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2. Research on the Cooperative Behavior of Academic Papers Published by Chinese Educational Scholars Based on Complex Networks
- Author
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Li, Bichu and Zhang, Ziliang
- Abstract
Research on mutual cooperation among scholars or research institutions has become more and more common. The purpose of this paper is to explore the current status of cooperation between scholars and research institutions in the field of Chinese education. In this paper, we use the method of the complex network to analyze the cooperative behavior of academic papers published by Chinese educational scholars by collecting academic papers on education leadership, education policy, quality education, and vocational education. Our conclusions show that most of the academic papers published by Chinese educational scholars are non-cooperative. In the authors of the co-authored papers, there is a significant "Matthew effect", that is, some key scholars in these fields that link the collaborators. Lastly, there is no obvious aggregation effect between the authors of the co-authored papers which indicating a widespread and extensive connection between the collaborators. The above conclusions provide valuable insights into our understanding of the cooperative behavior of Chinese education scholars.
- Published
- 2019
3. Annual Proceedings of Selected Research and Development Papers Presented at the Annual Convention of the Association for Educational Communications and Technology (42nd, Las Vegas, Nevada, 2019). Volume 1
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Association for Educational Communications and Technology, Simonson, Michael, and Seepersaud, Deborah
- Abstract
For the forty-second time, the Association for Educational Communications and Technology (AECT) is sponsoring the publication of these Proceedings. Papers published in this volume were presented at the annual AECT Convention in Las Vegas, Nevada. The Proceedings of AECT's Convention are published in two volumes. Volume 1 contains 37 papers dealing primarily with research and development topics. Papers dealing with the practice of instructional technology including instruction and training issues are contained in Volume 2. [For Volume 2, see ED609417.]
- Published
- 2019
4. Content-based quality evaluation of scientific papers using coarse feature and knowledge entity network
- Author
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Zhongyi Wang, Haoxuan Zhang, Haihua Chen, Yunhe Feng, and Junhua Ding
- Subjects
Paper quality evaluation ,Knowledge entity ,Network analysis ,Machine learning ,Novelty ,Structural entropy ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Pre-evaluating scientific paper quality aids in alleviating peer review pressure and fostering scientific advancement. Although prior studies have identified numerous quality-related features, their effectiveness and representativeness of paper content remain to be comprehensively investigated. Addressing this issue, we propose a content-based interpretable method for pre-evaluating the quality of scientific papers. Firstly, we define quality attributes of computer science (CS) papers as integrity, clarity, novelty, and significance, based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: Accepted/Disputed/Rejected (ADR) and Accepted/Rejected (AR). Subsequently, we construct fine-grained features from metadata and knowledge entity networks, including text structure, readability, references, citations, semantic novelty, and network structure. We empirically evaluate our method using the ICLR paper dataset, achieving optimal performance with the Random Forest model, yielding F1 scores of 0.715 and 0.762 for the two tasks, respectively. Through feature analysis and case studies employing SHAP interpretable methods, we demonstrate that the proposed features enhance the performance of machine learning models in scientific paper quality evaluation, offering interpretable evidence for model decisions.
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- 2024
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5. Social Networks and Skills Instruction: A Pilot Study of STEM College Educators and Employers in Wisconsin and New York. WCER Working Paper No. 2018-3
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University of Wisconsin-Madison, Wisconsin Center for Education Research (WCER), Benbow, Ross J., Lee, Changhee, and Hora, Matthew T.
- Abstract
Research indicates that teamwork, communication, self-directed learning, and problem-solving skills are strongly linked to individual academic and professional success, yet little is known regarding how college educators and employer trainers learn to better teach or train others in these valuable skills in postsecondary and employment STEM contexts. This pilot study uses social network analysis--a research perspective studying relationships or "social ties" to better understand the ways interactions influence behavior--to explore the dimensions of educator and trainer discussions regarding methods for helping students or employees acquire important skills. The study also examines whether educators and employers believe such discussions influence their instruction. A descriptive analysis of data from online surveys collected from educators (n=192) and employers (n=70) in technology and manufacturing fields in southern Wisconsin and western New York indicates respondents frequently engage in such teaching- and training-focused discussions with people inside and outside their colleges and businesses. Though more college educators are involved in such conversations than employers, employer trainers who engage in such conversations do so with individuals affiliated with more diverse organizations. Results also indicate that educators and employers who have these discussions do so at a similar frequency. Finally, most educators and employers with teaching- and training-focused social networks perceive them to be beneficial to their teamwork, communication, self-directed learning, and problem-solving instruction. In light of these findings, leaders hoping to further develop teaching- and training-focused social networks in education and employment fields may find more success in openly promoting the importance of such social ties as well as providing more opportunities for intra- and interorganizational professional development in instruction.
- Published
- 2018
6. Plant Blindness Intensity Throughout the School and University Years: A Cross-Age Study
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Amprazis, Alexandros, Papadopoulou, Penelope, Hammann, Marcus, Series Editor, Yarden, Anat, Series Editor, Ergazaki, Marida, Founding Editor, Kampourakis, Kostas, Founding Editor, Zabel, Jörg, Editorial Board Member, Korfiatis, Constantinos, Editorial Board Member, Jimenez Aleixandre, Maria Pilar, Editorial Board Member, Harms, Ute, Editorial Board Member, Reiss, Michael, Editorial Board Member, Gericke, Niklas, Editorial Board Member, El-Hani, Charbel Nino, Editorial Board Member, Dawson, Vaille, Editorial Board Member, Nehm, Ross, Editorial Board Member, McComas, William, Editorial Board Member, Passmore, Cynthia, Editorial Board Member, Grace, Marcus, Editorial Board Member, Knippels, Marie Christine, Editorial Board Member, and Korfiatis, Konstantinos, editor
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- 2024
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7. Moving towards an Integrated Theory of Policy Networks: A Multi-Theoretical Approach for Examining State-Level Policy Change in U.S. Subsystems. Working Paper #45
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Michigan State University, Education Policy Center, Galey, Sarah, and Youngs, Peter
- Abstract
Scholars have developed a wide range of theories to explain both stability and change in policy subsystems. In recent years, a burgeoning literature has emerged that focuses on the application of network analysis in policy research, more formally known as Policy Network Analysis (PNA). This approach, while still developing, has great potential as an integrated theoretical framework that brings together multiple theories under a single conceptual paradigm. In this paper, the authors demonstrate how the insights of three other established policy theories--namely, the Advocacy Coalition Framework (ACF), Punctuated-Equilibrium theory (PET), and the Policy Entrepreneur Model (PEM)--may be augmented under a synthesis theory of policy networks to provide a more conceptually coherent theory of policymaking that may be useful for explaining periods of both stability and change.
