# Visualizing Status, Hotspots, and Future Trends in Mathematical Literacy Research via Knowledge Graph

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Sources and Research Methods

#### 2.1. Data Sources and Screening

#### 2.2. Research Method

## 3. Results Analysis

#### 3.1. Analysis of Publication Trends

#### 3.2. Analysis of Research Actives

#### 3.2.1. Analysis of Country/Region

#### 3.2.2. Analysis of Organizations

#### 3.2.3. Analysis of Authors

#### 3.2.4. The Foundation of Mathematical Literacy Research

## 4. Research Hotspot and Evolution Analysis

#### 4.1. Keyword Co-Occurrence Analysis

_{s}to node $t$, and ${n}_{st}^{i}$ is the number of shortest paths passing through node $i$ among ${g}_{st}$ shortest paths from node s to node $t$. As can be seen from Table 5, among the keywords with more than 20 occurrences are “achievement”, “children”, “working memory”, “ability”, “gender difference”, “literacy”, and academic achievement. It shows that these keywords have received extensive attention in the field of mathematical literacy research.

#### 4.2. Keyword Cluster Analysis

#### 4.2.1. Children’s Working Memory and Mathematical Literacy

#### 4.2.2. Brain Science and Mathematical Literacy

#### 4.2.3. Math Achievement and Mathematical Literacy

#### 4.2.4. Teaching Strategies for Generating Mathematical Literacy

#### 4.3. Research Evolution Trend Analysis

## 5. Conclusions and Future Works

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Giftedness and Talent in the 21st Century: Adapting to the Turbulence of Globalization; Springer: Berlin/Heidelberg, Germany, 2016; Available online: https://sc.panda321.com/extdomains/books.google.com/books?hl=zh-CN&lr=&id=6mSvDAAAQBAJ&oi=fnd&pg=PR5&dq=Gifted-ness+and+talent+in+the+21st+century:+Adapting+to+the+turbulence+of+globalization&ots=0aNCYkPXlj&sig=9wnkFsoJHnm_Omt36Ma3tJfXIBs (accessed on 29 July 2022).
- OECD. PISA 2012 Assessment and Analytical Framework. Mathematics, Reading, Science, Problem Solving and Financial Literacy; OECD Publishing: Paris, France, 2013. [Google Scholar]
- Seitz, M.; Weinert, S. Numeracy skills in young children as predictors of mathematical competence. Br. J. Dev. Psychol.
**2022**, 40, 224–241. [Google Scholar] [CrossRef] [PubMed] - Meaney, T. Weighing up the Influence of Context on Judgements of Mathematical Literacy. Int. J. Sci. Math. Educ.
**2007**, 5, 681–704. [Google Scholar] [CrossRef] - Kilpatrick, J. Understanding mathematical literacy: The contribution of research. Educ. Stud. Math.
**2001**, 47, 101–116. [Google Scholar] [CrossRef] - Crowther, G. 15 to 18: A Report of the Central Advisory Council for Education (England); HM Stationery Office: London, UK, 1959. [Google Scholar]
- Cockcroft, W.H. Mathematics Counts; HM Stationery Office: London, UK, 1982; Available online: http://www.educationengland.org.uk/documents/cockcroft/cockcroft1982.html (accessed on 29 July 2022).
- DfEE. The National Numeracy Strategy: Framework for Teaching Mathematics from Reception to Year 6; DfEE Publications: London, UK, 1999. [Google Scholar]
- Department of Education. The National Curriculum in England: Framework for Key Stages 1–4. Available online: https://www.gov.uk/government/publications/national-curriculum-in-england-framework-for-key-stages-1-to-4 (accessed on 29 July 2022).
- National Council of Teachers of Mathematics. Curriculum and Evaluation Standards for School Mathematics; National Council of Teachers of Mathematics: Reston, VA, USA, 1989. [Google Scholar]
- National Council of Teachers of Mathematics, Inc. Principles and Standards for School Mathematics: Discussion Draft; National Council of Teachers of Mathematics: Reston, VA, USA, 1998; Available online: https://www.nctm.org/classroomresources/ (accessed on 1 August 2022).
