東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Preservice Secondary Mathematics Tea...

Smucker, Karoline.

Linked to FindBook

Google Book

Amazon

博客來

Preservice Secondary Mathematics Teachers' Approaches to Probabilistic and Stochastic Problem Solving using Computer Simulations.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Preservice Secondary Mathematics Teachers' Approaches to Probabilistic and Stochastic Problem Solving using Computer Simulations./
Author:	Smucker, Karoline.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	469 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-04, Section: B.
Contained By:	Dissertations Abstracts International84-04B.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30013423
ISBN:	9798351442938

Preservice Secondary Mathematics Teachers' Approaches to Probabilistic and Stochastic Problem Solving using Computer Simulations.
Smucker, Karoline.

Preservice Secondary Mathematics Teachers' Approaches to Probabilistic and Stochastic Problem Solving using Computer Simulations. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 469 p.

Source: Dissertations Abstracts International, Volume: 84-04, Section: B.

Thesis (Ph.D.)--The Ohio State University, 2022.

This item must not be sold to any third party vendors.

Probabilistic simulations have long served as instructional tools in statistics and probability education. With advances in technology, computer simulation environments where large quantities of data can be collected and analyzed have been suggested as venues for problem solving in contexts involving both known and unknown probability distributions. This research used task based interviews to examine how four secondary preservice mathematics teachers approached seven probabilistic and stochastic contexts which included designed computer simulation environments. The interviews included tasks involving both known and unknown probability distributions. Participants' problem solving and stochastic modeling practices were considered, along with the role the simulations may have played in their approaches.This research proposes an empirically grounded model for problem solving with simulations in stochastic contexts based on participants? tendencies. Results suggest that participants' problem solving varied based on whether the task involved a known or unknown distribution. When tasks involved known distributions, the simulations were used primarily to confirm or test mathematical ideas from outside the simulation. In contexts involving unknown distributions, participants used the simulation environment to investigate empirically and provide approximations and inferences. Prior experience in statistics and probability played a key role in problem solving outside the simulation, and included mathematizing the contexts using distributions, lists, charts, and formulas. In the simulation environments, participants worked mathematically with the data using calculations, proportions, counts, and graphs. Data collection in the simulation environments tended to be iterative, with participants testing, evaluating, and refining ideas over several rounds, though the amount of data which they considered to be necessary to draw conclusions varied significantly based on context.

ISBN: 9798351442938Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Statistics education

Preservice Secondary Mathematics Teachers' Approaches to Probabilistic and Stochastic Problem Solving using Computer Simulations.
LDR:03383nmm a2200445 4500 001 2394435
005 20240422070856.5
006 m o d
007 cr#unu||||||||
008 251215s2022 ||||||||||||||||| ||eng d
020 $a 9798351442938
035 $a (MiAaPQ)AAI30013423
035 $a AAI30013423
040 $a MiAaPQ $c MiAaPQ
100 1 $a Smucker, Karoline. $3 3763909
245 1 0 $a Preservice Secondary Mathematics Teachers' Approaches to Probabilistic and Stochastic Problem Solving using Computer Simulations.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 469 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-04, Section: B.
500 $a Advisor: Manouchehri, Azita.
502 $a Thesis (Ph.D.)--The Ohio State University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Probabilistic simulations have long served as instructional tools in statistics and probability education. With advances in technology, computer simulation environments where large quantities of data can be collected and analyzed have been suggested as venues for problem solving in contexts involving both known and unknown probability distributions. This research used task based interviews to examine how four secondary preservice mathematics teachers approached seven probabilistic and stochastic contexts which included designed computer simulation environments. The interviews included tasks involving both known and unknown probability distributions. Participants' problem solving and stochastic modeling practices were considered, along with the role the simulations may have played in their approaches.This research proposes an empirically grounded model for problem solving with simulations in stochastic contexts based on participants? tendencies. Results suggest that participants' problem solving varied based on whether the task involved a known or unknown distribution. When tasks involved known distributions, the simulations were used primarily to confirm or test mathematical ideas from outside the simulation. In contexts involving unknown distributions, participants used the simulation environment to investigate empirically and provide approximations and inferences. Prior experience in statistics and probability played a key role in problem solving outside the simulation, and included mathematizing the contexts using distributions, lists, charts, and formulas. In the simulation environments, participants worked mathematically with the data using calculations, proportions, counts, and graphs. Data collection in the simulation environments tended to be iterative, with participants testing, evaluating, and refining ideas over several rounds, though the amount of data which they considered to be necessary to draw conclusions varied significantly based on context.
590 $a School code: 0168.
650 4 $a Statistics. $3 517247
650 4 $a Pedagogy. $3 2122828
650 4 $a Mathematics education. $3 641129
650 4 $a Technology education. $3 3423978
650 4 $a Educational technology. $3 517670
650 4 $a Teacher education. $3 3172312
650 4 $a Computer science. $3 523869
653 $a Statistics education
653 $a Probability education
653 $a Preservice teachers
653 $a Simulation technology
653 $a Problem solving
653 $a Math teachers
690 $a 0710
690 $a 0280
690 $a 0530
690 $a 0463
690 $a 0456
690 $a 0984
710 2 $a The Ohio State University. $b EDU Teaching and Learning. $3 1672312
773 0 $t Dissertations Abstracts International $g 84-04B.
790 $a 0168
791 $a Ph.D.
792 $a 2022
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30013423