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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 aimed to develop the Computational Thinking Test for Elementary School Students (CTT-ES) to assess young children's CT competencies in non-programming contexts and also examine the relationship between CT competencies and CT dispositions. A survey including a pool of CTT-ES candidate items and the Computational Thinking Scale (CTS) was administered to 631 elementary school students. Rasch model of the Item Response Theory and the discrimination analysis of the Classical Testing Theory were conducted for item analyses. Pearson's correlation analyses and hierarchical multiple regression analyses were used to examine the relationships between CTT-ES and CTS scores. The results showed that the final CTT-ES including 16 items had a good fitness, discrimination, and reliability to evaluate elementary students' domain-general CT competencies. The convergent validity of CTT-ES was confirmed by its significant correlations with the CTS scores. The significant regression model not only showed students' CT competencies can be predicted by their CT dispositions but also supported The Developmental Model of CT. This study provided a valid and reliable tool for assessing young children's CT abilities. It also furthered our understanding about the developmental orders of CT abilities and contributed to the theoretical construction of CT.

A prior study developed the Computational Thinking Scale (CTS) for assessing individuals' computational thinking dispositions in five dimensions: decomposition, abstraction, algorithmic thinking, evaluation, and generalization. This study proposed the Developmental Model of Computational Thinking through validating the structural relationships among the five factors of the CTS. To examine the model, a questionnaire including the CTS was administered to 472 middle school students. A confirmatory factor analysis was used to confirm the construct of the measurements, and a PLS-SEM analysis was used to validate the structural relationships among the factors. The results confirmed that the 19-item CTS has good item reliability, internal consistency, and construct reliability for measuring computational thinking (CT). In the Developmental Model of CT, decomposition and abstraction significantly predict all other three CT dispositions, suggesting that they are the two fundamental factors required for CT development. Moreover, a significant linear prediction path was shown starting from algorithmic thinking, evaluation, until generalization. Thus, a multi-level model was confirmed for the conceptual framework of CT. This model suggests a possible sequence for CT development which may provide a guideline for the teaching objectives of CT for different learning stages in different school levels. Decomposition and abstraction are especially suggested to be emphasized in school curricula before teaching algorithmic thinking or algorithm designs.

The purpose of this study was to understand how students engage in computer-supported modeling-based activities (CSMBAs), and the relationship between their engagement and their modeling competence. Different facets of learning engagement were measured through multiple data, including performance on modeling tasks, self-reported level of engagement, and online behavior patterns of science modeling. The research participants were 76 11th-grade students in Taiwan. The research instruments included online student worksheets, an engagement questionnaire, computer logs, and modeling competence tests. Students' online worksheets were scored and used to group them into three performance groups--the low-level-performance group (LPG), the middle-level-performance group (MPG) and the high-level-performance group (HPG). ANOVA statistics lag sequential analysis (LSA), and ANCOVA statistics were used for the data analysis. The results showed that, first, in analyzing the engagement questionnaires, students' negative cognitive engagement, negative behavioral engagement, and negative social engagement all played important roles in their low performance in the CSMBAs. Second, through the use of LSA, it was found that the LPG students lacked evaluative behavior, while the HPG students emphasized reflective behavior. Third, analysis of the students' pre- and post-modeling competence tests showed that those who were in the HPG and MPG scored significantly higher than those in the LPG in two dimensions of the modeling competence post-tests. The results indicate that efforts made in completing tasks in CSMBAs can lead to better modeling competence. Implications for developing future CSMBAs and for promoting student engagement are suggested.