- National Research Council; Mathematics Learning Study Committee. Adding It Up: Helping Children Learn Mathematics; National Academy Press: Washington, DC, USA, 2001. [Google Scholar]
- Niss, M. Mathematical competencies and the learning of mathematics: The Danish KOM project. In Proceedings of the 3rd Mediterranean Conference on Mathematical Education, Athens, Greece, 3–5 January 2003; pp. 115–124. Available online: http://www.math.chalmers.se/Math/Grundutb/CTH/mve375/1213/docs/KOMkompetenser.pdf (accessed on 1 August 2022).
- Curriculum and Assessment Policy Statement (CAPS): Mathematical Literacy (Grades 10–12); Republic of South Africa Department of Basic Education: Pretoria, South Africa, 2012.
- OECD. The PISA 2003 Assessment Framework: Mathematics, Reading, Science and Problem Solving Knowledge and Skills; OECD Publishing: Paris, France, 2004. [Google Scholar] [CrossRef]
- Martin, M.O.; Mullis, S., IV; Foy, P. TIMSS 2015 Assessment Design; TIMSS: Amsterdam, The Netherlands, 2015; pp. 85–99. Available online: https://timssandpirls.bc.edu/timss2015/downloads/T15_FW_Chap4.pdf (accessed on 1 August 2022).
- Altun, M.; Bozkurt, I. A New Classification Proposal for Mathematical Literacy Problems. Egit. Bilim
**2017**, 42, 171–188. [Google Scholar] [CrossRef] [Green Version] - Bolstad, O.H. Lower secondary students’ encounters with mathematical literacy. Math. Educ. Res. J.
**2021**, 1–17. [Google Scholar] [CrossRef] - Gatabi, A.R.; Stacey, K.; Gooya, Z. Investigating grade nine textbook problems for characteristics related to mathematical literacy. Math. Educ. Res. J.
**2012**, 24, 403–421. [Google Scholar] [CrossRef] - Katranci, Y.; Sengul, S. The Relationship between Mathematical Literacy and Visual Math Literacy Self-Efficacy Perceptions of Middle School Students = Ortaokul ögrencilerinin matematik okuryazarligi ile görsel matematik okuryazarligi öz-yeterlik algilari arasindaki iliski. Pegem J. Educ. Instr.
**2019**, 9, 1113–1138. [Google Scholar] [CrossRef] [Green Version] - Guzel, C.I.; Berberoglu, G. Students’ affective characteristics and their relation to mathematical literacy measures in the Pro-gramme for International Student Assessment (PISA) 2003. Eurasian J. Educ. Res.
**2010**, 40, 93–113. [Google Scholar] - Kaur, B.; Areepattamannil, S. Influences of Metacognitive and Self-Regulated Learning Strategies for Reading on Mathematical Literacy of Adolescents in Australia and Singapore; Mathematics Education Research Group of Australasia: Payneham, Australia, 2012. [Google Scholar]
- Ozgen, K. An Analysis of High School Students’ Mathematical Literacy Self-efficacy Beliefs in Relation to Their Learning Styles. Asia-Pac. Educ. Res.
**2012**, 22, 91–100. [Google Scholar] [CrossRef] - Gabriel, F.; Buckley, S.; Barthakur, A. The impact of mathematics anxiety on self-regulated learning and mathematical literacy. Aust. J. Educ.
**2020**, 64, 227–242. [Google Scholar] [CrossRef] - Geary, D.C.; Hoard, M.K.; Nugent, L.; Ünal, Z.E. Sex differences in developmental pathways to mathematical competence. J. Educ. Psychol.
**2022**. [Google Scholar] [CrossRef] - Canbazoglu, H.B.; Tarim, K. An Activity-Based Practice for Improving Mathematical Literacy and Awareness of Elementary School Teacher Candidates= Sinif ögretmeni adaylarinin matematik okuryazarligi ve farkindaliklarinin gelistirilmesine yönelik etkinlik temelli bir uygulama. Pegem J. Educ. Instr.
**2020**, 10, 1183–1218. [Google Scholar] [CrossRef] - Kramarski, B.; Mizrachi, N. Online discussion and self-regulated learning: Effects of instructional methods on mathematical literacy. J. Educ. Res.
**2006**, 99, 218–231. [Google Scholar] [CrossRef] - Goffman, W.; Harmon, G. Mathematical Approach to the Prediction of Scientific Discovery. Nature
**1971**, 229, 103–104. [Google Scholar] [CrossRef] - Senturk, I.; Gursoy, N.K.; Oner, T.; Gursoy, A. A novel algorithmic construction for deductions of categorical polysyllogisms by Carroll’s diagrams. Inf. Sci.
**2021**, 578, 236–256. [Google Scholar] [CrossRef] - Moktefi, A.; Shin, S.J. (Eds.) Visual Reasoning with Diagrams; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2013. [Google Scholar]
- Garfield, E. From the science of science to Scientometrics visualizing the history of science with HistCite software. J. Inf.
