8,631 research outputs found

    The relationship of (perceived) epistemic cognition to interaction with resources on the internet

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    Information seeking and processing are key literacy practices. However, they are activities that students, across a range of ages, struggle with. These information seeking processes can be viewed through the lens of epistemic cognition: beliefs regarding the source, justification, complexity, and certainty of knowledge. In the research reported in this article we build on established research in this area, which has typically used self-report psychometric and behavior data, and information seeking tasks involving closed-document sets. We take a novel approach in applying established self-report measures to a large-scale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigation – including sites visited – and other trace data, to support more traditional self-report mechanisms. Our analysis suggests that prior work demonstrating relationships between self-report indicators is not paralleled in investigation of the hypothesized relationships between self-report and trace-indicators. However, there are clear epistemic features of this trace data. The article thus demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks

    Developing a multiple-document-processing performance assessment for epistemic literacy

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    The LAK15 theme “shifts the focus from data to impact”, noting the potential for Learning Analytics based on existing technologies to have scalable impact on learning for people of all ages. For such demand and potential in scalability to be met the challenges of addressing higher-order thinking skills should be addressed. This paper discuses one such approach – the creation of an analytic and task model to probe epistemic cognition in complex literacy tasks. The research uses existing technologies in novel ways to build a conceptually grounded model of trace-indicators for epistemic-commitments in information seeking behaviors. We argue that such an evidence centered approach is fundamental to realizing the potential of analytics, which should maintain a strong association with learning theory

    Internet source evaluation: The role of implicit associations and psychophysiological self-regulation

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    This study focused on middle school students\u2019 source evaluation skills as a key component of digital literacy. Specifically, it examined the role of two unexplored individual factors that may affect the evaluation of sources providing information about the controversial topic of the health risks associated with the use of mobile phones. The factors were the implicit association of mobile phone with health or no health, and psychophysiological self-regulation as reflected in basal Heart Rate Variability (HRV). Seventy-two seventh graders read six webpages that provided contrasting information on the unsettled topic of the potential health risks related to the use of mobile phones. Then they were asked to rank-order the six websites along the dimension of reliability (source evaluation). Findings revealed that students were able to discriminate between the most and least reliable websites, justifying their ranking in light of different criteria. However, overall, they were little accurate in rank-ordering all six Internet sources. Both implicit associations and HRV correlated with source evaluation. The interaction between the two individual variables was a significant predictor of participants\u2019 performance in rank-ordering the websites for reliability. A slope analysis revealed that when students had an average psychophysiological self-regulation, the stronger their association of the mobile phone with health, the better their performance on source evaluation. Theoretical and educational significances of the study are discussed

    CollabGraph: A graph-based collaborative search summary visualisation

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    Currently, the search history in search engines is presented in a list view of some combination of enumerated results by title, URL, or search query. However, this classical list view is not ideal in collaborative search environments as it does not always assist users in understanding collaborators' search history results and the project's status. We present CollabGraph, a system for graph-based summary visualization in collaborative search learning environments. Our system differentiates from existing solutions by visualizing the summary of the collaboration results in a graph and having its core personal knowledge graphs (PKGs) for each user. Our research questions concentrate around the CollabGraph's usefulness, preference, and enhancement of participation of student's and teacher's feedback compared to the list view of search history results. We evaluate our approach with an online questionnaire in six different project-based searching as learning (SaL) scenarios (LSs). The evaluation of users' experience indicates that the CollabGraph is useful, highly likeable, and could benefit users' participation and teacher's feedback by providing more precise insights into the project status. Our approach helps users better perceive about everyone's work, and it is a highly preferable feature alongside the list view. In addition, the results demonstrate that graph summary visualizations, such as the CollabGraph, are more suitable for closed-end scenarios and collaborative projects with many participants

    Predicting Students Performance in Online Education through Deep Learning Model

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    This epidemic has prompted the development of Education 4.0, virtual learning, and the demand to adapt educational practices to meet the needs of younger demographics. A rising epidemic has necessitated the shutdown of campuses where education programs are now being carried out online in educational institutions all over the globe. The report includes a study on the effectiveness and perceptions of students toward digital learning during the pandemic. A Convolutional Neural Network (CNN) and Particle swarm optimization model, which forecasts the student’s learning rates, are used to tackle this issue. This study will categorize student performance into low, medium, and high grades to forecast student achievement. The Kaggle student’s performance assessment database is utilized to gather the student information logs, which are then pre-processed to eliminate noise and redundant data. The CNN derives features based on the student’s attention and arbitrary patterns sequencing by examining the pre-processed information. Then, utilizing the Minimum Redundancy Maximum Relevance (mRMR) approach, the retrieved characteristics are evaluated. The lowest one that treats each characteristic individually is chosen as the greatest feature by mRMR. CNN uses stochastic Gradient Descent (SGD) to calculate the characteristic weights, which are then modified for improved extracting features. Finally, the CNN-WOA method forecasts the final academic achievement forecast outcome. Studies revealed that the suggested approach outperforms existing ones in terms of accuracy, precision, recall, and F-score while requiring less computing time

