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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.

Background: Collaborative computer simulations are available on some online platforms which support students at distributed locations to synchronously collaborate on the simulations to learn sciences. However, how students collaborate with each other in collaborative simulations is not clear. Objectives: The aim of this study was to investigate whether functional interdependency would influence learning. Methods: Two collaborative learning models with different functional interdependencies were examined (the synchronous model and the distributed accountability model). Multiple data sources obtained from 64 students were collected and analyzed. We discuss the collaborative learning with two levels of factors: the individual-level factors (i.e., individual perceptions of teamwork quality, visual attention and learning performance before and after a simulation activity) and the pair-level factors (i.e., discourse data and joint attention). Given that the pair-level factors in the collaborative learning environment may affect the individual-level factors, we further discuss the interactions of the two levels of factors in different collaborative learning models with hierarchical linear modeling (HLM) analysis. Results and Conclusions: The analytical results of the individual-level factors found participants in the distributed accountability model demonstrated more frequent and longer attention to the learning activity. Furthermore, the results indicated that the two simulation learning activities would enhance the participants' learning performance except in Q2. For the pair-level factors, the results demonstrated that the participants in the distributed accountability model showed more active discourse segments but similar joint attention patterns in the learning activity. The results of the HLM analysis indicated that the students in the distributed accountability model had to collaborate with their partners through more constructive dialogue, and focused on the same areas of interest to enhance their learning performance, as the complexity of the distributed accountability design was implemented in a simulation learning activity. Major takeaways: We found that the increase of functional interdependency cannot guarantee students' learning effect in collaborative learning and may hinder the communication that is needed during collaboration. Researcher may need to consider other collaboration tools to enhance the reciprocal reflection.

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 explore how students interacted with guidance to conduct a scientific inquiry in a physics simulation by using the eye-tracking techniques. The participants were 51 7th graders, and an eye-tracking system was used to record their visual behaviors and log data while they were using the simulation. As for data analysis, we first checked each participant's log data to examine whether they completed the requirement of the guidance, and then checked the correctness of her/his answer to the inquiry task. The participants were thus divided into two groups (correct vs. wrong), and the patterns of their visual behaviors were examined by a set of eye-movement indices, normalized heat maps and lag sequential analyses. The results indicate that both spatial distributions and temporal sequences of the participants' visual attention were associated with their performances on the inquiry task. Regarding the spatial distribution, the correct group tended to allocate more visual attention to the regions presenting the target phenomenon than the wrong group. Concerning the temporal sequence, the correct group tended to make more visual transitions among the content of the guidance, the relevant control panels and the target phenomena than the wrong group.

Robotics education has gradually been emphasized in contemporary school curricula; however, assessment tools for robotics learning are still limited. Based on Bloom's Taxonomy of educational objectives, this study aimed to develop the Robotics Learning Self-Efficacy Scale (RLSES) with a two-level construct of five dimensions for assessing students' self-efficacy for learning robotics. A total of 181 elementary, junior high and senior high school students (5th-12th graders) with robotics learning experience were selected as the sample of this study. A questionnaire including 32 candidate items designed for the initial version of the RLSES was administered to the sample. An exploratory factor analysis was conducted and, finally, 16 items were drawn for the final RLSES under five subscales ("Comprehension," "Practice," "Analysis," "Application," and "Collaboration"), with a total explained variance of 85.28%. The Cronbach's alpha reliability was 0.97 for the overall scale, ranging from 0.87 to 0.95 for the subscales. The inter-correlation analysis showed evidence of discriminant validity. Regression analysis results supported that "Practice" and "Comprehension" self-efficacy were significant predictors of "Analysis," "Application," and "Collaboration" self-efficacy, confirming the two-level (2 × 3) construct of the RLSES. Significant differences among school levels were found and are discussed.

Computational thinking has received tremendous attention from computer science educators and educational researchers in the last decade. However, most prior literature defines computational thinking as thinking outcomes rather than thinking processes. Based on Selby and Woodland's framework, this study developed and validated the Computational Thinking Scale (CTS) to assess all students' thought processes of computational thinking for both general and specific problem-solving contexts in five dimensions: abstraction, decomposition, algorithmic thinking, evaluation and generalization. A survey including 25 candidate items for CTS as well as demographic variables was administered to 388 junior high school students in Taiwan. An explorative factor analysis using the principal axis method with the oblimin rotation was used to validate the scale. Finally, 19 items were extracted successfully under the designed five dimensions, with a total explained variance of 64.03% and an overall reliability of 0.91. Results of the demographic comparisons showed that boys had a greater disposition than girls in decomposition thinking when solving problems using computer programming. In addition, programming learning experience, especially self-directed learning and after-school learning, had significant positive effects on all dimensions of CTS. Several future studies are suggested using this tool.