- Published
- 2014
8. Content-based quality evaluation of scientific papers using coarse feature and knowledge entity network.
- Author
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Wang, Zhongyi, Zhang, Haoxuan, Chen, Haihua, Feng, Yunhe, and Ding, Junhua
- Abstract
Pre-evaluating scientific paper quality aids in alleviating peer review pressure and fostering scientific advancement. Although prior studies have identified numerous quality-related features, their effectiveness and representativeness of paper content remain to be comprehensively investigated. Addressing this issue, we propose a content-based interpretable method for pre-evaluating the quality of scientific papers. Firstly, we define quality attributes of computer science (CS) papers as integrity , clarity , novelty , and significance , based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: Accepted/Disputed/Rejected (ADR) and Accepted/Rejected (AR). Subsequently, we construct fine-grained features from metadata and knowledge entity networks, including text structure, readability, references, citations, semantic novelty, and network structure. We empirically evaluate our method using the ICLR paper dataset, achieving optimal performance with the Random Forest model, yielding F1 scores of 0.715 and 0.762 for the two tasks, respectively. Through feature analysis and case studies employing SHAP interpretable methods, we demonstrate that the proposed features enhance the performance of machine learning models in scientific paper quality evaluation, offering interpretable evidence for model decisions. • Define four criteria for quality evaluation of scientific papers: integrity, clarity, novelty, and significance. • Propose a framework for quality evaluation of scientific papers based on coarse features and knowledge entity network. • An effective algorithm for measuring the novelty and significance of scientific papers based on knowledge entity networks. • Create and release a rigorous dataset, which could serve as the gold standard for quality evaluation of scientific papers. • Conduct extensive experiments to validate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]
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- 2024
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9. Using Learner Data from Duolingo to Detect Micro- and Macroscopic Granularity through Machine Learning Methods to Capture the Language Learning Journey
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Chiera, Belinda, Bédi, Branislav, and Zviel-Girshin, Rina
- Abstract
Modern language learning applications have become 'smarter' and 'intelligent' by including Artificial Intelligence (AI) and Machine Learning (ML) technologies to collect different kinds of data. This data can be used for analysis on a microscopic and/or macroscopic level to provide granulation of knowledge. We analyzed 1,213 French language learner data over a 30-day period, publicly available from Duolingo, to compare the progression of individual learners (microscopic granularity) and large groups of learners (macroscopic granularity). Using network modeling, we compared patterns of individual learners against one another and that of a learning community and determined what groups of learners typically practice across communities. Preliminary results suggest how applications for L2 learning can be designed to create an optimal path for learning. [For the complete volume, "Intelligent CALL, Granular Systems and Learner Data: Short Papers from EUROCALL 2022 (30th, Reykjavik, Iceland, August 17-19, 2022)," see ED624779.]
- Published
- 2022
10. Panama Papers' offshoring network behavior
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David Dominguez, Odette Pantoja, Pablo Pico, Miguel Mateos, María del Mar Alonso-Almeida, and Mario González
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Panama Papers ,Offshore societies ,Tax havens ,Geographical networks ,Graph Theory ,Network Analysis ,Science (General) ,Q1-390 ,Social sciences (General) ,H1-99 - Abstract
The present study analyzes the offshoring network constructed from the information contained in the Panama Papers, characterizing worldwide regions and countries as well as their intra- and inter-relationships. The Panama Papers 2016 divulgence is the largest leak of offshoring and tax avoidance documentation. The document leak, with a volume content of approximately 2.6 terabytes, involves more than two hundred thousand enterprises in more than two hundred countries. From this information, the offshore connections of individuals and companies are constructed and aggregated using their countries of origin. The top offshore financial regions and countries of the network are identified, and their intra- and inter-relationship are mapped and described. We are able to identify the top countries in the offshoring network and characterize their connectivity structure, discovering the more prominent actors in the worldwide offshoring scenario and their range of influence.
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- 2020
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11. The Public's Understanding of 'Evolution' as Seen through Online Spaces
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Park, Hyoung-Yong and Seo, Hae-Ae
- Abstract
Evolution is a central concept that unifies all areas of life sciences. Despite longstanding scientific efforts in science education, the public's scientific awareness of evolution still needs to improve. Furthermore, teaching evolution is subject to recurring controversy. This study aimed to investigate the gap between public understanding of evolution seen through online spaces and contents in a school curriculum and explore its reasons. A content analysis was conducted using data mining on a major online portal in Korea. It examined the characteristics of creating and consuming content on evolution through the online portal service based on analyzing the number of posts related to biological evolution and active participants. It also discussed the feasibility of automatic document classification to distinguish between scientific understanding and nonscientific beliefs on the evolution and related online circulating contents. The results show that there are tactics for public exposure and dissemination of creationism through online discussions. [For the full proceedings, see ED629086.]
- Published
- 2023
12. Predicting the Global Impact of Authors from the Learning Analytics Community--A Case Study Grounded in CNA
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Ionita, Remus Florentin, Dascalu, Mihai, Corlatescu, Dragos-Georgian, and McNamara, Danielle S
- Abstract
Exploring new or emerging research domains or subdomains can become overwhelming due to the magnitude of available resources and the high speed at which articles are published. As such, a tool that curates the information and underlines central entities, both authors and articles from a given research context, is highly desirable. Starting from the articles of the International Conference of Learning Analytics & Knowledge (LAK) in its first decade, this paper proposes a novel method grounded in Cohesion Network Analysis (CNA) to analyze subcommunities of authors based on the semantic similarities between authors and papers, and estimate their global impact. Paper abstracts are represented as embeddings using a fine-tuned SciBERT language model, alongside a custom trained LSA model. The extrapolation between the local LAK community to a worldwide importance was also underlined by the comparison between the rankings obtained from our method and statistics from ResearchGate. The accuracies for binary classifications in terms of high/low impact predictions were around 70% for authors, and around 80% for articles. Our method can guide researchers by providing valuable information on the interactions between the members of a knowledge community and by highlighting central local authors who may potentially have a high global impact.
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- 2021
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13. Extractive Summarization Using Cohesion Network Analysis and Submodular Set Functions
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Cioaca, Valentin Sergiu, Dascalu, Mihai, and McNamara, Danielle S.
- Abstract
Numerous approaches have been introduced to automate the process of text summarization, but only few can be easily adapted to multiple languages. This paper introduces a multilingual text processing pipeline integrated in the open-source "ReaderBench" framework, which can be retrofit to cover more than 50 languages. While considering the extensibility of the approach and the problem of missing labeled data for training in various languages besides English, an unsupervised algorithm was preferred to perform extractive summarization (i.e., select the most representative sentences from the original document). Specifically, two different approaches relying on text cohesion were implemented:(1) a graph-based text representation derived from Cohesion Network Analysis that extends TextRank; and (2) a class of submodular set functions. Evaluations were performed on the DUC dataset and use as baseline the implementation of TextRank from Gensim. Our results using the submodular set functions outperform the baseline. In addition, two use cases on English and Romanian languages are presented, with corresponding graphical representations for the two methods. [This paper was published in: 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Proceedings, 2020, pp. 161-168 (ISBN 978-1-7281-7628-4).]
