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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.

Research has addressed the benefits of immersive virtual reality (IVR) for affective learning rather than cognitive performance, indicating the necessity to explore what factors may influence learners' attitudinal learning, defined as the result of instruction for changing learners' attitudes, when engaging in immersive instructional mediums. This study proposes an affection-oriented model to understand the roles of young learners' epistemic curiosity traits and triggered situational interest in their attitudinal learning from the affective, cognitive, and behavioral dimensions in IVR learning contexts. Through a series of PLS-SEM analyses with 90 elementary school students, the results of the measurement model test have verified the reliability and validity of the instruments employed in this study. The structural relationships among epistemic curiosity, situational interest, and attitudinal learning were also confirmed. The triggered situational interests of "novelty" and "exploration intention" during the IVR learning process were further identified as the mediators in the research model. While the students' senses of "novelty" mediated the relationship between curiosity and cognitive attitudinal learning, their senses of interest in "exploration intention" mediated both the relationships between curiosity and affective attitudinal learning and between curiosity and behavioral attitudinal learning. This study contributed to establishing the theoretical framework for affection-oriented IVR learning.

Background: There exist shared competencies between computational thinking (CT) and mathematics, and these two domains also mutually benefit from various teaching approaches. However, the linkages between mathematics and computational thinking lack robust empirical support, particularly from student-centered learning perspectives. Our study aimed to enhance our understanding of the connections between students' mathematics learning and computational thinking. To assess students' mathematics learning, we measured their beliefs about mathematics learning and their level of mathematical literacy (ML). Our hypothesis posited that students' beliefs concerning mathematics learning, encompassing their views on the nature of mathematics and their attitude towards the subject, can both directly and indirectly influence their CT, with ML serving as a mediating factor. Our data were gathered through surveys and tests administered to eighth- and ninth-grade students. Data were analyzed using partial least squares--structural equation modeling (PLS-SEM). Results: The evaluation of the measurement model indicated strong internal consistency for each construct. Both convergent and discriminant validity were also established. Upon assessing the structural model, it was found that beliefs about the nature of mathematics positively predicted attitudes towards mathematics, and this belief also indirectly predicted ML through positive attitudes towards mathematics. In addition, ML directly and positively predicted both CT subscales. Notably, a comprehensive mediating effect of ML on beliefs about mathematics learning and CT was identified in the analysis. Conclusions: This study advances the understanding of the relationships between mathematics learning and CT. We have further confirmed the importance of mathematical literacy in predicting CT and its mediating role between beliefs about mathematics learning and CT. It is suggested that teachers could promote students' CT competence by enhancing their mathematical literacy or integrating mathematics and CT into the same learning activities. Finally, we propose that upcoming investigations treat CT assessments as formative constructs, diverging from their reflective counterparts.

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.

Promoting understanding of the epistemologies of science has long been the primary objective in science education, and can be viewed as a form of science learning outcome. Many studies have attempted to understand learners' conceptions of epistemology in science from various perspectives and methods; however, no recent reviews have focused on the measurement of various constructs and variables of epistemologies of science. The main purpose of this review study is to understand how these epistemologies in science teaching and learning were measured, and to provide an overview of recent developments with respect to the measurement issue in the epistemology of science. We searched for articles that were published between 2010 and 2019 and retained 225 eligible studies passing all review criteria for inclusion in this review. Major constructs of epistemologies of science emerging from the studies include epistemic beliefs and views, nature of science, epistemic emotions, epistemic metacognition, epistemic practices, and epistemic resources. These constructs were further categorized into personal, productive, and practical epistemologies. Among these studies, most of the quantitative studies used questionnaires for measuring personal epistemologies, while only a few utilized close-ended items. The majority of the studies adopting qualitative methods investigated personal epistemologies as well. Nevertheless, among studies of practical epistemologies, more studies adopted qualitative methods than quantitative methods. We summarize the characteristics and psychometric properties of the most frequently used quantitative research instruments. Examples of qualitative data collection and data analyses are provided. Relationships between epistemologies and science learning outcomes are discussed, and suggestions are made for future directions.

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 aim of the study was to investigate students' views of model evaluation through the lens of personal epistemology. We developed an integrated analytical framework by combining a developmental framework, including absolutist, multiplist, and evaluatist, with a multi-dimensional framework, including limits of knowing, certainty of knowing, and criteria of knowing. Furthermore, we examined the potential influence of the question contexts and the students' grade levels. A total of 188 secondary school students were surveyed. Students answered two sets of model evaluation questions based on two scientific contexts. After reading the information about the two models, the students had to choose from three epistemic assumptions and then provide written justifications explaining their choice of assumptions. Quantitative and qualitative analyses were conducted for the multiple-choice questions and the written responses. In both contexts there were higher percentages of 11th-grade students choosing the evaluatist assumptions than the eighth-grade students. For students choosing multiplist and evaluatist assumptions, the 11th-grade students were more likely than the eighth-grade students to think in terms of pragmatic and evidential criteria as "the criteria of knowing." Different contexts of the questions evoked different views of model evaluation particularly regarding the "limits of knowing." Four additional categories of epistemic levels also emerged from the data. This study provides a new framework for understanding students' thinking about model evaluation. Implications and suggestions for future research are provided.

