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In this research, we use a process data analysis approach to gather additional evidence about students' argumentation skills beyond their performance scores in a computer-based assessment. This game-enhanced scenario-based assessment (named Seaball) included five activities that require students to demonstrate their argumentation skills within a scenario about whether junk food should be sold to students. Our research sample included 104 middle school students. Process data analyses focused on an "Interview" activity in which students explored different locations and interviewed various characters to identify their opinions on the junk food issue and categorize each opinion as pro or con. Students could take various paths to complete the activity. Results indicated that the number of trials students made in the Interview activity predicted their performance on the Interview activity as well as the total Seaball scores. It was also found that most students improved their answers in the Interview activity after receiving automated feedback and making corresponding changes. Besides the connections between student activities and performance, results from analyzing the process data helped us to identify difficult items in the task. We conclude with implications for conducting process data analysis to better assess students' argumentation skills and to inform task design.

In Mainland China, the sports training process of most players is highly homogenized, the convergence of which makes them ineffectively be identified with their individual and specific profile and difficult for them to play the sports according to their strengths and characteristics. Moreover, existing sports training software does not differentiate between player types to provide customized persona. Therefore, efficient and personalized methods need to be provided to guide players towards more autonomous sports training. Current research shows that gamification design in the process of sports training can transform players' unique conscious behaviors into habits, thus increasing their autonomy. However, the current gamification design in sports training is only based on uniform gamification elements and does not take into account the player's motivation and gamification experience, which is one of the main reasons for the homogenization of sports training. Therefore, this study aimed to identify factors that contribute to the design of gamification systems in the field of sports training, as well as to determine the relationship between players' gamification experiences during sport. It will help the researchers to explore in depth the possibilities of learning environments for youth basketball training with the development of gamified experiences. This design-driven study performed both offline and online questionnaire research (N = 198), which was analyzed with the method of a 7-point Likert scale as well as the assistance of SPSS, identified potential for the establishment of a framework for analysing preferences for gamification design elements in the context of basketball training for young players. Based on the results, this paper finds that there is a correlation between immersion and achievement in gamification experiences and proposes a framework for gamification system design in the field of sports training and offers insight that may enable the development of gamification designs that can motivate players.

The research on using event logs and item response time to study test-taking processes is rapidly growing in the field of educational measurement. In this study, we analyzed the keystroke logs collected from 761 middle school students in the United States as they completed a persuasive writing task. Seven variables were extracted from the keystroke logs and compared with different score and gender groups. Group comparisons were also made using methodologies borrowed from sequence mining. Students' composition strategies over the course of the writing process were also investigated. The findings of this study have implications for gaining deeper understanding of observed group differences and for designing interventions to close the achievement gaps among population groups.

In this paper, we examine the student group discussion processes in a scenario-based assessment of engineering professional skills called Engineering Professional Skills Assessment (EPSA). In the assessment, the students were evaluated through a discussion on a scenario related to an engineering problem with no clear-cut solution. We applied various social network analysis (SNA) techniques and constructed 2 types of networks to model the patterns of the communication process in the student discussion and the connections among engineering professional skills (EPS) during the discussion process. We found that some network statistics were statistically related to team scores (or performance), such as the density of the EPS networks. We also investigated the temporal pattern of the discussion process by comparing the network statistics across 3 different time points. Results showed that the middle and end of the discussion processes differed significantly on 2 network measures: communication density and EPS connectedness. Results of the study have implications for instruction and scoring of such performance tasks. Limitations and next steps are also discussed.

Objective To explore the correlation between serum tumor markers and mutations of driver genes in non-small cell lung cancer (NSCLC) patients, so as to provide references for serum tumor markers-guided targeted therapies for NSCLC. Methods A cross-sectional study was performed on 1 008 NSCLC patients admitted in the respiratory, oncology and thoracic surgery departments of our medical center from December 2015 to March 2021. All these patients underwent tissue biopsies and driver gene tests. Their clinical data and results of serum tumor markers and lung cancer-specific gene test were collected. The correlation of mutations of driver genes with clinical characteristics and serum tumor markers were analyzed with correlation analysis and receiver operating characteristic (ROC) curve analysis. Then the patients were grouped according to the elevated levels of these tumor markers and presence of surgical indicators or not. The differences in mutation types and rates in the driver genes were observed among different groups of patients. Results In the 1 008 patients, there were 674 males and 334 females, with an average age of 58.4 years. The total incidence of driver gene mutations was 64.5%. Higher mutation rate of epidermal growth factor receptor (EGFR) was observed in the patients with increased serum carcinoembryonic antigen (CEA) level than those without (49.9% vs 32.6%, P < 0.001) and in the patients with unraised cytokeratin fragment 19 (CYFRA21-1) level than those with unchanged or elevated level (48.5% vs 38.3%, P=0.003). And, the serum CEA level was significantly higher in the EGFR mutant patients than those with EGFR wild-type [12.19 (3.49~79.05) vs 4.93 (2.47~17.29) ng/mL, P < 0.001]. With the increase of serum CEA level, the occurrence of EGFR mutations was gradually increased. Stratified analysis showed that in stage ⅢB to Ⅵ patients, the serum CEA level was obviously higher in patients with EGFR mutation than those with EGFR wild-type. The patients with abnormally elevated serum neuron-specific enolase (NSE) level showed higher mutation rate of kirsten rat sarcoma viral oncogene (KRAS) than those with normal NSE level. Conclusion In NSCLC patients, serum CEA level is correlated with occurrence of EGFR mutations. Especially for those with stage ⅢB to Ⅵ, higher serum CEA level indicates higher mutation rate of EGFR. Lung cancer-specific gene test should be recommended to carry out in advanced lung cancer patients with significantly elevated CEA in order to determine whether there are indications for target therapy.