- Published
- 2021
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14. Using Epistemic Network Analysis to Explore Discourse Patterns across Design Iterations of a Teacher Dashboard
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Adair, Amy, Owens, Jessica, and Gobert, Janice
- Abstract
Providing high-level support to students on NGSS inquiry practices can be challenging; however, teacher dashboards can help teachers provide just-in-time instruction to students, both in-person and online. Prior work has shown some success with a dashboard that alerts teachers in real time on students' science inquiry difficulties, but teachers differed in their use of the alerts. To further support teachers, we designed a second iteration, in which the alerts included actionable, evidence-based Teacher Inquiry Practice Supports (TIPS), a series of suggested scaffolds that teachers can use to support students on the practices with which they are struggling. In this study, we investigate how the discursive support patterns from one teacher differed when using the dashboard alerts "without" TIPS followed by "with" TIPS. Findings suggest that TIPS influenced how the teacher incorporated different types of support for her students, and further, that the support given varied across different virtual lab stages. [This paper was published in: "ICLS2022 Proceedings," International Society of the Learning Sciences, 2022, pp. 297-304.]
- Published
- 2022
15. Extended Multi-Document Cohesion Network Analysis Centered on Comprehension Prediction
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Nicula, Bogdan, Perret, Cecile A., Dascalu, Mihai, and McNamara, Danielle S.
- Abstract
Theories of discourse argue that comprehension depends on the coherence of the learner's mental representation. Our aim is to create a reliable automated representation to estimate readers' level of comprehension based on different productions, namely self-explanations and answers to open-ended questions. Previous work relied on Cohesion Network Analysis to model a cohesion graph composed of semantic links between multiple reference texts and student productions. From this graph, a set of features was derived and used to build machine learning models to predict student comprehension scores. In this paper, we build on top of the previous study by: (1) extending the CNA graph by adding new semantic links targeting specific sentences that should have been captured within the learner's productions; and (2) cleaning the self-explanations by eliminating frozen expression, as well as entries which seemed nearly identical to the source text. The results are in line with the conclusions of the previous study regarding the importance of both self-explanations and question answers in predicting the students' reading comprehension level. They also outline the limitations of our feature generation approach, in which no substantial improvements were detected, despite adding more fine-grained features. [This paper was published in: I. I. Bittencourt et al. (Eds.), "AIED 2020" (pp. 228-233). Switzerland: Springer Nature.]
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- 2020
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16. Scientific authorship and collaboration network analysis on malaria research in Benin: papers indexed in the web of science (1996–2016)
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Roseric Azondekon, Zachary James Harper, Fiacre Rodrigue Agossa, Charles Michael Welzig, and Susan McRoy
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Network analysis ,Scientific collaboration ,Co-authorship ,Malaria ,Benin ,Public aspects of medicine ,RA1-1270 - Abstract
Abstract Background To sustain the critical progress made, prioritization and a multidisciplinary approach to malaria research remain important to the national malaria control program in Benin. To document the structure of the malaria collaborative research in Benin, we analyze authorship of the scientific documents published on malaria from Benin. Methods We collected bibliographic data from the Web Of Science on malaria research in Benin from January 1996 to December 2016. From the collected data, a mulitigraph co-authorship network with authors representing vertices was generated. An edge was drawn between two authors when they co-author a paper. We computed vertex degree, betweenness, closeness, and eigenvectors among others to identify prolific authors. We further assess the weak points and how information flow in the network. Finally, we perform a hierarchical clustering analysis, and Monte-Carlo simulations. Results Overall, 427 publications were included in this study. The generated network contained 1792 authors and 116,388 parallel edges which converted in a weighted graph of 1792 vertices and 95,787 edges. Our results suggested that prolific authors with higher degrees tend to collaborate more. The hierarchical clustering revealed 23 clusters, seven of which form a giant component containing 94% of all the vertices in the network. This giant component has all the characteristics of a small-world network with a small shortest path distance between pairs of three, a diameter of 10 and a high clustering coefficient of 0.964. However, Monte-Carlo simulations suggested our observed network is an unusual type of small-world network. Sixteen vertices were identified as weak articulation points within the network. Conclusion The malaria research collaboration network in Benin is a complex network that seems to display the characteristics of a small-world network. This research reveals the presence of closed research groups where collaborative research likely happens only between members. Interdisciplinary collaboration tends to occur at higher levels between prolific researchers. Continuously supporting, stabilizing the identified key brokers and most productive authors in the Malaria research collaborative network is an urgent need in Benin. It will foster the malaria research network and ensure the promotion of junior scientists in the field.
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- 2018
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17. Predicting Multi-Document Comprehension: Cohesion Network Analysis
- Author
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Nicula, Bogdan, Perret, Cecile A., Dascalu, Mihai, and McNamara, Danielle S.
- Abstract
Theories of discourse argue that comprehension depends on the coherence of the learner's mental representation. Our aim is to create a reliable automated representation to estimate readers' level of comprehension based on different productions, namely self-explanations and answers to open-ended questions. Previous work relied on Cohesion Network Analysis to model a cohesion graph composed of semantic links between multiple reference texts and student productions. From this graph, a set of features was derived and used to build machine learning models to predict student comprehension scores. In this paper, we build on top of the previous study by: a) extending the CNA graph by adding new semantic links targeting specific sentences that should have been captured within the learner's productions, and b) cleaning the self-explanations by eliminating frozen expression, as well as entries which seemed nearly identical to the source text. The results are in line with the conclusions of the previous study regarding the importance of both self-explanations and question answers in predicting the students' reading comprehension level. They also outline the limitations of our feature generation approach, in which no substantial improvements were detected, despite adding more fine-grained features. [This paper was published in: Isotani, S., Millán, E.,Ogan, A., Hastings, P., McLaren, B. & Luckin, R. (Eds.), "Proceedings of the 20th International Conference of Artificial Intelligence in Education (AIED) in Chicago, IL" (pp. 358-369). Cham, Switzerland: Springer.]
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- 2019
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18. A Ten-Year Bibliometric Network Review on Massive Open Online Courses (MOOCs) Research: 2011-2020
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Boonroungrut, Chinun, Saroinsong, Wulan Patria, and Kim, One
- Abstract
Massive open online courses (MOOCs) have also received interest from researchers worldwide; however, there was no comprehensive review of the MOOC research. This paper aims to identify the MOOCs research scientific landscape as the trend from publications worldwide. In assessing research trends, the bibliometric network analysis using distance-based network mapping in VOSviewer was applied in this review. The 3,211 eligible articles published between 2011 and 2020 confirmed three main research clusters: learning system, human characteristics and higher education clusters. The results also showed that terms, such as 'learning systems', 'gender differences' and 'flipped classroom' emerged as ongoing research trends. In addition, these findings indicated that the overall productivity rates in the Middle East and Gulf regions were low. Besides, the authorship mapping indicated an absence of the small-world properties. A discussion of the findings and directions for further research are also provided. Based on the network analysis method, this paper presents the researchers' alternative method to review literature using an approach that possibly includes mostly published articles related to MOOCs.