The purpose of this study was to investigate the affordances of participatory simulations by comparing students' models of engagement and science learning outcomes in a multi-team participatory simulated game (MPSG) and a single-team participatory simulated game (SPSG). Two versions of a mobile-based game about marine fishery management were created. Participants were 105 seventh-grade students in Taiwan. Research instruments included a Science Game Engagement questionnaire and a Marine Ecosystem and Sustainability Test. Students' interviews and classroom videos were also collected. Students' models of engagement were analyzed by using Partial Least Squares Structural Equation Modeling, and comparisons of subscales of engagement and learning outcomes were made by Analysis of Covariance statistics. The results showed that the students in the MPSG group had a higher level of behavioral engagement and better learning achievement than the students in the SPSG group did. Also, in exploring the interrelationships among the subscales of students' engagement, we found that, in the SPSG group, students' behavioral engagement was positively predicted by their emotional engagement, while in the MPSG group, it was positively predicted by their social engagement. In both groups, behavioral engagement predicted cognitive engagement. This finding was supported by the video data in that more interactions among the group members were observed in the MPSG group. This indicated that the multi-agent, dynamic modeling in the MPSG may have shifted the quality of the learners' social interactions. The affordances of participatory simulated games are discussed, and future research directions are provided.

The purpose of this study was, first, to understand the item hierarchy regarding students' understanding of scientific models and modeling (USM). Secondly, this study investigated Taiwanese students' USM progression from 7th to 12th grade, and after participating in a model-based curriculum. The questionnaire items were developed based on 6 aspects of USM, namely, "model type," "model content," "constructed nature of models," "multiple models," "change of models", and "purpose of models". Moreover, 10 representations of models were included for surveying "what a model" is. Results show that the "purpose of models" and "model type" items covered a wide range of item difficulties. At the one end, items for the "purpose of models" are most likely to be endorsed by the students, except for the item "models are used to predict." At the other end, the "model type" items tended to be difficult. The students were least likely to agree that models can be text, mathematical, or dynamic. The items of the "constructed nature of models" were consistently located above the average, while the "change of models" items were consistently located around the mean level of difficulty. In terms of the natural progression of USM, the results show significant differences between 7th grade and all grades above 10th, and between 8th grade and 12th grade. The students in the 7th grade intervention group performed better than the students in the 7th and 8th grades who received no special instruction on models.

The purpose of this study was to develop and validate an online contextualized test for assessing students' understanding of epistemic knowledge of science. In addition, how students' understanding of epistemic knowledge of science interacts with learner factors, including time spent on science learning, interest, self-efficacy, and gender, was also explored. The participants were 489 senior high school students (244 males and 245 females) from eight different schools in Taiwan. Based on the result of an extensive literature review, we first identified six factors of epistemic knowledge of science, such as status of scientific knowledge, the nature of scientific enterprise, measurement in science, and so on. An online test was then created for assessing students' understanding of the epistemic knowledge of science. Also, a learner-factor survey was developed by adopting previous PISA survey items to measure the abovementioned learner factors. The results of this study show that: (1) by factor analysis, the six factors of epistemic knowledge of science could be grouped into two dimensions which reflect the nature of scientific knowledge and knowing in science, respectively; (2) there was a gender difference in the participants' understanding of the epistemic knowledge of science; and (3) students' interest in science learning and the time spent on science learning were positively correlated to their understanding of the epistemic knowledge of science.

This study investigated the relationships among college students' epistemic beliefs in biology (EBB), conceptions of learning biology (COLB), and strategies of learning biology (SLB). EBB includes four dimensions, namely "multiple-source," "uncertainty," "development," and "justification." COLB is further divided into "constructivist" and "reproductive" conceptions, while SLB represents deep strategies and surface learning strategies. Questionnaire responses were gathered from 303 college students. The results of the confirmatory factor analysis and structural equation modelling showed acceptable model fits. Mediation testing further revealed two paths with complete mediation. In sum, students' epistemic beliefs of "uncertainty" and "justification" in biology were statistically significant in explaining the constructivist and reproductive COLB, respectively; and "uncertainty" was statistically significant in explaining the deep SLB as well. The results of mediation testing further revealed that "uncertainty" predicted surface strategies through the mediation of "reproductive" conceptions; and the relationship between "justification" and deep strategies was mediated by "constructivist" COLB. This study provides evidence for the essential roles some epistemic beliefs play in predicting students' learning.

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]