Despite the common and growing use of and instruction on digital reading in schools, the ways in which reading comprehension may operate differently in paper and online environments are still underexplored. Using publ...
Despite the common and growing use of and instruction on digital reading in schools, the ways in which reading comprehension may operate differently in paper and online environments are still underexplored. Using publicly available national datasets, we explore similarities and differences in how varied comprehension processes relate to each other in these spaces. Analyses using a Bayesian unidimensional graded response model found that paper-based reading performance involved hierarchical comprehension processes consistent with traditional theories of reading comprehension while digital hypermedia reading did not involve these same processes. This research provides usable knowledge for supporting researchers, and teachers in uncovering the unique educational benefits and challenges of online reading.
Skilled reading is important in daily life. While the understanding of the neurofunctional organization of this uniquely human skill has advanced significantly, it does not take into consideration the common bilingual...
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Skilled reading is important in daily life. While the understanding of the neurofunctional organization of this uniquely human skill has advanced significantly, it does not take into consideration the common bilingual experiences around the world. To examine the role of early bilingualism on the neural substrates supporting English word processing, we compared brain activity, as well as functional connectivity, in Spanish-English early bilingual adults (N = 25) and English monolingual adults (N = 33) during single-word processing. Activation analysis revealed no significant differences between the two groups. A seed-to-voxel analysis using eight a priori selected seed-regions (placed in regions known to be involved in reading) revealed relatively stronger functional connectivity in bilinguals between two sets of regions: left superior temporal gyrus seed positively with left lingual gyrus and left middle frontal gyrus seed negatively with left anterior cingulate cortex. Together these results suggest that an early Spanish-English bilingual experience does not modulate local brain activity for English word reading. It does, however, have some influence on the functional intercommunication between brain regions during reading, specifically in two regions associated with reading, which are functionally connected to those inside and outside of the reading network. We conclude that brain regions involved in processing English words are not that different in Spanish-English early bilingual adults relative to monolingual adult users of English.
Digital transformation in the banking sector after the Covid-19 pandemic in Indonesia as a middle-income country in East Asia Pacific has accelerated with the emergence of digital banks. However, digital banks in Indo...
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Co-creativity has recently received increasing attention. However, few empirical studies explore individuals' creative performance in a group, and fewer have approached creative ideation in different task situatio...
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Co-creativity has recently received increasing attention. However, few empirical studies explore individuals' creative performance in a group, and fewer have approached creative ideation in different task situations. This study recruited 156 participants to complete creativity tests on an online creativity task platform. Participants were randomly assigned to either cooperative or competitive task situations. Their performance was analyzed using two creativity tests: the Alternative Uses Test (AUT) and Chinese Radical Remote Associates Test (CRRAT). Participants completed tasks alone (i.e., in single player mode) and in a cooperative or competitive situation (i.e., in paired-player mode). The results revealed that participants in the competitive task situation showed higher levels of competitive anxiety. Moreover, their AUT and CRRAT performances in paired-player mode were better than those in single player mode. In the cooperative task situation, participants' CRRAT performance was significantly better than in the competitive task situation. This study had two main findings. First, it strengthens the understanding of how group work enhances individual online creative performance. Second, it distinguishes the influences of cooperative or competitive task situations on different creative performance. This study revealed the differences in creative performance in distinct task situations.
Forming learners' science concepts and conceptual change entails adaptive epistemic beliefs to support a high degree of interactivity within a coherent knowledge structure. Adaptive epistemic beliefs are character...
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Forming learners' science concepts and conceptual change entails adaptive epistemic beliefs to support a high degree of interactivity within a coherent knowledge structure. Adaptive epistemic beliefs are characterized by beliefs that knowledge is uncertain and should be justified through experimentation or multiple sources dependent upon the task contexts. Thus, assessing and evaluating learners' adaptive epistemic beliefs is a complex process that requires laborious analysis of learner artifacts based on reliable and valid coding schemes. This article aims to describe new ways of assessing and applying technologies that can measure and foster adaptive epistemic beliefs. We propose new strategies for a theoretically-based human-and-machine symbiotic learning Analytics (LA) framework. The application of this LA framework may facilitate the development of real-time detecting and representation of the individual and collective epistemic belief networks as well as diagnosing and providing appropriate scaffolds to promote adaptive epistemic beliefs via the design of personalised pedagogical feedback with experts' input. The heuristic application of technology infrastructure may propel a movement for more tangible and personalised learning in science education. The current gaps of using AI-based emerging technologies in science learning and implications for science education are discussed to advance science education in new directions.
Immigration is a hotly debated and deeply polarizing topic in American society. The past few decades have seen an influx of immigrants from Asia, Africa, and the Americas who contend with having a double-minority stat...
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Immigration is a hotly debated and deeply polarizing topic in American society. The past few decades have seen an influx of immigrants from Asia, Africa, and the Americas who contend with having a double-minority status. This qualitative study advances an understanding of the lived experiences and acculturation process of immigrant academics of color within American academia. Findings indicate struggles of cultural disequilibrium, marginalization, and the challenges of gaining or regaining cultural, professional, and social capital. Their experiences and perspectives have explicit implications for adult learning.
This study used deep learning techniques with Moodle log data to predict student performance in introductory computer programming courses. Particularly, this study would like to use prediction results to identify pote...
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ISBN:
(数字)9781665495196
ISBN:
(纸本)9781665495196
This study used deep learning techniques with Moodle log data to predict student performance in introductory computer programming courses. Particularly, this study would like to use prediction results to identify potential low -performing students who may need assistance from teachers. The results suggested that deep learning models are promising to predict student performance and identify low-performing students in the researched context. What the prediction results provided by the models can inform teachers in learning settings was also further discussed in this paper.
We explore how Black and Latino/a students from economically marginalized communities drew upon dominant capitals accrued by virtue of attendance at elite secondary schools in conjunction with non-dominant family and ...
We explore how Black and Latino/a students from economically marginalized communities drew upon dominant capitals accrued by virtue of attendance at elite secondary schools in conjunction with non-dominant family and community capitals to chart their postsecondary lives through college and beyond. In so doing, we point to affordances offered by the authors' longitudinal qualitative research investigation, as we work to understand individual and collective class and race positioning practices and outcomes post high school.
Feature attribution methods from explainable artificial intelligence (XAI) provide explanations of machine learning models by quantifying feature importance for predictions of test instances. While features determinin...
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Feature attribution methods from explainable artificial intelligence (XAI) provide explanations of machine learning models by quantifying feature importance for predictions of test instances. While features determining individual predictions have frequently been identified in machine learning applications, the consistency of feature importance-based explanations of machine learning models using different attribution methods has not been thoroughly investigated. We have systematically compared model explanations in molecular machine learning. Therefore, a test system of highly accurate compound activity predictions for different targets using different machine learning methods was generated. For these predictions, explanations were computed using methodological variants of the Shapley value formalism, a popular feature attribution approach in machine learning adapted from game theory. Predictions of each model were assessed using a model-agnostic and model-specific Shapley value-based method. The resulting feature importance distributions were characterized and compared by a global statistical analysis using diverse measures. Unexpectedly, methodological variants for Shapley value calculations yielded distinct feature importance distributions for highly accurate predictions. There was only little agreement between alternative model explanations. Our findings suggest that feature importance-based explanations of machine learning predictions should include an assessment of consistency using alternative methods.
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