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How individuals process and understand controversial scientific issues with social implications has been linked to their beliefs about epistemic justification, which concern how knowledge claims can be justified. In this study, we used cluster analysis to classify undergraduate and graduate students (n = 46) based on their beliefs about epistemic justification and eye tracking to investigate how profiles of epistemic justification differed when processing and representing information about a particular socio-scientific issue. It was found that one cluster predominantly relied on justification by multiple sources, whereas two other clusters combined reliance on justification by multiple sources with either reliance on personal justification or justification by authority. When these three clusters were compared while reading conflicting information about a controversial socio-scientific issue, multiple heat-map analysis and lag sequential analysis of eye movement data indicated that participants who predominantly relied on justification by multiple sources displayed a more balanced and integrative processing pattern than participants in the two other groups. Further, the cluster characterized by strong, unique beliefs in justification by multiple sources represented conflicting information in a more balanced way in written accounts of the issue. This study provides new insights into the role of beliefs about epistemic justification when learners encounter conflicting information about a controversial socio-scientific issue that have both theoretical and educational implications.

Abstract Background Data visualizations transform data into visual representations such as graphs, diagrams, charts and so forth, and enable inquiries and decision-making in many professional fields, as well as in public and economic areas. How students’ data visualization literacy (DVL), including constructing, comprehending, and utilizing adequate data visualizations, can be developed is gaining increasing attention in STEM education. As fundamental steps, the purpose of this study was to understand common student difficulties and useful strategies during the process of constructing data visualization so that suggestions and principles can be made for the design of curricula and interventions to develop students’ DVL. Methods This study engaged 57 college and high school students in constructing data visualizations relating to the topic of air quality for a decision-making task. The students’ difficulties and strategies demonstrated during the process of data visualization were analyzed using multiple collected data sources including the students’ think-aloud transcripts, retrospective interview transcripts, and process videos that captured their actions with the data visualization tool. Qualitative coding was conducted to identify the students’ difficulties and strategies. Epistemic network analysis (ENA) was employed to generate network models revealing how the difficulties and strategies co-occurred, and how the college and high school students differed. Results Six types of student difficulties and seven types of strategies were identified. The strategies were further categorized into non-, basic- and high-level metavisual strategies. About three-quarters of the participants employed basic or high-level metavisual strategies to overcome the technological and content difficulties. The high school students demonstrated a greater need to develop content knowledge and representation skills, whereas the college students needed more support to know how to simplify data to construct the best data visualizations. Conclusions and implications The study specified metacognition needed for data visualization, which builds on and extends the cognitive model of drawing construction (CMDC) and theoretical perspectives of metavisualization. The results have implications for developing students’ data visualization literacy in STEM education by considering the difficulties and trajectories of metacognitive strategy development, and by addressing the different patterns and needs demonstrated by the college and high school students.

One of the major purposes of this study is to investigate the potential impact of gender and information and computer technology (ICT) resources on students’ computational thinking (CT) competencies. To this end, the Computational Thinking Test for Junior High Students (CTT-JH) was developed and validated. Research participants included 437 junior high school students in Taiwan. The surveyed schools were categorized into more or fewer ICT resources. Then, discrimination analyses and Rasch modeling for item analyses and two-way ANOVA were conducted. Results showed that the final version of CTT-JH is of good item quality. Students in schools with more ICT resources had higher CT test mean scores regardless of gender. Nevertheless, at schools with limited resources, male students had significantly lower CT test mean scores than female students did. This study provides new insights into how gender and ICT resources can interact with and impact on students’ CT competencies. It also provides a valid and reliable tool for assessing young adolescents’ CT abilities.

This study aims to examine the effects of instructional designs with a pre-training video and real-time cues on game-based learning process and task outcomes. Forty-four university students from different disciplines were randomly assigned into four groups under a 2 x 2 experimental design (with/without a pre-training video versus more/less-relevant cues). The participants' visual behaviors during the learning processes of the four groups were all tracked by an eye-tracking system. MANOVA tests, lag sequential analyses and heat map analyses were analysed among and within groups. The results revealed that the cues had positive effects on game performance, and the pre-training video may enhance the visual control of information processing of cuing information. Nevertheless, neither a pre-training video nor real-time cues had benefits on students’ conceptual learning achievement in game-based learning.