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- 2022
19. A bibliometric analysis of most cited papers on vesiculobullous oral lesions.
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Gopinathan, Pillai, Aboalela, Ali, Haq, Ikram, Iyer, Kiran, Siddeeqh, Salman, Khan, Sulthan, and Abbiramy, Gopala
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BIBLIOMETRICS ,CONSCIOUSNESS raising ,CITATION analysis ,ORAL medicine ,DATABASES - Abstract
Aim: A well-known method for quantitatively evaluating scholarly work is bibliometric analysis. Best-cited papers raise awareness of the influential publications and patterns in the literature on a specific subject. The aim was to conduct bibliometric analysis to determine most cited articles on vesiculobullous oral lesions. This is the first study on citation analysis with respect to vesiculobullous oral lesions. Materials and Methods: A retrospective data search was explored on December 2022 using the Scopus database. The articles were evaluated, and fundamental data for bibliometric analysis was reviewed. Standard details about the author, linked organizations, publishing year, and place of origin were noted. Statistical analysis was performed using Chi-square analysis. VOSviewer software was used to determine the bibliometric network analysis for co-occurrence among coauthors and commonly used keywords. Results: A total of 344 articles published from 1971 to 2022 were included in the study. A total of 6680 citations and 19.41 citations per article were observed. The journal Archives of Dermatology received the most citation. There was a significant association between the number of citations and the journal type (open access vs. non-open access) (P < 0.05). Four to five highly related clusters with the help of VOSviewer software were found during co-occurrence network analysis. Conclusions: The top 10 articles on vesiculobullous oral lesions that received the most citations were listed in detail in the present study. This will be a valuable resource for academics, clinicians, and researchers in the fields of dermatology, general pathology, oral pathology, and oral medicine. [ABSTRACT FROM AUTHOR]
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- 2024
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20. Exploring Homophily in Demographics and Academic Performance Using Spatial-Temporal Student Networks
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Nguyen, Quan, Poquet, Oleksandra, Brooks, Christopher, and Li, Warren
- Abstract
Network analysis in educational research has primarily relied on self-reported relationships or connections inferred from online learning environments, such as discussion forums. However, a large part of students' social connections through day-to-day on-campus encounters has remained underexplored. The paper examines spatial-temporal student networks using campus WiFi log data throughout a semester, and their relations to the student demographics and academic performance. A tie in the spatial-temporal network was inferred when two individuals connected to the same WiFi access point at the same time intervals at the 'beyond chance' frequency. Our findings revealed that students were more likely to co-locate with the individuals of similar gender, ethnic group identity, family income, and grades. Analysis of homophily over the semester showed that students of the same gender were more likely to co-locate as the semester progressed. However, co-location of the students similar on ethnic minority identity, family income, and grades remained consistent throughout the semester. Mixed-effect regression models demonstrated that features derived from spatial-temporal networks, such as degree, the grade of the most frequently co-located peer, and average grade of five most frequently co-located peers were positively associated with academic performance. This study offers a unique exploration of the potential use of WiFi log data in understanding of student relationships integral to the quality of college experience. [For the full proceedings, see ED607784.]
- Published
- 2020
21. Multi-Document Cohesion Network Analysis: Automated Prediction of Inferencing across Multiple Documents
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Nicula, Bogdan, Perret, Cecile A., Dascalu, Mihai, and McNamara, Danielle S.
- Abstract
Open-ended comprehension questions are a common type of assessment used to evaluate how well students understand one of multiple documents. Our aim is to use natural language processing (NLP) to infer the level and type of inferencing within readers' answers to comprehension questions using linguistic and semantic features within their responses. Our taxonomy considers three types of responses to comprehension questions from students (N = 146) who read four documents: a) "textbase responses" (i.e., information required for the answer is present in a contiguous short sequence of text); b) "single-document inference responses" (i.e., requiring information from multiple text segments in a single document); and c) "multi-document inference responses" (i.e., information spanning multiple documents is required). The classification task was approached in two ways. First, we extracted features from students' answers to the comprehension questions using linguistic and semantic indices related to textual complexity and an extended Cohesion Network Analysis (CNA) graph to assess semantic links between the answers and the reference documents. Second, we compared different Recurrent Neural Networks (RNNs) architectures that rely on word embeddings to encode both answers and reference documents. Our best model based on RNN's predicts the answer type with an accuracy of 81%.[This paper was published in: "2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)" (pp. 343-348). IEEE.]
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- 2020
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22. Scientific authorship and collaboration network analysis on malaria research in Benin: papers indexed in the web of science (1996–2016)
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Azondekon, Roseric, Harper, Zachary James, Agossa, Fiacre Rodrigue, Welzig, Charles Michael, and McRoy, Susan
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- 2018
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23. Exploring the Learning Analytics Equation: What about the 'Carpe Diem' of Teaching and Learning?
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Bozkurt, Aras and Sharma, Ramesh C.
- Abstract
Humans have always been lured by the idea that they can use data to understand a phenomenon and make predictions about it. Learning analytics, in this sense, promise to understand and optimize learning and the environments in which it occurs by collecting data from learners and learning contexts. In this regard, this study systematically examines research on learning analytics through bibliometric, data mining, and analytics approaches. This paper argues that research interest in learning analytics is increasing steadily; some countries, higher education institutions, and researchers have a specific research agenda that indicates their intention to specialize in that field. It is also noted that there is a need for more interdisciplinary studies on learning analytics and a further need to merge technological capabilities with pedagogy. Based on the findings of text-mining and social network analysis, the following themes were identified: (1) learning analytics to improve teaching and personalize learning, (2) hegemony of data-driven teaching and learning practices, (3) multimodal learning analytics as the next generation practice, (4) learning design for learning analytics, (5) formative assessment through learning analytics, (6) learning analytics for social online learning spaces, and (7) privacy and ethical concerns to overcome. This paper suggests focusing on issues such as ethics and privacy and warns researchers to pay attention to the risks of both an educational panoptic society and quantified decision-making processes. Furthermore, rather than relying on algorithms, it is suggested to incorporate social values and center the learners in the learning analytics processes. Finally, this paper asks the following: "If we quantify the learning processes, how can we benefit from the carpe diem of educational processes and then seize the beauty of teaching and learning?"
- Published
- 2022
24. Research Productivity in the Human Movement Sciences in the Philippines: A Descriptive Bibliometric and Social Network Analysis
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Fernandez, Paula Mae Q., Tolentino, Julius Ceazar G., Miranda, John Paul P., Guanlao, John Gerald B., and Sac, Joseph G.
