tools/2_extract_learning_gain_from_test_responses.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook extracts and exports measures of learning outcomes from the responses to the pre-test and the post-test data of the JUSThink Dialogue and Actions Corpus.\n",
    "\n",
    "In later notebooks, we focus on one feature to estimate the learning outcomes: relative learning gain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib as pl\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define paths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inputs.\n",
    "data_dir = pl.Path('../data')\n",
    "test_responses_dir = data_dir.joinpath('test_responses')\n",
    "pretest_file = test_responses_dir.joinpath('justhink19_pretest.csv')\n",
    "posttest_file = test_responses_dir.joinpath('justhink19_posttest.csv')\n",
    "\n",
    "# Outputs.\n",
    "processed_data_dir = pl.Path('../processed_data')\n",
    "output_dir = processed_data_dir.joinpath('learning_features')\n",
    "learning_file = output_dir.joinpath('justhink19_learning_features.csv')\n",
    "\n",
    "# Create output directories if they do not exist.\n",
    "for folder in [output_dir]:\n",
    "    if not folder.exists():\n",
    "        folder.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "code_folding": []
   },
   "source": [
    "## Load data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretest table contains 40 teams (including key)\n"
     ]
    }
   ],
   "source": [
    "pretest_df = pd.read_csv(pretest_file, index_col='team_no')\n",
    "print('Pretest table contains {} teams (including key)'.format(len(pretest_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretest table contains 40 teams (including key)\n"
     ]
    }
   ],
   "source": [
    "posttest_df = pd.read_csv(posttest_file, index_col='team_no')\n",
    "print('Pretest table contains {} teams (including key)'.format(len(pretest_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_participant_score(df, participant, \n",
    "                              key_index='key', inplace=True):\n",
    "    if not inplace:\n",
    "        df = df.copy()\n",
    "    \n",
    "    cols = [c for c in df.columns if '_{}'.format(participant) in c]\n",
    "    participant_df = df[cols].copy()\n",
    "\n",
    "    key_responses = participant_df.loc[key_index]\n",
    "    \n",
    "    c = '{}_score'.format(participant)\n",
    "    df[c] = [np.sum(row == key_responses)/len(key_responses) \n",
    "             for i, row in participant_df.iterrows()]\n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "for test_df in [pretest_df, posttest_df]:\n",
    "    for participant in ['A', 'B']:\n",
    "        compute_participant_score(test_df, participant, key_index='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute relative learning gain (RLG) [1].\n",
    "$$ \n",
    "RLG = \\begin{cases} \n",
    "      \\frac{posttest - pretest}{Max - pretest} & posttest \\geq pretest \\\\\n",
    "      \\frac{posttest - pretest}{pretest} & posttest < pretest.\n",
    "\\end{cases}\n",
    "$$\n",
    "\n",
    "\n",
    "[1] M. Sangin, G. Molinari, M.-A. Nüssli, and P. Dillenbourg, “Facilitating peer knowledge modeling: Effects of a knowledge awareness tool on collaborative learning outcomes and processes,” Computers in Human Behavior, vol. 27, no. 3, pp. 1059–1067, May 2011, doi: 10.1016/j.chb.2010.05.032."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>A_RLG</th>\n",
       "      <th>B_RLG</th>\n",
       "      <th>RLG</th>\n",
       "      <th>A_pretest</th>\n",
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       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.285714</td>\n",
       "      <td>-0.142857</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>-0.375000</td>\n",
       "      <td>-0.020833</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
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       "      <td>0.333333</td>\n",
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       "      <td>0.4</td>\n",
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       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>-0.750000</td>\n",
       "      <td>-0.375000</td>\n",
       "      <td>-0.562500</td>\n",
       "      <td>0.4</td>\n",
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       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
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       "      <td>0.6</td>\n",
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       "      <td>-0.375000</td>\n",
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       "      <td>0.8</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
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       "      <td>0.166667</td>\n",
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       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.500000</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.125000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.037500</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.333333</td>\n",
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       "      <td>0.166667</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>29</th>\n",
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       "      <td>0.6</td>\n",
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       "      <th>30</th>\n",
       "      <td>0.250000</td>\n",
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       "      <td>0.125000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-0.166667</td>\n",
       "      <td>-0.125000</td>\n",
       "      <td>-0.145833</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
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       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>-0.222222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.111111</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.125000</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
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       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.083333</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>-0.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
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       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
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       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>-0.111111</td>\n",
       "      <td>-0.142857</td>\n",
       "      <td>-0.126984</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A_RLG     B_RLG       RLG  A_pretest  A_posttest  B_pretest  \\\n",
       "team_no                                                                   \n",