**2009**, 3, 173–179. [Google Scholar] [CrossRef] [Green Version] - Chen, C. Searching for intellectual turning points: Progressive knowledge domain visualization. Proc. Natl. Acad. Sci. USA
**2004**, 101 (Suppl. S1), 5303–5310. [Google Scholar] [CrossRef] [Green Version] - Pillai, S.P.M.; Galloway, G.; Adu, E. Comparative Studies of Mathematical Literacy/Education: A Literature Review. Int. J. Educ. Sci.
**2017**, 16, 67–72. [Google Scholar] [CrossRef] - Ulger, T.K.; Bozkurt, I.; Altun, M. Thematic Analysis of Articles Focusing on Mathematical Literacy in Mathematics Teach-ing-Learning Process. Educ. Sci.
**2020**, 45, 1–38. [Google Scholar] - Hillman, A.M. A Literature Review on Disciplinary Literacy: How do secondary teachers apprentice students into mathematical literacy? J. Adolesc. Adult Lit.
**2013**, 57, 397–406. [Google Scholar] [CrossRef] - Glanzel, W. Bibliometrics as a Research Field a Course on Theory and Application of Bibliometric Indicators. 2003. Available online: http://nsdl.niscair.res.in/bitstream/123456789/968/1/Bib_Module_KUL.pdf (accessed on 20 October 2022).
- Du, H.; Li, B.; Brown, M.A.; Mao, G.; Rameezdeen, R.; Chen, H. Expanding and shifting trends in carbon market research: A quantitative bibliometric study. J. Clean. Prod.
**2015**, 103, 104–111. [Google Scholar] [CrossRef] - Chen, C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol.
**2006**, 57, 359–377. [Google Scholar] [CrossRef] [Green Version] - Farine, D.R. Proximity as a proxy for interactions: Issues of scale in social network analysis. Anim. Behav.
**2015**, 104, e1–e5. [Google Scholar] [CrossRef] [Green Version] - Rousseeuw, P.J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math.
**1987**, 20, 53–65. [Google Scholar] [CrossRef] [Green Version] - Wang, D.; Huangfu, Y.; Dong, Z.; Dong, Y. Research Hotspots and Evolution Trends of Carbon Neutrality—Visual Analysis of Bibliometrics Based on CiteSpace. Sustainability
**2022**, 14, 1078. [Google Scholar] [CrossRef] - Wang, Z.; Ma, D.; Pang, R.; Xie, F.; Zhang, J.; Sun, D. Research Progress and Development Trend of Social Media Big Data (SMBD): Knowledge Mapping Analysis Based on CiteSpace. ISPRS Int. J. Geo-Inf.
**2020**, 9, 632. [Google Scholar] [CrossRef] - Zhao, Y.; Guo, J.; Bao, C.; Liang, C.; Jain, H.K. Knowledge Graph Analysis of Human Health Research Related to Climate Change. Int. J. Environ. Res. Public Health
**2020**, 17, 7395. [Google Scholar] [CrossRef] - Shi, D.; Zhou, J.; Wang, D.; Wu, X. Research Status, Hotspots, and Evolutionary Trends of Intelligent Education from the Perspective of Knowledge Graph. Sustainability
**2022**, 14, 10934. [Google Scholar] [CrossRef] - Muthén, B.; Muthén, L. Mplus. In Handbook of Item Response Theory; Chapman and Hall/CRC: Boca Raton, FL, USA, 2017; pp. 507–518. Available online: https://www.taylorfrancis.com/chapters/edit/10.1201/9781315117430-28/mplus-bengt-muth%C3%A9n-linda-muth%C3%A9n (accessed on 2 August 2022).
- Gilmore, C.; Attridge, N.; Clayton, S.; Cragg, L.; Johnson, S.; Marlow, N.; Simms, V.; Inglis, M. Individual Differences in Inhibitory Control, Not Non-Verbal Number Acuity, Correlate with Mathematics Achievement. PLoS ONE