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The interdisciplinary field of human movement sciences (HMS) has gained massive interest among educational institutions around the world, not only in terms of academic programs but also in research. With this emergence, the researchers aimed to describe the productivity of HMS research in the Philippines. The descriptive bibliometric analysis phase of this paper considered papers published and indexed in Google Scholar from January 2010 to June 2021 and was analyzed after data cleaning and preprocessing. Results revealed that a total of 274 research publications were recorded between the years 2010 and 2021 with an average annual publication rate of 28.6% as far as the dataset was concerned. Also, public higher education institutions (HEIs) emerged to be the most productive generators of research outputs, specifically topped by the University of the Philippines (UP). Moreover, UP-based authors dominated the rankings of the most productive HMS researchers, with "Jeffrey Pagaduan" in the top rank. Results further indicated that 75% of the authors collaborated with fellow researchers within or outside their institution. Meanwhile, the term "physical education" was recorded to be the most frequently appearing word in most of the publications. Through the aid of the data mining approach social network analysis (SNA), five (5) researchers from public HEIs and two from private HEIs were identified to have the largest networks as discussed in the paper. The exploration of research in terms of productivity and the embedded social network of researchers may serve as a springboard in defining the future development of the field in the Philippines.
- Published
- 2022
25. Exploring Teacher Use of an Online Forum to Develop Game-Based Learning Literacy
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Barany, Amanda, Shah, Mamta, and Foster, Aroutis
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Game-based learning researchers have emphasized the importance of teachers' game literacy and knowledge of pedagogical approaches involved in successfully adopting an instructional approach (Bell and Gresalfi, 2017). In this paper, we describe findings from an online resource that teachers used to generate a repository of games for use both during their involvement in a Masters in Learning Technologies program and after the completion of the program. We argue that such a repository providing information on games in terms of their technology, pedagogy, and content may prove useful for teachers searching for games to align with their area of practice. This paper presents a descriptive analysis of a sample of 82 posts posted from September 2010-November 2016 to demonstrate participants' emerging proficiency in assessing games for their technological, pedagogical, and content-related affordances and constraints (as supported by the Game Network Analysis (GaNA) framework) (Shah & Foster, 2015). The paper also presents a case example to illustrate a forum user's developing game literacy and the community and contextual factors that influence post content. We conclude with implications for future research. [For the complete proceedings, see ED579395.]
- Published
- 2017
26. Stakeholders, Networks and Links in Early Childhood Policy: Network Analysis and the 'Transition to School: Position Statement'
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Wallis, Jake and Dockett, Sue
- Abstract
The importance of a positive start to school has been highlighted in a range of national and international research. This has stimulated considerable ongoing research attention, as well as initiatives across policy and practice, all with the aim of promoting a positive transition to school for all children. Despite the common interests across these sectors, the links and/or relationships between and among research, policy and practice remain unclear. This article maps the potential online users of the "Transition to School: Position Statement"--a document developed collaboratively by researchers, policymakers and practitioners--and organisations whose ambit includes transition to school. Using network analysis, the authors identify the online network of stakeholders involved in the field of early childhood and the links between these, before considering how such links might influence discourse and policy formation around transition to school. The analysis highlights weak cross-sectoral links and online networks dominated by government departments and agencies. Implications of these results are explored and the potential for digital research methods in research about transition to school is considered.
- Published
- 2015
- Full Text
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27. Network of public equity funds and investment performance in South Korea
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Kim, Yongwon, Song, Inwook, and Park, Young Kyu
- Published
- 2024
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28. A Cartography of Digital Literacy: Conceptual Categories and Main Issues in the Theorization and Study of Digital Literacies
- Author
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Samaniego, José Miguel
- Abstract
This paper presents a cartography of the digital literacy academic field. Such cartography is comprised of two sections: a categorization of the field through literature review and analysis, and an exploration of its main issues through thematic and network analysis. On the one hand, five conceptual categories of digital literacies are found: functional, sociocultural, critical, transformative, and sociomaterial. On the other, main issues are described with 21 recurring themes of digital literacy and a few networks depicting its most salient matters of concern, concluding with an interpretation of these in the composition of 8 encompassing issue spaces: digital literacies conceptions and practices, digital literacy in education, access and digital divide, digital texts and literacy, websites and social networks, digital technologies at the workplace and healthcare, digital technologies users and uses, and information issues. Finally, a few paragraphs are dedicated to the limitations of categorizing and issue mapping.
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- 2023
29. Panama Papers' offshoring network behavior
- Author
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Mario González, María del Mar Alonso-Almeida, Miguel Mateos, Odette Pantoja, David Dominguez, Pablo Pico, UAM. Departamento de Ingeniería Informática, UAM. Departamento de Organización de Empresas, and Neurocomputación Biológica (ING EPS-005)
- Subjects
0301 basic medicine ,Panama Papers ,Economics ,Offshore societies ,Article ,Economía ,03 medical and health sciences ,Globalization ,0302 clinical medicine ,Documentation ,Geographical networks ,Business ,Economic geography ,lcsh:Social sciences (General) ,lcsh:Science (General) ,Volume content ,Informática ,Panama ,Multidisciplinary ,Offshoring ,International Relations ,Money ,Network behavior ,Tax avoidance ,030104 developmental biology ,Graph Theory ,Tax havens ,lcsh:H1-99 ,030217 neurology & neurosurgery ,Network Analysis ,lcsh:Q1-390 - Abstract
The present study analyzes the offshoring network constructed from the information contained in the Panama Papers, characterizing worldwide regions and countries as well as their intra- and inter-relationships. The Panama Papers 2016 divulgence is the largest leak of offshoring and tax avoidance documentation. The document leak, with a volume content of approximately 2.6 terabytes, involves more than two hundred thousand enterprises in more than two hundred countries. From this information, the offshore connections of individuals and companies are constructed and aggregated using their countries of origin. The top offshore financial regions and countries of the network are identified, and their intra- and inter-relationship are mapped and described. We are able to identify the top countries in the offshoring network and characterize their connectivity structure, discovering the more prominent actors in the worldwide offshoring scenario and their range of influence., Panama Papers; Offshore societies; Tax havens; Geographical networks; Graph Theory; Network Analysis; International Relations; Money; Globalization; Business; Economics
- Published
- 2020
30. Bibliometric Analysis of Academic Papers Citing Dunleavy et al.'s (2006) "New Public Management Is Dead—Long Live Digital-Era Governance": Identifying Research Clusters and Future Research Agendas.
