{"cells":[{"cell_type":"markdown","metadata":{"id":"0ZgsJOlTV-cA"},"source":["# Tutorial on `edu-convokit` for the TalkMoves dataset\n","\n","Welcome to the tutorial on `edu-convokit` for the [TalkMoves dataset](https://github.com/SumnerLab/TalkMoves). This tutorial will walk you through the process of using `edu-convokit` to pre-process, annotate and analyze the TalkMoves dataset.\n","\n","If you are looking for a tutorial on the individual components of `edu-convokit`, please refer to the following tutorials to get started:\n","- [Text Pre-processing Colab](https://colab.research.google.com/drive/1a-EwYwkNYHSNcNThNTXe6DNpsis0bpQK)\n","- [Annotation Colab](https://colab.research.google.com/drive/1rBwEctFtmQowZHxralH2OGT5uV0zRIQw)\n","- [Analysis Colab](https://colab.research.google.com/drive/1xfrq5Ka3FZH7t9l87u4sa_oMlmMvuTfe)\n","\n","This tutorial will use all of the components!"]},{"cell_type":"markdown","metadata":{"id":"qyot5jipV-cB"},"source":["## Installation\n","\n","Let's start by installing `edu-convokit` and importing the necessary modules.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21593,"status":"ok","timestamp":1703936794770,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"E1IPd9HnV-cC","outputId":"170889eb-0ae8-44bf-f12c-31399220d963"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting git+https://github.com/rosewang2008/edu-convokit.git\n","  Cloning https://github.com/rosewang2008/edu-convokit.git to /tmp/pip-req-build-dgphjpe_\n","  Running command git clone --filter=blob:none --quiet https://github.com/rosewang2008/edu-convokit.git /tmp/pip-req-build-dgphjpe_\n","  Resolved https://github.com/rosewang2008/edu-convokit.git to commit 1e094c8836a3e3112cc1f996f5f12aeff013777c\n","  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from edu-convokit==0.0.1) 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Wang","userId":"08647070137360066467"},"user_tz":480},"id":"nf3j5W8AV-cC","outputId":"f2b9c0a7-dc15-4dc9-efb3-751c2b4e0a0a"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:root:Since the GPL-licensed package `unidecode` is not installed, using Python's `unicodedata` package which yields worse results.\n","[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data]   Unzipping corpora/stopwords.zip.\n"]}],"source":["from edu_convokit.preprocessors import TextPreprocessor\n","from edu_convokit.annotation import Annotator\n","from edu_convokit.analyzers import (\n","    QualitativeAnalyzer,\n","    QuantitativeAnalyzer,\n","    LexicalAnalyzer,\n","    TemporalAnalyzer\n",")\n","# For helping us load data\n","from edu_convokit import utils\n","\n","import os\n","import tqdm"]},{"cell_type":"markdown","metadata":{"id":"iwnxZ8sLV-cC"},"source":["## 📑 Data\n","\n","Let's download the dataset under `raw_data/`.\n","Note we're only download a subsample of the dataset for this tutorial; this cuts down the annotation time.\n","If you would like to annotate the entire dataset, feel free to upload the entire dataset to this Colab!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":550,"status":"ok","timestamp":1703936817307,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"e66OQumaV-cD","outputId":"fc466ccb-a7be-4573-cc28-9a0dcbbd0a40"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-12-30 11:46:56--  https://raw.githubusercontent.com/rosewang2008/edu-convokit/master/data/talkmoves.zip\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 346774 (339K) [application/zip]\n","Saving to: ‘talkmoves.zip’\n","\n","\rtalkmoves.zip         0%[                    ]       0  --.-KB/s               \rtalkmoves.zip       100%[===================>] 338.65K  --.-KB/s    in 0.02s   \n","\n","2023-12-30 11:46:56 (17.2 MB/s) - ‘talkmoves.zip’ saved [346774/346774]\n","\n"]}],"source":["# We will put the data here:\n","DATA_DIR = \"raw_data\"\n","!mkdir -p $DATA_DIR\n","\n","# We will put the annotated data here:\n","ANNOTATIONS_DIR = \"annotations\"\n","!mkdir -p $ANNOTATIONS_DIR\n","\n","# # Download the data\n","!wget \"https://raw.githubusercontent.com/rosewang2008/edu-convokit/master/data/talkmoves.zip\"\n","\n","# # Unzip the data\n","!unzip -n -q talkmoves.zip -d $DATA_DIR\n","\n","# Data directory is then raw_data/talkmoves\n","DATA_DIR = \"raw_data/talkmoves\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uFT_56rtV-cD"},"outputs":[],"source":["# We'll set the important variables specific to this dataset. If you open one of the files, you'll see that the\n","# speaker and text columns are defined as:\n","TEXT_COLUMN = \"Sentence\"\n","SPEAKER_COLUMN = \"Speaker\"\n","\n","# We will also define the annotation columns.\n","# For the purposes of this tutorial, we will only be using talktime, student_reasoning, and uptake.\n","TALK_TIME_COLUMN = \"talktime\"\n","STUDENT_REASONING_COLUMN = \"student_reasoning\"\n","UPTAKE_COLUMN = \"uptake\""]},{"cell_type":"markdown","metadata":{"id":"28sYVNWJV-cD"},"source":["One thing that will be important is knowing how the teacher/tutor and student are represented in the dataset.\n","Let's load some examples and see how they are represented."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LUqIcq6YV-cD"},"outputs":[],"source":["files = os.listdir(DATA_DIR)\n","files = [os.path.join(DATA_DIR, f) for f in files if utils.is_valid_file_extension(f)]\n","\n","df = utils.merge_dataframes_in_list(files)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":519},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1703936943212,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"yZzWrdaxV-cD","outputId":"30c85bab-849b-46ea-ba8e-804b7361e10a"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-776a0c55-8db3-4e9d-8450-5adfdea10a79\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>TimeStamp</th>\n","      <th>Turn</th>\n","      <th>Speaker</th>\n","      <th>Sentence</th>\n","      <th>Teacher Tag</th>\n","      <th>Student Tag</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>5</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>1.0</td>\n","      <td>T/R1</td>\n","      <td>Do you remember it  looks like this.</td>\n","      <td>1 - 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Keeping Everyone Together</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>839</th>\n","      <td>839.0</td>\n","      <td>NaN</td>\n","      <td>481.0</td>\n","      <td>T</td>\n","      <td>Okay, but why, how could she be sure?</td>\n","      <td>3 - Getting Students to Relate</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-776a0c55-8db3-4e9d-8450-5adfdea10a79')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-776a0c55-8db3-4e9d-8450-5adfdea10a79 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Liu' 'JAKE'\n"," 'ASHANK' 'Alan' 'ALYSSA' 'SN' 'KEVIN' 'SI' 'Amy' 'Jackie' 'PARTNER'\n"," 'TIMOTHY' 'Jacquelyn' 'T/R1' 'Students' 'Student' 'LINDA FISHER'\n"," 'DEBORAH' 'Jason' 'CHARLOTTE' 'Jeff' 'Michelle' 'Milin' 'Stephanie'\n"," 'Stephanie & Jeff' 'Michelle & Milin' 'Blonde' 'Milin,' '~23' 'All'\n"," 'Michelle & Jeff' 'R2' 'CECILIO DIMAS' 'STUDENT' 'Erik and Brian'\n"," 'SAMUEL' 'OSI' 'CLAIRE']\n"]}],"source":["speaker_names = df[SPEAKER_COLUMN].unique()\n","\n","print(\"Speaker names: \", speaker_names)\n"]},{"cell_type":"markdown","metadata":{"id":"b4luDlvVV-cD"},"source":["It seems like the teacher's speaker name starts with T.\n","The student's speaker name are all other names.\n","Let's split the names into two groups: teacher and student."