diff --git a/.ipynb_checkpoints/argument-checkpoint.ipynb b/.ipynb_checkpoints/argument-checkpoint.ipynb index 44a89d7..c15ce36 100644 --- a/.ipynb_checkpoints/argument-checkpoint.ipynb +++ b/.ipynb_checkpoints/argument-checkpoint.ipynb @@ -224,7 +224,7 @@ "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.*" + "*✏️ 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." ] }, { @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "basic-canadian", "metadata": {}, "outputs": [], @@ -250,7 +250,7 @@ "id": "recognized-positive", "metadata": {}, "source": [ - "## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n" + "## First Research Question: Do students using Pear Deck score higher than those who don’t?\n" ] }, { @@ -266,12 +266,11 @@ "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", " \n", - "✏️ *Write your answer below:*\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" ] }, @@ -285,26 +284,40 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "id": "negative-highlight", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "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 " + "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()" ] }, { @@ -312,7 +325,7 @@ "id": "collectible-puppy", "metadata": {}, "source": [ - "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n" + "## Second Research Question: Do students like learning with Pear Deck?\n" ] }, { @@ -328,12 +341,7 @@ "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" + "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" ] }, { @@ -346,26 +354,44 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "id": "pursuant-surrey", "metadata": {}, - "outputs": [], + "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": [ - "#######################################################################\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 " + "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)" ] }, { @@ -391,14 +417,7 @@ "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:*" + "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." ] }, { @@ -414,14 +433,7 @@ "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:*" + "✏️ 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." ] } ], diff --git a/.ipynb_checkpoints/pyproject-checkpoint.toml b/.ipynb_checkpoints/pyproject-checkpoint.toml new file mode 100644 index 0000000..85318b8 --- /dev/null +++ b/.ipynb_checkpoints/pyproject-checkpoint.toml @@ -0,0 +1,23 @@ +[project] +name = "project-argument" +version = "0.1.0" +description = "" +authors = [ + {name = "Chris Proctor",email = "chris@chrisproctor.net"} +] +license = {text = "MIT"} +readme = "README.md" +requires-python = ">=3.10,<4.0" +dependencies = [ + "jupyter (>=1.1.1,<2.0.0)", + "seaborn (>=0.13.2,<0.14.0)", + "pandas (>=2.2.3,<3.0.0)" +] + + +[build-system] +requires = ["poetry-core>=2.0.0,<3.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry] +package-mode = false diff --git a/argument.ipynb b/argument.ipynb index 44a89d7..c15ce36 100644 --- a/argument.ipynb +++ b/argument.ipynb @@ -224,7 +224,7 @@ "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.*" + "*✏️ 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." ] }, { @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "basic-canadian", "metadata": {}, "outputs": [], @@ -250,7 +250,7 @@ "id": "recognized-positive", "metadata": {}, "source": [ - "## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n" + "## First Research Question: Do students using Pear Deck score higher than those who don’t?\n" ] }, { @@ -266,12 +266,11 @@ "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", " \n", - "✏️ *Write your answer below:*\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" ] }, @@ -285,26 +284,40 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "id": "negative-highlight", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "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 " + "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()" ] }, { @@ -312,7 +325,7 @@ "id": "collectible-puppy", "metadata": {}, "source": [ - "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n" + "## Second Research Question: Do students like learning with Pear Deck?\n" ] }, { @@ -328,12 +341,7 @@ "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" + "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" ] }, { @@ -346,26 +354,44 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "id": "pursuant-surrey", "metadata": {}, - "outputs": [], + "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": [ - "#######################################################################\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 " + "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)" ] }, { @@ -391,14 +417,7 @@ "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:*" + "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." ] }, { @@ -414,14 +433,7 @@ "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:*" + "✏️ 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." ] } ],