- Author
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Cho, Beomgeun
- Subjects
NEW public management ,BIBLIOMETRICS ,ADMINISTRATIVE reform ,INTERNET in public administration ,PUBLIC value - Abstract
I trace the bibliometric evolution of "New Public Management Is Dead" by Dunleavy et al. to investigate how the seminal paper influenced the administrative reform debate. They suggested Digital-Era Governance as the main post-NPM idea. My bibliometric analysis discovers public value, administrative reform trajectories, and digital government as influential themes. Unlike Dunleavy et al., the literature found the managerial reform wave is not linear, reform ideas are supplementary, and NPM remains a major toolkit. Future research should focus on reintegration and need-based holism, linking digital government to administrative reform, and the negative impact of digital government on democracy. [ABSTRACT FROM AUTHOR]
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- 2023
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31. Scientific Attention to Sustainability and SDGs: Meta-Analysis of Academic Papers
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Kimitaka Asatani, Haruo Takeda, Hiroko Yamano, and Ichiro Sakata
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bibliometrics ,network analysis ,sdgs ,natural language processing ,information retrieval ,scientific foresight ,Technology - Abstract
Scientific research plays an important role in the achievement of a sustainable society. However, grasping the trends in sustainability research is difficult because studies are not devised and conducted in a top-down manner with Sustainable Development Goals (SDGs). To understand the bottom-up research activities, we analyzed over 300,000 publications concerned with sustainability by using citation network analysis and natural language processing. The results suggest that sustainability science’s diverse and dynamic changes have been occurring over the last few years; several new topics, such as nanocellulose and global health, have begun to attract widespread scientific attention. We further examined the relationship between sustainability research subjects and SDGs and found significant correspondence between the two. Moreover, we extracted SDG topics that were discussed following a convergent approach in academic studies, such as “inclusive society” and “early childhood development”, by observing the convergence of terms in the citation network. These results are valuable for government officials, private companies, and academic researchers, empowering them to understand current academic progress along with research attention devoted to SDGs.
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- 2020
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32. Recent Developments in Network Analysis and their Applications (Invited Paper)
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Stefan Pickl, Matthias Dehmer, and Zhonglin Wang
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NETWORK ANALYSIS ,Information technology ,T58.5-58.64 ,Communication. Mass media ,P87-96 - Abstract
In this paper we performed a brief survey of the recent literature on statistical analysis of networks. For instance, we reviewed contributions dealing with statistical properties of complex networks like the degree distribution, the clustering coefficient, and other statistical analysis techniques such as resampling, bootstrapping, randomization and so forth. We see that those statistical techniques are suitable to investigate so-called non-deterministic networks. That means, we refer to networks that cannot be inferred deterministically as in graph theory. Therefore we believe that these approaches complement classical ones meaningfully and, hence, we continue doing research in this field.
- Published
- 2015
33. Using Social Network Analysis to Review the Research in Open and Distance Learning
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Bozkurt, Aras, Zawacki-Richter, Olaf, and Aydin, Cengiz Hakan
- Abstract
This study presents a social network analysis of the keywords attached to articles published in the "Turkish Online Journal of Distance Education" ("TOJDE"), a prominent journal in the field of open and distance learning. The social network analysis applied was based on a data mining and analytics approach. A total of 1120 keywords from 784 selected articles constituted the sample of the study. The keyword analysis revealed that the articles published in the "TOJDE" largely focused on technology-related issues, suggesting that the issues related to educational technology are a popular research area. However, the analysis also found that there was an imbalance between educational technology-related topics and pedagogy-related topics, a critical issue that needs to be further considered. [This paper was published in: Proceedings of "The Association for Educational Communications and Technology (AECT) 2019 International Convention" (pp. 38-44). AECT.]
- Published
- 2019
34. Data on the social network of peregrines from Brasov on occasional printed papers from the early modern era**
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Andor Nagy
- Subjects
World Wide Web ,Social network ,business.industry ,General Arts and Humanities ,General Social Sciences ,Sociology ,business ,Network analysis - Abstract
During their university studies the Saxons of Brasov, who used to be one of the most influential urban communities of Transylvanian Saxons, had relationships with friends and colleagues. I want to particularly highlight the relationships documented by the occasional prints between 1650 and 1750. I want to find the answer to what social circles are mentioned in the occasional prints related to the Saxon students of Brasov during their peregrination. Therefore I will henceforth mostly make attempts to reconstruct their friendly and collegial relationships.Occasional texts transition between correspondences and few-word memorial notes (especially regarding the number of writers and the length of writings). Thus a comprehensive storing and analysing of the occasional works restricted to a certain group can provide an opportunity to get informed about family, friendly and collegial relationships. Such writings may also contain valuable implications for the research of relation history. The relations that can be seen through these might add a lot in terms of success, especially if it is possible to continue the relation historical exploratory work connected to certain people. Furthermore, these data can be compared with their positions held during a later period of their lives, as well as with their family relations and high reputation within their community.
- Published
- 2021
35. The Top 100 Most Cited Scientific Papers in the Public, Environmental & Occupational Health Category of Web of Science: A Bibliometric and Visualized Analysis
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Vicenç Hernández-González, Josep Maria Carné-Torrent, Carme Jové-Deltell, Álvaro Pano-Rodríguez, and Joaquin Reverter-Masia
- Subjects
Public health ,Citation analysis ,Spain ,Bibliometrics ,public health ,bibliometrics ,productivity ,network analysis ,citation analysis ,Web of Science ,Health, Toxicology and Mutagenesis ,Germany ,Public Health, Environmental and Occupational Health ,Network analysis ,France ,Occupational Health ,Productivity - Abstract
Background: The main basis for the public recognition of the merits of scientists has always been the system of scientific publications and citations. Our goal is to identify and analyze the most cited articles in the Public, Environmental & Occupational Health category. (2) Methods: We searched the Web of Science for all articles published in the “Public, Environmental & Occupational Health” category up to March 2022 and selected the 100 most cited articles. We recorded the number of citations, the journal, the year of publication, quartile, impact factor, institution, country, authors, topic, type of publication and collaborations. (3) Results: 926,665 documents were analyzed. The top 100 had 401,620 citations. The journal with the most articles was the Journal of Clinical Epidemiology and the one with the highest number of citations was Medical Care. The year with the highest number of articles in the top 100 was 1998. The country with the highest percentage of publications was the USA and the most productive institution was Harvard. The most frequent keywords were bias, quality, and extension. The largest collaboration node was between the USA, Canada, Germany, Spain, Australia, France, and Sweden. (4) Conclusions: This bibliometric study on Public, Environmental & Occupational Health provides valuable information not only to identify topics of interest in the analyzed category, but also to identify the differences in the topics they study. This research was funded by Human Movement Research Group: SGR-Cat 2021 grant number SGR 1463. Generalitat de Catalunya.
