diff --git a/.gitignore b/.gitignore index bc682cb..b6f6a7a 100644 --- a/.gitignore +++ b/.gitignore @@ -37,6 +37,7 @@ exp/server.log # latex extensions paper/**/*.aux paper/**/*.fdb_latexmk +paper/**/*.fff paper/**/*.fls paper/**/*.log paper/**/*.out @@ -45,3 +46,6 @@ paper/**/*.blg paper/**/*.bbl texput.log paper/.#compile.sh + +# MS Word temp files +paper/**/~$* \ No newline at end of file diff --git a/README.md b/README.md index 6d40049..427f7a4 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Text embedding models yield high-resolution insights into conceptual knowledge from short multiple-choice quizzes +# Text embedding models yield detailed conceptual knowledge maps derived from short multiple-choice quizzes

@@ -7,8 +7,8 @@

This repository contains all data and code used to produce the paper -"[_Text embedding models yield high-resolution insights into conceptual -knowledge from short multiple-choice quizzes_](https://psyarxiv.com/dh3q2)" by +"[_Text embedding models yield detailed conceptual +knowledge maps derived from short multiple-choice quizzes_](https://psyarxiv.com/dh3q2)" by Paxton C. Fitzpatrick, Andrew C. Heusser, and Jeremy R. Manning. We also include reproducible environments for running our experiment and @@ -17,7 +17,7 @@ analyses via [Docker](https://www.docker.com/). ## Table of Contents -- [Text embedding models yield high-resolution insights into conceptual knowledge from short multiple-choice quizzes](#text-embedding-models-yield-high-resolution-insights-into-conceptual-knowledge-from-short-multiple-choice-quizzes) +- [Text embedding models yield detailed conceptual knowledge maps derived from short multiple-choice quizzes](#text-embedding-models-yield-detailed-conceptual-knowledge-maps-derived-from-short-multiple-choice-quizzes) - [Table of Contents](#table-of-contents) - [Repo Organization](#repo-organization) - [Installing Docker](#installing-docker) diff --git a/code/notebooks/main/4_reconstructing-knowledge.ipynb b/code/notebooks/main/4_reconstructing-knowledge.ipynb index 98059c2..4759cd3 100644 --- a/code/notebooks/main/4_reconstructing-knowledge.ipynb +++ b/code/notebooks/main/4_reconstructing-knowledge.ipynb @@ -12,8 +12,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2023-01-26T05:49:02.080047Z", - "start_time": "2023-01-26T05:49:01.186876Z" + "end_time": "2025-11-28T02:17:24.495101Z", + "start_time": "2025-11-28T02:17:23.573709Z" } }, "outputs": [ @@ -63,8 +63,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2023-01-26T05:49:02.118317Z", - "start_time": "2023-01-26T05:49:02.081149Z" + "end_time": "2025-11-28T02:17:24.533965Z", + "start_time": "2025-11-28T02:17:24.496100Z" }, "scrolled": true }, @@ -201,10 +201,16 @@ " def get_kmap(self, kmap_key):\n", " """\n", " dict.get()-like access to self.knowledge_maps\n", - " :param trace_key: str\n", - " The key for the trace to be returned\n", - " :return: trace: np.ndarray\n", - " The trace stored under the given `trace_key`\n", + "\n", + " Parameters\n", + " ----------\n", + " kmap_key : str\n", + " The key for the knowledge map to be returned\n", + "\n", + " Returns\n", + " -------\n", + " kmap : numpy.ndarray\n", + " The knowledge map stored under the given `kmap_key`\n", " """\n", " try:\n", " return self.knowledge_maps[kmap_key]\n", @@ -218,10 +224,16 @@ " def get_trace(self, trace_key):\n", " """\n", " dict.get()-like access to self.traces\n", - " :param trace_key: str\n", - " The key for the trace to be returned\n", - " :return: trace: np.ndarray\n", - " The trace stored under the given `trace_key`\n", + "\n", + " Parameters\n", + " ----------\n", + " trace_key : str\n", + " The key for the trace to be returned\n", + "\n", + " Returns\n", + " -------\n", + " trace : numpy.ndarray\n", + " The trace stored under the given `trace_key`\n", " """\n", " try:\n", " return self.traces[trace_key]\n", @@ -267,8 +279,8 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2023-01-26T05:49:02.126030Z", - "start_time": "2023-01-26T05:49:02.121139Z" + "end_time": "2025-11-28T02:17:24.541164Z", + "start_time": "2025-11-28T02:17:24.534779Z" }, "scrolled": true }, @@ -284,14 +296,14 @@ "\n", " Parameters\n", " ----------\n", - " lecture: numpy.ndarray\n", + " lecture : numpy.ndarray\n", " `(n_coordinates, n_features)` matrix of coordinates for which to\n", " estimate knowledge.\n", - " questions: numpy.ndarray\n", + " questions : numpy.ndarray\n", " `(n_observations, n_features)` matrix of coordinates for the\n", " quiz questions used to estimate knowledge for each of the\n", " `n_coordinates` locations.\n", - " accuracy: array_like\n", + " accuracy : array_like\n", " `(n_observations,)` binary array denoting whether each question\n", " was answered correctly (`True`|`1`) or incorrectly\n", " (`False`/`0`).\n", @@ -330,8 +342,8 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2023-01-26T05:49:02.133555Z", - "start_time": "2023-01-26T05:49:02.126809Z" + "end_time": "2025-11-28T02:17:24.551006Z", + "start_time": "2025-11-28T02:17:24.542327Z" }, "scrolled": true }, @@ -372,7 +384,7 @@ " ignore_nan : bool, optional\n", " If True (default: False), ignore NaNs in all calculations\n", " (handle them with numpy NaN-aware functions and suppress common\n", - " NaN-related warnigns).\n", + " NaN-related warnings).\n", " color : str or tuple of float, optional\n", " Any color specification accepted by Matplotlib. See\n", " https://matplotlib.org/3.5.1/tutorials/colors/colors.html for a\n", @@ -399,7 +411,7 @@ "\n", " Returns\n", " -------\n", - " returns : matplotlib.axes.Axes or list of objects\n", + " returns : matplotlib.axes.Axes or tuple of objects\n", " Return value depends on the value passed to `return_bounds`. If\n", " False (default), the Axes object alone is returned. If True, a\n", " 3-tuple is returned, where the first item is the Axes object and\n", @@ -474,8 +486,8 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2023-01-26T05:49:02.156381Z", - "start_time": "2023-01-26T05:49:02.134795Z" + "end_time": "2025-11-28T02:17:24.579032Z", + "start_time": "2025-11-28T02:17:24.552162Z" } }, "outputs": [ @@ -697,8 +709,8 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2023-01-26T05:49:03.087810Z", - "start_time": "2023-01-26T05:49:02.157615Z" + "end_time": "2025-11-28T02:17:25.501238Z", + "start_time": "2025-11-28T02:17:24.579871Z" } }, "outputs": [ @@ -732,8 +744,8 @@ "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2023-01-26T05:49:03.095571Z", - "start_time": "2023-01-26T05:49:03.088882Z" + "end_time": "2025-11-28T02:17:25.510308Z", + "start_time": "2025-11-28T02:17:25.502079Z" }, "scrolled": false }, @@ -1095,8 +1107,8 @@ "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2023-01-26T05:49:03.238397Z", - "start_time": "2023-01-26T05:49:03.096666Z" + "end_time": "2025-11-28T02:17:25.663235Z", + "start_time": "2025-11-28T02:17:25.511475Z" } }, "outputs": [], @@ -1140,8 +1152,8 @@ "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2023-01-26T05:49:03.241576Z", - "start_time": "2023-01-26T05:49:03.239866Z" + "end_time": "2025-11-28T02:17:25.666956Z", + "start_time": "2025-11-28T02:17:25.664121Z" }, "scrolled": true }, @@ -1165,8 +1177,8 @@ "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2023-01-26T05:49:03.244378Z", - "start_time": "2023-01-26T05:49:03.242280Z" + "end_time": "2025-11-28T02:17:25.670966Z", + "start_time": "2025-11-28T02:17:25.668188Z" } }, "outputs": [], @@ -1180,8 +1192,8 @@ "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2023-01-26T05:49:06.079890Z", - "start_time": "2023-01-26T05:49:03.245522Z" + "end_time": "2025-11-28T02:17:28.264981Z", + "start_time": "2025-11-28T02:17:25.671967Z" }, "code_folding": [], "scrolled": false @@ -1189,7 +1201,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1205,6 +1217,7 @@ " sns.color_palette(np.array(sns.hls_palette())[[3, 2, 1]])\n", "):\n", " set_figure_style()\n", + " desat_palette = sns.color_palette(sns.color_palette(), desat=0.75)\n", " fig, axarr = plt.subplots(2, 2, sharey=True)\n", " fig.set_size_inches(15, 10)\n", " ((a, b),\n", @@ -1230,12 +1243,15 @@ " xycoords=a.yaxis.label, \n", " size=24, \n", " fontweight='semibold',\n", + " fontstyle='italic',\n", " rotation=90, \n", " ha='right', \n", " va='center')\n", "\n", " # across-moments mean\n", " sns.barplot(data=ff_traces.mean(axis=1).T, ax=b)\n", + " sns.stripplot(data=ff_traces.mean(axis=1).T, palette=desat_palette, jitter=1,\n", + " linewidth=1, edgecolor='gray', alpha=0.5, zorder=1, clip_on=False, ax=b)\n", " b.set_xticklabels(['Quiz 1', 'Quiz 2', 'Quiz 3'])\n", " b.set_ylim(0, 1)\n", " b.tick_params(axis='y', length=0)\n", @@ -1271,12 +1287,15 @@ " xycoords=c.yaxis.label, \n", " size=24, \n", " fontweight='semibold',\n", + " fontstyle='italic',\n", " rotation=90, \n", " ha='right', \n", " va='center')\n", "\n", " # across-moments mean\n", " sns.barplot(data=bos_traces.mean(axis=1).T, ax=d)\n", + " sns.stripplot(data=bos_traces.mean(axis=1).T, palette=desat_palette, jitter=1,\n", + " linewidth=1, edgecolor='gray', alpha=0.5, zorder=1, clip_on=False, ax=d)\n", " d.set_ylim(0, 1)\n", " d.set_xlabel('Quiz', fontsize=23)\n", " d.set_xticklabels(['Quiz 1', 'Quiz 2', 'Quiz 3'])\n", @@ -1324,8 +1343,8 @@ "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2023-01-26T05:49:06.085696Z", - "start_time": "2023-01-26T05:49:06.080773Z" + "end_time": "2025-11-28T02:17:28.271024Z", + "start_time": "2025-11-28T02:17:28.265881Z" } }, "outputs": [ @@ -1391,7 +1410,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.18" }, "toc": { "base_numbering": 1, diff --git a/code/notebooks/main/6_knowledge-smoothness.ipynb b/code/notebooks/main/6_knowledge-smoothness.ipynb index e35dc6c..8ca4cb4 100644 --- a/code/notebooks/main/6_knowledge-smoothness.ipynb +++ b/code/notebooks/main/6_knowledge-smoothness.ipynb @@ -121,7 +121,7 @@ " A `pandas.Series` whose values are iterables of length 2 \n", " representing lower and upper confidence interval bounds for the \n", " accross-participants mean overall/raw p(correct) for each quiz.\n", - " interp_func : float, optional\n", + " interp_freq : float, optional\n", " If provided, interpolate the series of by-distance p(correct)\n", " values for each quiz & accuracy of reference question to the \n", " given frequency before computing the intersections.\n", @@ -1296,7 +1296,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/paper/admin/Author_Checklist_NCOMMS-23-10640C__1762787328_45.docx b/paper/admin/Author_Checklist_NCOMMS-23-10640C__1762787328_45.docx new file mode 100644 index 0000000..489f7cf Binary 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for inline comments \newcommand{\comment}[1]{} @@ -42,10 +44,10 @@ \renewcommand{\nameref}[1]{\textit{\oldnameref{#1}}} \doublespacing -\linenumbers +%\linenumbers -\title{Text embedding models yield high-resolution insights into conceptual -knowledge from short multiple-choice quizzes} +\title{Text embedding models yield detailed conceptual knowledge maps +derived from short multiple-choice quizzes} \author{Paxton C. Fitzpatrick\textsuperscript{1}, Andrew C. Heusser\textsuperscript{1, 2}, and Jeremy R. Manning\textsuperscript{1, *}\\ @@ -74,21 +76,19 @@ these embeddings, along with participants' quiz responses, to track how the learners' knowledge changed after watching each video and predict their success on individual quiz questions. Our findings show how a small set of quiz -questions may be used to obtain rich and meaningful high-resolution insights +questions may be used to obtain rich and meaningful insights into what each learner knows, and how their knowledge changes over time as they learn. -\textbf{Keywords: education, learning, knowledge, concepts, natural language processing} - \end{abstract} \section*{Introduction} -Suppose that a teacher had access to a complete, tangible ``map'' of everything +Suppose that a teacher had access to a complete, tangible map of everything a student knows. Defining what such a map might even look like, let alone how it might be constructed or filled in, is itself a non-trivial problem. But if a -teacher \textit{were} to gain access to such a map, how might it change their +teacher were to gain access to such a map, how might it change their ability to teach that student? Perhaps they might start by checking how well the student knows the to-be-learned information already, or how much they know about related concepts. For some students, they could potentially optimize @@ -102,7 +102,7 @@ \section*{Introduction} A common approach to assessing a student's knowledge is to present them with a set of quiz questions, calculate the proportion they answer correctly, and provide them with feedback in the form of a simple numeric or letter grade. -While such a grade can provide \textit{some} indication of whether the student +While such a grade can provide some indication of whether the student has mastered the to-be-learned material, any univariate measure of performance on a complex task sacrifices certain relevant information, risks conflating underlying factors, and so on. For example, consider the relative utility of @@ -116,7 +116,7 @@ \section*{Introduction} Designing and building procedures and tools for mapping out knowledge touches on deep questions about what it means to learn. For example, how do we acquire conceptual knowledge? Memorizing course lectures or textbook chapters by rote -can lead to the superficial \textit{appearance} of understanding the underlying +can lead to the superficial appearance of understanding the underlying content, but achieving true conceptual understanding seems to require something deeper and richer. Does conceptual understanding entail connecting newly acquired information to the scaffolding of one's existing knowledge or @@ -139,7 +139,7 @@ \section*{Introduction} ability to answer simple questions about it. However, text embedding models~\citep[e.g.,][]{LandDuma97, DeerEtal90, BleiEtal03, BleiLaff06, MikoEtal13a, CerEtal18, BrowEtal20, ViswEtal17} also -attempt to capture the deeper meaning \textit{underlying} those atomic +attempt to capture the deeper meaning underlying those atomic elements. These models consider not only the co-occurrences of those elements within and across documents, but (in many cases) also patterns in how those elements appear across different scales (e.g., @@ -149,7 +149,7 @@ \section*{Introduction} To be clear, this is not to say that text embedding models themselves are capable of ``understanding'' deep conceptual meaning in any traditional sense. But rather, their ability to capture the underlying -\textit{structure} of text documents beyond their surface-level contents +structure of text documents beyond their surface-level contents provides a computational framework through which those documents' deeper conceptual meanings may be quantified, explored, and understood. According to these models, @@ -157,9 +157,9 @@ \section*{Introduction} vector in a high-dimensional representation space, wherein nearby vectors reflect conceptually related documents. A model that succeeds at capturing an analogue of ``understanding'' is able to assign nearby -feature vectors to two conceptually related documents \textit{even - when the specific words contained in those documents have limited - overlap}. In this way, ``concepts'' are defined implicitly by the +feature vectors to two conceptually related documents even +when the specific words contained in those documents have limited +overlap. In this way, ``concepts'' are defined implicitly by the model's geometry~\citep[e.g., how the embedding coordinate of a given word or document relates to the coordinates of other text embeddings; ][]{PianHill22}. @@ -173,17 +173,17 @@ \section*{Introduction} knowledge may have a critical dependency structure, such that knowing about a particular concept might require first knowing about a set of other concepts. For example, understanding the concept of a fish swimming in water first -requires understanding what fish and water \textit{are}. Fourth, as we learn, -our ``current state of knowledge'' should change accordingly. Learning new -concepts should both update our characterizations of ``what is known'' and also +requires understanding what fish and water are. Fourth, as we learn, +our current state of knowledge should change accordingly. Learning new +concepts should both update our characterizations of what is known and also unlock any now-satisfied dependencies of those newly learned concepts so that -they are ``tagged'' as available for future learning. +they are considered available for future learning. Here we develop a framework for modeling how conceptual knowledge is acquired during learning. The central idea behind our framework is to use text embedding -models to define the coordinate systems of two maps: a \textit{knowledge -map} that describes the extent to which each concept is currently known, and -a \textit{learning map} that describes changes in knowledge over time. Each +models to define the coordinate systems of two maps: a ``knowledge +map'' that describes the extent to which each concept is currently known, and +a ``learning map'' that describes changes in knowledge over time. Each location on these maps represents a single concept, and the maps' geometries are defined such that related concepts are located nearby in space. We use this framework to analyze and interpret behavioral data collected from an experiment @@ -195,17 +195,17 @@ \section*{Introduction} approaches to studying learning and memory (e.g., list-learning studies) often draw little distinction between memorization and understanding. Instead, these studies typically focus on whether information is effectively encoded or -retrieved, rather than whether the information is \textit{understood}. +retrieved, rather than whether the information is understood. Approaches to studying conceptual learning, such as category learning experiments, can begin to investigate the distinction between memorization and understanding, often by training participants to distinguish arbitrary or random features in otherwise meaningless categorized stimuli~\citep{ReilEtal82, Este86a, Este86b, GlucEtal02, AshbMadd05, HulbNorm15}. However, the objective of real-world training, or learning from life experiences more generally, is often -to develop new knowledge that may be applied in \textit{useful} ways in the +to develop new knowledge that may be applied in useful ways in the future. In this sense, the gap between modern learning theories and modern pedagogical approaches that inform classroom learning strategies is enormous: -most of our theories about \textit{how} people learn are inspired by +most of our theories about how people learn are inspired by experimental paradigms and models that have only peripheral relevance to the kinds of learning that students and teachers actually seek~\citep{Macl05, HallGree08}. To help bridge this gap, our study uses course materials from real @@ -214,7 +214,7 @@ \section*{Introduction} concepts presented during course lectures and tested by assessments, and that these relationships can be leveraged to predict students' success on individual quiz questions. We also provide a demonstration of how our models can be used to -construct ``maps'' of what students know, and how their knowledge changes with +construct maps of what students know, and how their knowledge changes with training. In addition to helping to visually capture knowledge (and changes in knowledge), we hope that such maps might lead to real-world tools for improving how we educate. Taken together, our work shows that existing course materials @@ -229,7 +229,7 @@ \section*{Results} From a geometric perspective, this assumption implies that knowledge is fundamentally ``smooth.'' In other words, as one moves through a space representing an individual's knowledge (where similar concepts occupy nearby -coordinates), their ``level of knowledge'' should change relatively gradually. +coordinates), their level of knowledge should change relatively gradually. To begin to test this smoothness assumption, we sought to track participants' knowledge and how it changed over time in response to training. Two overarching goals guide our approach. First, we want to gain detailed insights into what @@ -239,7 +239,7 @@ \section*{Results} variety of specific concepts. Second, we want our approach to be potentially scalable to large numbers of diverse concepts, courses, and students. This requires that the conceptual content of interest be discovered -\textit{automatically}, rather than relying on manually produced ratings or +automatically, rather than relying on manually produced ratings or labels. \begin{figure}[tp] @@ -285,7 +285,7 @@ \section*{Results} in either video (see Supp.~Tab.~\questions~for the full list of questions in our stimulus pool). Participants answered questions randomly drawn from each content area (Lecture~1, Lecture~2, and general physics knowledge) on each of -the three quizzes. Quiz~1 was intended to assess participants' ``baseline'' +the three quizzes. Quiz~1 was intended to assess participants' baseline knowledge before training, Quiz~2 assessed knowledge after watching the \textit{Four Fundamental Forces} video (i.e., Lecture~1), and Quiz~3 assessed knowledge after watching the \textit{Birth of Stars} video (i.e., Lecture~2). @@ -297,10 +297,10 @@ \section*{Results} \caption{\textbf{Modeling course content.} \textbf{A. Building a document pool from sliding windows of text.} We decompose each lecture's transcript into a series of overlapping sliding windows. The full set of transcript - snippets (across all windows) may be treated as a set of ``documents'' for + snippets (across all windows) may be treated as a set of documents for training a text embedding model. \textbf{B. Constructing lecture content - \textit{trajectories}.} After training the model on the sliding windows - from both lectures, we transform each lecture into a ``trajectory'' through + trajectories.} After training the model on the sliding windows + from both lectures, we transform each lecture into a trajectory through text embedding space by joining the embedding coordinates of successive sliding windows parsed from its transcript. \textbf{C. Embedding multiple lectures and questions in a shared space.} We apply the same model (trained @@ -334,7 +334,7 @@ \section*{Results} topic-proportions matrix) is analogous to a coordinate in a 15-dimensional space whose axes are topics discovered by the model. Within this space, each lecture's sequence of topic vectors (i.e., corresponding to its transcript's -overlapping text snippets across sliding windows) forms a \textit{trajectory} +overlapping text snippets across sliding windows) forms a trajectory that captures how its conceptual content unfolds over time (Fig.~\ref{fig:sliding-windows}B). We resampled these trajectories to a resolution of one topic vector for each second of video (i.e., 1~Hz). @@ -344,7 +344,7 @@ \section*{Results} indeed the topic model could capture information about the deeper conceptual content of the lectures (i.e., beyond surface-level details such as particular word choices), then we should be able to recover a correspondence between each -lecture and questions \textit{about} each lecture. Importantly, such a +lecture and questions about each lecture. Importantly, such a correspondence could not arise solely from superficial text matching between lecture transcripts and questions, since the lectures and questions often used different words (Supp.~Fig.~\jaccard) and phrasings. Simply comparing the @@ -371,7 +371,7 @@ \section*{Results} \caption{\textbf{Lecture and question topic overlap.} \textbf{A. Topic weight variability}. The bar plots display the variance in each topic's weight across lecture timepoints (top row) and questions (bottom row); - colors denote topics. The top-weighted words from the most ``expressive'' + colors denote topics. The top-weighted words from the most expressive (i.e., variable across observations) topic from each lecture are displayed in the upper right (orange: topic 2; yellow-green: topic 4). The top-weighted words from the full set of topics may be found in @@ -384,22 +384,22 @@ \section*{Results} \end{figure} Another, more sensitive, way of summarizing the conceptual content of the -lectures and questions is to look at \textit{variability} in how topics are +lectures and questions is to look at variability in how topics are weighted over time and across different questions (Fig.~\ref{fig:topics}). Intuitively, the variability in the expression of a given topic relates to how much ``information''~\citep{Fish22} the lecture (or question set) reflects about that topic. For example, suppose a given topic is weighted on heavily throughout a lecture. That topic might be characteristic of some aspect or -property of the lecture \textit{overall} (conceptual or otherwise), but unless +property of the lecture overall (conceptual or otherwise), but unless the topic's weights change in meaningful ways over time, it would be a -poor indicator of any \textit{specific} conceptual content in the lecture. We +poor indicator of any specific conceptual content in the lecture. We therefore also compared the variances in topic weights (over time and across questions) between the lectures and questions. The variability in topic expression was similar for the Lecture~1 video and questions ($r(13) = 0.824,~p<0.001$, 95\% CI~$= [0.696,~0.973]$), and for the Lecture~2 video and questions ($r(13) = 0.801,~p<0.001$, 95\% CI~$= [0.539,~0.958]$). Simultaneously, as reported in Figure~\ref{fig:topics}B, the -variabilities in topic expression across \textit{different} videos and +variabilities in topic expression across different videos and lecture-specific questions (i.e., Lecture~1 video vs.~Lecture~2 questions; Lecture~2 video vs.