This research aimed to investigate the structural relationships among teachers' computational thinking (CT), design thinking (DT), robotics teaching beliefs, and robotics pedagogical content knowledge (RPCK). A total of 98 in-service and pre-service teachers who participated in a robotics teaching professional development workshop served as the sample of the study. A survey including the Computational Thinking Scale, the Design Thinking Disposition Scale, the Robotics Teaching Beliefs Scale and the Technological Pedagogical Content Knowledge-Robotics Scale was conducted after the workshop. A confirmatory factor analysis was employed to validate the measurement constructs, and Partial Least Squares - Structural Equation Modeling (PLS-SEM) analysis was utilized to examine the relationships among the factors. The results revealed that both CT and DT dispositions could positively predict teachers' robotics teaching beliefs, which subsequently predicted their RPCK. Moreover, a direct positive relationship between CT and RPCK was identified, while such a relationship was not evident for DT. The model demonstrates the critical role of CT in shaping teachers' beliefs and pedagogical strategies of robotics teaching, and provides insights into the indirect influence of DT. Finally, the Model of Robotics Teaching Professional Development (MRTPD) was proposed to profile how to promote teachers' pedagogical content knowledge of robotics teaching from their CT and DT dispositions.

This study aims to disclose how the magnetic resonance imaging (MRI) neuroimaging approach has been applied in education studies, and what kind of learning themes has been investigated in the reviewed MRI neuroimaging research. Based on the keywords “brain or neuroimaging or neuroscience” and “MRI or diffusion tensor imaging (DTI) or white matter or gray matter or resting-state,” a total of 25 papers were selected from the subject areas “Educational Psychology” and “Education and Educational Research” from the Web of Science and Scopus from 2000 to 2019. Content analysis showed that MRI neuroimaging and learning were studied under the following three major topics and nine subtopics: cognitive function (language, creativity, music, physical activity), science education (mathematical learning, biology learning, physics learning), and brain development (parenting, personality development). As for the type of MRI neuroimaging research, the most frequently used approaches were functional MRI, followed by structural MRI and DTI, although the choice of approach was often motivated by the specific research question. Research development trends show that the neural plasticity theme has become more prominent recently. This study concludes that in educational research, the MRI neuroimaging approach provides objective and empirical evidence to connect learning processes, outcomes, and brain mechanisms.

Reactions of ZnSO4∙7H2O, N-(pyridin-3-ylmethyl)-4-(pyridin-4-yl)-1,8-naphthalimide (NI-mbpy-34), and 5-bromobenzene-1,3-dicarboxylic acid (Br-1,3-H2bdc) afforded a luminescent coordination polymer, [Zn(Br-1,3-bdc)(NI-mbpy-34)]n (1), under hydro(solvo)thermal conditions. Single-crystal X-ray structure analysis revealed that 1 features a three-dimensional (3-D) 2-fold interpenetrating cds (or CdSO4) net topology with the point symbol of (65·8), where the Zn(II) centers are considered as 4-connected square-planar nodes. X-ray powder diffraction (XRPD) patterns and thermogravimetric (TG) analysis confirmed that 1 shows high chemical and thermal stabilities. Notably, 1 displayed solvent dependent photoluminescence properties; the fluorescence intensity and emission maximum of 1 in different solvent suspensions varied when a solvent was changed. Furthermore, the H2O suspension of 1 exhibited blue fluorescence emission and thus can be treated as a selective and sensitive fluorescent probe for turn-on detection of Cr3+ cations through absorbance caused enhancement (ACE) mechanism and turn-off detection of Cr2O72−/CrO42− anions through collaboration of the absorption competition and energy transfer process, with limit of detection (LOD) as low as μM scale.

