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Paper Citation Record · LEDGER

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata

As of 9 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 0 inbound Pith citation observations for arXiv:2510.22266.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2510.22266 v2

Coverage vector

measured 73 of 73 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:12:19.207638Z

measured 73 of 73 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

73 of 73 outbound references displayed

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External citation measurements

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Outbound references

Observation 3f0c8349-c92b-4072-abd4-21065730e153 · outbound

This paper cites , Al-Samarraie , H.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Al-Samarraie , H

Reference 1

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Observation f2406847-233f-4aec-babd-47f7bc9adf63 · outbound

This paper cites , Abduljabbar , Z.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Abduljabbar , Z

Reference 2

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Observation 35e6dc03-be80-4d8c-b7d6-0b8f1ebb27ff · outbound

This paper cites , Berrada , M.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Berrada , M

Reference 3

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Observation 7d991572-6f13-43ec-ad29-a86f2dd8eb53 · outbound

This paper cites , G \'a mez , M.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , G \'a mez , M

Reference 4

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Observation 3b760852-ad5c-45aa-ba4f-2689dc87f952 · outbound

This paper cites , Karabatak , S.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Karabatak , S

Reference 5

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Observation f5c4ace9-bda0-4ff8-a430-45bfa0f5c149 · outbound

This paper cites , Soares , J.F.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Soares , J.F

Reference 6

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This paper cites , Sehgal , R.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Sehgal , R

Reference 7

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Observation 021baeee-8ec0-4c0d-847d-7f0e1922aae6 · outbound

This paper cites , Zaki , N.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Zaki , N

Reference 8

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This paper cites , Artar , M.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Artar , M

Reference 9

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Observation 0f7c9cd6-a698-4c73-b4d7-d3b003e61ff0 · outbound

This paper cites : The world bank and unesco institute for statistics (uis).

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata : The world bank and unesco institute for statistics (uis)

Reference 10

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Observation 93a86455-0caa-4e05-8d32-599f6d614e76 · outbound

This paper cites , B \`a ssolo , \'A.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , B \`a ssolo , \'A

Reference 11

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This paper cites , Csorgo , A.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Csorgo , A

Reference 12

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Observation 0a3aba89-621e-4d55-b45c-80203b8a0e5c · outbound

This paper cites , Gintis , H.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Gintis , H

Reference 13

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Guzm \'a n Rinc \'o n , A

Reference 14

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Gonz \'a lez T \'a mara , L

Reference 15

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Observation 8172e498-2fc6-412d-ace9-87cbeb06a87f · outbound

This paper cites : The Forms of Capital.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata : The Forms of Capital

Reference 16

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata : Random forests

Reference 17

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Observation 329c745b-e77a-4bbe-b433-704ccc499f27 · outbound

This paper cites , Dutra , B.L.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Dutra , B.L

Reference 18

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Observation a7db4735-2a54-4602-ab19-01afd7add852 · outbound

This paper cites , Ezeife , C.I.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Ezeife , C.I

Reference 19

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Guestrin , C

Reference 20

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Observation 58f8e546-beba-458e-adea-bfe9440b5d0e · outbound

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Khoroshavin , A

Reference 21

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Pereira , E.M

Reference 22

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Thompsen Primo , T

Reference 23

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Dia , N.J

Reference 24

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Ershov , V

Reference 25

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata : Ensemble methods in machine learning

Reference 26

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This paper cites Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey

Reference 27

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This paper cites : O saeb - sistema de avaliação da educação básica: potencialidades, problemas e desafios.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata : O saeb - sistema de avaliação da educação básica: potencialidades, problemas e desafios

Reference 28

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata : Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement , ( 2009 )

Reference 29

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Appiah , O

Reference 30

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Beikmohammadi , A

Reference 31

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata : Exploring the role of explainable artificial intelligence(xai) in adaptive learning systems

Reference 32

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata Technical report, Ministério da Educação, Brasília, DF (2023)

Reference 33

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/saeb

Reference 34

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Choi , S

Reference 35

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Engelke , A

Reference 36

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A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Kiraz , S

Reference 37

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Observation 3c1d7344-e910-438f-a966-cb165a5f073f · outbound

This paper cites , Meng , Q.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Meng , Q

Reference 38

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Observation 704ae8ea-012d-46b3-b39c-66e9bc7d5059 · outbound

This paper cites , Saltarelli , A.J.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Saltarelli , A.J

Reference 39

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Observation 619f2fe4-b7cf-4ed4-aca1-31e9af8879eb · outbound