~Lecture~1 questions) were negatively correlated, and neither video's topic variability was reliably correlated with the topic @@ -417,7 +417,7 @@ \section*{Results} text embedding model might additionally capture these conceptual relationships at a finer scale. For example, if a particular question asks about the content from one small part of a lecture, we wondered whether the text embeddings could -be used to automatically identify the ``matching'' moment(s) in the lecture. To +be used to automatically identify the matching moment(s) in the lecture. To explore this, we computed the correlation between each question's topic weights and the topic weights for each second of its corresponding lecture, and found that each question appeared to be temporally specific @@ -453,29 +453,29 @@ \section*{Results} \label{fig:question-correlations} \end{figure} -The ability to quantify how much each question is ``asking about'' the content -from each moment of the lectures could enable high-resolution insights into +The ability to quantify how much each question is asking about the content +from each moment of the lectures could enable more detailed insights into participants' knowledge. Traditional approaches to estimating how much a -student ``knows'' about the content of a given lecture entail administering some form of assessment (e.g., a quiz) and computing the +student knows about the content of a given lecture entail administering some form of assessment (e.g., a quiz) and computing the proportion of questions the student answered correctly. But if two students receive identical scores on such an assessment, might our modeling framework help us to gain more -nuanced insights into the \textit{specific} content that each student has +nuanced insights into the specific content that each student has mastered (or failed to master)? For example, a student who misses three questions that were all about the same concept (e.g., concept $A$) will have -gotten the same \textit{proportion} of questions correct as another student who -missed three questions about three \textit{different} concepts (e.g., $A$, $B$, -and $C$). But if we wanted to help these two students fill in the ``gaps'' in +gotten the same proportion of questions correct as another student who +missed three questions about three different concepts (e.g., $A$, $B$, +and $C$). But if we wanted to help these two students fill in the gaps in their understandings, we might do well to focus specifically on concept $A$ for the first student, but to also add in materials pertaining to concepts $B$ and -$C$ for the second student. In other words, raw ``proportion-correct'' measures -may capture \textit{how much} a student knows, but not \textit{what} they know. +$C$ for the second student. In other words, raw proportion-correct measures +may capture how much a student knows, but not what they know. We wondered whether our modeling framework might enable us to (formally and automatically) infer participants' knowledge at the scale of individual concepts (e.g., as captured by a single moment of a lecture). We developed a simple formula (Eqn.~\ref{eqn:prop}) for using a participant's responses to a small set of multiple-choice questions to estimate how much that -participant ``knows'' about the concept reflected by any arbitrary coordinate +participant knows about the concept reflected by any arbitrary coordinate $x$ in text embedding space (e.g., the content reflected by any moment in a lecture they had watched; see \nameref{subsec:traces}). Essentially, the estimated knowledge at coordinate $x$ is given by the weighted @@ -483,7 +483,7 @@ \section*{Results} weights reflect how much each question is ``about'' the content at $x$. When we apply this approach to estimate the participant's knowledge about the content presented in each moment of each lecture, we can obtain a detailed time course -describing how much ``knowledge'' that participant has about the content +describing how much knowledge that participant has about the content presented at any part of the lecture. As shown in Figure~\ref{fig:knowledge-timeseries}A and C, we can apply this approach separately for the questions from each quiz participants took throughout the @@ -513,16 +513,16 @@ \section*{Results} of Stars}.} The panel is in the same format as Panel B, but here the knowledge estimates are for the content of the \textit{Birth of Stars} lecture. \textbf{All panels.} Error ribbons and error bars denote 95\% confidence - intervals, estimated across participants.} + intervals of the mean, estimated across participants ($n = 50$).} \label{fig:knowledge-timeseries} \end{figure} While the time courses in Figure~\ref{fig:knowledge-timeseries}A and C provide -detailed \textit{estimates} about participants' knowlege, these estimates are -of course only \textit{useful} to the extent that they accurately reflect what +detailed estimates about participants' knowlege, these estimates are +of course only useful to the extent that they accurately reflect what participants actually know. As one sanity check, we anticipated that the -knowledge estimates should reflect a content-specific ``boost'' in +knowledge estimates should reflect a content-specific boost in participants' knowledge after watching each lecture. In other words, if participants learn about each lecture's content upon watching it, the knowledge estimates should capture that. After watching the \textit{Four @@ -542,7 +542,7 @@ \section*{Results} (versus before) watching it (Fig.~\ref{fig:knowledge-timeseries}D). Specifically, since participants watched that lecture after taking Quiz~2 (but before Quiz~3), we hypothesized that their knowledge estimates should be -relatively low on Quizzes~1 and 2, but should show a ``boost'' on Quiz~3. +relatively low on Quizzes~1 and 2, but should show a boost on Quiz~3. Consistent with this prediction, we found no reliable differences in estimated knowledge about the \textit{Birth of Stars} lecture content on Quiz~1 versus 2 ($t(49) = 1.013,~p = 0.316$), but estimated knowledge was substantially @@ -563,11 +563,11 @@ \section*{Results} embedding coordinate, while accounting for varied effects of individual participants and questions (see \nameref{subsec:glmm}). To assess the predictive value of the knowledge estimates, we compared each GLMM to an analogous (i.e., -nested) ``null'' model that assumed these estimates carried no predictive information using parametric bootstrap likelihood-ratio tests. +nested) null model that assumed these estimates carried no predictive information using parametric bootstrap likelihood-ratio tests. \begin{figure}[tp] \centering - \includegraphics[width=0.75\textwidth]{figs/predict-knowledge-questions} + \includegraphics[width=0.71\textwidth]{figs/predict-knowledge-questions} \caption{\textbf{Predicting success on held-out questions using estimated knowledge.