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1703936956147,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"uoDt1DEPV-cD","outputId":"3b2564ce-7a58-49bc-f291-1bdf19eb66cf"},"outputs":[{"name":"stdout","output_type":"stream","text":["Teacher speaker:  ['T', 'T 2', 'T2', 'TIMOTHY', 'T/R1']\n","Student speaker:  ['David', 'Meredith', 'Beth', 'Meredith and David', 'Danielle', 'Gregory', 'Michael', 'Andrew', 'Laura', 'Jessica', 'Audra', 'Kelly', 'Brian', 'Jessica and Audra', 'SS', 'Erik', 'Mark', 'Graham', 'Others', 'S', 'BRYAN', 'DANIEL', 'ANDREW', 'CYNTHIA', 'SAURABH', 'STUDENT 1', 'MS. Liu', 'JAKE', 'ASHANK', 'Alan', 'ALYSSA', 'SN', 'KEVIN', 'SI', 'Amy', 'Jackie', 'PARTNER', 'Jacquelyn', 'Students', 'Student', 'LINDA FISHER', 'DEBORAH', 'Jason', 'CHARLOTTE', 'Jeff', 'Michelle', 'Milin', 'Stephanie', 'Stephanie & Jeff', 'Michelle & Milin', 'Blonde', 'Milin,', '~23', 'All', 'Michelle & Jeff', 'R2', 'CECILIO DIMAS', 'STUDENT', 'Erik and Brian', 'SAMUEL', 'OSI', 'CLAIRE']\n"]}],"source":["# Let's remove nan speakers\n","speaker_names = [_ for _ in speaker_names if str(_) != \"nan\"]\n","\n","# And let's make sure the names are interpreted as strings\n","speaker_names = [str(_) for _ in speaker_names]\n","\n","# Create a regex for the teacher names\n","TEACHER_START_LETTER = \"T\"\n","TEACHER_SPEAKER = [_ for _ in speaker_names if _.startswith(TEACHER_START_LETTER)]\n","STUDENT_SPEAKER = [_ for _ in speaker_names if _ not in TEACHER_SPEAKER]\n","\n","print(\"Teacher speaker: \", TEACHER_SPEAKER)\n","print(\"Student speaker: \", STUDENT_SPEAKER)"]},{"cell_type":"markdown","metadata":{"id":"YdFGVGlnV-cD"},"source":["There are some names in the teacher list that belong to the student. Let's just manually fix that:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":141,"status":"ok","timestamp":1703936973106,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"ZdIu96VfV-cD","outputId":"bf16e813-e3f4-4fd3-904a-3409d33f6cbb"},"outputs":[{"name":"stdout","output_type":"stream","text":["Teacher speaker:  ['T', 'T 2', 'T2', 'T/R1']\n","Student speaker:  ['David', 'Meredith', 'Beth', 'Meredith and David', 'Danielle', 'Gregory', 'Michael', 'Andrew', 'Laura', 'Jessica', 'Audra', 'Kelly', 'Brian', 'Jessica and Audra', 'SS', 'Erik', 'Mark', 'Graham', 'Others', 'S', 'BRYAN', 'DANIEL', 'ANDREW', 'CYNTHIA', 'SAURABH', 'STUDENT 1', 'MS. Liu', 'JAKE', 'ASHANK', 'Alan', 'ALYSSA', 'SN', 'KEVIN', 'SI', 'Amy', 'Jackie', 'PARTNER', 'Jacquelyn', 'Students', 'Student', 'LINDA FISHER', 'DEBORAH', 'Jason', 'CHARLOTTE', 'Jeff', 'Michelle', 'Milin', 'Stephanie', 'Stephanie & Jeff', 'Michelle & Milin', 'Blonde', 'Milin,', '~23', 'All', 'Michelle & Jeff', 'R2', 'CECILIO DIMAS', 'STUDENT', 'Erik and Brian', 'SAMUEL', 'OSI', 'CLAIRE', 'TIMOTHY']\n"]}],"source":["FALSE_POSITIVE_NAMES = [\"TIMOTHY\"]\n","\n","# Remove from the teacher speaker list\n","TEACHER_SPEAKER = [_ for _ in TEACHER_SPEAKER if _ not in FALSE_POSITIVE_NAMES]\n","\n","# Add to the student speaker list\n","STUDENT_SPEAKER.extend(FALSE_POSITIVE_NAMES)\n","\n","print(\"Teacher speaker: \", TEACHER_SPEAKER)\n","print(\"Student speaker: \", STUDENT_SPEAKER)"]},{"cell_type":"markdown","metadata":{"id":"blTi-uo4V-cE"},"source":["## 📝 Text Pre-Processing and Annotation\n","\n","Let's first preprocess and annotate the dataset with `edu-convokit`. The following section will:\n","- Read each file in the dataset and preprocess it using `edu-convokit`'s `Preprocessor`. We'll need to anonymize the names and merge the utterances by speaker.\n","- Then, annotate the file using `edu-convokit`'s `Annotator` for talktime, student reasoning and uptake.\n","- Finally, save the annotated file under `data/annotated/`.\n","\n","Let's get started!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ynNwS9H7V-cE"},"outputs":[],"source":["# First, let's create the replacement names for the teacher and student speakers\n","TEACHER_REPLACEMENT_NAMES = [\"[TEACHER]\"] * len(TEACHER_SPEAKER)\n","\n","# We will replace the student names with [STUDENT_0], [STUDENT_1], etc.\n","# This will approximately preserve the unique identity of each student, while also anonymizing them.\n","STUDENT_REPLACEMENT_NAMES = [f\"[STUDENT_{i}]\" for i in range(len(STUDENT_SPEAKER))]\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["9b4763d9c03c4ffca64ad682a5ae0f55","57ee5dd5d8ea4ad69bc1ab257bfd1813","67a4f0df25364ef498de81a379637412","7c674773bdb14a8b8981f1682a843bf0","4573417126674438887830f36a5d2b15","63d2f514465241fdbd4139db60578f05","366b9c430d264df2a7bd8ccf72fd7f02","082b744a84144fb29d7b140dde18b69d","4f6297a807e4464688e054bf661c3c15","8352d4b57c8b426ab8aefe54d6170376","cfa501518d5d4ea09758d33e3689d582","c8a7d3dbcf194abfb22538b9a620b2d2","e61d650fd3bf41d79f3408577967caad","9358bd9840ab4cb4aa7fefd82b65bddb","915036b052114b6a8cd78b17bdcf7d26","210052ece30648b5b04402f06b56c3a3","2785f28465ac47ada4ea86b560d68ede","7435e8d174f640078df20cfad45a9e2a","c62cf5556dd3410e892481e892b9184f","53a2f9c210894c538cfd8f9d64ac0e20","430601f6fcbb4f9c8d2921bf78b70e1e","ff8992e1e65e45e1bbf69020c5ce1ccf","d848d59514d54253a4d1e609436c603a","da84d4b341e947a3a9ed7aeeb129bb7e","94131245d1294befba8e6a9ba81b7a8d","499448698bb3464ca189dfdad49ba164","dd367d1589874a2d9d7db57e98ca7a88","0014799175bc430e9a25ca4b8ec682bd","4317e92e97044e029418e93f2172a49a","f4047591c0184f4baf84efb130831096","c51ea360d85b42e4ac35fc4154d8fb79","f9a621d46ca84430a55eeddb338f48e8","5b106cfbd4a8419390f2d010a914dfb4","66f02c19850546c59a69c4f2ecde17b5","9fc4abe082564145ae4ea544a20e4fe5","3659dff7761a46c9ab84cb07130e9037","b1dd339af58c4807b8fe2f199ddfa664","e4e469f9b49a4e2eac09a3039788933c","36c90fcd3dac48ce89ca47b2ae586f95","415c51f4458c498ab750fe080fe5a6cb","dc5520b56bd444f7b568eab23b85d9c8","c54b490bd2a64069ae141ef6852c09e6","f1b1738ea375490fa811c40a980e1228","06d2d509efb34c239fec3d8dc0bed8ae","d6fdf448dfb248cc9eb7654fda5a5c50","43fd023035984ee180f5599c7570f38d","9af5e5ac5d5a44998ace8cbe5a1ce8e6","57b158a2f4a94d6383f31faaf8361657","90d64e9e5be14fd79a64ebc2535a62a3","ab1368d7fef24cde93323212f8b961a6","61b61d88843341b48940e2cf7ef5e47a","431f2bcd27a94ddc99b9f083be783ed0","bec7663bd6ec462fbe6572fbf100d4f4","47f592dec0f24ef7b6c4b5e06362b9b4","1cbe3fe49064418eb0541b2eca24086f","dca60baf19364cdebfd5aed821cc0d07","19409eb01527439cab7f72103bf61ed7","9dd314b86fa04bdabb388b8c67404f80","fd9c06b823de435cbe785e3cf9e86023","fd83e472b11c4b34abd5f37101962520","aaee717f3e3e4de38c1053d147bed63e","4f438225124943d29a62cce0491fe238","7cf31e9ace8f4b6aad4dfaeaa8e2a273","422d8f1cdd1e4958b97a8bec8d0cd36f","f821914d858b47beb3ca939bf4bf89c6","fa125aa396bb49539248ba78b7827018","9856018958e342c2adc644a68e9ad997","b5606fc1c761472d959c3f26c0f7e059","374df26d1fc3476481e7063bb2046ab2","d4a6bc1ebe35412ebc615a7e0a12e174","a1ec7f2ef8004bc68ff11d20ea7e476d","a7f931732d9f4fd1ac64d36606487316","8ded43738a34414e8ea3760f1aaaee6d","06469507823f46f09b8ddfcebeda615e","fe5c73fabfcd41878c4f65738f6a321e","6dfbea73480a48eab4ceeb28a0b270d7","2ec35e33bdbb448d9beaf8fc5c679327","3738f536813b4aec87b7430694fcafb7","757a456e7a04408195b7b070834c304a","6e9dfc30b9da46ca909ed045b4292acf","c2385b00c83641cd91dd025937b015d4","f1cf797b228943569a67a4e9cffc6170","858582b165184c2a917bfec55bf01307","1c9b83c72adf4613b4a58c3cf10be5b0","0a980333095b4f0994629d84e7553d5f","24f7d01772ec488cad57cfe3f5146940","0733b24750804a1382c7e8ed64a7011a","d84127b4eb664ffbafcac142d01f423d","6ea551d5f14948a3a9a51f9ac7731556","98298ba7d9d144deb338ef979b0d1732","4ebc10335e8c4c4ca663f4fc6f6a5bc1","a5652eb34d5e47c7a2ee8437994fea06","2fd6d7e09a2b4c8ab462dadb7e89e1fa","35a9949fb8274e779a63a2468fd97371","ddf58472475841f5bf27413db13ea732","7750912eba2a404f9443b728ce840b23","d4ac8d92f28d46849bb1273fc440cceb","a493cbc8b0bd4f76954298eb2be5d85b","bd29562a9b32436c86000a1b6c95035e","d09822f5fd55424d8ee1135b854673ea","c026a9232e264cd8a0dce335e979a9d6","d8c3ece26866425cb52e2677c98a1a2f","a2d5ca78b959418d9ffdfb04bbbcdd48","ac5a57f770614ab4a7033c2536aa9f43","c9ce4a2004774b04b7ab93a99a8d2317","460fa36ba7994c6b936804bf00193216","5ac69ddad72f41e6894b79c2fd94d762","400b5518eb404e4b86c116855654498e","6d1546777c754bc6bae750cd5521ccc3","63c567abeb5e49658ed368da2968620c","dbaf178021cb4b78b16b2e4e5c575718","56feed4fa32149148032db716c962b34","c447442d742e4353b9cbb512638844cb","fcb640929dc44a80aaf9c33db8418a5e","41ac1d4ccc694abf8798d2e09057dcd1","dcfeff4e518445658e1165338481ddd8","47b2e643634645f9bee6742b3db6abcf","4d0a261e60504b3e8f3b0f4a7dbf2dc2","e561670a6d2848098247337737726e02","4e59d0eff683490083adc8c6a855a3b5","a9422b5598cf45f7a275aac9f2262e04"]},"executionInfo":{"elapsed":326273,"status":"ok","timestamp":1703937302564,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"wqRB6A3AV-cE","outputId":"3fad5115-bf67-4eaf-bc99-30d4cb2adb01"},"outputs":[{"name":"stderr","output_type":"stream","text":["\r  