Hydro(solvo)thermal reactions of Cd(NO3)2, N-(pyridin-3-ylmethyl)-4-(pyridin-4-yl)-1,8-naphthalimide (NI-mbpy-34), and 5-bromobenzene-1,3-dicarboxylic acid (Br-1,3-H2bdc) afforded a luminescent coordination polymer, {[Cd(Br-1,3-bdc)(NI-mbpy-34)(H2O)]∙2H2O}n (1). Single-crystal X-ray diffraction analysis showed that 1 features a two-dimensional (2-D) gridlike sql layer with the point symbol of (44·62), where the Cd(II) center adopts a {CdO5N2} pentagonal bipyramidal geometry. Thermogravimetric (TG) analysis confirmed the thermal stability of 1 up to about 340 °C, whereas XRPD patterns proved the maintenance of crystallinity and framework integrity of 1 in CH2Cl2, H2O, CH3OH, and toluene. Photoluminescence studies indicated that 1 displayed intense blue fluorescence emissions in both solid-state and H2O suspension-phase. Owing to the good fluorescent properties, 1 could serve as an excellent turn-off fluorescence sensor for selective and sensitive Cr(VI) detection in water, with LOD = 15.15 μM for CrO42− and 14.91 μM for Cr2O72−, through energy competition absorption mechanism. In addition, 1 could also sensitively detect Cr3+, Fe3+, and Al3+ ions in aqueous medium via fluorescence-enhancement responses, with LOD = 2.81 μM for Cr3+, 3.82 μM for Fe3+, and 3.37 μM for Al3+, mainly through an absorbance-caused enhancement (ACE) mechanism.

While research has shown that students' epistemic beliefs can be a strong predictor of their academic performance, cognitive abilities, or self-efficacy, studies of this topic in computer education are rare. The purpose of this study was twofold. First, it aimed to validate a newly developed questionnaire for measuring students' epistemic beliefs about computer programming at middle school level. Second, the study aimed to understand how students' epistemic beliefs predict their computational thinking disposition and computer programming self-efficacy. A total of 406 middle school students completed a survey consisting of three research instruments: the Computer Programming Epistemic Belief Inventory (CPEBI), the Computational Thinking Scale (CTS), and the Computer Programming Self-Efficacy Scale (CPSES). The results indicated that the epistemic belief of "uncertainty" was a significant and positive predictor of the "abstraction" and "generalization" dimensions of computational thinking dispositions. In addition, the belief of "complexity" was an antecedent to three dimensions of computational thinking, namely, "algorithmic thinking," "evaluation," and "generalization." Only the "evaluation" dimension of CT disposition had significant and positive relationships with "programming self-efficacy." The roles of "uncertainty" and "complexity" of computer programming epistemic beliefs are discussed. Implications and suggestions for future computer programming education, especially for young students, are provided.

In this research, we propose recurrent neural networks (RNNs) to build a relationship between rainfalls and water level patterns of an urban sewerage system based on historical torrential rain/storm events. The RNN allows signals to propagate in both forward and backward directions, which offers the network dynamic memories. Besides, the information at the current time-step with a feedback operation can yield a time-delay unit that provides internal input information at the next time-step to effectively deal with time-varying systems. The RNN is implemented at both gauged and ungauged sites for 5-, 10-, 15-, and 20-min-ahead water level predictions. The results show that the RNN is capable of learning the nonlinear sewerage system and producing satisfactory predictions at the gauged sites. Concerning the ungauged sites, there are no historical data of water level to support prediction. In order to overcome such problem, a set of synthetic data, generated from a storm water management model (SWMM) under cautious verification process of applicability based on the data from nearby gauging stations, are introduced as the learning target to the training procedure of the RNN and moreover evaluating the performance of the RNN at the ungauged sites. The results demonstrate that the potential role of the SWMM coupled with nearby rainfall and water level information can be of great use in enhancing the capability of the RNN at the ungauged sites. Hence we can conclude that the RNN is an effective and suitable model for successfully predicting the water levels at both gauged and ungauged sites in urban sewerage systems.

One of the major purposes of this study is to investigate the potential impact of gender and information and computer technology (ICT) resources on students' computational thinking (CT) competencies. To this end, the Computational Thinking Test for Junior High Students (CTT-JH) was developed and validated. Research participants included 437 junior high school students in Taiwan. The surveyed schools were categorized into more or fewer ICT resources. Then, discrimination analyses and Rasch modeling for item analyses and two-way ANOVA were conducted. Results showed that the final version of CTT-JH is of good item quality. Students in schools with more ICT resources had higher CT test mean scores regardless of gender. Nevertheless, at schools with limited resources, male students had significantly lower CT test mean scores than female students did. This study provides new insights into how gender and ICT resources can interact with and impact on students' CT competencies. It also provides a valid and reliable tool for assessing young adolescents' CT abilities. [ABSTRACT FROM AUTHOR]