**2013**, 8, e67374. [Google Scholar] [CrossRef] [Green Version] - Purpura, D.J.; Reid, E.E. Mathematics and language: Individual and group differences in mathematical language skills in young children. Early Child. Res. Q.
**2016**, 36, 259–268. [Google Scholar] [CrossRef] - Holloway, I.D.; Ansari, D. Mapping numerical magnitudes onto symbols: The numerical distance effect and individual differences in children’s mathematics achievement. J. Exp. Child Psychol.
**2009**, 103, 17–29. [Google Scholar] [CrossRef] [PubMed] - Thompson, R.J.; Napoli, A.R.; Purpura, D.J. Age-related differences in the relation between the home numeracy environment and numeracy skills. Infant Child Dev.
**2017**, 26, e2019. [Google Scholar] [CrossRef] - Nguyen, T.; Watts, T.W.; Duncan, G.J.; Clements, D.H.; Sarama, J.S.; Wolfe, C.; Spitler, M.E. Which preschool mathematics competencies are most predictive of fifth grade achievement? Early Child. Res. Q.
**2016**, 36, 550–560. [Google Scholar] [CrossRef] [Green Version] - Missall, K.; Hojnoski, R.L.; Caskie, G.I.L.; Repasky, P. Home Numeracy Environments of Preschoolers: Examining Relations Among Mathematical Activities, Parent Mathematical Beliefs, and Early Mathematical Skills. Early Educ. Dev.
**2014**, 26, 356–376. [Google Scholar] [CrossRef] - Schneider, M.; Beeres, K.; Coban, L.; Merz, S.; Schmidt, S.S.; Stricker, J.; De Smedt, B. Associations of non-symbolic and symbolic numerical magnitude processing with mathematical competence: A meta-analysis. Dev. Sci.
**2016**, 20, e12372. [Google Scholar] [CrossRef] - De Smedt, B.; Noël, M.-P.; Gilmore, C.; Ansari, D. How do symbolic and non-symbolic numerical magnitude processing skills relate to individual differences in children’s mathematical skills? A review of evidence from brain and behavior. Trends Neurosci. Educ.
**2013**, 2, 48–55. [Google Scholar] [CrossRef] [Green Version] - Wang, X.; Guo, J.; Gu, D.; Yang, Y.; Yang, X.; Zhu, K. Tracking knowledge evolution, hotspots and future directions of emerging technologies in cancers research: A bibliometrics review. J. Cancer
**2019**, 10, 2643–2653. [Google Scholar] [CrossRef] [Green Version] - Yang, Y.; Qu, G.; Hua, L. Research Status, Hotspots, and Evolution Trend of Decision-Making in Marine Management Using VOSviewer and CiteSpace. Math. Probl. Eng.
**2022**, 2022, 8283417. [Google Scholar] [CrossRef] - Li, Y.; Fang, R.; Liu, Z.; Jiang, L.; Zhang, J.; Li, H.; Liu, C.; Li, F. The association between toxic pesticide environmental exposure and Alzheimer’s disease: A scientometric and visualization analysis. Chemosphere
**2020**, 263, 128238. [Google Scholar] [CrossRef] - Alloway, T.P.; Gathercole, S.E.; Adams, A.-M.; Willis, C.; Eaglen, R.; Lamont, E. Working memory and phonological awareness as predictors of progress towards early learning goals at school entry. Br. J. Dev. Psychol.
**2005**, 23, 417–426. [Google Scholar] [CrossRef] [Green Version] - Geary, D.C. Mathematics and Learning Disabilities. J. Learn. Disabil.
**2004**, 37, 4–15. [Google Scholar] [CrossRef] [PubMed] - Kyttälä, M.; Aunio, P.; Hautamäki, J. Working memory resources in young children with mathematical difficulties. Scand. J. Psychol.
**2010**, 51, 1–15. [Google Scholar] [CrossRef] [PubMed] - Jordan, A.-K.; Duchhardt, C.; Heinze, A.; Tresp, T.; Grüßing, M. Mehr als numerische Basiskompetenzen? Zur Dimensionalität und Struktur mathematischer Kompetenz von Kindergartenkindern. Psychol. Erzieh. Unterr.
**2015**, 62, 205–217. [Google Scholar] [CrossRef] - Passolunghi, M.C.; Costa, H.M. Working memory and early numeracy training in preschool children. Child Neuropsychol.
**2014**, 22, 81–98. [Google Scholar] [CrossRef] - Kroesbergen, E.H.; Noordende, J.E.V.; Kolkman, M.E. Training working memory in kindergarten children: Effects on working memory and early numeracy. Child Neuropsychol.
**2012**, 20, 23–37. [Google Scholar] [CrossRef] - Toll, S.W.; Van Luit, J.E. Accelerating the early numeracy development of kindergartners with limited working memory skills through remedial education. Res. Dev. Disabil.
