{"cells":[{"cell_type":"markdown","metadata":{"id":"tkZFagfLQul7"},"source":["# Tutorial on Annotating for Education Language Data\n","\n","Welcome to this tutorial on using [`edu-convokit`](https://github.com/rosewang2008/edu-convokit) for annotating your education language data!\n","Annotation is a critical step in understanding your data, and it is important to do it right & consistently across datasets.\n","`edu-convokit` is designed to help you do just that.\n","\n","Annotation is useful because:\n","- It creates descriptive statistics about your data, which can help you understand the data.\n","- It quantifies the language used by your students and educators, which can help you understand the language.\n","- It measures the interaction between the student and the educator, which can help you understand the interaction.\n","\n","`edu-convokit` is designed to support these purposes.\n","\n","## πŸ“š Learning Objectives\n","\n","In this tutorial, you will learn how to use `Annotator` to annotate your data. Some of the annotations we'll cover include:\n","- Section Link πŸ”— Talk Time: We will annotate the amount of time the student and educator talk.\n","- Section Link πŸ”— Student Reasoning: We will annotate use of reasoning in the student's speech.\n","- Section Link πŸ”— Teacher Focusing Questions: We will annotate the use of focusing questions by the educator.\n","- Section Link πŸ”— Conversational Uptake: We will annotate instances of high conversational uptake by the educator.\n","\n","For other annotations, please refer to the [documentation](https://edu-convokit.readthedocs.io/en/latest/) for more information.\n","If you want to add your own annotations, please make a pull request to the [repo](https://github.com/rosewang2008/edu-convokit/).\n","\n","Without further ado, let's get started!"]},{"cell_type":"markdown","metadata":{"id":"3F8CtV9cQul9"},"source":["## Installation\n","\n","First, install `edu-convokit`:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26511,"status":"ok","timestamp":1703932069788,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"ErJys4_TQul9","outputId":"da1f1783-49a1-4d70-bf05-ca97a8e3f48c"},"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-s81zucpt\n"," Running command git clone --filter=blob:none --quiet https://github.com/rosewang2008/edu-convokit.git /tmp/pip-req-build-s81zucpt\n"," Resolved https://github.com/rosewang2008/edu-convokit.git to commit 8eb087b51abfa36a7031bf1de4e3dc40d8848186\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":"Ow3SgJIzQul-","outputId":"e8277eb9-9e86-4570-9817-bb50e5d1a8cb"},"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"]}],"source":["from edu_convokit.annotation import Annotator\n","\n","# We're going to standardize the text with TextPreprocessor.\n","# In this tutorial, we're going to assume you're familiar with TextPreprocessor.\n","# For the tutorial on TextPreprocessor, see: https://colab.research.google.com/drive/1a-EwYwkNYHSNcNThNTXe6DNpsis0bpQK\n","from edu_convokit.preprocessors import TextPreprocessor\n","\n","# For helping us flexibly load data\n","from edu_convokit import utils"]},{"cell_type":"markdown","metadata":{"id":"GJh0MxIsQul-"},"source":["## πŸ“‘ Data\n","\n","Let's load the data we'll be working with. We're going to be using a transcript from the [TalkMoves dataset](https://github.com/SumnerLab/TalkMoves).