Research purposes and design: This study aimed to compare the effects of different explicit approaches used in modeling-based instruction on students' understanding of scientific models and modeling (USMM). In Study 1, a self-study approach was compared to a preliminary explicit approach. In Study 2, preliminary explicit approach and embedded explicit approach were compared. Methods: For the data analysis, t tests and analysis of covariance (ANCOVA) were used to compare students' USMM under the conditions of using different explicit approaches. Findings: Results showed that in the first study, no statistically significant difference was found between self-study approach and the preliminary explicit approach in any of the USMM subscales. In the second study, students who learned with the embedded explicit approach had better understandings in all subscales of USMM, but not in the changing nature of scientific models (CNM) subscale, than those who learned with the preliminary explicit approach. Through qualitative analysis of the data, categories and examples of the teacher's embedded epistemic guidance were illustrated. Suggestions for future professional development and research instrument design are provided. [ABSTRACT FROM AUTHOR]

In this study, we reviewed 76 journal articles on employing drawing assessment as a research tool in science education. Findings from the systematic review suggest four justifications for using drawing as a type of research tool, including assessment via drawing as (a) an alternative method considering young participants' verbal or writing abilities, and affective or economic reasons, (b) a unique method that can reveal aspects not easily measured by other methods, (c) a major method that reflects characteristics of science subjects, and (d) a formative assessment to diagnose students' ideas to benefit their learning. Furthermore, five research trends of studies using drawing as assessment tools are identified, including: (a) students' conceptions of scientists from the Draw-a-Scientist-Test (DAST) and evolving studies, (b) students' understanding or mental models of science concepts, (c) participants' conceptions of science learning or teaching, (d) students' inquiry abilities and modelling skills via drawing, and (e) technology to support drawing. For each trend, we synthesised and commented on the current findings. A framework conceptualising phases and issues when designing research and instruments employing drawing assessments is proposed. The review provides insights into the design and future direction of research employing drawing assessments in science education.

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 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 aim of this study was to examine the potential impact of the representational characteristics of models and students' educational levels on students' views of scientific models and modeling (VSMM). An online multimedia questionnaire was designed to address three major aspects of VSMM, namely the "nature of models," the "nature of modeling," and the "purpose of models." The three scales of representational characteristics included "modality," "dimensionality," and "dynamics." A total of 102 eighth graders and 87 eleventh graders were surveyed. Both quantitative data and written responses were analyzed. The influence of the representational characteristics seemed to be more salient on the "nature of models" and the "purpose of models." Some interactions between the educational levels and the representational characteristics showed that the high school students were more likely to recognize textual representations and pictorial representations as models, while also being more likely to appreciate the differences between 2D and 3D models. However, some other differences between educational levels did not necessarily suggest that the high school students attained more sophisticated VSMM. For instance, in considering what information should be included in a model, students' attention to particular affordances of the representation can lead to a more naive view of modeling. Implications for developing future questionnaires and for teaching modeling are suggested in this study.

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.

Studies that incorporate technologies into school science laboratories have proliferated in the recent two decades. A total of 42 studies published from 1990 to 2011 that incorporated technologies to support school science laboratories are reviewed here. Simulations, microcomputer-based laboratories (MBLs), and virtual laboratories are commonly used to assist students in engaging in laboratory activities, followed by remote laboratories, databases, and other miscellaneous technologies. We report the demographics and characteristics of these technology-assisted laboratory studies and provide examples to illustrate how technologies have facilitated science learning in laboratories for different subjects and science domains, with various levels of student involvement and components of investigations. Major findings of the reviewed articles are summarized to understand the effects of applying technologies in school laboratories. Incorporation of technologies in school science laboratories has changed students' learning experiences in terms of the phenomena to be explored, their interactions with the natural phenomena or materials, and approaches to handling and making sense of data. Based on our findings, possible directions for future research and emerging instructional issues regarding technology-assisted laboratories are discussed.