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- 2022
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36. Developing a Data-Driven Emerging Skill Network Analytics Framework for Automated Employment Advert Evaluation
- Author
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Liu, Xiaoming and Schwieger, Dana
- Abstract
Rapid advancements and emergent technologies add an additional layer of complexity to preparing computer science and information technology higher education students for entering the post pandemic job market. Knowing and predicting employers' technical skill needs is essential for shaping curriculum development to address the emergent skill gap. Examining online advertisements to determine the skills sought by employers of new hires for these emerging areas and ensuring that program course content addresses these skills can be a daunting task. In this paper, the authors describe the development of a data-driven analytics framework that can be used for evaluating emerging skill clusters in online job adverts and the application of the framework to a mobile computing course at the authors' institution.
- Published
- 2023
37. Using Fair AI with Debiased Network Embeddings to Support Help Seeking in an Online Math Learning Platform
- Author
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Li, Chenglu, Xing, Wanli, and Leite, Walter L.
- Abstract
There has been a long-standing issue of sparse discussion forums participation in online learning, which can impede students' help seeking practices. Researchers have examined AI techniques such as link prediction with network analysis to connect help seekers with help providers. However, little is known whether these AI systems will treat students fairly. In this study, we aim to start a foundation work to build a recommender system that can (1) fairly suggest peers who are likely to answer a question and (2) predict the response quality of students. [This chapter will be published in: I. Roll et al. (Eds.) "Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, The Netherlands, June 14-18, 2021, Proceedings, Part II." Switzerland: Springer Nature. 2021.]
- Published
- 2021
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38. Gaining Insights on Student Course Selection in Higher Education with Community Detection
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Sturludóttir, Erla Guðrún, Arnardóttir, Eydís, Hjálmtýsson, Gísli, and Óskarsdóttir, María
- Abstract
Gaining insight into course choices holds significant value for universities, especially those who aim for flexibility in their programs and wish to adapt quickly to changing demands of the job market. However, little emphasis has been put on utilizing the large amount of educational data to understand these course choices. Here, we use network analysis of the course selection of all students who enrolled in an undergraduate program in engineering, business or computer science at a Nordic university over a five year period. With these methods, we have explored student choices to identify their distinct fields of interest. This was done by applying community detection (CD) to a network of courses, where two courses were connected if a student had taken both. We compared our CD results to actual major specializations within the computer science department and found strong similarities. Analysis with our proposed methodology can be used to offer more tailored education, which in turn allows students to follow their interests and adapt to the ever-changing career market. [For the full proceedings, see ED615472.]
- Published
- 2021
39. Key Student Nodes Mining in the In-Class Social Network Based on Combined Weighted GRA-TOPSIS Method
- Author
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Shou, Zhaoyu, Tang, Mengxue, Wen, Hui, Liu, Jinghua, Mo, Jianwen, and Zhang, Huibing
- Abstract
In this paper, a key node mining algorithm of entropy-CRITIC combined weighted GRA-TOPSIS method is proposed, which is based on the network structure features. First, the method obtained multi-dimensional data of students' identities, seating relationships, social relationships, and so on to build a database. Then, the seating similarity among students was used to construct the in-class social networks and analyze the structural characteristics of them. Finally, the CRITIC and entropy weight method was introduced for obtaining the combined weight values and the GRA-TOPSIS multi-decision fusion algorithm to mine the key student nodes that have negative impact. The experiments showed that the algorithm of this paper can evaluate students objectively based on their classroom social networks, providing technical support for process-oriented comprehensive quality education evaluation.
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- 2023
- Full Text
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40. How Many Friends Can You Make in a Week?: Evolving Social Relationships in MOOCs over Time
- Author
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Xu, Yiqiao, Lynch, Collin F., and Barnes, Tiffany
- Abstract
Massive Open Online Courses (MOOCs) are designed on the assumption that good students will help poor students thus offloading the individual support tasks from the instructor to the class. However prior research has shown that this is not always true. Students in MOOCs tend to form distinct sub-communities and their grades are closely correlated with those of their closest peers. That work, however, was only based on analyzing the final social network in a MOOC. In this paper, we study the evolution of these co-performing clusters over time. We explore a longitudinal approach to detect how students form their social connections on the discussion forum and we show that students form close coequal communities early in the course and maintain them over the duration of the course. [For the full proceedings, see ED593090.]
- Published
- 2018
41. Mapping the Open Education Landscape: Citation Network Analysis of Historical Open and Distance Education Research
- Author
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Weller, Martin, Jordan, Katy, DeVries, Irwin, and Rolfe, Viv
- Abstract
The term open education has recently been used to refer to topics such as Open Educational Resources (OERs) and Massive Open Online Courses (MOOCs). Historically its roots lie in civil approaches to education and open universities, but this research is rarely referenced or acknowledged in current interpretations. In this article the antecedents of the modern open educational movement are examined, as the basis for connecting the various strands of research. Using a citation analysis method the key references are extracted and their relationships mapped. This work reveals eight distinct sub-topics within the broad open education area, with relatively little overlap. The implications for this are discussed and methods of improving inter-topic research are proposed. [This paper was presented at the 2018 Open Education Consortium Global Conference, held in Delft (The Netherlands) April 24th-26th, 2018.]
- Published
- 2018
42. Social Network Analysis as a Driver of Continuous Improvement: A Case Study
- Author
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Matthew B. Courtney and Kelly A. Foster
- Abstract
Social network analysis (SNA) is a research method that, when applied to improvement science, can help leaders understand the strength of relationships within their organization. The COVID-19 pandemic has had a lasting impact on organizational norms, and it has interrupted relationship building efforts. This paper documents a case study of the Kentucky Department of Education (KDE), which deployed SNA techniques to strategically identify areas of growth within its network and design intentional, targeted solutions to improve the network health. As organizations emerge from the pandemic environment and begin to plan continuous improvement efforts, they would be well served to examine the impact of the pandemic on their level of connectedness. The broader impact and generalizability of the case study as well as considerations for replication are also discussed.
- Published
- 2023
43. Micro-Celebrities or Teacher Leaders? An Analysis of Spanish Educators' Behaviors on Twitter
- Author
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Carlos Marcelo, Paulino Murillo, Paula Marcelo-Martínez, Carmen Yot-Domínguez, and Cristina Yanes Cabrera
- Abstract
Social networking sites have become affinity spaces for teachers. Many teachers use them with different intentions and motivations, including learning. On social media platforms there are active teachers who have developed a certain leadership and recognition from many teachers. In some areas, like marketing or fashion, people with influence are called influencers. This paper investigates who they are, how their network is configured and how they perceive themselves. The questions that directed our research were: Who are the predominant Spanish teacher leaders on Twitter? What is the network structure that characterizes them? What perceptions do these teacher leaders have about their role and its impact on their professional development as teachers and others? This study has two distinct but interrelated phases. We investigated the structure and relationships among 54 Spanish teacher leaders. Using a social network analysis (SNA) approach, through the analysis of the social behavior of these teachers on the social network Twitter, we first identify educational profiles who have a high degree of centrality in the network. These are teachers who are recognized as opinion leaders by a significant proportion of their fellows. In addition to the degree of centrality that tells us how relevant a user is in a specific digital community, we identified teachers who play a key role in the circulation of information in the network studied. In some way, these teachers share common characteristics with activists in other fields. Of the 54 teachers, we selected 20 who were then interviewed. The findings demonstrate that they don't consider themselves micro-celebrities or influencers. We found a lack of identification not only with the term, but also with the image of an influencer which was understood as banal, superficial, commercial, and far from what they do in social networks. These teachers develop their identity as new digital artisans who foster a culture of collaboration and create affinity spaces that allow informal learning. Their motivation is intrinsic, through recognition and prestige among other teachers, which leads them to build a kind of constructivist leadership.