**2013**, 34, 745–755. [Google Scholar] [CrossRef] - Price, G.R.; Mazzocco, M.M.M.; Ansari, D. Why Mental Arithmetic Counts: Brain Activation during Single Digit Arithmetic Predicts High School Math Scores. J. Neurosci.
**2013**, 33, 156–163. [Google Scholar] [CrossRef] [Green Version] - Price, G.R.; Wilkey, E.D.; Yeo, D.J.; Cutting, L.E. The relation between 1st grade grey matter volume and 2nd grade math competence. NeuroImage
**2016**, 124, 232–237. [Google Scholar] [CrossRef] [Green Version] - Grabner, R.H.; Ansari, D.; Reishofer, G.; Stern, E.; Ebner, F.; Neuper, C. Individual differences in mathematical competence predict parietal brain activation during mental calculation. NeuroImage
**2007**, 38, 346–356. [Google Scholar] [CrossRef] - Grabner, R.H.; Ischebeck, A.; Reishofer, G.; Koschutnig, K.; Delazer, M.; Ebner, F.; Neuper, C. Fact learning in complex arithmetic and figural-spatial tasks: The role of the angular gyrus and its relation to mathematical competence. Hum. Brain Mapp.
**2009**, 30, 2936–2952. [Google Scholar] [CrossRef] - Ansari, D.; Grabner, R.H.; Koschutnig, K.; Reishofer, G.; Ebner, F. Individual differences in mathematical competence modulate brain responses to arithmetic errors: An fMRI study. Learn. Individ. Differ.
**2011**, 21, 636–643. [Google Scholar] [CrossRef] - Lin, S.-W.; Tai, W.-C. Latent Class Analysis of Students’ Mathematics Learning Strategies and the Relationship between Learning Strategy and Mathematical Literacy. Univers. J. Educ. Res.
**2015**, 3, 390–395. [Google Scholar] [CrossRef] - Yılmazer, G.; Masal, M. The Relationship between Secondary School Students’ Arithmetic Performance and their Mathematical Literacy. Procedia-Soc. Behav. Sci.
**2014**, 152, 619–623. [Google Scholar] [CrossRef] [Green Version] - Zhao, N.; Valcke, M.; Desoete, A.; Verhaeghe, J.; Xu, K. A multilevel analysis on predicting mathematics performance in Chinese primary schools: Implications for practice. Asia-Pac. Educ. Res.
**2011**, 20, 503–520. [Google Scholar] - Clark, C.A.C.; Sheffield, T.D.; Wiebe, S.; Espy, K.A. Longitudinal Associations Between Executive Control and Developing Mathematical Competence in Preschool Boys and Girls. Child Dev.
**2012**, 84, 662–677. [Google Scholar] [CrossRef] - Ryan, K.E.; Ryan, A.M. Psychological Processes Underlying Stereotype Threat and Standardized Math Test Performance. Educ. Psychol.
**2005**, 40, 53–63. [Google Scholar] [CrossRef] - Fleckenstein, J.; Gebauer, S.K.; Möller, J. Promoting mathematics achievement in one-way immersion: Performance development over four years of elementary school. Contemp. Educ. Psychol.
**2019**, 56, 228–235. [Google Scholar] [CrossRef] - Papadakis, S.; Kalogiannakis, M.; Zaranis, N. Improving Mathematics Teaching in Kindergarten with Realistic Mathematical Education. Day Care Early Educ.
**2016**, 45, 369–378. [Google Scholar] [CrossRef] - García-Perales, R.; Palomares-Ruiz, A. Education in Programming and Mathematical Learning: Functionality of a Programming Language in Educational Processes. Sustainability
**2020**, 12, 10129. [Google Scholar] [CrossRef] - Frith, V.; Jaftha, J.; Prince, R. Evaluating the effectiveness of interactive computer tutorials for an undergraduate mathematical literacy course. Br. J. Educ. Technol.
**2004**, 35, 159–171. [Google Scholar] [CrossRef] - Albert, M.J.; Blazquez-Merino, M.; Lopez-Rey, A.; Castro, M. Influence of Technological Resources on the Development of Mathematical Competence in High School. IT Prof.
**2021**, 23, 19–25. [Google Scholar] [CrossRef] - Cichy, I.; Kaczmarczyk, M.; Wawrzyniak, S.; Kruszwicka, A.; Przybyla, T.; Klichowski, M.; Rokita, A. Participating in Physical Classes Using Eduball Stimulates Acquisition of Mathematical Knowledge and Skills by Primary School Students. Front. Psychol.
**2020**, 11, 2194. [Google Scholar] [CrossRef] [PubMed]