0%|          | 0/30 [00:00<?, ?it/s]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9b4763d9c03c4ffca64ad682a5ae0f55","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json:   0%|          | 0.00/429 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c8a7d3dbcf194abfb22538b9a620b2d2","version_major":2,"version_minor":0},"text/plain":["vocab.txt:   0%|          | 0.00/238k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d848d59514d54253a4d1e609436c603a","version_major":2,"version_minor":0},"text/plain":["special_tokens_map.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"66f02c19850546c59a69c4f2ecde17b5","version_major":2,"version_minor":0},"text/plain":["config.json:   0%|          | 0.00/778 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d6fdf448dfb248cc9eb7654fda5a5c50","version_major":2,"version_minor":0},"text/plain":["pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"dca60baf19364cdebfd5aed821cc0d07","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9856018958e342c2adc644a68e9ad997","version_major":2,"version_minor":0},"text/plain":["vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3738f536813b4aec87b7430694fcafb7","version_major":2,"version_minor":0},"text/plain":["tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6ea551d5f14948a3a9a51f9ac7731556","version_major":2,"version_minor":0},"text/plain":["config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d09822f5fd55424d8ee1135b854673ea","version_major":2,"version_minor":0},"text/plain":["config.json:   0%|          | 0.00/585 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"dbaf178021cb4b78b16b2e4e5c575718","version_major":2,"version_minor":0},"text/plain":["pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["\r  3%|▎         | 1/30 [00:30<14:37, 30.25s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n","  7%|▋         | 2/30 [00:39<08:28, 18.17s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 10%|█         | 3/30 [00:51<06:48, 15.13s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 13%|█▎        | 4/30 [01:05<06:22, 14.70s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 17%|█▋        | 5/30 [01:14<05:19, 12.79s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 20%|██        | 6/30 [01:20<04:10, 10.42s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 23%|██▎       | 7/30 [01:29<03:45,  9.78s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 27%|██▋       | 8/30 [01:35<03:12,  8.77s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 30%|███       | 9/30 [01:42<02:49,  8.10s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 33%|███▎      | 10/30 [01:49<02:32,  7.64s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 37%|███▋      | 11/30 [01:56<02:21,  7.46s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 40%|████      | 12/30 [02:02<02:06,  7.01s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 43%|████▎     | 13/30 [02:12<02:14,  7.93s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 47%|████▋     | 14/30 [02:21<02:15,  8.45s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 50%|█████     | 15/30 [02:32<02:17,  9.18s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 53%|█████▎    | 16/30 [02:41<02:08,  9.17s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 57%|█████▋    | 17/30 [02:54<02:11, 10.15s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 60%|██████    | 18/30 [02:58<01:41,  8.48s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 63%|██████▎   | 19/30 [03:05<01:28,  8.09s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 67%|██████▋   | 20/30 [03:21<01:43, 10.31s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 70%|███████   | 21/30 [03:26<01:18,  8.73s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 73%|███████▎  | 22/30 [03:33<01:05,  8.21s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 77%|███████▋  | 23/30 [04:31<02:42, 23.28s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 80%|████████  | 24/30 [04:39<01:51, 18.63s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 83%|████████▎ | 25/30 [04:52<01:23, 16.74s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 87%|████████▋ | 26/30 [04:59<00:56, 14.03s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 90%|█████████ | 27/30 [05:05<00:34, 11.56s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 93%|█████████▎| 28/30 [05:15<00:22, 11.21s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n"," 97%|█████████▋| 29/30 [05:20<00:09,  9.32s/it]WARNING:root:Note: This model was trained on student reasoning, so it should be used on student utterances.\n","    For more details on the model, see https://arxiv.org/pdf/2211.11772.pdf\n","WARNING:root:Note: This model was trained on teacher's uptake of student's utterances. So, speaker1 should be the student and speaker2 should be the teacher.\n","    For more details on the model, see https://arxiv.org/pdf/2106.03873.pdf\n","WARNING:root:Note: It's recommended that you merge utterances from the same speaker before running this model. You can do that with edu_convokit.text_preprocessing.merge_utterances_from_same_speaker.\n","100%|██████████| 30/30 [05:26<00:00, 10.87s/it]\n"]}],"source":["# Initialize the preprocessor and annotator\n","processor = TextPreprocessor()\n","annotator = Annotator()\n","\n","# This takes about 50 minutes on Colab, CPU\n","# Though this time varies depending on bandwidth\n","for filename in tqdm.tqdm(os.listdir(DATA_DIR)):\n","    if utils.is_valid_file_extension(filename):\n","        df = utils.load_data(os.path.join(DATA_DIR, filename))\n","\n","    # Preprocess the data. Let's anonymize the names in the speaker column.\n","    df = processor.anonymize_known_names(\n","        df=df,\n","        text_column=SPEAKER_COLUMN,\n","        # We're going to directly replace the text in the speaker column with the anonymized text.\n","        target_text_column=SPEAKER_COLUMN,\n","        names=TEACHER_SPEAKER + STUDENT_SPEAKER,\n","        replacement_names=TEACHER_REPLACEMENT_NAMES + STUDENT_REPLACEMENT_NAMES\n","    )\n","\n","    # Now let's anonymize the names in the text column.\n","    df = processor.anonymize_known_names(\n","        df=df,\n","        text_column=TEXT_COLUMN,\n","        target_text_column=TEXT_COLUMN,\n","        names=TEACHER_SPEAKER + STUDENT_SPEAKER,\n","        replacement_names=TEACHER_REPLACEMENT_NAMES + STUDENT_REPLACEMENT_NAMES\n","    )\n","\n","    # Now let's merge the utterances of the same speaker together and directly update the dataframe.\n","    df = processor.merge_utterances_from_same_speaker(\n","        df=df,\n","        text_column=TEXT_COLUMN,\n","        speaker_column=SPEAKER_COLUMN,\n","        target_text_column=TEXT_COLUMN\n","    )\n","\n","    # Now we're going to annotate the data.\n","    df = annotator.get_talktime(\n","        df=df,\n","        text_column=TEXT_COLUMN,\n","        output_column=TALK_TIME_COLUMN\n","    )\n","\n","    df = annotator.get_student_reasoning(\n","        df=df,\n","        text_column=TEXT_COLUMN,\n","        speaker_column=SPEAKER_COLUMN,\n","        output_column=STUDENT_REASONING_COLUMN,\n","        # We just want to annotate the student utterances. So we're going to specify the speaker value as STUDENT_SPEAKER.