This paper cites A Unified Approach to Interpreting Model Predictions.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata A Unified Approach to Interpreting Model Predictions

Reference 40

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Observation 70efb5c9-ceaa-4fb0-9519-e73a50e3f32e · outbound

This paper cites , Lee , S.-I.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Lee , S.-I

Reference 41

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Observation fa2a49ec-ab5e-4b82-887f-edaef39ab600 · outbound

This paper cites , Nguyen , A.J.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Nguyen , A.J

Reference 42

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source=arxiv_source observed=2026-08-04T08:12:16.372717Z digest=sha256:fd88dc8deb773a1970c52cc763039423984aaef6ffa855c561e1aa24aea4de09

Observation 3cd45840-1299-4d5e-be50-af7299e669e5 · outbound

This paper cites , Porter , L.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Porter , L

Reference 43

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Observation 3dbaae90-d02a-4645-9c4f-4f6af121657f · outbound

This paper cites , Weininger , E.B.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Weininger , E.B

Reference 44

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source=arxiv_source observed=2026-08-04T08:12:16.608407Z digest=sha256:60f5456bcdd6f829dff844d37214b98d9c58c1d611a36dab9b8b0a11a85af6bb

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This paper cites Human-Centric eXplainable AI in Education.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata Human-Centric eXplainable AI in Education

Reference 45

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Observation c32997dc-50d8-4cc9-a1ac-1956121596f1 · outbound

This paper cites , Dehghani , T.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Dehghani , T

Reference 46

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Observation 0e37caa5-4dcf-44de-be9d-7e78fc05cde6 · outbound

This paper cites : Explanation in artificial intelligence: Insights from the social sciences.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata : Explanation in artificial intelligence: Insights from the social sciences

Reference 47

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source=arxiv_source observed=2026-08-04T08:12:16.838883Z digest=sha256:200a69866f670eb0b3dc1d7f9af82abdab6846d6cf36f9246a5ab88591f45581

Observation 27cd526c-184e-4815-b667-dd22de6aff28 · outbound

This paper cites , Oliveira , D.A.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Oliveira , D.A

Reference 48

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Observation 352a9f5d-93a1-43dd-b8e2-861f682b2343 · outbound

This paper cites , Souza , S.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Souza , S

Reference 49

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source=arxiv_source observed=2026-08-04T08:12:16.984724Z digest=sha256:51d45c91eb986a168a39221a2d399fef71e8d5f559a6e7b4d8defc55aac504ba

Observation 4b66c6a0-5470-4c29-8ab6-e6c1b533c112 · outbound

This paper cites Early Detection of At-Risk Students Using Machine Learning.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata Early Detection of At-Risk Students Using Machine Learning

Reference 50

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source=arxiv_source observed=2026-08-04T08:12:17.056099Z digest=sha256:56ce4b1361501b26c2cbea4e9575ff671a26e576ae3e08b32f1663c2ce7e76bb

Observation 2f85e1c6-eba6-4d94-8082-3278a9fcb8a5 · outbound

This paper cites PISA 2022 Results (Volume I): The State of Learning and Equity in Education.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata PISA 2022 Results (Volume I): The State of Learning and Equity in Education

Reference 51

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source=arxiv_source observed=2026-08-04T08:12:17.126630Z digest=sha256:3cc79f03ef165c54044f78759f46437cabbb58bc2d19d80bbce955eba9689aa8

Observation d16e13ed-b467-40e9-95a5-3e7a1cda976f · outbound

This paper cites , Gulbandilar , E.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Gulbandilar , E

Reference 52

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source=arxiv_source observed=2026-08-04T08:12:17.256730Z digest=sha256:48136b4f40e29a52707c6ef67e494d540e2f5a1f7ca5721ad7eb0346a4e03a07

Observation 68c113dd-32c1-427f-9360-6381f18448bc · outbound

This paper cites , Boulesteix , A.-L.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Boulesteix , A.-L

Reference 53

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Observation 8cbcc5d0-a04e-47ea-a9c8-577049f220b2 · outbound

This paper cites , Bortolotti , S.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Bortolotti , S

Reference 54

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source=arxiv_source observed=2026-08-04T08:12:17.523676Z digest=sha256:d0a3a818bf1aa25e9503784f5301302e9009040640db3c132678d25161e753c9

Observation 3250a266-3420-425f-ae1e-3bd7ce62daac · outbound

This paper cites , Fonseca , S.C.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Fonseca , S.C