\n","\n","We're also going to use `TextPreprocessor` to anonymize and pre-process the data. This is optional, but recommended.\n","\n","For the tutorial on `TextPreprocessor`, please refer to [this tutorial](https://colab.research.google.com/drive/1a-EwYwkNYHSNcNThNTXe6DNpsis0bpQK). Here, we're going to assume you're familiar with `TextPreprocessor`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":397},"executionInfo":{"elapsed":1138,"status":"ok","timestamp":1703932179178,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"YuXBRDplQul-","outputId":"84c669ed-a4b6-4232-eb79-3b36fc7e4215"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2023-12-30 10:29:37-- https://raw.githubusercontent.com/rosewang2008/edu-convokit/master/data/talkmoves/Boats%20and%20Fish%202_Grade%204.xlsx\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...\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: 10528 (10K) [application/octet-stream]\n","Saving to: β€˜Boats and Fish 2_Grade 4.xlsx’\n","\n","Boats and Fish 2_Gr 100%[===================>] 10.28K --.-KB/s in 0.001s \n","\n","2023-12-30 10:29:38 (14.2 MB/s) - β€˜Boats and Fish 2_Grade 4.xlsx’ saved [10528/10528]\n","\n"]},{"data":{"text/html":["\n","
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0I'm wondering which is bigger, one half or two...T
1Try the purples. Get three purples. It doesn’t...[STUDENT_0]
2What was it? Two thirds?[STUDENT_1]
3It would be like brown or something like that.[STUDENT_0]
4Ok[STUDENT_1]
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\n"],"text/plain":[" Sentence Speaker\n","0 I'm wondering which is bigger, one half or two... T\n","1 Try the purples. Get three purples. It doesn’t... [STUDENT_0]\n","2 What was it? Two thirds? [STUDENT_1]\n","3 It would be like brown or something like that. [STUDENT_0]\n","4 Ok [STUDENT_1]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["!wget \"https://raw.githubusercontent.com/rosewang2008/edu-convokit/master/data/talkmoves/Boats and Fish 2_Grade 4.xlsx\"\n","\n","data_fname = \"Boats and Fish 2_Grade 4.xlsx\"\n","df = utils.load_data(data_fname) # Handles loading data from different file types including: .csv, .xlsx, .json\n","\n","# We're going to first standardize the text with TextPreprocessor as done from our last tutorial: anonymize and merge utterances from the same speaker\n","processor = TextPreprocessor()\n","TEXT_COLUMN = \"Sentence\"\n","SPEAKER_COLUMN = \"Speaker\"\n","known_names = [\"David\", \"Meredith\", \"Beth\"]\n","known_replacement_names = [f\"[STUDENT_{i}]\" for i in range(len(known_names))]\n","\n","# Anonymize text\n","df = processor.anonymize_known_names(\n"," df=df,\n"," text_column=TEXT_COLUMN,\n"," names=known_names,\n"," replacement_names=known_replacement_names,\n"," target_text_column=TEXT_COLUMN\n",")\n","\n","# Anonymize speakers\n","df = processor.anonymize_known_names(\n"," df=df,\n"," text_column=SPEAKER_COLUMN,\n"," target_text_column=SPEAKER_COLUMN,\n"," names=known_names,\n"," replacement_names=known_replacement_names\n",")\n","\n","# Merge utterances\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","# Show\n","df.head()"]},{"cell_type":"markdown","metadata":{"id":"22vG_XAqQul-"},"source":["## πŸ“ Annotating Talk Time\n","\n","Let's start by annotating the amount of time the student and educator talk.