\n","        speaker_value=STUDENT_REPLACEMENT_NAMES\n","    )\n","\n","    df = annotator.get_uptake(\n","        df=df,\n","        text_column=TEXT_COLUMN,\n","        speaker_column=SPEAKER_COLUMN,\n","        output_column=UPTAKE_COLUMN,\n","        # We want to annotate the teacher's uptake of the student's utterances.\n","        # So we're looking for instances where the student first speaks, then the teacher speaks.\n","        speaker1=STUDENT_REPLACEMENT_NAMES,\n","        speaker2=TEACHER_REPLACEMENT_NAMES\n","    )\n","\n","    # And we're done! Let's now save the annotated data as a csv file.\n","    filename = filename.split(\".\")[0] + \".csv\"\n","    df.to_csv(os.path.join(ANNOTATIONS_DIR, filename), index=False)"]},{"cell_type":"markdown","metadata":{"id":"mXfkvZb3V-cE"},"source":["## Analysis\n","\n","Now that we have annotated the dataset, let's analyze it using `edu-convokit`'s `Analyzer`. We'll be doing the following:\n","- We'll use `QualitativeAnalyzer` to look at some examples of the talktime, student reasoning and uptake annotations.\n","- We'll use `QuantitativeAnalyzer` to look at the aggregate statistics of the talktime, student reasoning and uptake annotations.\n","- We'll use `LexicalAnalyzer` to compare the student and tutor's vocabulary.\n","- We'll use `TemporalAnalyzer` to look at the temporal trends of the talktime, student reasoning and uptake annotations.\n","\n","Let's get started!!!"]},{"cell_type":"markdown","metadata":{"id":"UbVTWaijV-cE"},"source":["## 🔍 Qualitative Analysis"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":296,"status":"ok","timestamp":1703937329060,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"FtrQmPUOV-cE","outputId":"532151bf-4ea5-4af6-bb6b-cef92812293e"},"outputs":[{"name":"stdout","output_type":"stream","text":["talktime: 54\n",">> [TEACHER]: Okay, that’s really great. Okay, you are really wonderful. We have about five more minutes where we can do some  problem solving [STUDENT_3] then put these things away. This is what  I would like you to do. I would like you to take a turn to  make a problem that will challenge your partner…\n","\n","talktime: 54\n",">> [TEACHER]: I'm wondering which is bigger, one half or two thirds. Now  before you model it you might think in your head, before you begin  to model it what you is bigger [STUDENT_3] if so, if one is bigger, by how  much. Why don’t you work with your partner [STUDENT_3] see what you can  do.\n","\n","talktime: 54\n",">> [TEACHER]: Let me write this  down. This, what you are saying here is so important, here. Let me  see if I can write this down. You're saying that you're calling the red,  you're giving red the number name, right? The length of the red,  right? We'll give it the number name, what did you say?\n","\n","talktime: 4\n",">> [STUDENT_15]: I made a problem.\n","\n","talktime: 4\n",">> [STUDENT_15]: You don’t know mine.\n","\n","talktime: 4\n",">> [TEACHER]: What would be what?\n","\n","talktime: 6\n",">> [TEACHER]: And ask your partner that problem,\n","\n","talktime: 6\n",">> [TEACHER] [TEACHER]: This looks interesting. Are you experimenting?\n","\n","talktime: 6\n",">> [TEACHER] [TEACHER]: You have to make it convincing.\n","\n"]}],"source":["# We're going to look at examples from the entire dataset.\n","qualitative_analyzer = QualitativeAnalyzer(data_dir=ANNOTATIONS_DIR)\n","\n","# Examples of talktime. Will show random examples from the dataset.\n","qualitative_analyzer.print_examples(\n","    speaker_column=SPEAKER_COLUMN,\n","    text_column=TEXT_COLUMN,\n","    feature_column=TALK_TIME_COLUMN,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1703937329492,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"VGrnPunzV-cE","outputId":"ddbddb92-f4f7-4cca-96ed-a4dd8f5f8502"},"outputs":[{"name":"stdout","output_type":"stream","text":["student_reasoning: 1.0\n",">> [STUDENT_29]: [Puts three light green rods on top of the blue rod]  There,  that would, if you look down it would equal up to a blue.\n","\n","student_reasoning: 1.0\n",">> [STUDENT_15]: And I know that that’s half of [He points to the orange rod],  [STUDENT_3] I know that yellow is half of orange, which is ten.\n","\n","student_reasoning: 1.0\n",">> [STUDENT_1]: one half by one sixth. Cause if you put six ones up to a whole\n","\n"]}],"source":["# Examples of student reasoning. Let's look at positive examples:\n","qualitative_analyzer.print_examples(\n","    speaker_column=SPEAKER_COLUMN,\n","    text_column=TEXT_COLUMN,\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    feature_value=1.0,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1,"status":"ok","timestamp":1703937330005,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"I8X0orSvV-cE","outputId":"24c53dc9-6fa5-4b47-d811-3ea0230be63a"},"outputs":[{"name":"stdout","output_type":"stream","text":["student_reasoning: 0.0\n",">> [STUDENT_15]: You just don’t know the problem I just made up. You don’t  know the problem I made up.\n","\n","student_reasoning: 0.0\n",">> [STUDENT_15]: I have a problem already. I have a problem. I have a  problem, remember it? If a light green, um, no, no.\n","\n","student_reasoning: 0.0\n",">> [STUDENT_15]: I have one for you, too. [[TEACHER] [TEACHER] walks over]  I have a  problem.\n","\n"]}],"source":["# We can also look at negative examples:\n","qualitative_analyzer.print_examples(\n","    speaker_column=SPEAKER_COLUMN,\n","    text_column=TEXT_COLUMN,\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    feature_value=0.0,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":192,"status":"ok","timestamp":1703937330392,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"guK26yJRV-cE","outputId":"0124d7f9-b4a7-4d5f-f72d-885cc8633889"},"outputs":[{"name":"stdout","output_type":"stream","text":["uptake: 1.0\n","[STUDENT_15]: If a light green was one third, what would be a whole?\n",">> [TEACHER]: What would be what?\n","\n","uptake: 1.0\n","[STUDENT_15]: If a light green was one third, what would be a whole?\n",">> [TEACHER]: One, what would one be.\n","\n","uptake: 1.0\n","[STUDENT_29]: [Puts three light green rods on top of the blue rod]  There,  that would, if you look down it would equal up to a blue.\n",">> [TEACHER]: Hold on, I’m a little confused. Tell me again. Six ones? You called  this one? What are you calling these?\n","\n"]}],"source":["# Examples of uptake.\n","qualitative_analyzer.print_examples(\n","    speaker_column=SPEAKER_COLUMN,\n","    text_column=TEXT_COLUMN,\n","    feature_column=UPTAKE_COLUMN,\n","    # I want to look at positive examples of uptake (uptake = 1.0)\n","    feature_value=1.0,\n","    # ... and look at the previous student utterance (show_k_previous_lines = 1).\n","    # This is interesting because it will show us how the teacher is responding to the student's utterance.