- Published
- 2023
44. A State-Level Analysis of Mexican Education and Its Impact on Regional, Economic, and Social Development: Two-Stage Network DEA Approach
- Author
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Martin Flegl, Sonia Valeria Avilés-Sacoto, David Güemes-Castorena, and Estefanía Caridad Avilés-Sacoto
- Abstract
Education has been considered a cornerstone for human and economic development. Although there is a national educational strategy in most countries, various implementations are at the state level. This paper studies academic efficiency at the primary and secondary levels and the human development dimensions -- long and healthy life, being knowledgeable, and enjoying a decent standard of life -- at the state level. For this purpose, a network data envelopment analysis (NDEA) with two stages was proposed. The first stage studies the educational process efficiency, while the second evaluates its impact in the form of the human development index. The study found significant differences between the evaluated states in the education stage, where the lowest efficiencies are mainly in the southwest of Mexico. The results also indicate that better education quality leads to greater regional, economic, and social development at the state level. This study contributes to the NDEA applications on the understanding of the impact that education has in improving the development of the regions holistically.
- Published
- 2023
45. Asymmetry in the Perception of Friendship in Students Groups
- Author
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Lancieri, Luigi
- Abstract
Several studies point out the link between sociability and academic results. In this paper, we highlight a phenomenon of asymmetry in the perception of friendship. This occurs when a student think he has more or less friends than he really has. We present an experimental method that allows us to analyze this question in relation with the academic performances of 15 groups of students. We show that students having a symmetric view of their friendship relations tend to have the better results. Furthermore, our study shows that the link between sociability and results improvement is stronger for lower grades (i.e younger students). [For the complete proceedings, see ED579395.]
- Published
- 2017
46. Utilizing Network Analysis to Explore Student Qualitative Inferential Reasoning Chains
- Author
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J. Caleb Speirs, MacKenzie R. Stetzer, and Beth A. Lindsey
- Abstract
Over the course of the introductory calculus-based physics course, students are often expected to build conceptual understanding and develop and refine skills in problem solving and qualitative inferential reasoning. Many of the research-based materials developed over the past 30 years by the physics education research community use sequences of scaffolded questions to step students through a qualitative inferential reasoning chain. It is often tacitly assumed that, in addition to building conceptual understanding, such materials improve qualitative reasoning skills. However, clear documentation of the impact of such materials on qualitative reasoning skills is critical. New methodologies are needed to better study reasoning processes and to disentangle, to the extent possible, processes related to physics content from processes general to all human reasoning. As a result, we have employed network analysis methodologies to examine student responses to reasoning-related tasks in order to gain deeper insight into the nature of student reasoning in physics. In this paper, we show that network analysis metrics are both interpretable and valuable when applied to student reasoning data generated from "reasoning chain construction tasks." We also demonstrate that documentation of improvements in the articulation of specific lines of reasoning can be obtained from a network analysis of responses to reasoning chain construction tasks.
- Published
- 2024
- Full Text
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47. CSCLRec: Personalized Recommendation of Forum Posts to Support Socio-Collaborative Learning
- Author
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Chen, Zhaorui and Demmans, Carrie
- Abstract
Discussion forums are used to support socio-collaborative learning processes among students in online courses. However, complex forum structures and lengthy discourse require that students spend their limited time searching and filtering through posts to find those that are relevant to them rather than spending that time engaged in other meaningful learning activities (i.e., discussion). Moreover, existing adaptive systems do not accommodate individual learner needs in these contexts. In this work, we propose a multi-relational graph-based recommendation approach that mines student interaction logs to address the above problems within discussionbased socio-collaborative online courses. To account for the social aspects of learning, our approach incorporates learner modeling, social network analysis, and natural language processing techniques; it offers tailored recommendations of forum posts for learners with different types of interaction behaviors. In our experiments with small online courses, our approach outperformed competitor approaches in terms of recommendation precision while meeting expectations with respect to diversity and novelty. The results illustrate the proposed algorithm's effectiveness in predicting student preferences, suggesting its potential to increase student participation in discussion-related learning activities. [For the full proceedings, see ED607784.]
- Published
- 2020
48. Keyword network analysis of North Korean youth adaptation papers
- Author
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Eon Ha Park
- Subjects
Information retrieval ,Computer science ,Keyword analysis ,Adaptation (computer science) ,Network analysis - Published
- 2020
49. Analysis of the impact of network characteristics on the industry's value-added rate
- Author
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Roh, Jae-Whak
- Published
- 2023
- Full Text
- View/download PDF
50. Network Analysis of a Virtual Community of Learning of Economics Educators
- Author
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Fontainha, Elsa, Martins, Jorge Tiago, and Vasconcelos, Ana Cristina
- Abstract
Introduction: This paper aims at understanding virtual communities of learning in terms of dynamics, types of knowledge shared by participants, and network characteristics such as size, relationships, density, and centrality of participants. It looks at the relationships between these aspects and the evolution of communities of learning. It focuses specifically on a virtual community of learning concerned with Economics education (CoL01). Method: The methods employed include descriptive statistics, content and network analysis. Analysis: We examined in detail the case of a virtual community of Economics educators using the digital archive of exchanged messages during 15 years, and focusing on: community of learning characteristics and dynamics, its density, the centrality of participants and type of knowledge shared. We compared it with three other virtual communities in the same disciplinary domain. Results: The community of learning under analysis revealed a cyclical behaviour in its development. However, the behaviour of participants (i.e. ratio of answers per topic of discussion) remains stable. Leadership is not the responsibility of a single individual. Instead it is shared by a nucleus of persons inside the community of learning. Knowledge sharing is student and teaching-oriented. Conclusions: Contrary to the literature that emphasises the importance of the role of a moderator for community interaction, leadership in debate in the studied community is not centralised in a single person or a very small group. Instead, there is a strong nucleus of members in the community of learning which is more active during the whole period under analysis and may play a brokering role that is key to maintaining the dynamics of participation. Large digital public archives proved to be useful for a deep analysis of the characteristics and dynamics of communities of learning. [This paper was published in: "Proceedings of ISIC: The Behaviour Conference" (Leeds, England, Sep 2-5, 2014), Part 2, Paper isic20.]
- Published
- 2015
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