國立虎尾科技大學 |

The Mathematics of Facial Recognition : = Statistics Teachers' Adaptations to a Machine Learning Curriculum.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	The Mathematics of Facial Recognition :/
Reminder of title:	Statistics Teachers' Adaptations to a Machine Learning Curriculum.
Author:	Delaney, Victoria Leah.
Description:	1 online resource (440 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
Contained By:	Dissertations Abstracts International86-04B.
Subject:	Pedagogy. -
Online resource:	click for full text (PQDT)
ISBN:	9798342137614

The Mathematics of Facial Recognition : = Statistics Teachers' Adaptations to a Machine Learning Curriculum.
Delaney, Victoria Leah.

The Mathematics of Facial Recognition :Statistics Teachers' Adaptations to a Machine Learning Curriculum. - 1 online resource (440 pages)

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

Thesis (Ph.D.)--Stanford University, 2024.

Includes bibliographical references

Today's students are surrounded by machine learning (ML)-powered tools. Yet, few understand how they work. While there are numerous opportunities for students to learn about ML in informal settings (e.g., Alvarez et al., 2022; Druga et al., 2022) and online (e.g., code.org, Scratch), there are far fewer opportunities in the United States for students to learn about how ML works while they are in school. Although curriculum and standards frameworks have been developed to help educators navigate machine learning education, it is not yet known if teachers can or want to enact ML curriculum in their practice.As a former classroom teacher and ML graduate student, I saw interdisciplinary commonalities between machine learning and statistics, the subject I used to teach. In response, I co-designed a one-unit curriculum, The Math of Facial Recognition (FaceID),with a practicing high school teacher. We built the curriculum with three goals: (1) that it would aim to help teachers and students build high-level intuitions about ML, (2) that it would draw from teachers' and students' prior statistics knowledge, and (3) that it would bridge statistics and ML concepts by exploring facial recognition in smartphones, a ML application that students likely use everyday. We built custom web applications, termed "widgets," with the purpose of enabling students to explore and test hypotheses about facial recognition using images of themselves. Finally, we designed the curriculum with features that intentionally encouraged statistics teachers to adapt segments and activities of lessons for their students.That teachers would adapt a curriculum for instruction is not surprising; in fact, it is an expected component of good teaching. Drawing from studies in curriculum theory, I argue that teachers' adaptations of curriculum - from written documents, to teacher-intended curricula, to jointly-enacted curricula between teachers and students - are supported by their pedagogical design capacity (PDC) (Brown, 2009). In the context of my study, PDC refers to statistics teachers' diverse resources as designers of instruction: their statistics pedagogical content knowledge (PCK), subject-matter knowledge of statistics, students PCK, general knowledge of pedagogy, and their goals. Teachers' goals may or may not align with goals stated in the written curriculum, which can influence their adaptations. Two key goals of this work are to investigate how teachers' PDC supported their ability to adapt FaceID,and to identify their rationales for making adaptations.To implement my study, I partnered with three experienced high school statistics teachers who agreed to enact FaceID in their classrooms. I observed each teacher's enactment, noting where and how teachers made adaptations. I interviewed each teacher before and after they taught the curriculum to learn about their prior knowledge, experiences, goals, and post-implementation, the nature of their adaptations. I also asked each teacher to identify their adaptations separately in a reflection document, because I was curious to understand how teachers defined an "adaptation" in their own practice. After concluding my field work, I analyzed the data to determine what adaptations teachers made to FaceIDand their rationales for adapting segments and activities, taking note of instances where teachers interpreted their adaptations differently than the researchers.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798342137614Subjects--Topical Terms:

1148703
Pedagogy.
Index Terms--Genre/Form:

554714
Electronic books.

The Mathematics of Facial Recognition : = Statistics Teachers' Adaptations to a Machine Learning Curriculum.
LDR:04796ntm a22003977 4500 001 1152939
005 20241216064802.5
006 m o d
007 cr mn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798342137614
035 $a (MiAaPQ)AAI31591776
035 $a (MiAaPQ)Stanfordgf326jc6533
035 $a AAI31591776
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Delaney, Victoria Leah. $3 1480051
245 1 4 $a The Mathematics of Facial Recognition : $b Statistics Teachers' Adaptations to a Machine Learning Curriculum.
264 0 $c 2024
300 $a 1 online resource (440 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
500 $a Advisor: Borko, Hilda.
502 $a Thesis (Ph.D.)--Stanford University, 2024.
504 $a Includes bibliographical references
520 $a Today's students are surrounded by machine learning (ML)-powered tools. Yet, few understand how they work. While there are numerous opportunities for students to learn about ML in informal settings (e.g., Alvarez et al., 2022; Druga et al., 2022) and online (e.g., code.org, Scratch), there are far fewer opportunities in the United States for students to learn about how ML works while they are in school. Although curriculum and standards frameworks have been developed to help educators navigate machine learning education, it is not yet known if teachers can or want to enact ML curriculum in their practice.As a former classroom teacher and ML graduate student, I saw interdisciplinary commonalities between machine learning and statistics, the subject I used to teach. In response, I co-designed a one-unit curriculum, The Math of Facial Recognition (FaceID),with a practicing high school teacher. We built the curriculum with three goals: (1) that it would aim to help teachers and students build high-level intuitions about ML, (2) that it would draw from teachers' and students' prior statistics knowledge, and (3) that it would bridge statistics and ML concepts by exploring facial recognition in smartphones, a ML application that students likely use everyday. We built custom web applications, termed "widgets," with the purpose of enabling students to explore and test hypotheses about facial recognition using images of themselves. Finally, we designed the curriculum with features that intentionally encouraged statistics teachers to adapt segments and activities of lessons for their students.That teachers would adapt a curriculum for instruction is not surprising; in fact, it is an expected component of good teaching. Drawing from studies in curriculum theory, I argue that teachers' adaptations of curriculum - from written documents, to teacher-intended curricula, to jointly-enacted curricula between teachers and students - are supported by their pedagogical design capacity (PDC) (Brown, 2009). In the context of my study, PDC refers to statistics teachers' diverse resources as designers of instruction: their statistics pedagogical content knowledge (PCK), subject-matter knowledge of statistics, students PCK, general knowledge of pedagogy, and their goals. Teachers' goals may or may not align with goals stated in the written curriculum, which can influence their adaptations. Two key goals of this work are to investigate how teachers' PDC supported their ability to adapt FaceID,and to identify their rationales for making adaptations.To implement my study, I partnered with three experienced high school statistics teachers who agreed to enact FaceID in their classrooms. I observed each teacher's enactment, noting where and how teachers made adaptations. I interviewed each teacher before and after they taught the curriculum to learn about their prior knowledge, experiences, goals, and post-implementation, the nature of their adaptations. I also asked each teacher to identify their adaptations separately in a reflection document, because I was curious to understand how teachers defined an "adaptation" in their own practice. After concluding my field work, I analyzed the data to determine what adaptations teachers made to FaceIDand their rationales for adapting segments and activities, taking note of instances where teachers interpreted their adaptations differently than the researchers.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Pedagogy. $3 1148703
650 4 $a Computer science. $3 573171
650 4 $a Writing. $3 561099
650 4 $a Adaptation. $3 1465331
650 4 $a Educational technology. $3 556755
650 4 $a Core curriculum. $3 1466386
650 4 $a Teachers. $3 881385
650 4 $a Classrooms. $3 859839
650 4 $a Schools. $3 557489
650 4 $a Science education. $3 1151737
650 4 $a Design. $3 595500
650 4 $a Digital technology. $3 1465919
650 4 $a Curriculum development. $3 1148494
655 7 $a Electronic books. $2 local $3 554714
690 $a 0389
690 $a 0984
690 $a 0800
690 $a 0456
690 $a 0710
690 $a 0714
690 $a 0727
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Stanford University. $3 1184533
773 0 $t Dissertations Abstracts International $g 86-04B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31591776 $z click for full text (PQDT)