To explore the role of design thinking in contemporary computer literacy education, this study aimed to examine the relationship between young students' design thinking disposition and their computer programming self-efficacy. To assess students' design thinking disposition, this study developed the Design Thinking Disposition Scale (DTDS) with a sample of 350 junior high school students who had computer programming experience in a STEAM course. A principle axis factor analysis with the promax rotation method was used to verify the DTDS's construct under the four dimensions: "empathize," "define," "ideate" and "prototype." The Cronbach's alpha reliability was 0.90 for the overall scale. Correlation analyses results showed that all the four dimensions were significantly correlated with computer programming self-efficacy assessed by CPSES. A significant regression model was found in which the three factors, "ideate," "prototype" and "define," significantly predicted the overall computer programming self-efficacy. Meanwhile, except for the "ideate" subscale, no gender difference was found in the young students' design thinking dispositions. The students' self-directed programming learning experience was shown to benefit their design thinking disposition. The DTDS can be applied to design-thinking-embedded computer literacy curricula such as makers, STEAM, or robotics education. Several further studies are also suggested.

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. [ABSTRACT FROM AUTHOR]

Computer programming has been gradually emphasized in recent computer literacy education and regarded as a requirement for all middle school students in some countries. To understand young students' perceptions about their own learning in computer programming, this study aimed to develop an instrument, Computer Programming Self-Efficacy Scale (CPSES), for all students above middle school levels. Based on Berland and Lee's computational thinking framework, this study developed the CPSES items at a literacy level and finally the instrument included the five subscales: "Logical Thinking," "Algorithm," "Debug," "Control," and "Cooperation." An exploratory factor analysis and reliability tests were conducted in this study. The reliability alpha was 0.96 for the overall scale, and ranged from 0.84 to 0.96 for the subscales. This study also confirmed the positive correlation between computer programming experience and computer programming self-efficacy. In addition, for low- and middle-experienced learners, significant gender differences were found in two subscales: Algorithm and Debug. The CPSES can be applied as an evaluation tool in computer education, robotics education, as well as integrated STEM or STEAM education in which computer programming was regarded as a part of computer literacy.

This study was to investigate the role of students' game self-efficacy in their game performance as well as in their visual behavior distributions and transfer patterns during their gameplay. A total of 48 university students were recruited for participating in a gameplaying task while wearing with an ASL Mobile Eye eye-tracker. After the gameplay, each participant was asked to report their self-efficacy of the gameplay. Based on the self-reported game self-efficacy (GSE) score, the participants were divided into the higher and lower GSE groups. Independent t tests were used to compare their game performances and eye-tracking indices for each area of interest. A lag sequential analysis was used to profile the visual transfer pattern for each group. The results showed that the higher GSE group had better performances than the lower GSE group. During the gameplay, the higher GSE students tended to pay more attention to the critical information for a successful play. In addition, the lag sequential analyses revealed different visual transfer patterns for the two GSE groups. The higher GSE group tended to show a critical-factor-centralized visual-trasfer pattern which demonstrated more planning and monitoring gameplaying strategies; while the lower GSE students, probably lacking the self-confidence of gameplaying, tended to pay more attention to the remaining time for the game.This study may provide suggestions for future game-based leraning system designs.

This study aimed to examine how different forms (still pictures vs. animations) of seductive illustrations impact text-and-graphic learning processes, perceptions, and outcomes. An eye-tracking experiment of three groups (static, dynamic, and control) was conducted with 60 college and graduate students while learning with PowerPoint slides about infant motor development milestones. Prior knowledge, learning performance, learning perception, and visual attention were assessed by achievement tests, self-rated scales, and eye-tracking measures. Analysis of variance and t test results showed that, under a low task-load condition, no seductive details effect was found for learning achievement but was found for learning process and perception. Decreased attention was found in the relevant pictures in both experimental groups. With more deeply and intensively processing on the seductive animations, the dynamic group perceived more distractions than the static group. Lag sequential analysis results revealed different visual transitional patterns for the groups, providing deep understandings about the process of seductive details effects.

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. [ABSTRACT FROM AUTHOR]

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.

The main purpose of this study was to provide instructional suggestions for supporting science learning in digital environments based on a review of eye tracking studies in e-learning related areas. Thirty-three eye-tracking studies from 2005 to 2014 were selected from the Social Science Citation Index (SSCI) database for review. Through a literature analysis program, CATA, and in-depth content analysis of the research methods and findings, five research theme clusters were abstracted from the selected papers, namely, cognitive activities in processing multimedia presentations, multimedia effects, roles of personal factors, effects of instructional design, and learning with dynamic e-platforms. Based on the results of the in-depth theme analyses, it is suggested that the design of e-learning instruction should consider placing related text and graphics in adjacent areas, using one verbal mode at a time, and providing explicit and clear verbal explanations. When using animations, instructors need to explain carefully the goals and contents of the animations to reduce the extraneous cognitive load. In the dynamic learning environment, a pre-training program is necessary for students to become familiar with the new environment. Finally, individual differences such as background knowledge, cognitive abilities and cognitive styles should be taken into consideration in the instructional design.

The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V.