\n","We will define talk time as the number of words in `TEXT_COLUMN`.\n","However, if you have metadata about the length of the audio, you can also use that to annotate talk time.\n","Please refer to the [documentation](https://edu-convokit.readthedocs.io/en/latest/) for more information."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":281,"status":"ok","timestamp":1703932184452,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"e4TNhPfSQul-","outputId":"a9c3a44b-8571-4bf5-cbc0-0be9c74833ca"},"outputs":[{"data":{"text/html":["\n","
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SentenceSpeakertalktime
0I'm wondering which is bigger, one half or two...T54
1Try the purples. Get three purples. It doesn’t...[STUDENT_0]12
2What was it? Two thirds?[STUDENT_1]5
3It would be like brown or something like that.[STUDENT_0]9
4Ok[STUDENT_1]1
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\n"],"text/plain":[" Sentence Speaker talktime\n","0 I'm wondering which is bigger, one half or two... T 54\n","1 Try the purples. Get three purples. It doesn’t... [STUDENT_0] 12\n","2 What was it? Two thirds? [STUDENT_1] 5\n","3 It would be like brown or something like that. [STUDENT_0] 9\n","4 Ok [STUDENT_1] 1"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["annotator = Annotator()\n","\n","# The talktime values will be populated in this column\n","TALK_TIME_COLUMN = \"talktime\"\n","\n","df = annotator.get_talktime(\n"," df=df,\n"," text_column=TEXT_COLUMN,\n"," analysis_unit=\"words\",\n"," output_column=TALK_TIME_COLUMN\n",")\n","\n","df.head()"]},{"cell_type":"markdown","metadata":{"id":"IhrfyJVJQul-"},"source":["πŸŽ‰ We can see with a single function call, we've added our first annotation -- `talktime` -- to our data!\n","\n","All the other annotations work in a similar way. Let's continue!"]},{"cell_type":"markdown","metadata":{"id":"CRCqQlZjQul-"},"source":["## πŸ“ Annotating Student Reasoning\n","\n","Next, let's annotate the student's reasoning.\n","Under the hood, we're using a model trained on student's math reasoning from [prior work](https://github.com/ddemszky/classroom-transcript-analysis).\n","So...\n","\n","πŸ’‘ Note:\n","- This model is trained on math reasoning, so it may not work well on other subjects.\n","- This model will run slow on CPU, so we recommend using a GPU. If you have a GPU, this library will automatically use it.\n","- This model is trained on _student_ utterances. `edu-convokit` has a simple way to only annotate student utterances, which we'll see below.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":400,"referenced_widgets":["b498c129c0e44dff81191cc677cb9aa8","65b4483312c44f00ab70e1da216bd2b2","a1f36fc2fb364873aebb933943d147ff","bb0378211ba5450c90958820777491c8","c40c55b78e9245e68d1368efd2116b55","6297e6ceff8e42fcbe39eb8c2442aad7","621b899ab7904e1eb1b90bb8ab56bcf5","ab2c6f60eb7f468eb1acb40902adb01a","f38caab569ea40b593a79bdb0ff9c01b","9025e414ce274dd69d9d63bee970479b","b7bb0d6c8ae84f39b2b2336c9e7caf39","93b4cf1453b34c899fe2f49084b580a0","01638eb1127f4d6eacc0376d0a88b306","f04988b571234ea59702b12f090856e4","b5ee3f1c61e14eddb13bc0f500898c76","23af1474081f4e4fb04ca78fdd4c999e","cc7a2fc33e704c93a89a3833c2325212","e72d8293f91242dd905d16db75a8f861","0df67a54af9c454cb15b3bcb678f83f2","2cacb213ca0a4e86b5bc9ded5859651d","e659ac12ac4843df87a9e4c031d865d7","8a85e58a32cb4c4d8d