Understanding Genetic Breast Cancer Risk: Processing Loci of the BRCA Gist Intelligent Tutoring System.

The BRCA Gist Intelligent Tutoring System helps women understand and make decisions about genetic testing for breast cancer risk. BRCA Gist is guided by Fuzzy-Trace Theory, (FTT) and built using AutoTutor Lite. It responds differently to participants depending on what they say. Seven tutorial dialogues requiring explanation and argumentation are guided by three FTT concepts: forming gist explanations in one's own words, emphasizing decision-relevant information, and deliberating the consequences of decision alternatives. Participants were randomly assigned to BRCA Gist, a control, or impoverished BRCA Gist conditions removing gist explanation dialogues, argumentation dialogues, or FTT images. All BRCA Gist conditions performed significantly better than controls on knowledge, comprehension, and risk assessment. Significant differences in knowledge, comprehension, and fine-grained dialogue analyses demonstrate the efficacy of gist explanation dialogues. FTT images significantly increased knowledge. Providing more elements in arguments against testing correlated with increased knowledge and comprehension.

The development and analysis of tutorial dialogues in AutoTutor Lite.

The goal of intelligent tutoring systems (ITS) that interact in natural language is to emulate the benefits that a well-trained human tutor provides to students, by interpreting student answers and appropriately responding in order to encourage elaboration. BRCA Gist is an ITS developed using AutoTutor Lite, a Web-based version of AutoTutor. Fuzzy-trace theory theoretically motivated the development of BRCA Gist, which engages people in tutorial dialogues to teach them about genetic breast cancer risk. We describe an empirical method to create tutorial dialogues and fine-tune the calibration of BRCA Gist's semantic processing engine without a team of computer scientists. We created five interactive dialogues centered on pedagogic questions such as "What should someone do if she receives a positive result for genetic risk of breast cancer?" This method involved an iterative refinement process of repeated testing with different texts and successively making adjustments to the tutor's expectations and settings in order to improve performance. The goal of this method was to enable BRCA Gist to interpret and respond to answers in a manner that best facilitated learning. We developed a method to analyze the efficacy of the tutor's dialogues. We found that BRCA Gist's assessment of participants' answers was highly correlated with the quality of the answers found by trained human judges using a reliable rubric. The dialogue quality between users and BRCA Gist predicted performance on a breast cancer risk knowledge test completed after exposure to the tutor. The appropriateness of BRCA Gist's feedback also predicted the quality of answers and breast cancer risk knowledge test scores.

Status quo and predictors of Weibo users' attitudes toward lesbians and gay men in 31 provinces in the Chinese mainland: Analysis based on supervised machine learning and provincial panel data.

Introduction: Public 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. Methods: Natural 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. Results: Although 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. Conclusion: Weibo 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.

The double-edged sword effect of argumentative scaffolding on group discussion in an adaptive discussion system.

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.

Improved EM algorithm for MPT model analysis.

Multinomialprocessing tree (MPT) models are in wide use as measurement models for analyzing categorical data in cognitive experiments. The approach involves estimating parameters and conducting hypothesis tests involving parameters that are arrayed in a tree structure designed to represent latent cognitive processes. The standard inference algorithm for these models is based on the well-known expectationmaximization (EM) algorithm. On the basis of the original use of the EMalgorithm for MPT models, this article presents an approach that accelerates the convergence speed of the algorithm by (1) adjusting suitable initial positions for certain parameters to reduce required iterative times and (2) using a series of operations between/among a set of matrices that are specific to the original model structure and information to reduce the time required for a single iteration. As compared with traditional algorithms, the simulation results show that the proposed algorithm has superior efficiency in interpreted languages and also has better algorithm readability and structure flexibility.

Relationship Between Item and Source Memory: Explanation of Connection-Strength Model.

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.

Detecting changes in attitudes toward depression on Chinese social media: A text analysis.

