Math Self Efficacy

Shane Goodwin and other researchers studied factors that affect a student’s confidence on a multiple-choice Mathematics exam. A group of n = 139 students in an Intermediate Algebra course at BYU-Idaho participated in the study. The exam consisted of 30 multiple-choice problems worth a total of 100 points. The students’ scores out of 100 points are given in the variable “Scores.” For each test question, the students evaluated their confidence in their response on a scale of 1 to 6. Confidence Rating Scale: 1 - Random guess (no clue) 2 - Very unsure 3 - Somewhat unsure 4 - Somewhat sure 5 - Very sure 6 - Certain (absolutely sure) Confidence ratings were not relayed to the instructor, and they did not affect the grade on the exam. The mean confidence rating marked by each student is given in the variable “ConfidenceRatingMean.”
MATH221
education
Author

MATH 221

Published

April 30, 2024

Data details

There are 139 rows and 3 columns. The data source1 is used to create our data that is stored in our pins table. You can access this pin from a connection to posit.byui.edu using hathawayj/math_self_efficacy.

This data is available to all.

Variable description

  • Gender: Gender (M, F)
  • Score: Percent of questions right on exam
  • ConfidenceRatingMean: Mean of confidence ratings for each student (see description for scale)

Variable summary

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Score 0 1 74.72 16.37 30.00 66.70 76.70 86.70 100 ▂▃▃▇▇
ConfidenceRatingMean 0 1 4.41 0.94 1.57 3.83 4.43 5.17 6 ▁▂▆▇▆

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Gender 0 1 1 1 0 2 0
Explore generating code using R 
library(tidyverse)
library(pins)
library(connectapi)

math_self_efficacy <- read_csv('https://github.com/byuistats/data/raw/master/MathSelfEfficacy/MathSelfEfficacy.csv') %>%
  select(!Comments)

# Publish the data to the server with Bro. Hathaway as the owner.
board <- board_connect()
pin_write(board, math_self_efficacy, type = "parquet", access_type = "all")

pin_name <- "math_self_efficacy"
meta <- pin_meta(board, paste0("hathawayj/", pin_name))
client <- connect()
my_app <- content_item(client, meta$local$content_id)
set_vanity_url(my_app, paste0("data/", pin_name))

Access data

This data is available to all.

Direct Download: math_self_efficacy.parquet

R and Python Download:

URL Connections:

For public data, any user can connect and read the data using pins::board_connect_url() in R.

library(pins)
url_data <- "https://posit.byui.edu/data/math_self_efficacy/"
board_url <- board_connect_url(c("dat" = url_data))
dat <- pin_read(board_url, "dat")

Use this custom function in Python to have the data in a Pandas DataFrame.

import pandas as pd
import requests
from io import BytesIO

def read_url_pin(name):
  url = "https://posit.byui.edu/data/" + name + "/" + name + ".parquet"
  response = requests.get(url)
  if response.status_code == 200:
    parquet_content = BytesIO(response.content)
    pandas_dataframe = pd.read_parquet(parquet_content)
    return pandas_dataframe
  else:
    print(f"Failed to retrieve data. Status code: {response.status_code}")
    return None

# Example usage:
pandas_df = read_url_pin("math_self_efficacy")

Authenticated Connection:

Our connect server is https://posit.byui.edu which you assign to your CONNECT_SERVER environment variable. You must create an API key and store it in your environment under CONNECT_API_KEY.

Read more about environment variables and the pins package to understand how these environment variables are stored and accessed in R and Python with pins.

library(pins)
board <- board_connect(auth = "auto")
dat <- pin_read(board, "hathawayj/math_self_efficacy")
import os
from pins import board_rsconnect
from dotenv import load_dotenv
load_dotenv()
API_KEY = os.getenv('CONNECT_API_KEY')
SERVER = os.getenv('CONNECT_SERVER')

board = board_rsconnect(server_url=SERVER, api_key=API_KEY)
dat = board.pin_read("hathawayj/math_self_efficacy")