\n","    show_k_previous_lines=1,\n",")"]},{"cell_type":"markdown","metadata":{"id":"as3Ho8MNV-cE"},"source":["## 📊 Quantitative Analysis"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":2053,"status":"ok","timestamp":1703937741538,"user":{"displayName":"Rose 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","text/plain":["<Figure size 3350x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["talktime\n","\n","Proportion statistics\n","                                       count      mean       std       min       25%       50%       75%       max\n","speaker                                                                                                           \n","[STUDENT_0]                              3.0  0.082532  0.055057  0.038502  0.051667  0.064833  0.104548  0.144262\n","[STUDENT_10]                             1.0  0.052941       NaN  0.052941  0.052941  0.052941  0.052941  0.052941\n","[STUDENT_11]                             2.0  0.050219  0.010940  0.042484  0.046351  0.050219  0.054087  0.057955\n","[STUDENT_12]                             6.0  0.103688  0.088372  0.004112  0.022533  0.114360  0.180103  0.195356\n","[STUDENT_14]                             5.0  0.006621  0.006594  0.000435  0.000936  0.004612  0.012336  0.014786\n","[STUDENT_15]                            11.0  0.149584  0.111150  0.002649  0.083878  0.131579  0.187356  0.379571\n","[STUDENT_15] [STUDENT_3] [STUDENT_12]    1.0  0.000801       NaN  0.000801  0.000801  0.000801  0.000801  0.000801\n","[STUDENT_16]                             2.0  0.013095  0.004014  0.010256  0.011676  0.013095  0.014514  0.015933\n","[STUDENT_17]                             3.0  0.016949  0.017947  0.001677  0.007066  0.012454  0.024586  0.036717\n","[STUDENT_18]                             1.0  0.000419       NaN  0.000419  0.000419  0.000419  0.000419  0.000419\n","[STUDENT_19]                            13.0  0.158800  0.186038  0.004929  0.051454  0.079588  0.146341  0.584345\n","[STUDENT_1]                              6.0  0.144681  0.167141  0.013691  0.025632  0.059223  0.273155  0.377049\n","[STUDENT_1] [STUDENT_3] [STUDENT_0]      1.0  0.005464       NaN  0.005464  0.005464  0.005464  0.005464  0.005464\n","[STUDENT_20]                             1.0  0.040346       NaN  0.040346  0.040346  0.040346  0.040346  0.040346\n","[STUDENT_21]                             2.0  0.170696  0.017246  0.158501  0.164599  0.170696  0.176794  0.182891\n","[STUDENT_22]                             6.0  0.085113  0.077821  0.044715  0.045282  0.050777  0.072001  0.241883\n","[STUDENT_23]                             2.0  0.087668  0.080432  0.030794  0.059231  0.087668  0.116106  0.144543\n","[STUDENT_24]                             4.0  0.090441  0.077632  0.031746  0.048115  0.062901  0.105227  0.204214\n","[STUDENT_25]                             2.0  0.161549  0.145949  0.058347  0.109948  0.161549  0.213149  0.264750\n","[STUDENT_26]                             1.0  0.011345       NaN  0.011345  0.011345  0.011345  0.011345  0.011345\n","[STUDENT_27]                             3.0  0.076598  0.056412  0.030794  0.045092  0.059390  0.099500  0.139610\n","[STUDENT_28]                             3.0  0.092144  0.015845  0.077796  0.083641  0.089485  0.099317  0.109149\n","[STUDENT_29]                             5.0  0.126505  0.055537  0.042045  0.104058  0.142857  0.158890  0.184676\n","[STUDENT_2]                              3.0  0.009829  0.007439  0.002186  0.006221  0.010256  0.013651  0.017045\n","[STUDENT_30]                             1.0  0.064877       NaN  0.064877  0.064877  0.064877  0.064877  0.064877\n","[STUDENT_31]                             4.0  0.066166  0.090450  0.002237  0.018617  0.031213  0.078761  0.200000\n","[STUDENT_32]                             3.0  0.089839  0.021829  0.077236  0.077236  0.077236  0.096140  0.115044\n","[STUDENT_33]                             2.0  0.069106  0.000000  0.069106  0.069106  0.069106  0.069106  0.069106\n","[STUDENT_34]                             2.0  0.023347  0.029145  0.002738  0.013043  0.023347  0.033652  0.043956\n","[STUDENT_35]                             3.0  0.054487  0.073891  0.001965  0.012241  0.022517  0.080748  0.138980\n","[STUDENT_36]                             1.0  0.011364       NaN  0.011364  0.011364  0.011364  0.011364  0.011364\n","[STUDENT_37]                             2.0  0.055127  0.010618  0.047619  0.051373  0.055127  0.058881  0.062635\n","[STUDENT_38]                             1.0  0.012500       NaN  0.012500  0.012500  0.012500  0.012500  0.012500\n","[STUDENT_39]                             1.0  0.005682       NaN  0.005682  0.005682  0.005682  0.005682  0.005682\n","[STUDENT_40]                             1.0  0.021669       NaN  0.021669  0.021669  0.021669  0.021669  0.021669\n","[STUDENT_41]                             1.0  0.048957       NaN  0.048957  0.048957  0.048957  0.048957  0.048957\n","[STUDENT_42]                             1.0  0.047352       NaN  0.047352  0.047352  0.047352  0.047352  0.047352\n","[STUDENT_43]                             2.0  0.026003  0.016571  0.014286  0.020144  0.026003  0.031862  0.037721\n","[STUDENT_44]                             1.0  0.132927       NaN  0.132927  0.132927  0.132927  0.132927  0.132927\n","[STUDENT_45]                             1.0  0.094036       NaN  0.094036  0.094036  0.094036  0.094036  0.094036\n","[STUDENT_45] & [STUDENT_44]              1.0  0.000145       NaN  0.000145  0.000145  0.000145  0.000145  0.000145\n","[STUDENT_45] & [STUDENT_46]              1.0  0.000871       NaN  0.000871  0.000871  0.000871  0.000871  0.000871\n","[STUDENT_46]                             1.0  0.133507       NaN  0.133507  0.133507  0.133507  0.133507  0.133507\n","[STUDENT_46],                            1.0  0.001016       NaN  0.001016  0.001016  0.001016  0.001016  0.001016\n","[STUDENT_47]                             1.0  0.193441       NaN  0.193441  0.193441  0.193441  0.193441  0.193441\n","[STUDENT_47] & [STUDENT_44]              1.0  0.000726       NaN  0.000726  0.000726  0.000726  0.000726  0.000726\n","[STUDENT_4]                              4.0  0.092072  0.110127  0.025918  0.028582  0.043212  0.106702  0.255947\n","[STUDENT_50]                             1.0  0.000145       NaN  0.000145  0.000145  0.000145  0.000145  0.000145\n","[STUDENT_53]                             1.0  0.000871       NaN  0.000871  0.000871  0.000871  0.000871  0.000871\n","[STUDENT_55]                             1.0  0.001887       NaN  0.001887  0.001887  0.001887  0.001887  0.001887\n","[STUDENT_56]                             1.0  0.735250       NaN  0.735250  0.735250  0.735250  0.735250  0.735250\n","[STUDENT_59]                             1.0  0.007937       NaN  0.007937  0.007937  0.007937  0.007937  0.007937\n","[STUDENT_5]                              1.0  0.019981       NaN  0.019981  0.019981  0.019981  0.019981  0.019981\n","[STUDENT_60]                             1.0  0.004762       NaN  0.004762  0.004762  0.004762  0.004762  0.004762\n","[STUDENT_61]                             1.0  0.004762       NaN  0.004762  0.004762  0.004762  0.004762  0.004762\n","[STUDENT_62]                             2.0  0.137668  0.115241  0.056180  0.096924  0.137668  0.178412  0.219156\n","[STUDENT_6]                              8.0  0.041322  0.042725  0.002649  0.009801  0.029687  0.059497  0.130818\n","[STUDENT_7]                              1.0  0.021569       NaN  0.021569  0.021569  0.021569  0.021569  0.021569\n","[STUDENT_8]                              2.0  0.004774  0.000643  0.004320  0.004547  0.004774  0.005001  0.005229\n","[STUDENT_9]                              6.0  0.047136  0.046222  0.005298  0.014495  0.040520  0.054591  0.131373\n","[STUDENT_9] [STUDENT_3] [STUDENT_10]     1.0  0.026797       NaN  0.026797  0.026797  0.026797  0.026797  0.026797\n","[TEACHER]                               27.0  0.577200  0.194013  0.252459  0.448436  0.586710  0.700936  0.936508\n","[TEACHER] [TEACHER]                      2.0  0.206389  0.017239  0.194199  0.200294  0.206389  0.212484  0.218579\n","[TEACHER]/R1                             2.0  0.722468  0.036595  0.696591  0.709529  0.722468  0.735406  0.748344\n","~23                                      1.0  0.002612       NaN  0.002612  0.002612  0.002612  0.002612  0.002612\n"]},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["quantitative_analyzer = QuantitativeAnalyzer(data_dir=ANNOTATIONS_DIR)\n","\n","# Let's create a speaker mapping to shorten the speaker names. Teacher -> T, Student{i} -> S{i}\n","speaker_mapping = {\n","    **{_: \"T\" for _ in TEACHER_REPLACEMENT_NAMES},\n","    **{_: f\"S{i}\" for i, _ in enumerate(STUDENT_REPLACEMENT_NAMES)}\n","}\n","\n","# For figure formatting because there are a lot of students\n","import matplotlib.pyplot as plt\n","plt.figure(figsize=(len(TEACHER_REPLACEMENT_NAMES + STUDENT_REPLACEMENT_NAMES)/2, 6))\n","\n","# Let's plot the talk time ratio between the speakers.