BACKGROUND & AIMS: Depression is a common and sometimes severe form of mental illness, and public attitudes towards depression can impact the psychological and social functioning of depressed patients. The purpose of the present study was to investigate public attitudes toward depression and three-year trends in these attitudes using big data analysis of social media posts in China. METHODS: A search of publically available Sina Weibo posts from January 2014 to July 2017 identified 20,129 hot posts with the keyword term "depression". We first used a Chinese Linguistic Psychological Text Analysis System (TextMind) to analyze linguistic features of the posts. And, then we used topic models to conduct semantic content analysis to identify specific themes in Weibo users' attitudes toward depression. RESULTS: Linguistic features analysis showed a significant increase over time in the frequency of terms related to affect, positive emotion, anger, cognition (including the subcategory of insight), and conjunctions. Semantic content analysis identified five common themes: severe effects of depression, stigma, combating stigma, appeals for understanding, and providing support. There was a significant increase over time in references to social (as opposed to professional) support, and a significant decrease over time in references to the severe consequences of depression. CONCLUSIONS: Big data analysis of Weibo posts is likely to provide less biased information than other methods about the public's attitudes toward depression. The results suggest that although there is ongoing stigma about depression, there is also an upward trend in mentions of social support for depressed persons. A supervised learning statistical model can be developed in future research to provide an even more precise analysis of specific attitudes.

Does high teacher autonomy support reduce smartphone use disorder in Chinese adolescents? A moderated mediation model.

The Interaction of Person-Affect-Cognition-Execution model (I-PACE; Brand, Young, Laier, Wölfling, & Potenza, 2016) proposes that addictive behavior is the result of the interaction of multiple factors. According to I-PACE model, perceived social support (teacher autonomy support), self-esteem, and gratification (life satisfaction) contribute to adolescent smartphone use disorder (SUD) (Brand et al., 2016). However, previous studies have rarely examined the interactive effects of teacher autonomy support, self-esteem and life satisfaction on adolescent SUD. The present study examined these relationships using a moderated mediation model in which self-esteem played a mediating role and life satisfaction played a moderating role in the relation between teacher autonomy support and adolescent SUD. A sample of 1912 Chinese adolescents completed measures of teacher autonomy support, self-esteem, life satisfaction, and adolescent SUD. Self-esteem mediated the association between teacher autonomy support and adolescent SUD. In addition, the relation between teacher autonomy support and SUD was moderated by life satisfaction: when the effect of life satisfaction was high, teacher autonomy support negatively predicted adolescent SUD, whereas when the effect of life satisfaction was low, teacher autonomy support was positively related to adolescent SUD. These findings advance our understanding of the effect of teacher autonomy support, self-esteem and life satisfaction on adolescent SUD. Limitations and implications of this study are discussed, such as teacher autonomy support may not reduce adolescent SUD, especially when their life satisfaction is low.

Are Posttraumatic Stress Symptoms and Avoidant Coping Inhibitory Factors? The Association Between Posttraumatic Growth and Quality of Life Among Low-Grade Gliomas Patients in China.

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.

Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach.

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.

What Are the Effects of Self-Regulation Phases and Strategies for Chinese Students? A Meta-Analysis of Two Decades Research of the Association Between Self-Regulation and Academic Performance.

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.

Health-Related Quality of Life and Posttraumatic Growth in Low-Grade Gliomas in China: A Prospective Study.

OBJECTIVE: The present study aimed to describe the quality of life (QoL) changes of survivors of low-grade gliomas (LGGs) 1 year after surgery and to identify determinants of QoL with an emphasis on the role of perceived posttraumatic growth (PTG). We also tried to examine the linear and quadratic relationship between QoL and PTG. METHODS: Two hundred sixty participants were included in the final data analysis. The Chinese version of posttraumatic growth inventory and the Functional Assessment of Cancer Therapy-Brain scale were used to measure PTG and QoL. Hierarchical linear models were fitted to explore the individual time trajectories in change of QoL and examine the relationship between demographics, clinical features, PTG, and QoL. RESULTS: All dimensions of QoL and PTG increased over time except physical well-being, social well-being in QoL, and new possibilities in PTG. Time, PTG score, insurance, socioeconomic status, and right hemisphere tumor position were positive predictors of QoL. Seizure and depression negatively predicted QoL. The quadratic of PTG predicted QoL; however, the coefficient of quadratic PTG approached zero. CONCLUSIONS: In general, PTG and QoL increased over time. Perceived PTG could significantly predict QoL of LGGs survivors 1 year after surgery. A quadratic relation between PTG and QoL was not found. Although our data suggested that the growth of QoL may vary across different patients, there were only 2 time points in this study. Future studies should set more time points to examine this relationship.