8fa35d53775da2","9d57e99d888a44afb0e5cc20e435e152","ac0f91a4e0ef4a1ea6d9e71e4380a0b4","48b80bb0d53f42f8ac42cd5c7f7e9e44","59f49da854f44be0a8ff1ed35c8b565b","c171a76161f0423a9d1e49bbb5a2518d","0f6b499a447241d98cc68371f6b1e3be","6db7b439727f4be6af017421b6327307","907f3a585f2f44fa915e7522bce75aa6","fe4192f5366843bc9e61080c3873b567","9ccfbfb1ffcb4a41a51e2b4b09582cea","307e7a54861743c6aac2beac39f9bd20","72468ac03d5742a4ac92bc87eeda45f2","ba93e5464871467f98153e0d2109b201","45c76ab2cbba48719bc7ed65892b8241","ebb6c044743e4f03a6c67e3831f75190","d05726ae49d646a5b3c261c0a4563aaa","09ead46f1b0a43bda66b55d17b5fed6f","22df55bbc0ea40c6b822f9968d357b28","ab7f9724e5944d9090631b430feb6718","fe01285359134618aada2ca8b5b2b027","c6f087be78f246028707f2c206c880e1","c8b15823ffd3486a8c26f70812df92d1","80c3d98985564c63a0e865fc35237bed","84c3e028066544edb4072f1b751f4199","45a1a278d80244aba54bae287165295a","f0bd2f00d42245bc98c0d6afeb4cacfb","9e67c4447efa473bbe9b16099367d14e","1054c4f96f0c4d02b227b89688d6e219","07dd9ef8633143efaf919e09cd63188c","19fad3225a7d4a0fb72b08418dc67f01","4360e2343dec464b9d0ac4b27843759e","6a56e72a1a284b078220b67fbf5a77af","42bd78b0bc7245ff9468deba2211303f"]},"executionInfo":{"elapsed":35372,"status":"ok","timestamp":1703932221823,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"aHNmmI_CQul-","outputId":"87846a85-eefe-43b7-987c-aca447013bf8"},"outputs":[{"name":"stderr","output_type":"stream","text":["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":"b498c129c0e44dff81191cc677cb9aa8","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json: 0%| | 0.00/429 [00:00\n","
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SentenceSpeakertalktimestudent_reasoning
0I'm wondering which is bigger, one half or two...T54NaN
1Try the purples. Get three purples. It doesn’t...[STUDENT_0]120.0
2What was it? Two thirds?[STUDENT_1]5NaN
3It would be like brown or something like that.[STUDENT_0]90.0
4Ok[STUDENT_1]1NaN
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\n"," \n"],"text/plain":[" Sentence Speaker talktime \\\n","0 I'm wondering which is bigger, one half or two... T 54 \n","1 Try the purples. Get three purples. It doesn’t... [STUDENT_0] 12 \n","2 What was it? Two thirds? [STUDENT_1] 5 \n","3 It would be like brown or something like that. [STUDENT_0] 9 \n","4 Ok [STUDENT_1] 1 \n","\n"," student_reasoning \n","0 NaN \n","1 0.0 \n","2 NaN \n","3 0.0 \n","4 NaN "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# The reasoning annotations will be populated in this column\n","STUDENT_REASONING_COLUMN = \"student_reasoning\"\n","\n","df = annotator.get_student_reasoning(\n"," df=df,\n"," speaker_column=SPEAKER_COLUMN,\n"," text_column=TEXT_COLUMN,\n"," output_column=STUDENT_REASONING_COLUMN,\n"," # Since this model is only trained on _student_ utterances,\n"," # we can explicitly pass in the speaker names associated to students.\n"," # It will only annotate utterances from these speakers.\n"," speaker_value=known_replacement_names,\n",")\n","\n","df.head()"]},{"cell_type":"markdown","metadata":{"id":"PG8J_g_iQul-"},"source":["πŸŽ‰ Great! We've added our second annotation -- `student_reasoning` -- to our data!\n","\n","πŸ’‘ Note:\n","- `student_reasoning` is NaN for the educator's utterances as desired.\n","- Otherwise, for the students, `student_reasoning` is either 1.0 or 0.0. 