\n","quantitative_analyzer.plot_statistics(\n","    feature_column=TALK_TIME_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    # Proportion of talk time for each speaker.\n","    value_as=\"prop\",\n","    label_mapping=speaker_mapping\n",")\n","\n","# We can also print the statistics:\n","quantitative_analyzer.print_statistics(\n","    feature_column=TALK_TIME_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    # Proportion of talk time for each speaker.\n","    value_as=\"prop\"\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":1820,"status":"ok","timestamp":1703937775548,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"kn8Kxif3V-cE","outputId":"8a0e669b-22de-42bd-c77c-b420afca9964"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"image/png":"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size 3350x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["student_reasoning\n","\n","Average statistics\n","                                       count      mean       std       min       25%       50%       75%       max\n","speaker                                                                                                           \n","[STUDENT_0]                              3.0  0.714286  0.494872  0.142857  0.571429  1.000000  1.000000  1.000000\n","[STUDENT_10]                             1.0  0.333333       NaN  0.333333  0.333333  0.333333  0.333333  0.333333\n","[STUDENT_11]                             2.0  0.250000  0.353553  0.000000  0.125000  0.250000  0.375000  0.500000\n","[STUDENT_12]                             5.0  0.466667  0.361325  0.000000  0.333333  0.500000  0.500000  1.000000\n","[STUDENT_14]                             0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_15]                             9.0  0.373016  0.363437  0.000000  0.071429  0.285714  0.500000  1.000000\n","[STUDENT_15] [STUDENT_3] [STUDENT_12]    0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_16]                             2.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000\n","[STUDENT_17]                             2.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000\n","[STUDENT_18]                             0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_19]                            12.0  0.250000  0.349964  0.000000  0.000000  0.000000  0.437500  1.000000\n","[STUDENT_1]                              6.0  0.352381  0.377063  0.000000  0.053571  0.307143  0.475000  1.000000\n","[STUDENT_1] [STUDENT_3] [STUDENT_0]      0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_20]                             1.0  0.000000       NaN  0.000000  0.000000  0.000000  0.000000  0.000000\n","[STUDENT_21]                             2.0  0.750000  0.353553  0.500000  0.625000  0.750000  0.875000  1.000000\n","[STUDENT_22]                             6.0  0.222222  0.403687  0.000000  0.000000  0.000000  0.250000  1.000000\n","[STUDENT_23]                             2.0  0.500000  0.707107  0.000000  0.250000  0.500000  0.750000  1.000000\n","[STUDENT_24]                             4.0  0.562500  0.515388  0.000000  0.187500  0.625000  1.000000  1.000000\n","[STUDENT_25]                             2.0  0.700000  0.424264  0.400000  0.550000  0.700000  0.850000  1.000000\n","[STUDENT_26]                             0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_27]                             3.0  0.666667  0.288675  0.500000  0.500000  0.500000  0.750000  1.000000\n","[STUDENT_28]                             3.0  0.833333  0.288675  0.500000  0.750000  1.000000  1.000000  1.000000\n","[STUDENT_29]                             5.0  0.428182  0.445552  0.000000  0.090909  0.250000  0.800000  1.000000\n","[STUDENT_2]                              2.0  0.500000  0.707107  0.000000  0.250000  0.500000  0.750000  1.000000\n","[STUDENT_30]                             1.0  0.000000       NaN  0.000000  0.000000  0.000000  0.000000  0.000000\n","[STUDENT_31]                             3.0  0.277778  0.254588  0.000000  0.166667  0.333333  0.416667  0.500000\n","[STUDENT_32]                             3.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000\n","[STUDENT_33]                             2.0  1.000000  0.000000  1.000000  1.000000  1.000000  1.000000  1.000000\n","[STUDENT_34]                             1.0  1.000000       NaN  1.000000  1.000000  1.000000  1.000000  1.000000\n","[STUDENT_35]                             2.0  0.392857  0.151523  0.285714  0.339286  0.392857  0.446429  0.500000\n","[STUDENT_36]                             0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_37]                             2.0  0.833333  0.235702  0.666667  0.750000  0.833333  0.916667  1.000000\n","[STUDENT_38]                             0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_39]                             0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_40]                             1.0  0.000000       NaN  0.000000  0.000000  0.000000  0.000000  0.000000\n","[STUDENT_41]                             1.0  0.333333       NaN  0.333333  0.333333  0.333333  0.333333  0.333333\n","[STUDENT_42]                             1.0  1.000000       NaN  1.000000  1.000000  1.000000  1.000000  1.000000\n","[STUDENT_43]                             2.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000\n","[STUDENT_44]                             1.0  0.146341       NaN  0.146341  0.146341  0.146341  0.146341  0.146341\n","[STUDENT_45]                             1.0  0.189189       NaN  0.189189  0.189189  0.189189  0.189189  0.189189\n","[STUDENT_45] & [STUDENT_44]              0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_45] & [STUDENT_46]              0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_46]                             1.0  0.153846       NaN  0.153846  0.153846  0.153846  0.153846  0.153846\n","[STUDENT_46],                            0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_47]                             1.0  0.096154       NaN  0.096154  0.096154  0.096154  0.096154  0.096154\n","[STUDENT_47] & [STUDENT_44]              0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_4]                              4.0  0.221154  0.259675  0.000000  0.000000  0.192308  0.413462  0.500000\n","[STUDENT_50]                             0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_53]                             0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_55]                             0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_56]                             1.0  0.000000       NaN  0.000000  0.000000  0.000000  0.000000  0.000000\n","[STUDENT_59]                             0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_5]                              1.0  0.000000       NaN  0.000000  0.000000  0.000000  0.000000  0.000000\n","[STUDENT_60]                             0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_61]                             0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_62]                             2.0  0.250000  0.353553  