**Figure 1.**The annual number of published articles. (The broken blue line represents the annual publication volume, and the red scatter is the trend of annual publication volume obtained by fitting the exponential function between the annual publication volume and the year.)

**Table 1.**The countries/regions with more than 13 publications and their numbers of published articles.

Country | Number of Published Articles | LCS | GCS |
---|---|---|---|

USA | 218 | 299 | 6404 |

England | 53 | 27 | 1478 |

Germany | 44 | 62 | 1206 |

Australia | 41 | 28 | 430 |

China | 29 | 23 | 672 |

Canada | 29 | 128 | 1684 |

Holland | 26 | 63 | 730 |

Spain | 26 | 10 | 295 |

South Africa | 20 | 10 | 156 |

Belgium | 15 | 5 | 52 |

Turkey | 13 | 5 | 78 |

Sweden | 13 | 4 | 177 |

Institution | Number of Published Articles | Local Citation Score (LCS) | Global Citation Score (GCS) | Country |
---|---|---|---|---|

University of Utrecht | 14 | 22 | 426 | Holland |

Purdue University | 14 | 55 | 327 | the United States |

Vanderbilt University | 11 | 13 | 189 | the United States |

University of Illinois | 11 | 52 | 539 | the United States |

Katholieke University Leuven | 9 | 4 | 185 | Belgium |

University of Oslo | 9 | 2 | 57 | Norway |

University Missouri | 8 | 8 | 200 | the United States |

Australian Catholic University | 8 | 2 | 27 | Australia |

Beijing Normal University | 8 | 8 | 835 | China |

Carleton University | 7 | 71 | 762 | Canada |

University of Oregon | 7 | 7 | 155 | the United States |

The University of Western Ontario | 7 | 37 | 544 | Canada |

Stanford University | 6 | 0 | 55 | the United States |

Emory University | 6 | 10 | 278 | the United States |

University of Oxford | 6 | 3 | 128 | England |

University of Granada | 6 | 4 | 174 | Spain |

leibniz Inst Educ trajectories | 6 | 1 | 22 | Germany |

Carnegie Mellon University | 6 | 3 | 248 | the United States |

The University of Iowa | 6 | 17 | 159 | the United States |

NYU | 6 | 3 | 110 | the United States |

University of California, Berkeley | 6 | 1 | 63 | the United States |

University of Cape Town | 6 | 2 | 15 | South Africa |

University of Hong Kong | 6 | 7 | 199 | China |

University of Texas at Austin | 6 | 2 | 30 | the United States |

University of Helsinki | 6 | 22 | 213 | Finland |

Author | Recs | LCS | GCS |
---|---|---|---|

Purpura D.J. | 14 | 88 | 502 |

Ansari D. | 8 | 37 | 551 |

Grabner R.H. | 8 | 32 | 444 |

Van Luit J.E.H. | 8 | 21 | 337 |

Verschaffel L. | 7 | 2 | 52 |

Price G.R. | 6 | 7 | 138 |

Aunio P. | 5 | 22 | 196 |

Ebner F. | 5 | 30 | 381 |

Geary D.C. | 5 | 8 | 193 |

Gnambs T. | 5 | 0 | 12 |

LeFevre J.A. | 5 | 71 | 726 |

Lourenco S.F. | 5 | 9 | 237 |

Reishofer G. | 5 | 30 | 381 |

Schmitt S.A. | 5 | 2 | 41 |

Authors | Title | References Cited Times | Year |
---|---|---|---|

Muthen L., et al. [45] | MPLUS USERS GUIDE | 24 | |

Schneider, Michael, et al. [52] | Associations of nonsymbolic and symbolic numerical magnitude processing with mathematical competence: A meta-analysis | 16 | 2017 |

Nguyen, Tutrang, et al. [50] | Which preschool mathematics competencies are most predictive of fifth grade achievement? | 13 | 2016 |