    A Literature Review on Intelligent Services Applied to Distance Learning

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    Distance learning has assumed a relevant role in the educational scenario. The use of Virtual Learning Environments contributes to obtaining a substantial amount of educational data. In this sense, the analyzed data generate knowledge used by institutions to assist managers and professors in strategic planning and teaching. The discovery of students’ behaviors enables a wide variety of intelligent services for assisting in the learning process. This article presents a literature review in order to identify the intelligent services applied in distance learning. The research covers the period from January 2010 to May 2021. The initial search found 1316 articles, among which 51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems or models, and 6% (3/51) use assessment tools. This review allowed for the observation that the principal services offered are recommendation systems and learning systems. In these services, the analysis of student profiles stands out to identify patterns of behavior, detect low performance, and identify probabilities of dropouts from courses.info:eu-repo/semantics/publishedVersio

    Machine and expert judgments of student perceptions of teaching behavior in secondary education:Added value of topic modeling with big data

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    Research shows that effective teaching behavior is important for students' learning and outcomes, and scholars have developed various instruments for measuring effective teaching behavior domains. Although student assessments are frequently used for evaluating teaching behavior, they are mainly in Likert-scale or categorical forms, which precludes students from freely expressing their perceptions of teaching. Drawing on an open-ended questionnaire from large-scale student surveys, this study uses a machine learning tool aiming to extract teaching behavior topics from large-scale students’ open-ended answers and to test the convergent validity of the outcomes by comparing them with theory-driven manual coding outcomes based on expert judgments. We applied a latent Dirichlet allocation (LDA) topic modeling analysis, together with a visualization tool (LDAvis), to qualitative data collected from 173,858 secondary education students in the Netherlands. This data-driven machine learning analysis yielded eight topics of teaching behavior domains: Clear explanation, Student-centered supportive learning climate, Lesson variety, Likable characteristics of the teacher, Evoking interest, Monitoring understanding, Inclusiveness and equity, Lesson objectives and formative assessment. In addition, we subjected 864 randomly selected student responses from the same dataset to manual coding, and performed theory-driven content analysis, which resulted in nine teaching behavior domains and 19 sub-domains. Results suggest that the relation between machine learning and human analysis is complementary. By comparing the bottom-up (machine learning analysis) and top-down (content analysis), we found that the proposed topic modeling approach reveals unique domains of teaching behavior, and confirmed the validity of the topic modeling outcomes evident from the overlapping topics

    Filling the Reading Void: Studying Reading Stamina in a Rural High School Through Action Research: A Companion Research Study

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    Experts agree best practice in elementary education allows students time for silent reading during school due to the benefits to students in vocabulary development and reading stamina. As students age, this practice typically declines in favor of activities meant to teach vocabulary, prepare for standardized tests, and study novels together as a class to explore and analyze literature, resulting in less reading and subsequently decreasing reading stamina. -- Teachers at a high poverty South Carolina high school, recognizing reading stamina as an issue, implemented a protocol described by Penny Kittle (2013) in Book Love: Developing Depth, Stamina, and Passion in Adolescent Readers, a method of individualized instruction centered on silent reading of choice material. A researcher studied the project called the Book Love Initiative guided by the central question, what happens in a secondary English classroom when a teacher creates and utilizes a balanced approach of appropriate level choice reading, text study, and novel study? -- Employing a QUAN + QUAL mixed-methods approach, the researcher used Kittle\u27s (2013) reading survey, the Sydney Attribution Scale (Marsh, 1983), student conferencing data, weekly reading records, behavioral observation checklists, and interviews with teacher participants to measure the impact on four constructs identified in research questions: attitudes toward reading, reading volume, stamina behaviors, and classroom environments. -- A companion study at a low poverty high school in North Carolina employed similar methods so researchers could determine if there were further reaching implications. Findings at both sites determined the initiative positively impacted the constructs identified in research questions. Companion researchers made recommendations for practice, policy, and research as a result of the study\u27s findings
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