0.000000  0.125000  0.250000  0.375000  0.500000\n","[STUDENT_6]                              6.0  0.472222  0.452360  0.000000  0.083333  0.416667  0.875000  1.000000\n","[STUDENT_7]                              1.0  0.000000       NaN  0.000000  0.000000  0.000000  0.000000  0.000000\n","[STUDENT_8]                              0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[STUDENT_9]                              6.0  0.305556  0.400231  0.000000  0.000000  0.166667  0.458333  1.000000\n","[STUDENT_9] [STUDENT_3] [STUDENT_10]     0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[TEACHER]                                0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[TEACHER] [TEACHER]                      0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","[TEACHER]/R1                             0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","~23                                      0.0       NaN       NaN       NaN       NaN       NaN       NaN       NaN\n","\n","\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["\n","# For figure formatting because there are a lot of students\n","import matplotlib.pyplot as plt\n","plt.figure(figsize=(len(TEACHER_REPLACEMENT_NAMES + STUDENT_REPLACEMENT_NAMES)/2, 6))\n","\n","# What about the student reasoning? How often does the student use reasoning?\n","quantitative_analyzer.plot_statistics(\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    # We change this to \"avg\" because we're now looking at within-speaker statistics.\n","    value_as=\"avg\",\n","    # We can set the y-axis limits to [0, 1] because the student reasoning column is a binary column.\n","    yrange=(0, 1),\n","    label_mapping=speaker_mapping\n",")\n","\n","# We can also print the statistics:\n","quantitative_analyzer.print_statistics(\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\"\n",")"]},{"cell_type":"markdown","metadata":{"id":"raKKayZaV-cF"},"source":["Note, the tutor has no `student_reasoning` because we did not annotate the tutor's utterances for student reasoning.\n","We can easily remove the tutor from the plot by dropping na values:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":354},"executionInfo":{"elapsed":1440,"status":"ok","timestamp":1703937787994,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"gVqMKLBBV-cF","outputId":"88e06990-866e-4a21-bcbc-6b664e0571e8"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n","/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/quantitative_analyzer.py:51: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / 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size 3350x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["\n","# For figure formatting because there are a lot of students\n","import matplotlib.pyplot as plt\n","plt.figure(figsize=(len(TEACHER_REPLACEMENT_NAMES + STUDENT_REPLACEMENT_NAMES)/2, 6))\n","\n","quantitative_analyzer.plot_statistics(\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\",\n","    yrange=(0, 1),\n","    dropna=True,\n","    label_mapping=speaker_mapping\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":484},"executionInfo":{"elapsed":430,"status":"ok","timestamp":1703937795198,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"1De0gvSvV-cF","outputId":"13f4a8ad-88d6-46cb-abef-fa3c239142f7"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Finally, let's look at the tutor's uptake of the student's utterances.\n","quantitative_analyzer.plot_statistics(\n","    feature_column=UPTAKE_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\",\n","    yrange=(0, 1),\n","    dropna=True\n",")"]},{"cell_type":"markdown","metadata":{"id":"6ns8Bau4V-cF"},"source":["## 💬 Lexical Analysis"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4127,"status":"ok","timestamp":1703937804816,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"Y3pX55STV-cF","outputId":"a0b958e7-5ea5-4f60-8634-c20494a3804c"},"outputs":[{"name":"stdout","output_type":"stream","text":["Top Words By Speaker\n","[TEACHER]\n","student: 695\n","one: 343\n","okay: 229\n","think: 170\n","would: 134\n","red: 101\n","right: 91\n","want: 87\n","two: 83\n","see: 82\n","\n","\n","[STUDENT_15]\n","one: 75\n","student: 58\n","two: 34\n","green: 23\n","yeah: 21\n","would: 20\n","half: 20\n","rod: 20\n","three: 20\n","call: 20\n","\n","\n","[TEACHER] [TEACHER]\n","one: 13\n","two: 10\n","okay: 9\n","student: 9\n","let: 8\n","interesting: 6\n","see: 5\n","twelfths: 4\n","hear: 3\n","clever: 3\n","\n","\n","[STUDENT_29]\n","one: 28\n","would: 16\n","student: 12\n","green: 11\n","rod: 10\n","three: 9\n","considered: 9\n","thirds: 9\n","red: 7\n","fifth: 6\n","\n","\n","[STUDENT_0]\n","one: 12\n","would: 8\n","two: 6\n","third: 4\n","student: 4\n","put: 4\n","green: 4\n","purples: 3\n","three: 3\n","yeah: 3\n","\n","\n","[STUDENT_1]\n","student: 43\n","two: 33\n","one: 30\n","put: 17\n","rod: 14\n","rods: 13\n","take: 11\n","bigger: 11\n","orange: 10\n","yeah: 10\n","\n","\n","[STUDENT_2]\n","one: 4\n","whole: 2\n","third: 2\n","half: 1\n","green: 1\n","would: 1\n","blue: 1\n","student: 1\n","put: 1\n","three: 1\n","\n","\n","[STUDENT_1] [STUDENT_3] [STUDENT_0]\n","well: 1\n","bigger: 1\n","one: 1\n","\n","\n","[STUDENT_14]\n","yeah: 8\n","one: 6\n","sixth: 5\n","two: 4\n","yes: 3\n","hmm: 2\n","thirds: 2\n","twelfths: 2\n","red: 1\n","well: 1\n","\n","\n","[STUDENT_6]\n","one: 20\n","student: 16\n","bigger: 10\n","would: 10\n","two: 8\n","like: 7\n","red: 7\n","yeah: 6\n","sixth: 6\n","equal: 5\n","\n","\n","[STUDENT_16]\n","bigger: 2\n","boat: 2\n","children: 2\n","well: 1\n","thinking: 1\n","sizes: 1\n","made: 1\n","sea: 1\n","monster: 1\n","biggest: 1\n","\n","\n","[STUDENT_17]\n","rod: 5\n","orange: 4\n","red: 2\n","well: 1\n","fit: 1\n","would: 1\n","take: 1\n","white: 1\n","student: 1\n","stick: 1\n","\n","\n","[STUDENT_18]\n","yes: 1\n","\n","\n","[STUDENT_7]\n","student: 2\n","one: 2\n","yeah: 1\n","working: 1\n","write: 1\n","example: 1\n","see: 1\n","bigger: 1\n","half: 1\n","third: 1\n","\n","\n","[STUDENT_8]\n","one: 3\n","half: 2\n","third: 1\n","red: 1\n","\n","\n","[STUDENT_9]\n","one: 30\n","student: 27\n","third: 9\n","half: 9\n","would: 8\n","rod: 7\n","green: 7\n","purple: 6\n","red: 6\n","think: 6\n","\n","\n","[STUDENT_10]\n","saw: 4\n","student: 4\n","half: 3\n","third: 3\n","two: 3\n","agree: 2\n","bigger: 2\n","comes: 1\n","overhead: 1\n","places: 1\n","\n","\n","[STUDENT_11]\n","student: 5\n","well: 3\n","red: 3\n","like: 3\n","one: 3\n","third: 2\n","put: 2\n","tallest: 2\n","black: 2\n","green: 2\n","\n","\n","[STUDENT_12]\n","one: 34\n","student: 27\n","well: 22\n","half: 22\n","like: 22\n","two: 16\n","sixth: 15\n","split: 12\n","three: 11\n","third: 10\n","\n","\n","[STUDENT_9] [STUDENT_3] [STUDENT_10]\n","one: 3\n","three: 2\n","girls: 1\n","agreeing: 1\n","demonstrates: 1\n","said: 1\n","yeah: 1\n","put: 1\n","reds: 1\n","green: 1\n","\n","\n","[STUDENT_34]\n","student: 5\n","brown: 4\n","took: 4\n","purple: 4\n","tried: 2\n","another: 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23\n","red: 22\n","one: 18\n","teacher: 15\n","yeah: 15\n","different: 9\n","know: 8\n","see: 8\n","make: 7\n","\n","\n","[STUDENT_45]\n","teacher: 28\n","would: 20\n","student: 15\n","towers: 9\n","see: 8\n","high: 8\n","like: 7\n","add: 6\n","make: 5\n","keep: 5\n","\n","\n","[STUDENT_46]\n","student: 27\n","teacher: 19\n","blue: 19\n","put: 19\n","red: 18\n","one: 16\n","could: 15\n","see: 15\n","top: 12\n","yeah: 9\n","\n","\n","[STUDENT_47]\n","blue: 48\n","student: 47\n","red: 27\n","one: 22\n","okay: 20\n","like: 17\n","right: 15\n","could: 14\n","yeah: 13\n","teacher: 13\n","\n","\n","[STUDENT_47] & [STUDENT_44]\n","towers: 1\n","yeah: 1\n","\n","\n","[STUDENT_45] & [STUDENT_46]\n","teacher: 