The purpose of this study was to explore the relationships between the self-efficacies and the attitudes toward Web-based Recipe Learning (WRL) of 296 culinary college students in Taiwan. Factors of the WRL Self-Efficacy Scale and the WRL Attitudes Scale were identified in this study using exploratory factor analyses. Both scales showed a good inter-correlation matrix. Correlation and mediational effect analyses were used to examine the relationships between the students' self-efficacy and attitudes, and several significant findings are reported. First, students' self-efficacies had positive relations with their attitudes toward WRL. In addition, students' 'Ingredient Recognition' self-efficacy was found to significantly predict the other two dimensions of self-efficacy: 'Knife Skills' and 'Cooking Procedure.' Moreover, students' 'Ingredient Recognition' self-efficacy was found to significantly predict their WRL attitudes of 'Usefulness' via a mediated variable, 'Cooking Procedure' self-efficacy. To sum up, the results suggest that 'Ingredient Recognition' is a fundamental level of WRL self-efficacy, while both 'Knife Skills' and 'Cooking Procedure' are advanced levels of WRL self-efficacies. And, when considering the mediational effect, culinary college students' self-efficacy for cooking procedure is an important mediator between the fundamental self-efficacy and attitudes in web-based culinary learning.

Using the Game-based-learning Teaching Belief Scale (GTBS) and the Technological Pedagogical Content Knowledge--Games questionnaire (TPACK-G), this study investigated 316 Taiwanese in-service teachers' teaching beliefs about game-based learning and their perceptions of game-based pedagogical content knowledge (GPCK). Both t-tests and ANOVA analyses were used to examine the differences due to their demographic backgrounds. Correlation and regression analyses were used to examine the predicting variables of the teachers' GPCK. The results showed that, among the factors of GTBS and TPACK-G, GPK plays the most critical role in predicting GPCK. In addition, the elementary school teachers had stronger Belief, Confidence and Motivation for using game-based teaching approaches, and had higher GPK and GPCK than middle school teachers. A gender difference only existed in GK. Younger teachers had better GK and GCK than older teachers. Compared with experienced teachers, novice teachers tended to believe that digital games can assist learning and instruction, and perceived higher self-efficacy in their TPACK-G.

This study examined the gender difference in students' perceived discussion strategies in face-to-face and online asynchronous contexts. A survey of 363 university students and follow-up interviews of 20 participants was conducted to examine any gender differences within each context and between the two contexts. The Discussion Strategies Scale was developed to examine students' discussion strategies for both contexts in four dimensions: comprehension, interaction, elaboration and anxiety. The results show that no gender difference was found within the face-to-face context; however, within the online asynchronous context, the females perceived themselves better than did the males regarding their elaboration strategies. Although both genders experienced less anxiety in asynchronous discussion, the males perceived themselves as having better strategies in face-to-face discussion than in asynchronous discussion and the females perceived themselves as having about the same level of sophistication in both contexts. This study provides an in-depth observation of how both genders adapt themselves to different discussion contexts. We conclude that female students adapted themselves, as strategic learners, better than the males in asynchronous learning situations in which the male students were not as active as they were in traditional face-to-face discussion contexts.

Online information searching tasks are usually implemented in a technology-enhanced science curriculum or merged in an inquiry-based science curriculum. The purpose of this study was to examine the role students' different levels of scientific epistemic beliefs (SEBs) play in their online information searching strategies and behaviors. Based on the measurement of an SEB survey, 42 undergraduate and graduate students in Taiwan were recruited from a pool of 240 students and were divided into sophisticated and naïve SEB groups. The students' self-perceived online searching strategies were evaluated by the Online Information Searching Strategies Inventory, and their search behaviors were recorded by screen-capture videos. A sequential analysis was further used to analyze the students' searching behavioral patterns. The results showed that those students with more sophisticated SEBs tended to employ more advanced online searching strategies and to demonstrate a more metacognitive searching pattern.

This study employed eye-tracking technology to examine how students with different levels of prior knowledge process text and data diagrams when reading a web-based scientific report. Students' visual behaviors were tracked and recorded when they read a report demonstrating the relationship between the greenhouse effect and global climate change in 2 diagrams and 4 textual sections. Based on the pretest scores, 13 participants were categorized into high and low prior knowledge (PK) groups. Eye-tracking measures including the total reading time, total fixation duration, and total regression number on each area of interest of the 2 groups were compared. A heat map was further used to show the overall visual distribution of each group. In addition, the inter-scanning transitions between the textual and graphical information of the 2 groups were compared and further confirmed by the patterns of the scan paths. The results revealed that overall students spent more time reading the textual than the graphical information. The high PK students showed longer fixation durations and more regressions on the graphics than the low PK students. Meanwhile, the high PK students showed more inter-scanning transitions than the low PK students not only between the text and graphics but also between the 2 data diagrams. This suggests that the high PK students were more able to integrate text and graphic information and inspect scientific data which is essential for online inquiry learning. This study provides eye-tracking evidence to show that low PK students have difficulties integrating scientific diagrams with expository texts and inspecting scientific data diagrams that are commonly shown in websites. Suggestions are made for future studies and instructional design for online inquiry-based science learning.