Preparation, Practice, and Beliefs: A Machine Learning Approach to Understanding Teacher Effectiveness
This paper uses machine learning methods to identify key predictors of teacher effectiveness, proxied by student learning gains linked to a teacher over an academic year. Conditional inference forests and the least absolute shrinkage and selection operator are applied to matched student-teacher data...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Washington, D.C
The World Bank
2021
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This paper uses machine learning methods to identify key predictors of teacher effectiveness, proxied by student learning gains linked to a teacher over an academic year. Conditional inference forests and the least absolute shrinkage and selection operator are applied to matched student-teacher data for math and Kiswahili from grades 2 and 3 in 392 schools across Tanzania. These two machine learning methods produce consistent results and outperform standard ordinary least squares in out-of-sample prediction by 14-24 percent. As in previous research, commonly used teacher covariates like teacher gender, Education, experience, and so forth are not good predictors of teacher effectiveness. Instead, teacher practice (what teachers do, measured through classroom observations and student surveys) and teacher beliefs (measured through teacher surveys) emerge as much more important. Overall, teacher covariates are stronger predictors of teacher effectiveness in math than in Kiswahili. Teacher beliefs that they can help disadvantaged and struggling students learn (for math) and they have good relationships within schools (for Kiswahili), teacher practice of providing written feedback and reviewing key concepts at the end of class (for math), and spending extra time with struggling students (for Kiswahili) are highly predictive of teacher effectiveness. As is teacher preparation on how to teach foundational topics (for both Math and Kiswahili). These results demonstrate the need to pay more systematic attention to teacher preparation, practice, and beliefs in teacher research and policy |
| Beschreibung: | 1 Online-Ressource (53 Seiten) |
| DOI: | 10.1596/1813-9450-9847 |
Internformat
MARC
| LEADER | 00000nmm a22000001c 4500 | ||
|---|---|---|---|
| 001 | BV049080787 | ||
| 003 | DE-604 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 230731s2021 xxu|||| o||u| ||||||eng d | ||
| 024 | 7 | |a 10.1596/1813-9450-9847 |2 doi | |
| 035 | |a (ZDB-1-WBA)076553205 | ||
| 035 | |a (OCoLC)1392151873 | ||
| 035 | |a (DE-599)KEP076553205 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a xxu |c XD-US | ||
| 049 | |a DE-12 |a DE-521 |a DE-573 |a DE-523 |a DE-Re13 |a DE-19 |a DE-355 |a DE-703 |a DE-91 |a DE-706 |a DE-29 |a DE-M347 |a DE-473 |a DE-824 |a DE-20 |a DE-739 |a DE-1043 |a DE-863 |a DE-862 | ||
| 100 | 1 | |a Filmer, Deon |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Preparation, Practice, and Beliefs |b A Machine Learning Approach to Understanding Teacher Effectiveness |c Deon Filmer |
| 264 | 1 | |a Washington, D.C |b The World Bank |c 2021 | |
| 300 | |a 1 Online-Ressource (53 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 520 | 3 | |a This paper uses machine learning methods to identify key predictors of teacher effectiveness, proxied by student learning gains linked to a teacher over an academic year. Conditional inference forests and the least absolute shrinkage and selection operator are applied to matched student-teacher data for math and Kiswahili from grades 2 and 3 in 392 schools across Tanzania. These two machine learning methods produce consistent results and outperform standard ordinary least squares in out-of-sample prediction by 14-24 percent. As in previous research, commonly used teacher covariates like teacher gender, Education, experience, and so forth are not good predictors of teacher effectiveness. Instead, teacher practice (what teachers do, measured through classroom observations and student surveys) and teacher beliefs (measured through teacher surveys) emerge as much more important. Overall, teacher covariates are stronger predictors of teacher effectiveness in math than in Kiswahili. Teacher beliefs that they can help disadvantaged and struggling students learn (for math) and they have good relationships within schools (for Kiswahili), teacher practice of providing written feedback and reviewing key concepts at the end of class (for math), and spending extra time with struggling students (for Kiswahili) are highly predictive of teacher effectiveness. As is teacher preparation on how to teach foundational topics (for both Math and Kiswahili). These results demonstrate the need to pay more systematic attention to teacher preparation, practice, and beliefs in teacher research and policy | |