1\n","colors: 1\n","\n","\n","[STUDENT_50]\n","teacher: 1\n","\n","\n","[STUDENT_46],\n","draw: 1\n","think: 1\n","\n","\n","~23\n","student: 3\n","easier: 1\n","maybe: 1\n","like: 1\n","shelly: 1\n","pattern: 1\n","put: 1\n","different: 1\n","category: 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1\n","\n","\n","[STUDENT_30]\n","candies: 2\n","cindy: 2\n","reading: 1\n","valerie: 1\n","shares: 1\n","box: 1\n","gave: 1\n","student: 1\n","candy: 1\n","every: 1\n","\n","\n","[TEACHER]/R1\n","student: 66\n","one: 42\n","number: 34\n","two: 32\n","name: 30\n","think: 23\n","names: 19\n","candy: 17\n","bar: 16\n","agree: 15\n","\n","\n","[STUDENT_38]\n","one: 2\n","third: 2\n","tentatively: 1\n","yes: 1\n","mmm: 1\n","hmm: 1\n","\n","\n","[STUDENT_39]\n","yeah: 1\n","dark: 1\n","blue: 1\n","\n","\n","[STUDENT_20]\n","problem: 2\n","think: 1\n","person: 1\n","answered: 1\n","read: 1\n","whole: 1\n","\n","\n","[STUDENT_15] [STUDENT_3] [STUDENT_12]\n","yeah: 1\n","\n","\n","\n"]}],"source":["lexical_analyzer = LexicalAnalyzer(data_dir=ANNOTATIONS_DIR)\n","\n","# Cast as str\n","lexical_analyzer._df[TEXT_COLUMN] = lexical_analyzer._df[TEXT_COLUMN].astype(str)\n","\n","# Let's look at the most common words per speaker in the dataset.\n","lexical_analyzer.print_word_frequency(\n","    text_column=TEXT_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    # We want to look at the top 10 words per speaker.\n","    topk=10,\n","    # Let's also format the text (e.g., remove punctuation, lowercase the text, etc.)\n","    run_text_formatting=True\n",")\n"]},{"cell_type":"markdown","metadata":{"id":"eRTIjn9CV-cF"},"source":["It's a bit hard to see how the student and teacher's vocabulary compare. Let's run a log-odds analysis to see which words are more likely to be used by the student or teacher."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":516},"executionInfo":{"elapsed":8619,"status":"ok","timestamp":1703937816319,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"cTCSBWB1V-cF","outputId":"d49800bf-f02d-4b82-b23a-9f75accf18af"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 600x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# This returns the merged dataframe of the annotated files in DATA_DIR.\n","df = lexical_analyzer.get_df()\n","\n","# We want to create two groups of df: one for the student and one for the tutor.\n","student_df = df[df[SPEAKER_COLUMN].isin(STUDENT_REPLACEMENT_NAMES)]\n","teacher_df = df[df[SPEAKER_COLUMN].isin(TEACHER_REPLACEMENT_NAMES)]\n","\n","# Now we can run the log-odds analysis:\n","lexical_analyzer.plot_log_odds(\n","    df1=student_df,\n","    df2=teacher_df,\n","    text_column1=TEXT_COLUMN,\n","    text_column2=TEXT_COLUMN,\n","    # Let's name the df groups to show on the plot\n","    group1_name=\"Student\",\n","    group2_name=\"Teacher\",\n","    # Let's also run the text formatting\n","    run_text_formatting=True,\n",")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":901},"executionInfo":{"elapsed":5791,"status":"ok","timestamp":1703937827022,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"C6HEqYfoV-cF","outputId":"ebb2b14d-3a51-4e07-ec27-6b660e93ea46"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 600x1000 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# We might also be interested in other n-grams. Let's look at the top 10 bigrams per speaker.\n","lexical_analyzer.plot_log_odds(\n","    df1=student_df,\n","    df2=teacher_df,\n","    text_column1=TEXT_COLUMN,\n","    text_column2=TEXT_COLUMN,\n","    group1_name=\"Student\",\n","    group2_name=\"Teacher\",\n","    run_text_formatting=True,\n","    # n-grams:\n","    run_ngrams=True,\n","    n=2,\n","    topk=10,\n","    logodds_factor=0.5\n",")"]},{"cell_type":"markdown","metadata":{"id":"_G8YeqhKV-cF"},"source":["## 📈 Temporal Analysis\n","\n","Let's look at the temporal trends of the talktime, student reasoning and uptake annotations!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":477},"executionInfo":{"elapsed":1782,"status":"ok","timestamp":1703938012315,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"EhOdXcIqV-cF","outputId":"f05da105-5486-49ea-ef93-b54157a4a61e"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Cutting max num transcripts to 3 to show speakers\n","temporal_analyzer = TemporalAnalyzer(data_dir=ANNOTATIONS_DIR, max_transcripts=3)\n","\n","# First let's look at the talk time ratio between the speakers over time.\n","temporal_analyzer.plot_temporal_statistics(\n","    feature_column=TALK_TIME_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"prop\",\n","    # Let's create 10 bins for the x-axis.\n","    num_bins=10,\n","    label_mapping=speaker_mapping\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":532},"executionInfo":{"elapsed":1914,"status":"ok","timestamp":1703938036226,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"tTjBlmJGV-cF","outputId":"a35fefbc-52b2-4289-f322-ecde8d627750"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/temporal_analyzer.py:57: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Now student reasoning over time.\n","temporal_analyzer.plot_temporal_statistics(\n","    feature_column=STUDENT_REASONING_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\",\n","    # Let's create 5 bins for the x-axis.\n","    num_bins=10,\n","    label_mapping=speaker_mapping\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":532},"executionInfo":{"elapsed":896,"status":"ok","timestamp":1703938042303,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"_cWE8v-HV-cI","outputId":"02c3b1c1-b8af-48dc-8751-d38126301972"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/edu_convokit/analyzers/temporal_analyzer.py:57: RuntimeWarning: invalid value encountered in double_scalars\n","  f\"prop_speaker_{feature_column}\": speaker_df[feature_column].sum() / feature_sum,\n"]},{"data":{"image/png":"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size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["<Figure size 640x480 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Finally, let's look at the tutor's uptake of the student's utterances over time.\n","temporal_analyzer.plot_temporal_statistics(\n","    feature_column=UPTAKE_COLUMN,\n","    speaker_column=SPEAKER_COLUMN,\n","    value_as=\"avg\",\n","    # Let's create 5 bins for the x-axis.\n","    num_bins=10,\n","    label_mapping=speaker_mapping\n",")"]},{"cell_type":"markdown","metadata":{"id":"fhjxVEuLV-cI"},"source":["## 📚 Conclusions and Next Steps\n","\n","Great! From this tutorial, we learned how to use `edu-convokit` to preprocess, annotate and analyze the Amber dataset.\n","We saw how very simple principles built into `edu-convokit` can be used to analyze the dataset and gain insights into the data from various perspectives (qualitative, quantitative, lexical and temporal).\n","\n","Other resources you can check out include:\n","- [Tutorial on edu-convokit for the NCTE dataset](https://colab.research.google.com/drive/1k3fn6uY4QRMtPUZN6hpMd6o-0g7fYotg)\n","- [Tutorial on edu-convokit for the Amber dataset](https://colab.research.google.com/drive/1Q3anUPcemMils4cz2gwEwDdKCjEdm6T9)\n","- [`edu-convokit` documentation](https://edu-convokit.readthedocs.io/en/latest/index.html)\n","- [`edu-convokit` GitHub repository](https://github.com/rosewang2008/edu-convokit/tree/main)\n","\n","If you have any questions, please feel free to reach out to us on [`edu-convokit`'s GitHub](https://github.com/rosewang2008/edu-convokit).\n","\n","👋 Happy exploring your data with `edu-convokit`!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"k7mq7jBNqBWw"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 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