} We used generalized linear mixed models (GLMMs) to model the probability of correctly answering a quiz question as a function of @@ -577,16 +577,19 @@ \section*{Results} knowledge for each question based on all other questions the same participant answered on the same quiz (``All questions''; top row), knowledge for each question about one lecture based on all other questions - (from the same participant and quiz) about the \textit{same} lecture + (from the same participant and quiz) about the same lecture (``Within-lecture''; middle rows), and knowledge for each question about one lecture based on all questions (from the same participant and quiz) - about the \textit{other} lecture (``Across-lecture''; bottom rows). The + about the other lecture (``Across-lecture''; bottom rows). The backgrounds in each panel display kernel density estimates of the relative observed proportions of correctly (blue) versus incorrectly (red) answered questions, for each level of estimated knowledge along the $x$-axis. The black curves display the (population-level) GLMM-predicted probabilities of correctly answering a question as a function of estimated knowledge. Error - ribbons denote 95\% confidence intervals.} + ribbons denote 95\% confidence intervals of the predicted mean probabilities. + $OR$ denotes the model-estimated odds ratio. $\lambda_{LR}$ denotes the result + of a likelihood-ratio test for the effect of estimated knowledge. $p$-values + were estimated via parametric bootstrapping.} \label{fig:predictions} \end{figure} @@ -594,20 +597,20 @@ \section*{Results} We carried out three different versions of the analyses described above, wherein we considered different sources of information in our estimates of participants' knowledge for each quiz question. First, we estimated knowledge -at each held-out question's embedding coordinate using \textit{all} other questions +at each held-out question's embedding coordinate using all other questions answered by the same participant on the same quiz (``All questions''; Fig.~\ref{fig:predictions}, top row). This test was intended to assess the overall predictive power of our approach. Second, we estimated knowledge for each question about a given lecture using only the other questions (from the -same participant and quiz) about that \textit{same} lecture +same participant and quiz) about that same lecture (``Within-lecture''; Fig.~\ref{fig:predictions}, middle rows). This test was -intended to assess the \textit{specificity} of our approach by asking whether +intended to assess the specificity of our approach by asking whether our predictions could distinguish between questions about different content covered by the same lecture. Third, we estimated knowledge for each question about one lecture using only the questions (from the same participant and quiz) -about the \textit{other} lecture (``Across-lecture''; +about the other lecture (``Across-lecture''; Fig.~\ref{fig:predictions}, bottom rows). This test was intended to assess the -\textit{generalizability} of our approach by asking whether our predictions +generalizability of our approach by asking whether our predictions could extend across the content areas of the two lectures. When estimating participants' knowledge, we used a rebalancing procedure to ensure that (for a given participant and quiz) their knowledge estimates for correctly and @@ -629,7 +632,7 @@ \section*{Results} We observed a similar set of results when we restricted our estimates of participants' knowledge to consider only -their performance on other questions about the \textit{same} lecture. +their performance on other questions about the same lecture. Specifically, for Quiz~1, participants' knowledge of \textit{Four Fundamental Forces}-related questions, estimated from their performance on other \textit{Four Fundamental Forces}-related questions, was predictive of their @@ -642,44 +645,43 @@ \section*{Results} participants' Quiz~2 responses (\textit{Four Fundamental Forces}: $OR = 35.126,\ 95\%\ \textnormal{CI} = [5.113,\ 123.868],\ \lambda_{LR} = 32.251,\ p < 0.001$; \textit{Birth of Stars}: $OR = 4.717,\ 95\%\ \textnormal{CI} = -[2.021,\ 9.844],\ \lambda_{LR} = 16.788,\ p < 0.001$) and partially for -their Quiz~3 responses (\textit{Birth of Stars}: $OR = 16.902,\ 95\%\ -\textnormal{CI} = [3.353,\ 53.265],\ \lambda_{LR} = 23.233,\ p < 0.001$; +[2.021,\ 9.844],\ \lambda_{LR} = 16.788,\ p < 0.001$) and partially for +their Quiz~3 responses (\textit{Birth of Stars}: $OR = 16.902,\ 95\%\ +\textnormal{CI} = [3.353,\ 53.265],\ \lambda_{LR} = 23.233,\ p < 0.001$; \textit{Four Fundamental Forces}: $OR = 2.485,\ 95\%\ \textnormal{CI} = -[0.724,\ 8.366],\ \lambda_{LR} = 1.984,\ p = 0.170$). Speculatively, the Quiz~3 -results suggest that the within-lecture knowledge estimates may be -susceptible to ceiling effects in -participants' quiz performance. On Quiz~3, after viewing both lectures, no +[0.724,\ 8.366],\ \lambda_{LR} = 1.984,\ p = 0.170$). We note that +the within-lecture knowledge estimates are susceptible to ceiling effects in +participants' quiz performance. For example, on Quiz~3, after viewing both lectures, no participant answered more than three \textit{Four Fundamental Forces}-related questions incorrectly, and all but five participants (out of 50) answered two or -fewer incorrectly. (This was the only subset of questions about either lecture, -across all three quizzes, for which this was true.) Consequently, for 90\% of -participants, our within-lecture estimates of their knowledge for \textit{Four -Fundamental Forces}-related questions that they answered incorrectly leveraged -information from at most a single other question they were \textit{not} able to -correctly answer. This likely hampered our ability to accurately characterize -the specific (and by the time they took Quiz~3, relatively few) aspects of the -lecture content these participants did \textit{not} know about, and successfully -distinguish them from the far more numerous aspects of the lecture content they -now \textit{did} know about. Taken together, these within-lecture results suggest that our +fewer incorrectly. (This was the only subset of questions about either lecture, +across all three quizzes, for which this was true.) Consequently, for 90\% of +participants, our within-lecture estimates of their knowledge for \textit{Four +Fundamental Forces}-related questions that they answered incorrectly leveraged +information from at most a single other