| 650 | 4 | |a Education | |
| 650 | 4 | |a Effective Schools and Teachers | |
| 650 | 4 | |a Machine Learning | |
| 650 | 4 | |a Student Achievement | |
| 650 | 4 | |a Teacher Effectiveness | |
| 650 | 4 | |a Teacher Mindset | |
| 650 | 4 | |a Teacher Performance | |
| 650 | 4 | |a Teacher Value-Added | |
| 700 | 1 | |a Nahata, Vatsal |e Sonstige |4 oth | |
| 700 | 1 | |a Sabarwal, Shwetlena |e Sonstige |4 oth | |
| 856 | 4 | 0 | |u https://doi.org/10.1596/1813-9450-9847 |x Verlag |z kostenfrei |3 Volltext |
| 912 | |a ZDB-1-WBA | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-034342677 | |
Datensatz im Suchindex
| _version_ | 1812671843184672768 |
|---|---|
| adam_text | |
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Filmer, Deon |
| author_facet | Filmer, Deon |
| author_role | aut |
| author_sort | Filmer, Deon |
| author_variant | d f df |
| building | Verbundindex |
| bvnumber | BV049080787 |
| collection | ZDB-1-WBA |
| ctrlnum | (ZDB-1-WBA)076553205 (OCoLC)1392151873 (DE-599)KEP076553205 |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| doi_str_mv | 10.1596/1813-9450-9847 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nmm a22000001c 4500</leader><controlfield tag="001">BV049080787</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">230731s2021 xxu|||| o||u| ||||||eng d</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1596/1813-9450-9847</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-1-WBA)076553205</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1392151873</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP076553205</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">xxu</subfield><subfield code="c">XD-US</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-12</subfield><subfield code="a">DE-521</subfield><subfield code="a">DE-573</subfield><subfield code="a">DE-523</subfield><subfield code="a">DE-Re13</subfield><subfield code="a">DE-19</subfield><subfield code="a">DE-355</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-706</subfield><subfield code="a">DE-29</subfield><subfield code="a">DE-M347</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-824</subfield><subfield code="a">DE-20</subfield><subfield code="a">DE-739</subfield><subfield code="a">DE-1043</subfield><subfield code="a">DE-863</subfield><subfield code="a">DE-862</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Filmer, Deon</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Preparation, Practice, and Beliefs</subfield><subfield code="b">A Machine Learning Approach to Understanding Teacher Effectiveness</subfield><subfield code="c">Deon Filmer</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Washington, D.C</subfield><subfield code="b">The World Bank</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (53 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">This paper uses machine learning methods to identify key predictors of teacher effectiveness, proxied by student learning gains linked to a teacher over an academic year. Conditional inference forests and the least absolute shrinkage and selection operator are applied to matched student-teacher data for math and Kiswahili from grades 2 and 3 in 392 schools across Tanzania. These two machine learning methods produce consistent results and outperform standard ordinary least squares in out-of-sample prediction by 14-24 percent. As in previous research, commonly used teacher covariates like teacher gender, Education, experience, and so forth are not good predictors of teacher effectiveness. Instead, teacher practice (what teachers do, measured through classroom observations and student surveys) and teacher beliefs (measured through teacher surveys) emerge as much more important. Overall, teacher covariates are stronger predictors of teacher effectiveness in math than in Kiswahili. Teacher beliefs that they can help disadvantaged and struggling students learn (for math) and they have good relationships within schools (for Kiswahili), teacher practice of providing