Reference 55

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source=arxiv_source observed=2026-08-04T08:12:17.618491Z digest=sha256:5b21accec4f10ffd6752423bb73444b5865e62b2d025ef91114ea4efc6b8d05e

Observation 3169733d-f660-439a-b452-366eb3ec38a8 · outbound

This paper cites , Wallace , M.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Wallace , M

Reference 56

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Observation 7f0b0b1d-6c3a-41c6-89e7-83481c6063a0 · outbound

This paper cites , Lourens , M.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Lourens , M

Reference 57

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Observation 1ce4a962-365b-4519-9619-a41a903bd14f · outbound

This paper cites , Lakshmi , K.P.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Lakshmi , K.P

Reference 58

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source=arxiv_source observed=2026-08-04T08:12:17.889154Z digest=sha256:30ed0e556bf7be3613320cff624ccccf224371d43049403b9e5e231294106dd3

Observation 0b1149f9-538a-4533-bc0d-685e6bd45bf1 · outbound

This paper cites , Soares , J.F.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Soares , J.F

Reference 59

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Observation b1f7f6b7-a852-474d-92cc-cd9c488a456b · outbound

This paper cites , Ventura , S.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Ventura , S

Reference 60

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Observation 557cb04a-02bf-4883-848b-0e7711390179 · outbound

This paper cites , Balby , L.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Balby , L

Reference 61

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Observation 19725772-e9b1-4887-a1b5-58ca49cb33f8 · outbound

This paper cites , Lin , Y.-R.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Lin , Y.-R

Reference 62

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Observation f0e6570b-3e6a-4429-8a4e-1a7ed83f60e4 · outbound

This paper cites : Predictive analysis of students’ learning performance using data mining techniques: A comparative study of feature selection methods.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata : Predictive analysis of students’ learning performance using data mining techniques: A comparative study of feature selection methods

Reference 63

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Observation 59fd77c5-ef97-4030-be8e-1655088062af · outbound

This paper cites , Newman , A.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Newman , A

Reference 64

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Observation e933fb6e-00dc-47a9-98f4-de79d4cedac6 · outbound

This paper cites : O modelo de tr \^e s n \' veis: uma alternativa para a an \'a lise de dados de avalia c \ a o de sistemas educacionais.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata : O modelo de tr \^e s n \' veis: uma alternativa para a an \'a lise de dados de avalia c \ a o de sistemas educacionais

Reference 65

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Observation 0ed0c231-15f4-46b9-988f-8e199f91c1aa · outbound

This paper cites , Chen , Y.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Chen , Y

Reference 66

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Observation a0ffacbb-8fd6-4701-a6ae-41b2a5c97ef9 · outbound

This paper cites , Kappel , M.A.A.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Kappel , M.A.A

Reference 67

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Observation b8795429-12ff-45de-84c2-e81861c330ab · outbound

This paper cites , Chinmulgund , A.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Chinmulgund , A

Reference 68

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source=arxiv_source observed=2026-08-04T08:12:18.728356Z digest=sha256:f0354234c322061ec734b2290150a8c19b5417fcfe5ab172b96b4ab3883817eb

Observation bed04468-3e3a-4532-a368-38e4763b4dbe · outbound

This paper cites , Development , O.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Development , O

Reference 69

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Observation bae9ccaf-0505-45fc-89b5-f9f1b8860967 · outbound

This paper cites , Minkovska , D.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , Minkovska , D

Reference 70

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source=arxiv_source observed=2026-08-04T08:12:18.879622Z digest=sha256:0a16ea0f24010caf93a824c590f99078135ca7e0ed5b42df70b74f1da5469e6b

Observation a09d700b-066a-4f5a-8a89-1ec70b923923 · outbound

This paper cites Global Education Monitoring Report 2020: Inclusion and Education: All Means All.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata Global Education Monitoring Report 2020: Inclusion and Education: All Means All

Reference 71

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Observation cf0c52df-1089-43bb-ba05-5e1d9c7d1856 · outbound

This paper cites : School composition and contextual effects on student outcomes.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata : School composition and contextual effects on student outcomes

Reference 72

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Observation 76ba9bfa-bd32-41a6-994e-1dea0a2cee72 · outbound

This paper cites , C evik , M.

A Multi-level Analysis of Factors Associated with Student Performance: A Machine Learning Approach to the SAEB Microdata , C evik , M

Reference 73

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source=arxiv_source observed=2026-08-04T08:12:19.207638Z digest=sha256:136a839c70cd671540d18673208bfa444c75cf4f1f61f11bd6946737b3c6c3d5

Pith citing papers

No inbound Pith citation observations are available.