Holloway, I.D., and Ansari, D. [48] | Mapping numerical magnitudes onto symbols: The numerical distance effect and individual differences in children’s mathematics achievement | 13 | 2009 |

Missall, Kristen, et al. [51] | Home numeracy environments of preschoolers: Examining relations among mathematical activities, parent mathematical beliefs, and early mathematical skills | 12 | 2015 |

De Smedt, et al. [53] | How do symbolic and nonsymbolic numerical magnitude processing relate to individual differences in children’s mathematical skills? A review of evidence from brain and behavior | 12 | 2013 |

Thompson, et al. [49] | Age-related differences in the relation between the home numeracy environment and numeracy skills | 12 | 2017 |

Purpura, D.J., and Reid, E.E. [47] | Mathematics and language: Individual and group differences in mathematical language skills in young children | 10 | 2016 |

Gilmore, Camilla, et al. [46] | Individual differences in inhibitory control, not non-verbal number acuity, correlate with mathematics achievement | 10 | 2013 |

Count | Centrality | Year | Keywords | Count | Centrality | Year | Keywords |
---|---|---|---|---|---|---|---|

97 | 0.12 | 1995 | achievement | 31 | 0.00 | 2009 | early numeracy |

78 | 0.20 | 1995 | children | 29 | 0.08 | 1995 | language |

70 | 0.03 | 2007 | Individual difference | 28 | 0.00 | 2010 | education |

69 | 0.10 | 2002 | working memory | 28 | 0.04 | 2006 | number sense |

57 | 0.07 | 1997 | skill | 26 | 0.00 | 2009 | executive function |

55 | 0.05 | 2001 | mathematics | 25 | 0.08 | 2011 | school readiness |

55 | 0.01 | 1999 | performance | 24 | 0.11 | 1999 | gender difference |

55 | 0.09 | 2002 | student | 24 | 0.01 | 2011 | predictor |

49 | 0.00 | 2007 | knowledge | 24 | 0.12 | 2007 | literacy |

38 | 0.01 | 2008 | mathematical literacy | 23 | 0.01 | 2010 | math |

37 | 0.00 | 2007 | mathematical competence | 23 | 0.26 | 2003 | academic achievement |

35 | 0.00 | 2009 | kindergarten | 22 | 0.01 | 1998 | number |

34 | 0.21 | 2001 | ability | 22 | 0.07 | 2008 | representation |

31 | 0.02 | 1997 | quantitative literacy |

Cluster ID | Size | Silhouette | Mean (Year) | Label |
---|---|---|---|---|

0 | 28 | 0.908 | 2008 | Mathematical literacy |

1 | 26 | 0.936 | 2008 | Working memory |

2 | 24 | 0.851 | 2013 | Parietal cortex |

3 | 21 | 0.94 | 2013 | Math performance |

4 | 19 | 0.921 | 2007 | Mathematical education |

5 | 19 | 0.833 | 2012 | Early childhood |

6 | 18 | 0.89 | 2010 | Parental beliefs |

7 | 18 | 0.971 | 2012 | Fractions |

8 | 17 | 0.971 | 2009 | Cognitive development |

9 | 16 | 0.918 | 2015 | Student |

10 | 15 | 0.951 | 2012 | Academic performance |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Chen, X.; Zhou, J.; Wang, J.; Wang, D.; Liu, J.; Shi, D.; Yang, D.; Pan, Q.
Visualizing Status, Hotspots, and Future Trends in Mathematical Literacy Research via Knowledge Graph. *Sustainability* **2022**, *14*, 13842.
https://doi.org/10.3390/su142113842

**AMA Style**

Chen X, Zhou J, Wang J, Wang D, Liu J, Shi D, Yang D, Pan Q.
Visualizing Status, Hotspots, and Future Trends in Mathematical Literacy Research via Knowledge Graph. *Sustainability*. 2022; 14(21):13842.
https://doi.org/10.3390/su142113842

**Chicago/Turabian Style**

Chen, Xiaohong, Jincheng Zhou, Jinqiu Wang, Dan Wang, Jiu Liu, Dingpu Shi, Duo Yang, and Qingna Pan.
2022. "Visualizing Status, Hotspots, and Future Trends in Mathematical Literacy Research via Knowledge Graph" *Sustainability* 14, no. 21: 13842.
https://doi.org/10.3390/su142113842