1.0 means the model thinks the student is using reasoning, and 0.0 means the model thinks the student is not using reasoning.\n","\n","πŸ’‘ Are you wondering whether there's an easy way to **view examples** of the student's reasoning?\n","\n","`edu-convokit` has a simple way to do this with our `Analyzer`s. This will be covered in the tutorial on `Analyzer`s: [link](https://colab.research.google.com/drive/1xfrq5Ka3FZH7t9l87u4sa_oMlmMvuTfe).\n","For now, let's continue annotating!\n"]},{"cell_type":"markdown","metadata":{"id":"jTx57LEWQul-"},"source":["## πŸ“ Annotating Teacher Focusing Questions\n","\n","Let's annotate the educator's use of focusing questions.\n","Under the hood, we're using a model trained on focusing questions in math classrooms from [prior work](https://github.com/sterlingalic/funneling-focusing).\n","So...\n","\n","πŸ’‘ Note:\n","- This model is trained on math classroom data, so it may not work well on other subjects.\n","- This model will run slow on CPU, so we recommend using a GPU. If you have a GPU, this library will automatically use it.\n","- This model is trained on _teacher_ utterances. `edu-convokit` has a simple way to only annotate teacher utterances which is similar to the one we saw above for student utterances."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":400,"referenced_widgets":["ba517f6122364672b315aa24a2634661","2d77343195954f7ebeed13c96bef129f","a5c805ee43ef409497d77176166b733e","7e0fa7d7425f497fb4bffa179aa58f42","507061a7fce8407395df923b3835a459","a9502a1f190549c4b153c6fff8ffba52","b0d3068009d64075b8b5933554e23f51","e09d85fad1ec4a5895de00cb27460bfd","3271f0583be64fbaa6e1763e8ba5a8e9","2f84e834e9804d14b85b14ce99b0f1d9","65365d4c95ca4534adb67c3641089e9c","baaaff9d41a64a63aab3812f21bc1a75","a7b3fa02c488432bb5abf6accea1a1a0","c52643ecdfb643c4a8a3ccebbbe6aba6","cd1440cd012247038c48473083354872","e95c83bac2864b45b5055221ccd3fce1","8b20866690fb4c7891cd0ff5a39ae75a","e673e3f41a75402e857135cd1d9487f6","644aff0ba7e840da8be9cb12ef9d065d","ff03f47ff7174bc2a026001e8f14e98c","6b5cc4a88c3245b3885097b118bde164","03efb2afce3f49a18af1d7aa47dd5cd9","83481c4e1e5b476e96c63df634866e57","22326af4c10a497ab5e523c58b2deae8","38961b46b75e44b1b33f76072007cd24","6d572c05837c4fc8bee3ce64a5bbe5e1","6d252ef949884fc29207c80f6d27b346","a6d5de84bc5b438ca3a80143e0548569","8d947a3e32e6451e98a9748dc0412a06","efbfe6f1498d4f23aec3a28c038a9ceb","2a513a04efed495ba91634390bc5a23a","7b6a03d0832342478974e7dd6eb9ba93","9408971f972043f7937cde0a6434ec72","54b34d286b774fd6bbaeddbbd80cb205","d46cc2d8bd554bc3a07371d7adb7a8b3","fca29d97559645bf8662202895ec194a","95d1c637afa3438dba8dc62f05c8418c","39cce37df81d48bfbd61c4e2695771e3","38395c616fe240978c50e840bdf6fdc6","40243b24bff04db3b527506f6176840a","9fd09b82c0994b9db3ca79f16462144a","32e3719b4b1b46fb98b49923d143e95e","dbca212ddc0647858ba9d7547aa9de08","4a9eeef234684b709d3aa549ecc7fd0e","7a36e29432764cf3a0f2e7805a988f7b","d4a0d8491bbb4951a3c7a5121ab63134","f91dabe95efb48ea86664cef13f9e023","1b15cfd57d7c484c84ffdda41914cbaa","6a95104da66b44ea91664af379ff721f","f09a537483ae46dabe047e2d8bc54863","219cdc6a2b614e5fac57585d7292b037","0ad7dc9fffe74600969e05a108e54a74","5a859cd96be3482d88c2d73fc13909f8","fd752ede9296422c9ca049549e0e323c","1f3f0f09eb964921a3df59099dc98c14"]},"executionInfo":{"elapsed":31356,"status":"ok","timestamp":1703932257256,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"TGeIj2g1Qul-","outputId":"bbaff2a7-c67b-49e6-f89e-1b9155f1ccba"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:root:Note: This model was trained on teacher focusing questions, so it should be used on teacher utterances.\n"," For more details on the model, see https://aclanthology.org/2022.bea-1.27.pdf\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ba517f6122364672b315aa24a2634661","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json: 0%| | 0.00/443 [00:00\n","