SKOPE-IT (Shareable Knowledge Objects as Portable Intelligent Tutors): overlaying natural language tutoring on an adaptive learning system for mathematics.

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.

ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics.

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.

Optimization of a multinomial model for investigating hallucinations and delusions with source monitoring.

Studies of source monitoring have played an important role in cognitive investigations of the inner/outer confusions that characterize hallucinations and delusions in schizophrenia, and multinomial modelling is a statistical/cognitive modelling technique that provides a powerful method for analyzing source monitoring data. The purpose of the current work is to describe how multinomial models can be optimized to answer direct questions about hallucinations and delusions in schizophrenia research. To demonstrate this, we present a reanalysis of previously published source monitoring data, comparing a group of patients with schneiderian first rank symptoms to a group without schneiderian first rank symptoms. The main findings of this analysis were (1) impaired recognition of self-generated items and (2) evidence that impaired source discrimination of perceived items is accompanied by an internalization bias in the target symptom group. Statistical and cognitive interpretations of the findings are discussed.

Impairment of specific episodic memory processes by sub-psychotic doses of ketamine: the effects of levels of processing at encoding and of the subsequent retrieval task.

RATIONALE: The precise nature of the impact of the N-methyl-D-aspartate antagonist, ketamine, upon human episodic memory, has yet to be elucidated fully. OBJECTIVES: This study sought to assess the effects of ketamine on the sub-processes facilitating memory encoding and retrieval. METHODS: We evaluated the effects of the drug on a series of memory performance measures depending upon whether it was administered at the encoding or retrieval stage and on the nature of the encoding task used. Twelve healthy volunteers participated in a double-blind, placebo-controlled, randomized, within-subjects study. Intravenous infusions of placebo, 50 ng/ml ketamine or 100 ng/ml ketamine were administered. We investigated the effects of ketamine on three key aspects of episodic memory: encoding vs retrieval processes, source memory, and depth of processing. Data were analysed using both multinomial modelling and standard measures of item discrimination and response bias. RESULTS: Deleterious effects of ketamine on episodic memory were primarily attributable to its effects on encoding, rather than retrieval processes. Recognition memory was impaired for items encoded at an intermediate level of processing, but preserved for shallowly and deeply encoded items. Increased source guessing bias was also observed when encoding took place under ketamine. CONCLUSIONS: The effects of ketamine upon episodic memory seem, therefore, to predominate at encoding. Furthermore, our results are also consistent with a specific impairment of encoding processes that result in subsequent recollective, as opposed to familiarity-based, retrieval. The observed effects are compatible with memory deficits seen in schizophrenia and thus provide some support for the ketamine model of the disease.

Extending General Processing Tree Models to Analyze Reaction Time Experiments.

General processing tree (GPT) models are usually used to analyze categorical data collected in psychological experiments. Such models assume functional relations between probabilities of the observed behavior categories and the unobservable choice probabilities involved in a cognitive task. This paper extends GPT models for categorical data to the analysis of continuous data in a class of response time (RT) experiments in cognitive psychology. Suppose that a cognitive task involves several discrete processing stages and both accuracy (categorical) and latency (continuous) measures are obtained for each of the response categories. Furthermore, suppose that the task can be modeled by a GPT model that assumes serialization among the stages. The observed latencies of the response categories are functions of the choice probabilities and processing times (PT) at each of the processing stages. The functional relations are determined by the processing structure of the task. A general framework is presented and it is applied to a set of data obtained from a source monitoring experiment. Copyright 2001 Academic Press.

Human use regulatory affairs advisor (HURAA): learning about research ethics with intelligent learning modules.

The Human Use Regulatory Affairs Advisor (HURAA) is a Web-based facility that provides help and training on the ethical use of human subjects in research, based on documents and regulations in United States federal agencies. HURAA has a number of standard features of conventional Web facilities and computer-based training, such as hypertext, multimedia, help modules, glossaries, archives, links to other sites, and page-turning didactic instruction. HURAA also has these intelligent features: (1) an animated conversational agent that serves as a navigational guide for the Web facility, (2) lessons with case-based and explanation-based reasoning, (3) document retrieval through natural language queries, and (4) a context-sensitive Frequently Asked Questions segment, called Point & Query. This article describes the functional learning components of HURAA, specifies its computational architecture, and summarizes empirical tests of the facility on learners.