{ "cells": [ { "cell_type": "markdown", "id": "worldwide-blood", "metadata": {}, "source": [ "# Impact of Technological apps on Education?" ] }, { "cell_type": "markdown", "id": "understanding-numbers", "metadata": {}, "source": [ "*✏️ This research looks at whether using educational apps like Pear Deck helps students learn better and stay more engaged. It compares test scores and survey results from students who used Pear Deck with those who had regular lessons. The goal is to see if learning with technology improves student performance and interest.*" ] }, { "cell_type": "markdown", "id": "greater-circular", "metadata": {}, "source": [ "## Overarching Question: Do Educational apps like pear deck improve student learning outcomes?" ] }, { "cell_type": "markdown", "id": "appreciated-testimony", "metadata": {}, "source": [ "*✏️I am interested in exploring the question: “Do educational apps like Pear Deck improve student learning outcomes?” I chose this question because educational technology is becoming increasingly common in classrooms, and I have observed its use firsthand during my placement in a 3rd-grade classroom. My students regularly use ELA apps such as Pear Assessment and math apps like Pear Deck to complete their classwork. I am curious about whether regular use of educational apps impacts student performance. Investigating this question can reveal patterns in how technology influences learning and provide evidence-based recommendations for teachers.*" ] }, { "cell_type": "markdown", "id": "permanent-pollution", "metadata": {}, "source": [ "# Data" ] }, { "cell_type": "code", "execution_count": 2, "id": "c4330642-cdf6-447a-af17-94afb4261a30", "metadata": {}, "outputs": [], "source": [ "#Include any import statements you will need\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 9, "id": "overhead-sigma", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Student Traditional_Studying Pear_Deck_Studying\n", "0 1 10 14.0\n", "1 2 10 8.0\n", "2 3 10 10.0\n", "3 4 10 15.0\n", "4 5 10 14.0\n", "5 6 10 9.0\n", "6 7 10 13.0\n", "7 8 10 15.0\n", "8 9 10 9.0\n", "9 10 10 15.0\n", "10 11 10 15.0\n", "11 12 10 15.0\n", "12 13 10 14.0\n", "13 14 10 NaN\n", "14 15 10 NaN" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "data = {\n", " \"Student\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],\n", " \"Traditional_Studying\": [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10],\n", " \"Pear_Deck_Studying\": [14, 8, 10, 15, 14, 9, 13, 15, 9, 15, 15, 15, 14, None, None]\n", "}\n", "\n", "df = pd.DataFrame(data)\n", "df" ] }, { "cell_type": "markdown", "id": "continental-franklin", "metadata": {}, "source": [ "**Data Overview**\n", "\n", "*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*" ] }, { "cell_type": "markdown", "id": "infinite-instrument", "metadata": {}, "source": [ "# Methods and Results" ] }, { "cell_type": "code", "execution_count": null, "id": "basic-canadian", "metadata": {}, "outputs": [], "source": [ "#Import any helper files you need here" ] }, { "cell_type": "markdown", "id": "recognized-positive", "metadata": {}, "source": [ "## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n" ] }, { "cell_type": "markdown", "id": "graduate-palmer", "metadata": {}, "source": [ "### Methods" ] }, { "cell_type": "markdown", "id": "endless-variation", "metadata": {}, "source": [ "*Explain how you will approach this research question below. Consider the following:* \n", " - *Which aspects of the dataset will you use?* \n", " - *How will you reorganize/store the data?* \n", " - *What data science tools/functions will you use and why?* \n", " \n", "✏️ *Write your answer below:*\n", "\n" ] }, { "cell_type": "markdown", "id": "portuguese-japan", "metadata": {}, "source": [ "### Results " ] }, { "cell_type": "code", "execution_count": 17, "id": "negative-highlight", "metadata": {}, "outputs": [], "source": [ "#######################################################################\n", "### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n", "### \n", "### Your data analysis may include a statistic and/or a data visualization\n", "#######################################################################" ] }, { "cell_type": "code", "execution_count": 16, "id": "victorian-burning", "metadata": {}, "outputs": [], "source": [ "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON " ] }, { "cell_type": "markdown", "id": "collectible-puppy", "metadata": {}, "source": [ "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n" ] }, { "cell_type": "markdown", "id": "demographic-future", "metadata": {}, "source": [ "### Methods" ] }, { "cell_type": "markdown", "id": "incorporate-roller", "metadata": {}, "source": [ "*Explain how you will approach this research question below. Consider the following:* \n", " - *Which aspects of the dataset will you use?* \n", " - *How will you reorganize/store the data?* \n", " - *What data science tools/functions will you use and why?* \n", "\n", "✏️ *Write your answer below:*\n" ] }, { "cell_type": "markdown", "id": "juvenile-creation", "metadata": {}, "source": [ "### Results " ] }, { "cell_type": "code", "execution_count": 14, "id": "pursuant-surrey", "metadata": {}, "outputs": [], "source": [ "#######################################################################\n", "### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n", "###\n", "### Your data analysis may include a statistic and/or a data visualization\n", "#######################################################################" ] }, { "cell_type": "code", "execution_count": 15, "id": "located-night", "metadata": {}, "outputs": [], "source": [ "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON " ] }, { "cell_type": "markdown", "id": "infectious-symbol", "metadata": {}, "source": [ "# Discussion" ] }, { "cell_type": "markdown", "id": "furnished-camping", "metadata": { "code_folding": [] }, "source": [ "## Considerations" ] }, { "cell_type": "markdown", "id": "bearing-stadium", "metadata": {}, "source": [ "*It's important to recognize the limitations of our research.\n", "Consider the following:*\n", "\n", "- *Do the results give an accurate depiction of your research question? Why or why not?*\n", "- *What were limitations of your datset?*\n", "- *Are there any known biases in the data?*\n", "\n", "✏️ *Write your answer below:*" ] }, { "cell_type": "markdown", "id": "beneficial-invasion", "metadata": {}, "source": [ "## Summary" ] }, { "cell_type": "markdown", "id": "about-raise", "metadata": {}, "source": [ "*Summarize what you discovered through the research. Consider the following:*\n", "\n", "- *What did you learn about your media consumption/digital habits?*\n", "- *Did the results make sense?*\n", "- *What was most surprising?*\n", "- *How will this project impact you going forward?*\n", "\n", "✏️ *Write your answer below:*" ] } ], "metadata": { "jupytext": { "cell_metadata_json": true, "text_representation": { "extension": ".Rmd", "format_name": "rmarkdown", "format_version": "1.2", "jupytext_version": "1.9.1" } }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 5 }