       "6       -0.200000  0.000000 -0.100000        0.5         0.4        0.6   \n",
       "7        0.000000  0.285714  0.142857        0.7         0.7        0.3   \n",
       "8        0.200000 -0.375000 -0.087500        0.5         0.6        0.8   \n",
       "9        0.200000  0.250000  0.225000        0.5         0.6        0.6   \n",
       "10       0.333333  0.400000  0.366667        0.4         0.6        0.5   \n",
       "11       0.333333  0.400000  0.366667        0.7         0.8        0.5   \n",
       "12       0.000000  0.500000  0.250000        0.5         0.5        0.6   \n",
       "14       0.000000 -0.285714 -0.142857        0.6         0.6        0.7   \n",
       "15       0.333333 -0.375000 -0.020833        0.4         0.6        0.8   \n",
       "16       0.000000  0.200000  0.100000        0.6         0.6        0.5   \n",
       "17       0.166667  0.333333  0.250000        0.4         0.5        0.4   \n",
       "18      -0.750000 -0.375000 -0.562500        0.4         0.1        0.8   \n",
       "19       0.000000 -0.166667 -0.083333        0.7         0.7        0.6   \n",
       "20      -0.375000 -0.666667 -0.520833        0.8         0.5        0.3   \n",
       "21       0.000000  0.200000  0.100000        0.6         0.6        0.5   \n",
       "22       0.200000 -0.200000  0.000000        0.5         0.6        0.5   \n",
       "23       0.166667  0.166667  0.166667        0.4         0.5        0.4   \n",
       "25       0.000000  1.000000  0.500000        0.8         0.8        0.7   \n",
       "26      -0.125000  0.200000  0.037500        0.8         0.7        0.5   \n",
       "27       0.333333  0.000000  0.166667        0.4         0.6        0.5   \n",
       "28       0.000000  0.000000  0.000000        1.0         1.0        0.6   \n",
       "29       0.250000 -0.285714 -0.017857        0.6         0.7        0.7   \n",
       "30       0.250000  0.000000  0.125000        0.6         0.7        0.7   \n",
       "31      -0.100000  0.000000 -0.050000        1.0         0.9        0.5   \n",
       "32       0.750000  0.000000  0.375000        0.6         0.9        0.6   \n",
       "33      -0.166667 -0.125000 -0.145833        0.6         0.5        0.8   \n",
       "34       0.200000  0.600000  0.400000        0.5         0.6        0.5   \n",
       "35       0.000000  0.000000  0.000000        0.6         0.6        0.6   \n",
       "36       0.200000  0.000000  0.100000        0.5         0.6        0.4   \n",
       "37      -0.222222  0.000000 -0.111111        0.9         0.7        0.5   \n",
       "38       0.200000 -0.166667  0.016667        0.5         0.6        0.6   \n",
       "39      -0.250000  0.000000 -0.125000        0.8         0.6        0.8   \n",
       "40       0.000000  0.000000  0.000000        0.3         0.3        0.4   \n",
       "41       0.000000  0.000000  0.000000        0.6         0.6        0.6   \n",
       "42      -0.166667  0.000000 -0.083333        0.6         0.5        0.5   \n",
       "44      -0.400000  0.500000  0.050000        0.5         0.3        0.6   \n",
       "45       0.333333  0.166667  0.250000        0.7         0.8        0.4   \n",
       "46      -0.166667  0.333333  0.083333        0.6         0.5        0.4   \n",
       "47      -0.111111 -0.142857 -0.126984        0.9         0.8        0.7   \n",
       "\n",
       "         B_posttest  \n",
       "team_no              \n",
       "6               0.6  \n",
       "7               0.5  \n",
       "8               0.5  \n",
       "9               0.7  \n",
       "10              0.7  \n",
       "11              0.7  \n",
       "12              0.8  \n",
       "14              0.5  \n",
       "15              0.5  \n",
       "16              0.6  \n",
       "17              0.6  \n",
       "18              0.5  \n",
       "19              0.5  \n",
       "20              0.1  \n",
       "21              0.6  \n",
       "22              0.4  \n",
       "23              0.5  \n",
       "25              1.0  \n",
       "26              0.6  \n",
       "27              0.5  \n",
       "28              0.6  \n",
       "29              0.5  \n",
       "30              0.7  \n",
       "31              0.5  \n",
       "32              0.6  \n",
       "33              0.7  \n",
       "34              0.8  \n",
       "35              0.6  \n",
       "36              0.4  \n",
       "37              0.5  \n",
       "38              0.5  \n",
       "39              0.8  \n",
       "40              0.4  \n",
       "41              0.6  \n",
       "42              0.5  \n",
       "44              0.8  \n",
       "45              0.5  \n",
       "46              0.6  \n",
       "47              0.6  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learning_df = pretest_df.copy()[[]]\n",
    "\n",
    "# Compute the relative learning gain per participant.\n",
    "for participant in ['A', 'B']:\n",
    "    l = list()\n",
    "    for team_no in pretest_df.index:\n",
    "        pre = pretest_df.loc[team_no, '{}_score'.format(participant)]\n",
    "        post = posttest_df.loc[team_no, '{}_score'.format(participant)]\n",
    "        if post > pre:\n",
    "            v = 1.0 * (post - pre) / (1 - pre)\n",
    "        else:\n",
    "            v = 1.0 * (post - pre) / pre\n",
    "        l.append(v)\n",
    "\n",
    "    # [(post - pre)/(1-pre) if post > pre else (post - pre)/pre]\n",
    "    learning_df['{}_RLG'.format(participant)] = l\n",
    "\n",
    "# Compute the averaged team relative learning gain.\n",
    "learning_df['RLG'] = learning_df[['A_RLG', 'B_RLG']].mean(axis=1)\n",
    "\n",
    "# Add pretest and posttest scores.\n",
    "for participant in ['A', 'B']:\n",
    "    for test_name, test_df in [('pretest', pretest_df), ('posttest', posttest_df)]:\n",
    "        scores = test_df['{}_score'.format(participant)]\n",
    "        c = '{}_{}'.format(participant, test_name)\n",
    "        learning_df[c] = scores\n",
    "\n",
    "\n",
    "learning_df.drop(['key'], inplace=True)\n",
    "learning_df.index = learning_df.index.map(int)\n",
    "\n",
    "# Round the values to 6 decimal places.\n",
    "learning_df = learning_df.round(6)\n",
    "\n",
    "learning_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export the learning outcomes table to file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written to ../processed_data/learning_features/justhink19_learning_features.csv\n"
     ]
    }
   ],
   "source": [
    "learning_df.to_csv(learning_file)\n",
    "print('Written to {}'.format(learning_file))"
   ]
  }
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