written feedback and reviewing key concepts at the end of class (for math), and spending extra time with struggling students (for Kiswahili) are highly predictive of teacher effectiveness. As is teacher preparation on how to teach foundational topics (for both Math and Kiswahili). These results demonstrate the need to pay more systematic attention to teacher preparation, practice, and beliefs in teacher research and policy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Education</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Effective Schools and Teachers</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine Learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Student Achievement</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Teacher Effectiveness</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Teacher Mindset</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Teacher Performance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Teacher Value-Added</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nahata, Vatsal</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sabarwal, Shwetlena</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1596/1813-9450-9847</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-WBA</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034342677</subfield></datafield></record></collection> |
| id | DE-604.BV049080787 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T22:27:58Z |
| indexdate | 2024-10-12T04:03:00Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034342677 |
| oclc_num | 1392151873 |
| open_access_boolean | 1 |
| owner | DE-12 DE-521 DE-573 DE-523 DE-Re13 DE-BY-UBR DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-703 DE-91 DE-BY-TUM DE-706 DE-29 DE-M347 DE-473 DE-BY-UBG DE-824 DE-20 DE-739 DE-1043 DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| owner_facet | DE-12 DE-521 DE-573 DE-523 DE-Re13 DE-BY-UBR DE-19 DE-BY-UBM DE-355 DE-BY-UBR DE-703 DE-91 DE-BY-TUM DE-706 DE-29 DE-M347 DE-473 DE-BY-UBG DE-824 DE-20 DE-739 DE-1043 DE-863 DE-BY-FWS DE-862 DE-BY-FWS |
| physical | 1 Online-Ressource (53 Seiten) |
| psigel | ZDB-1-WBA |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | The World Bank |
| record_format | marc |
| spellingShingle | Filmer, Deon Preparation, Practice, and Beliefs A Machine Learning Approach to Understanding Teacher Effectiveness Education Effective Schools and Teachers Machine Learning Student Achievement Teacher Effectiveness Teacher Mindset Teacher Performance Teacher Value-Added |
| title | Preparation, Practice, and Beliefs A Machine Learning Approach to Understanding Teacher Effectiveness |
| title_auth | Preparation, Practice, and Beliefs A Machine Learning Approach to Understanding Teacher Effectiveness |
| title_exact_search | Preparation, Practice, and Beliefs A Machine Learning Approach to Understanding Teacher Effectiveness |
| title_exact_search_txtP | Preparation, Practice, and Beliefs A Machine Learning Approach to Understanding Teacher Effectiveness |
| title_full | Preparation, Practice, and Beliefs A Machine Learning Approach to Understanding Teacher Effectiveness Deon Filmer |
| title_fullStr | Preparation, Practice, and Beliefs A Machine Learning Approach to Understanding Teacher Effectiveness Deon Filmer |
| title_full_unstemmed | Preparation, Practice, and Beliefs A Machine Learning Approach to Understanding Teacher Effectiveness Deon Filmer |
| title_short | Preparation, Practice, and Beliefs |
| title_sort | preparation practice and beliefs a machine learning approach to understanding teacher effectiveness |
| title_sub | A Machine Learning Approach to Understanding Teacher Effectiveness |
| topic | Education Effective Schools and Teachers Machine Learning Student Achievement Teacher Effectiveness Teacher Mindset Teacher Performance Teacher Value-Added |
| topic_facet | Education Effective Schools and Teachers Machine Learning Student Achievement Teacher Effectiveness Teacher Mindset Teacher Performance Teacher Value-Added |
| url | https://doi.org/10.1596/1813-9450-9847 |
| work_keys_str_mv | AT filmerdeon preparationpracticeandbeliefsamachinelearningapproachtounderstandingteachereffectiveness AT nahatavatsal preparationpracticeandbeliefsamachinelearningapproachtounderstandingteachereffectiveness AT sabarwalshwetlena preparationpracticeandbeliefsamachinelearningapproachtounderstandingteachereffectiveness |