question they were not able to +correctly answer. This hampered our ability to accurately characterize +the specific (and by the time they took Quiz~3, relatively few) aspects of the +lecture content these participants did not know about, and successfully +distinguish them from the far more numerous aspects of the lecture content they +now did know about. Taken together, these within-lecture results suggest that our knowledge estimates can reliably distinguish between questions about different -content covered by a single lecture, provided there is sufficient diversity in -participants' quiz responses to extract meaningful information about both what +content covered by a single lecture, provided there is sufficient diversity in +participants' quiz responses to extract meaningful information about both what they know and what they do not know. Finally, we estimated participants' knowledge for each question about each lecture using only their performance on questions (from the same quiz) about the -\textit{other} lecture. This is an especially stringent test of our approach. +other lecture. This is an especially stringent test of our approach. Our primary assumption in constructing our knowledge estimates is that knowledge about a given concept is similar to knowledge about other concepts that are nearby in the embedding space. However, our analyses in Figure~\ref{fig:topics} and Supplementary Figure~\topicWeights\ show that the embeddings of content from -the two lectures (and of their associated quiz questions) are largely distinct -from each other. Therefore, any predictive power of these across-lecture -knowledge estimates must overcome large distances in the embedding space. To put -this in concrete terms, this test requires predicting participants' performance -on individual, highly specific questions about the formation of stars from their -responses to just five multiple-choice questions about the fundamental forces of +the two lectures (and of their associated quiz questions) are largely distinct +from each other. Therefore, any predictive power of these across-lecture +knowledge estimates must overcome large distances in the embedding space. To put +this in concrete terms, this test requires predicting participants' performance +on individual, highly specific questions about the formation of stars from their +responses to just five multiple-choice questions about the fundamental forces of the universe (and vice versa). We found that, before viewing either lecture (i.e., on Quiz~1), participants' @@ -698,40 +700,40 @@ \section*{Results} [0.739,\ 12.849],\ \lambda_{LR} = 3.266,\ p = 0.083$; \textit{Birth of Stars} questions given \textit{Four Fundamental Forces} questions: $OR = 2.199,\ 95\%\ \textnormal{CI} = [0.711,\ 5.623],\ \lambda_{LR} = 2.304,\ p = 0.141$). Only -after viewing \textit{both} lectures (i.e., on Quiz~3) did these across-lecture +after viewing both lectures (i.e., on Quiz~3) did these across-lecture knowledge estimates reliably predict participants' success on individual quiz questions (\textit{Four Fundamental Forces} questions given \textit{Birth of Stars} questions: $OR = 11.294,\ 95\%\ \textnormal{CI} = [1.375,\ 47.744],\ \lambda_{LR} = 10.396,\ p < 0.001$; \textit{Birth of Stars} questions given \textit{Four Fundamental Forces} questions: $OR = 7.302,\ 95\%\ \textnormal{CI} = [1.077,\ 44.879],\ \lambda_{LR} = 4.708,\ p = 0.038$). Taken together, these -results suggest that our ability to form estimates solely across different +results indicate that our ability to form estimates solely across different content areas is more limited than our ability to form estimates that -incorporate responses to questions from both content areas (as in -Fig.~\ref{fig:predictions}, ``All questions'') or within a single content area -(as in Fig.~\ref{fig:predictions}, ``Within-lecture''). However, if participants -have recently received some training on both content areas, the knowledge +incorporate responses to questions from both content areas (as in +Fig.~\ref{fig:predictions}, ``All questions'') or within a single content area +(as in Fig.~\ref{fig:predictions}, ``Within-lecture''). However, if participants +have recently received some training on both content areas, the knowledge estimates appear to be informative even across content areas. We speculate that these ``Across-lecture'' results might relate to some of our -earlier work on the nature of semantic representations~\citep{MannKaha12}. In -that work, we asked whether semantic similarities could be captured through -behavioral measures, even if participants' ``true'' internal representations -differed from the embeddings used to \textit{characterize} their behaviors. We -found that mismatches between an individual's internal representation of a set -of concepts and the representation used to characterize their behaviors can lead -to underestimates of how semantically driven those behaviors are. Along similar -lines, we suspect that in our current study, participants' conceptual -representations may initially differ from the representations learned by our -topic model. (Although the topic model's representations are still -\textit{related} to participants' initial internal representations; otherwise we -would have found that knowledge estimates derived from Quizzes~1 and 2 had no -predictive power in the other tests we conducted.) After watching both lectures, -however, participants' internal representations may become more aligned with the -embeddings used to estimate their knowledge (since those embeddings were trained -on the lectures' transcripts). This could help explain why the knowledge -estimates derived from Quizzes~1 and 2 (before both lectures had been watched) -do not reliably predict performance across content areas, whereas estimates +earlier work on the nature of semantic representations~\citep{MannKaha12}. In +that work, we asked whether semantic similarities could be captured through +behavioral measures, even if participants' true internal representations +differed from the embeddings used to characterize their behaviors. We +found that mismatches between an individual's internal representation of a set +of concepts and the representation used to characterize their behaviors can lead +to underestimates of how semantically driven those behaviors are. Along similar +lines, we suspect that in our current study, participants' conceptual +representations may initially differ from the representations learned by our +topic model. (Although the topic