{ "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", "*✏️ This dataset comes from a classroom study that compared how students performed after learning a Global Studies lesson in two different ways—through a traditional teacher-led lecture and with the interactive Pear Deck tool. It includes the scores of 15 students on a 15-point formative assessment, showing how each student did under both teaching methods. The data was used to see whether using Pear Deck helped students score higher than those who learned through the regular lecture format." ] }, { "cell_type": "markdown", "id": "infinite-instrument", "metadata": {}, "source": [ "# Methods and Results" ] }, { "cell_type": "code", "execution_count": 1, "id": "basic-canadian", "metadata": {}, "outputs": [], "source": [ "#Import any helper files you need here" ] }, { "cell_type": "markdown", "id": "recognized-positive", "metadata": {}, "source": [ "## First Research Question: Do students using Pear Deck score higher than those who don’t?\n" ] }, { "cell_type": "markdown", "id": "graduate-palmer", "metadata": {}, "source": [ "### Methods" ] }, { "cell_type": "markdown", "id": "endless-variation", "metadata": {}, "source": [ "** \n", " \n", "✏️ To find out if students who used Pear Deck scored higher than those who didn’t, I’ll focus on the two score columns in the dataset: Traditional_Studying and Pear_Deck_Studying. I’ll first clean and organize the data by removing any missing values so I can fairly compare both groups. Then, I will create a bar graph showing the average scores for each group. This visual will clearly show which group performed better, helping to answer whether using Pear Deck led to higher student achievement.\n", "\n", "The data in this table came from the research paper \"The Impact of Pear Deck on Student Achievement and Perceptions\" by Eric Gross.\n", "\n" ] }, { "cell_type": "markdown", "id": "portuguese-japan", "metadata": {}, "source": [ "### Results " ] }, { "cell_type": "code", "execution_count": 10, "id": "negative-highlight", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "data = {\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", "\n", "df = df.dropna(subset=[\"Pear_Deck_Studying\"])\n", "\n", "avg_scores = [df[\"Traditional_Studying\"].mean(), df[\"Pear_Deck_Studying\"].mean()]\n", "\n", "plt.bar([\"Traditional\", \"Pear Deck\"], avg_scores, color=[\"skyblue\", \"lightgreen\"])\n", "plt.title(\"Average Scores: Traditional vs. Pear Deck\")\n", "plt.ylabel(\"Average Score (out of 15)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "collectible-puppy", "metadata": {}, "source": [ "## Second Research Question: Do students like learning with Pear Deck?\n" ] }, { "cell_type": "markdown", "id": "demographic-future", "metadata": {}, "source": [ "### Methods" ] }, { "cell_type": "markdown", "id": "incorporate-roller", "metadata": {}, "source": [ "I will use the data from the article “The Impact of Pear Deck on Student Achievement and Perceptions” by Eric Gross to show if students like learning with pear deck. After this research, the students completed a three-question survey using a Likert scale (strongly agree, agree, disagree, strongly disagree) about motivation, learning, and preference for Pear Deck. I would put the survey results into a table to show the percentages of each response. This makes it easy to see how many students liked learning with Pear Deck compared to a traditional lecture.\n" ] }, { "cell_type": "markdown", "id": "juvenile-creation", "metadata": {}, "source": [ "### Results " ] }, { "cell_type": "code", "execution_count": 9, "id": "pursuant-surrey", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Survey Question Strongly Agree (%) \\\n", "0 You were more motivated to learn using Pear De... 15.4 \n", "1 You feel you learned more with Pear Deck than ... 15.4 \n", "2 You prefer Pear Deck when learning 38.5 \n", "\n", " Agree (%) Disagree (%) Strongly Disagree (%) \n", "0 76.9 7.7 0 \n", "1 76.9 7.7 0 \n", "2 46.2 15.4 0 \n" ] } ], "source": [ "import pandas as pd\n", "\n", "data = {\n", " \"Survey Question\": [\n", " \"You were more motivated to learn using Pear Deck than traditional lecture\",\n", " \"You feel you learned more with Pear Deck than traditional lecture\",\n", " \"You prefer Pear Deck when learning\"\n", " ],\n", " \"Strongly Agree (%)\": [15.4, 15.4, 38.5],\n", " \"Agree (%)\": [76.9, 76.9, 46.2],\n", " \"Disagree (%)\": [7.7, 7.7, 15.4],\n", " \"Strongly Disagree (%)\": [0, 0, 0]\n", "}\n", "\n", "df = pd.DataFrame(data)\n", "\n", "print(df)" ] }, { "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": [ "The results show that students did better and liked learning with Pear Deck, but they might not represent all students. The dataset is small, with only 15 students, and two students didn’t have Pear Deck scores. The survey responses might also be biased because students could answer more positively since they know they are being observed or because they enjoyed trying something new." ] }, { "cell_type": "markdown", "id": "beneficial-invasion", "metadata": {}, "source": [ "## Summary" ] }, { "cell_type": "markdown", "id": "about-raise", "metadata": {}, "source": [ "✏️ Through this research, I learned that students who used Pear Deck not only scored higher on the formative assessment but also reported enjoying and preferring this interactive method over traditional lectures. The results made sense because interactive, learner-centered tools often help students stay engaged and motivated. What was most surprising was how strong the positive perceptions were—over 90% of students felt more motivated and learned more with Pear Deck. Going forward, this project shows me the value of using interactive digital tools in learning, and I plan to consider incorporating more engaging technologies in teaching to improve both learning outcomes and student experience." ] } ], "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 }