explored the role of question types and prior knowledge in vicarious learning with an intelligent tutoring system. In experiment 1, the participants were assigned to three conditions (deep questions, shallow questions, control), the results showed that participants in the deep questions condition had higher retention test scores than those in the shallow questions condition and those in the control condition. In experiment 2, we conducted a 2 (prior knowledge: high, low) × 3 (question types: deep question, shallow question, control) between-subjects experimental design, and participants in the deep question condition again had higher retention test scores than the other conditions. Also, participants in the shallow question condition had higher retention test scores than those in the control condition. Additionally, no interaction between prior knowledge and question types was observed. In conclusion, deep questions can promote learning of experimental design, which replicates prior research results on deep level questions.

Background: Little is known about the effectiveness of instructors when presenting content in videos alone. In recent years, researchers have increasingly begun to explore the effects of instructors' social cues (e.g., eye gaze, body orientation, etc.) on learning. However, previous studies exploring the effects of eye gaze have confounded the role of body orientation, while studies exploring body orientation have confounded the role of eye gaze. Objectives: To explore the role of direct gaze and body orientation in learning with instructional videos, absent an instructional screen, in a less confounded manner. Methods: A total of 63 subjects were presented select concepts regarding schizophrenia. Eye-tracking technology combined with current theories of parasocial interaction and social agency was used to explore different social cues that affect learning performance. Students were randomly assigned to one of three experimental conditions: frontal body + direct gaze group (FD group), frontal body + no direct gaze group (FND group), and lateral body + no direct gaze group (LND group). Result and Conclusions: Direct gaze facilitated students' immediate retention and transfer scores, reduced learners' extraneous cognitive load, and guided students' attention to the instructor. However, counter to our expectations, parasocial interaction did not affect social cues. Implications: In both online and offline classes, instruction is expected to be enhanced by focusing on the camera to the extent possible, particularly when instructional screens are absent.

Resonance is generally used as a metaphor to describe the manner how the information from different sources is combined. Although it is an attractive and fundamental phenomenon in human behavior studies, most studies observed semantic resonances in well-controlled experimental settings at word level. To make up the missing link between word and document level resonances, we devoted our contributions to topic resonances in a novel and natural setting: academic commentaries. Ninety-three academic commentaries from ninety-three authors, along with their references and original papers, are analyzed by a latent Dirichlet allocation based natural language processing approach. This approach can decompose a corpus written and read by an author into several topics with different weights, which can reveal the phenomena ignored at word or document level. We found that (1) topic resonances commonly exist between commenters' fundamental input and output topics; (2) output words are re-allocated by commenters to echo salient input topics; (3) commenters are more prone to associate references which focus on the non-dominant input topics; and (4) topic resonance can even be predicted by a Hebbian-like model which matches the aforementioned findings. These findings will continue to enrich our understanding on the relationship among probe, feedback and context.

Big consumer data promises to be a game changer in applied and empirical marketing research. However, investigations of how big data helps inform consumers’ psychological aspects have, thus far, only received scant attention. Psychographics has been shown to be a valuable market segmentation path in understanding consumer preferences. Although in the context of e-commerce, as a component of psychographic segmentation, personality has been proven to be effective for prediction of e-commerce user preferences, it still remains unclear whether psychographic segmentation is practically influential in understanding user preferences across different product categories. To the best of our knowledge, we provide the first quantitative demonstration of the promising effect and relative importance of psychographic segmentation in predicting users’ online purchasing preferences across different product categories in e-commerce by using a data-driven approach. We first construct two online psychographic lexicons that include the Big Five Factor (BFF) personality traits and Schwartz Value Survey (SVS) using natural language processing (NLP) methods that are based on behavior measurements of users’ word use. We then incorporate the lexicons in a deep neural network (DNN)-based recommender system to predict users’ online purchasing preferences considering the new progress in segmentation-based user preference prediction methods. Overall, segmenting consumers into heterogeneous groups surprisingly does not demonstrate a significant improvement in understanding consumer preferences. Psychographic variables (both BFF and SVS) significantly improve the explanatory power of e-consumer preferences, whereas the improvement in prediction power is not significant. The SVS tends to outperform BFF segmentation, except for some product categories. Additionally, the DNN significantly outperforms previous methods. An e-commerce-oriented SVS measurement and segmentation approach that integrates both BFF and the SVS is recommended. The strong empirical evidence provides both practical guidance for e-commerce product development, marketing and recommendations, and a methodological reference for big data-driven marketing research.

To better understand the self-regulated learning process in online learning environments, this research applied a data mining method, the two-layer hidden Markov model (TL-HMM), to explore the patterns of learning activities. We analyzed 25,818 entries of behavior log data from an intelligent tutoring system. Results indicated that students with different learning outcomes demonstrated distinct learning patterns. Students who failed a problem set exhibited more passive learning behaviors and could hardly learn from practice, while students who mastered a problem set could effectively regulate their learning. Furthermore, we extended the use of TL-HMM to predicting learning outcome from behavior sequences and checked through crossvalidation. TL-HMM is demonstrated helpful to gain insight into learners' interactions with online learning environments. In practice, TLHMM could be embedded in intelligent tutoring systems to monitor learning behaviors and learner status, so as to detect the difficulties of learners and facilitate learning. [ABSTRACT FROM AUTHOR]