Collaborative problem solving (CPS) is a critical competency in a variety of contexts, including the workplace, school, and home. However, only recently have assessment and curriculum reformers begun to focus to a greater extent on the acquisition and development of CPS skill. One of the major challenges in psychometric modeling of CPS is collecting large-scale data on teams and processes. In this study, we explore the use of agent-based modeling (ABM) to model the CPS process, test the sensitivity of outcomes to different population characteristics, and generate simulated data that can provide a novel means by which to refine and develop psychometric models. Methods of adapting trait-based stochastic processes to a specific task are described, and preliminary results are presented.

The development of social networks provides a broad platform for the dissemination of information and also leads to the proliferation of fake news and false information, which we collectively refer to as rumors. The spread of rumors causes unnecessary panic and loss to individuals and society. To reduce the negative impacts of rumors, an appropriate rumor control strategy is necessary. To come up with some reasonable strategies, we need to have a clearer understanding of the spread of rumors. In this paper, we analyze crowd attitudes during the spreading of rumors by setting the misinformation prevalent progress on the social network as a dynamic system. Considering that most people do not have a clear supportive or opposing attitude when exposed to rumor information, we introduce a new group, stiflers who remain neutral, based on the infectious disease model scheme. By deriving the mean-field equation describing the rumor propagation process, we judge the stability of the constructed model. Finally, we use the model to fit the real-world data related to COVID-19, and based on this, we discuss the properties of the model and propose related strategies. [ABSTRACT FROM AUTHOR]

While climate change discourse on Western platforms like Twitter often reveals signs of polarization and misinformation, discussions on Chinese social media remain less explored. Building on the theoretical framework of the green public sphere, this study aims to explore the features of the content (topics and veracity), the characteristics of engaged users (regular users and social bots), and the communication strategies adopted by engaged users in climate change discussions on Chinese social media. We employed machine learning methods to analyze 452,167 climate change-related posts generated by 311,214 users from 2010 to 2020 on Weibo, finding that climate change discourse concentrated on environmental and health impacts and action advocacy, and misinformation was not prevalent. Regarding the composition of engaged users, only a small proportion were social bots which concentrated on action advocacy and politics and governance, rather than skeptical and denialist discourses. In terms of communication strategies, we found that social bots on Weibo were more likely to forward a post or mention another user than regular users. This study expands our understanding of climate change discourse and the green public sphere on social media and provides insights into leveraging social bots in climate change communication in an AI-powered society. [ABSTRACT FROM AUTHOR]

This study investigates the role of automated scoring and feedback in supporting students' construction of written scientific arguments while learning about factors that affect climate change in the classroom. The automated scoring and feedback technology was integrated into an online module. Students' written scientific argumentation occurred when they responded to structured argumentation prompts. After submitting the open-ended responses, students received scores generated by a scoring engine and written feedback associated with the scores in real-time. Using the log data that recorded argumentation scores as well as argument submission and revisions activities, we answer three research questions. First, how students behaved after receiving the feedback; second, whether and how students' revisions improved their argumentation scores; and third, did item difficulties shift with the availability of the automated feedback. Results showed that the majority of students (77%) made revisions after receiving the feedback, and students with higher initial scores were more likely to revise their responses. Students who revised had significantly higher final scores than those who did not, and each revision was associated with an average increase of 0.55 on the final scores. Analysis on item difficulty shifts showed that written scientific argumentation became easier after students used the automated feedback.

New technology enables interactive and adaptive scenario-based tasks (SBTs) to be adopted in educational measurement. At the same time, it is a challenging problem to build appropriate psychometric models to analyze data collected from these tasks, due to the complexity of the data. This study focuses on process data collected from SBTs. We explore the potential of using concepts and methods from social network analysis to represent and analyze process data. Empirical data were collected from the assessment of Technology and Engineering Literacy, conducted as part of the National Assessment of Educational Progress. For the activity sequences in the process data, we created a transition network using weighted directed networks, with nodes representing actions and directed links connecting two actions only if the first action is followed by the second action in the sequence. This study shows how visualization of the transition networks represents process data and provides insights for item design. This study also explores how network measures are related to existing scoring rubrics and how detailed network measures can be used to make intergroup comparisons.