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SentenceSpeakertalktimestudent_reasoningfocusing_questions
0I'm wondering which is bigger, one half or two...T54NaN0.0
1Try the purples. Get three purples. It doesn’t...[STUDENT_0]120.0NaN
2What was it? Two thirds?[STUDENT_1]5NaNNaN
3It would be like brown or something like that.[STUDENT_0]90.0NaN
4Ok[STUDENT_1]1NaNNaN
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[STUDENT_0] 9 \n","4 Ok [STUDENT_1] 1 \n","\n"," student_reasoning focusing_questions \n","0 NaN 0.0 \n","1 0.0 NaN \n","2 NaN NaN \n","3 0.0 NaN \n","4 NaN NaN "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["# The focusing questions annotation will be populated in this column\n","FOCUSING_QUESTIONS_COLUMN = \"focusing_questions\"\n","\n","df = annotator.get_focusing_questions(\n"," df=df,\n"," speaker_column=SPEAKER_COLUMN,\n"," text_column=TEXT_COLUMN,\n"," output_column=FOCUSING_QUESTIONS_COLUMN,\n"," # Since this model is only trained on _teacher_ utterances,\n"," # we can explicitly pass in the speaker names associated to the teacher.\n"," speaker_value=['T']\n",")\n","\n","df.head()"]},{"cell_type":"markdown","metadata":{"id":"8XK4ZHg7Qul_"},"source":["πŸŽ‰ Great! We've added our third annotation -- `focusing_questions` -- to our data!\n","\n","πŸ’‘ Note:\n","- `focusing_questions` is NaN for the student utterances.\n","- Similar to before, `focusing_questions` is either 1.0 or 0.0. 1.0 means the model thinks the educator is using a focusing question, and 0.0 means the model thinks the educator is not using a focusing question."]},{"cell_type":"markdown","metadata":{"id":"jpXVgdFIQul_"},"source":["## πŸ“ Annotating Conversational Uptake\n","\n","Let's annotate the educator's conversational uptake of the student.\n","Under the hood, we're using a model trained from [prior work](https://github.com/ddemszky/conversational-uptake).\n","It measures whether the educator builds on the contribution of the student's utterance.\n","\n","So...\n","\n","πŸ’‘ Note:\n","- This model will run slow on CPU, so we recommend using a GPU. If you have a GPU, this library will automatically use it.\n","- This model is trained on teacher utterances following student utterances. `edu-convokit` has a simple way to only annotate these teacher utterances which is similar to the function calls we saw before.