This study aimed to understand the relationship among conceptions of teaching and learning (COLT), classroom management self-efficacy (CMSe), and pedagogical content knowledge self-efficacy (PCKSe). A total of 485 in-...
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The aim of this study was to develop a scale to measure students’ blended learning courseexperience. A total of 792 undergraduate students from Malaysia participated in this study. exploratory factor analysis (eFA) ...
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extensive research has highlighted the significant impact of students’ approaches to learning and self-efficacy on academic achievement. It has been suggested that deep learning approaches, high self-efficacy, and st...
extensive research has highlighted the significant impact of students’ approaches to learning and self-efficacy on academic achievement. It has been suggested that deep learning approaches, high self-efficacy, and strong academic performance are crucial for developing undergraduates’ intentions to engage in sustainable lifelong learning after graduation. However, the interrelationships between students’ approaches to learning and self-efficacy within the context of design education remain underexplored. This study addresses this gap by investigating how students’ approaches to learning design are related to and predict their learning design self-efficacy. Two validated questionnaires were developed to assess these constructs: Approaches to learning Design (ALD) and Self-efficacy in learning Design (SeLD). The study involved 296 university students majoring in design. Confirmatory Factor Analysis (CFA) confirmed the validity and reliability of both questionnaires, identifying eight ALD and three SeLD subscales. Additionally, partial least squares structural equation (PLS-SeM) modeling demonstrated that students’ deep learning approaches strongly predict their learning design self-efficacy, while surfacelearning approaches, such as Fear of failure, negatively predict self-efficacy. This study offers valuable insights into the relationship between students’ approaches to learning and self-efficacy in design education, with important implications for enhancing classroom interactions and assessment practices.
Background: Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While ...
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Background: Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy. Objectives: This research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums. Methods: Data from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions. Results and Conclusions: Using an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi-directional long short-term memory model for emotion detection yielded the highest predictive performance, with an impressive F-measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low-level interaction group experienced moreemotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups
There have been many empirical studies on humor styles and humor comprehension and appreciation, but relatively few have directly addressed the relationship between these two topics of humor research, especially studi...
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A sense of humor constitutes a core factor that influences teacher-student interactions. The humor that teachers utilize in class may influence teacher-student relationships, either positively or negatively. Moreover,...
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The purpose of this paper is to investigate the factors affecting the passenger traffic volume on the high-capacity MRT, to analyze the traffic volume trends, and to make traffic volume predictions more accurately. Th...
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In theera of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of ...
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Cancer immunotherapy harnesses the immune system to combat tumors and has emerged as a major cancer treatment modality. The PD-1/PD-L1 immune checkpoint modulates interactions between tumor cells and T cells and has b...
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Differentiation of induced pluripotent stem cells (iPSCs) is an extremely complex process that has proven difficult to study. In this research, we utilized nanotopography to elucidate details regarding iPSC differenti...
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