Although augmented reality (AR) has gained much research attention in recent years, the term AR was given different meanings by varying researchers. In this article, we first provide an overview of definitions, taxonomies, and technologies of AR. We argue that viewing AR as a concept rather than a type of technology would be more productive for educators, researchers, and designers. Then we identify certain features and affordances of AR systems and applications. Yet, these compelling features may not be unique to AR applications and can be found in other technological systems or learning environments (e.g., ubiquitous and mobile learning environments). The instructional approach adopted by an AR system and the alignment among technology design, instructional approach, and learning experiences may be more important. Thus, we classify three categories of instructional approaches that emphasize the "roles," "tasks," and "locations," and discuss what and how different categories of AR approaches may help students learn. While AR offers new learning opportunities, it also creates new challenges for educators. We outline technological, pedagogical, learning issues related to the implementation of AR in education. For example, students in AR environments may be cognitively overloaded by the large amount of information they encounter, the multiple technological devices they are required to use, and the complex tasks they have to complete. This article provides possible solutions for some of the challenges and suggests topics and issues for future research. (Contains 1 table.)

Research has addressed the benefits of immersive virtual reality (IVR) for affective learning rather than cognitive performance, indicating the necessity to explore what factors may influence learners' attitudinal learning, defined as the result of instruction for changing learners' attitudes, when engaging in immersive instructional mediums. This study proposes an affection-oriented model to understand the roles of young learners' epistemic curiosity traits and triggered situational interest in their attitudinal learning from the affective, cognitive, and behavioral dimensions in IVR learning contexts. Through a series of PLS-SEM analyses with 90 elementary school students, the results of the measurement model test have verified the reliability and validity of the instruments employed in this study. The structural relationships among epistemic curiosity, situational interest, and attitudinal learning were also confirmed. The triggered situational interests of novelty and exploration intention during the IVR learning process were further identified as the mediators in the research model. While the students' senses of novelty mediated the relationship between curiosity and cognitive attitudinal learning, their senses of interest in exploration intention mediated both the relationships between curiosity and affective attitudinal learning and between curiosity and behavioral attitudinal learning. This study contributed to establishing the theoretical framework for affection-oriented IVR learning. [ABSTRACT FROM AUTHOR]

Despite the fact that some educational researchers believe that laboratory courses promote outcomes in cognitive and affective domains in science learning, others have argued that laboratory courses are costly in relation to their value. Moreover, effective measurement of student learning in the laboratory is an area requiring further investigation. The present study set out to examine learning outcomes by measuring students' academic performance and their skill in writing research proposals and investigated students' views about laboratory courses through interviews. Comparisons were made between students taking lectures only (control group) and students enrolled in both lecture and laboratory courses (experimental group). A total of 78 undergraduate students participated in the study. Stepwise discriminant analysis, analysis of covariance, and multidimensional scaling were used to analyse the data. Results showed that students in the experimental group did not perform as well as students in the control group in examination questions that required rote memorisation but they performed better than their counterparts in classification of species and in essay writing. In addition, in terms of research study design, proposals written by students in the experimental group were not only of higher quality but also more consistent in quality than those written by students in the control group. Implications for curriculum design and future research are described with respect to the innovative measurements and analytical methods we used to evaluate laboratory learning outcomes, the role of laboratory courses in biology teaching, and students' learning styles. (Contains 3 tables and 1 figure.)

This study investigated 6 weeks of online asynchronous discussions in a graduate-level course involving eleven students. The research purposes of this study were to identify patterns of collaborative knowledge exploration (CKE) and to suggest new analytical dimensions for investigating online discussions. Based upon a unique theoretical framework integrating the concepts of transition community and discourse community, three major dimensions of online asynchronous learning were adopted in the analysis. The first dimension focuses on the various forms of social negotiation in the discourse. The second dimension regards references to various learning resources, while the third is related to the coherence of the resources utilized. Based upon these dimensions and the analysis of the participating students' online asynchronous discussions, this study suggested ten different patterns of CKE representing the cognitive processes of Elaborating, Challenging, Correcting, and Debating. We argue that the proposed analytical framework allows us to interpret the sociocultural and cognitive aspects of students' interactions for online problem-based learning. In addition, the results suggest that the more cognitively demanding the pattern is, the less frequently it occurs. Potential factors contributing to the observed findings are discussed in this study.

Internet-based science learning has been advocated by many science educators for more than a decade. This review examines relevant research on this topic. Sixty-five papers are included in the review. The review consists of the following two major categories: (1) the role of demographics and learners' characteristics in Internet-based science learning, such as demographic background, prior knowledge, and self-efficacy; and (2) the learning outcomes derived from Internet-based science learning, such as attitude, motivation, conceptual understanding, and conceptual change. Some important conclusions are drawn from the review. For example, Internet-based science learning is equally favorable, or in some cases more so, to learning for female students compared to male students. The learner's control is essential for enhancing students' attitudes and motivation toward learning in Internet-based science learning environments. Nevertheless, appropriate guidance from teachers, moderators, or the Internet-based learning environment itself is still quite crucial in Internet-based science learning. Recommendations for future research related to the effects of Internet-based science learning on students' metacognitive reflections, epistemological development, and worldviews are suggested. (Contains 2 tables.)

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. [ABSTRACT FROM AUTHOR]