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":281,"referenced_widgets":["f391db4cd84b4824b3aefbbdf9e89e49","fa2b3523edc643e6aacf294a3d17f975","662c005df3044164a86879c1285d4634","7efaabb625b84523942d7a6a158a5397","d9ca2ffd1d864148b32bba97809225eb","5195985e5fd84185b9bf74b73fe00ae6","2bdf27bb6f58409a8e1a348cb6bb87dc","e399e03dafd44fafa07fbeee05040e29","7ea0910eb50549feb0f1ec2684c5c62e","1c48e61ba92b411eb86b344475caa67b","2fd336f3e5f74626822462f63a144a7b","2e1ac0e3114e4bc89fa3c8b5cd681d34","56dcda8f819f4d1cbb73498328fc29da","1e4d7d8fa2ad40f7bcd734cac4d8f918","7b46e3d597624645aa95dab93a62b11b","669f9ea5fbc44b1a90f4f23371d3a3f4","43e8669e08e44c49975c903995e4c7b2","73042b370e354153a06dec2c1da989cc","95df46cf71cb4b1abb952e37db5d9b81","04ec5f5de26046f9837d6897afb46e71","e0917120f96c40a3aae53517a6a34453","228d988379ee4f39bbf7de8f238a21d8","d0b3cf8f953e44b4bf991851573c9b75","42cf702d413b476f9874634a8e64fdcb","1fae955ff2524bc5ab83a527c071676a","beb1f5caa1f748caa03c9a50d5fa2e99","3062fd004213433883961df72d959129","6adc65375461463aa77f5be0cff206c0","a128d671d27d46369e13bb291f69d3f9","cb1aebe61e014b148c72f43f257a4a37","3cf50235ec1743fea0ac8de5861d9e1a","83c6982d84ba4800934862113ab8ba4e","99369c4a7d2446c09fb14093d164b406","1ed87af90d734a4796fda6085eb08a97","46b39d17e33f4d9eb8dc47be95846eeb","afe2a61fed624fe2843a59abb7b69aef","2e976ac6861246d7880857a666adb63f","706d65d6c6cd41b595ccf3e83a94bdff","b4032d0fe796450e8697364194d98b88","d4f7903534834d808d34b164f745afe1","8261daed1a184be2a08ab47e04e61bbf","7bbfdde00b394382a0e13bec21dcadd0","26cc55ab692d45759376cef9898a8b07","e5df7a663d624bca8443e4e040fdd64b","5a0a99b11b694fc5bd2bdc6ef6bf4909","682c28862a624dcb99ab3b35851fe3e6","92389dcacedf48fd8df564784d4c5abb","62d4f80d9a6b45679174ae52fa0fa555","9782ae0213f74183b049c2b05e82083c","360f9e67f367423c8bf74433d92005c8","efab379638bf49aba3b9dc2a6310f36d","b7bca0c9dd3d4c3ca17d0709d69bde13","62a081bfde68472fb25fddb1856345c3","ae77ee3b6149496f9e9063e85711ca15","fa53aa83f8544f9191a539957b3805ff","d36ff1170f4b46b59622718e36197b31","6515923aefa042feac0318dc66111874","a31d2cd3f39643f78fb4c75a9e9e8448","386f24a73da44170aac2690ce4a0123a","1f5cf0cb2d624e2db4f4e0d7dcfd0586","f49d48cf1c6a497aa66c5adff086bbea","15afa9db892345269f84b1f30e8f032a","be10389831194348b7a4ef1922b4c048","0bf259e1b0ba47a592723bd4b4f0cf96","901de4a180df447a977b4f655a70318d","5f7730b6dfd8459f9a7b883ff9821970"]},"executionInfo":{"elapsed":32460,"status":"ok","timestamp":1703932294276,"user":{"displayName":"Rose Wang","userId":"08647070137360066467"},"user_tz":480},"id":"UbGtELCFQul_","outputId":"1d4a27f0-dce6-4cb6-d3cd-f36f118574ef"},"outputs":[{"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":"f391db4cd84b4824b3aefbbdf9e89e49","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json: 0%| | 0.00/28.0 [00:00\n","
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SentenceSpeakertalktimestudent_reasoningfocusing_questionsuptake
0I'm wondering which is bigger, one half or two...T54NaN0.0NaN
1Try the purples. Get three purples. It doesn’t...[STUDENT_0]120.0NaNNaN
2What was it? Two thirds?[STUDENT_1]5NaNNaNNaN
3It would be like brown or something like that.[STUDENT_0]90.0NaNNaN
4Ok[STUDENT_1]1NaNNaNNaN
5We’re not doing the one third, we’re doing two...[STUDENT_0]140.0NaNNaN
6First we’ve got to find out what a third of it...[STUDENT_1]180.0NaNNaN
7One third?[STUDENT_0]2NaNNaNNaN
8What’s third of an orange? Let’s start a diffe...[STUDENT_1]210.0NaNNaN
9Alright, yeah, I was thinking of that way before[STUDENT_0]90.0NaNNaN
10And you can take the take the red, and the lig...[STUDENT_1]350.0NaNNaN
11She asked, which is bigger, one half or two th...[STUDENT_0]100.0NaNNaN
12One half or two thirds? Now take six of the ones[STUDENT_1]110.0NaNNaN
13Yeah, I know, and put β€˜em up to there, and tha...[STUDENT_0]300.0NaNNaN
14Now take six of the ones Which is bigger?T9NaN0.01.0
15One half[STUDENT_2]2NaNNaNNaN
16I think one half is...[STUDENT_0]5NaNNaNNaN
17Yes, [STUDENT_0] and [STUDENT_1]?T4NaN0.00.0
18What do you have?[STUDENT_0]4NaNNaNNaN
19Well[STUDENT_1] and [STUDENT_0]1NaNNaNNaN
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Now take six of the ones \n","13 Yeah, I know, and put β€˜em up to there, and tha... \n","14 Now take six of the ones Which is bigger? \n","15 One half \n","16 I think one half is... \n","17 Yes, [STUDENT_0] and [STUDENT_1]? \n","18 What do you have? \n","19 Well \n","\n"," Speaker talktime student_reasoning \\\n","0 T 54 NaN \n","1 [STUDENT_0] 12 0.0 \n","2 [STUDENT_1] 5 NaN \n","3 [STUDENT_0] 9 0.0 \n","4 [STUDENT_1] 1 NaN \n","5 [STUDENT_0] 14 0.0 \n","6 [STUDENT_1] 18 0.0 \n","7 [STUDENT_0] 2 NaN \n","8 [STUDENT_1] 21 0.0 \n","9 [STUDENT_0] 9 0.0 \n","10 [STUDENT_1] 35 0.0 \n","11 [STUDENT_0] 10 0.0 \n","12 [STUDENT_1] 11 0.0 \n","13 [STUDENT_0] 30 0.0 \n","14 T 9 NaN \n","15 [STUDENT_2] 2 NaN \n","16 [STUDENT_0] 5 NaN \n","17 T 4 NaN \n","18 [STUDENT_0] 4 NaN \n","19 [STUDENT_1] and [STUDENT_0] 1 NaN \n","\n"," focusing_questions uptake \n","0 0.0 NaN \n","1 NaN NaN \n","2 NaN NaN \n","3 NaN NaN \n","4 NaN NaN \n","5 NaN NaN \n","6 NaN NaN \n","7 NaN NaN \n","8 NaN NaN \n","9 NaN NaN \n","10 NaN NaN \n","11 NaN NaN \n","12 NaN NaN \n","13 NaN NaN \n","14 0.0 1.0 \n","15 NaN NaN \n","16 NaN NaN \n","17 0.0 0.0 \n","18 NaN NaN \n","19 NaN NaN "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df.head(20)"]},{"cell_type":"markdown","metadata":{"id":"YdSyylvvQul_"},"source":["πŸŽ‰ Great, we finished our last annotation of the tutorial!\n","\n","With these annotations, we can now do some analysis on our data.\n","\n","We can save our annotated data to a file which we'll use in the next tutorial on `Analyzer`s: [link](TODO).\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1zspJNWjQul_"},"outputs":[],"source":["df.to_csv(\"annotated_data.csv\", index=False)"]},{"cell_type":"markdown","metadata":{"id":"Pp_W7hJXQul_"},"source":["## πŸ“ Conclusion and Where to Go From Here\n","\n","In this tutorial, we learned how to use `Annotator` to annotate our data. With one simple function call, we were able to annotate:\n","- Talk Time\n","- Student Reasoning\n","- Teacher Focusing Questions\n","- Conversational Uptake\n","\n","What are some natural next steps?\n","- You can annotate with other features. Please refer to the [documentation](https://edu-convokit.readthedocs.io/en/latest/) for an exhaustive list of features. Or, you can add your own features by making a pull request to the [repo](https://github.com/rosewang2008/edu-convokit).\n","- You can analyze your data with [`edu-convokit`'s `Analyzer`](https://colab.research.google.com/drive/1xfrq5Ka3FZH7t9l87u4sa_oMlmMvuTfe).\n","- For a tutorial on `Analyzer`, please refer to [this tutorial](https://colab.research.google.com/drive/1xfrq5Ka3FZH7t9l87u4sa_oMlmMvuTfe).\n","- For the documentation on `Analyzer`, please refer to [this documentation](https://edu-convokit.readthedocs.io/en/latest/analyzer.html).\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","πŸ‘‹ Happy exploring your data with `edu-convokit`!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XV4_KzKFpdJv"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 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