model's representations are still +related to participants' initial internal representations; otherwise we +would have found that knowledge estimates derived from Quizzes~1 and 2 had no +predictive power in the other tests we conducted.) After watching both lectures, +however, participants' internal representations may become more aligned with the +embeddings used to estimate their knowledge (since those embeddings were trained +on the lectures' transcripts). This could help explain why the knowledge +estimates derived from Quizzes~1 and 2 (before both lectures had been watched) +do not reliably predict performance across content areas, whereas estimates derived from Quiz~3 do. That the knowledge predictions derived from the text embedding space reliably @@ -741,10 +743,10 @@ \section*{Results} explanatory power extend? For example, suppose we know that a participant correctly answered a question at embedding coordinate $x$. As we move farther away from $x$ in the embedding space, how does the likelihood that the -participant knows about the content at a given location ``fall off'' with +participant knows about the content at a given location fall off with distance? Conversely, suppose the participant instead answered that same -question \textit{incorrectly}. Again, as we move farther away from $x$ in the -embedding space, how does the likelihood that the participant does \textit{not} +question incorrectly. Again, as we move farther away from $x$ in the +embedding space, how does the likelihood that the participant does not know about the content at a given coordinate change with distance? We reasoned that, assuming our embedding space is capturing something about how individuals actually organize their knowledge, a participant's ability to answer questions @@ -753,7 +755,7 @@ \section*{Results} reach some sufficiently large distance from $x$, our ability to infer whether or not a participant will correctly answer a question based on their ability to answer the question at $x$ should be no better than guessing based on their -\textit{overall} proportion of correctly answered questions. In other words, +overall proportion of correctly answered questions. In other words, beyond the maximum distance at which a participant's ability to answer the question at $x$ is informative of their ability to answer a second question at location $y$, guessing the outcome at $y$ based on the outcome at $x$ should be no more @@ -765,21 +767,23 @@ \section*{Results} \includegraphics[width=0.8\textwidth]{figs/knowledge-smoothness} \caption{\textbf{Knowledge falls off gradually in text embedding space.} - \textbf{A. Performance versus distance.} For each participant, for each + \textbf{A. Performance versus distance.} For each participant, and for each correctly answered question (blue) or incorrectly answered question (red), we computed the proportion of correctly answered questions within a given distance of that question's embedding coordinate. We used these proportions as a proxy for participants' knowledge about the content within that region of the embedding space. We repeated this analysis for all questions and participants, and separately for each quiz (column). The black lines denote - the average proportion correct across \textit{all} questions included in - the analysis at the given distance. \textbf{B. Maximum distance for which + the average proportion correct including all questions a participant answered + on the given quiz (i.e., not restricted by distance from the reference question). + Error ribbons denote bootstrap-estimated 95\% confidence intervals of the across-participants mean. + \textbf{B. Maximum distance for which performance is reliably different from the average.} We used a bootstrap procedure (see~\nameref{subsec:smoothness}) to estimate the point at which the blue and red lines in Panel A reliably diverged from the black line. We repeated this analysis separately for correctly and incorrectly answered - questions from each quiz. \textbf{All panels.} Error ribbons denote - bootstrap-estimated 95\% confidence intervals.} + questions from each quiz. Error ribbons denote 95\% confidence intervals of + the mean distance over 10,000 subsamples.} \label{fig:smoothness} \end{figure} @@ -790,7 +794,7 @@ \section*{Results} plotted this proportion as a function of $r$ for questions that participants answered correctly, and for questions they answered incorrectly. As shown in Figure~\ref{fig:smoothness}, we found that quiz -performance falls off smoothly with distance, and the ``rate'' at which it falls off +performance falls off smoothly with distance, and the rate at which it falls off does not appear to differ across quizzes, as measured by the distance at which performance becomes statistically indistinguishable from a simple proportion-correct score (see~\nameref{subsec:smoothness}). This @@ -803,7 +807,7 @@ \section*{Results} Knowledge estimates need not be limited to the contents of these particular lectures and quizzes. As illustrated in Figure~\ref{fig:knowledge-maps}, our general approach to estimating knowledge from a small number of quiz questions -may be extended to \textit{any} content, given its text embedding coordinate. To +may be extended to any content, given its text embedding coordinate. To visualize how knowledge ``spreads'' through text embedding space to content beyond the lectures participants watched and the questions they answered, we first fit a new topic model to the lectures' sliding windows with $k = @@ -821,9 +825,9 @@ \section*{Results} \includegraphics[width=\textwidth]{figs/knowledge_and_learning_maps} \caption{\textbf{Mapping out the geometry of knowledge and learning.} - \textbf{A. Average ``knowledge maps'' estimated using each quiz.} Each map + \textbf{A. Average knowledge maps estimated using each quiz.} Each map displays a 2D projection of participants' estimated knowledge about the content - reflected by \textit{all} regions of topic space (see + reflected by all regions of topic space (see \nameref{subsec:knowledge-maps}). The topic trajectories of the two lectures are indicated by dotted lines (blue: Lecture~1; green: Lecture~2), and the coordinates of each question are indicated by dots (light blue: @@ -831,12 +835,12 @@ \section*{Results} knowledge). Each map reflects an average across all participants. For individual participants' maps, see Supplementary Figures~\individualKnowledgeMapsA,~\individualKnowledgeMapsB, - and~\individualKnowledgeMapsC. \textbf{B. Average ``learning maps'' + and~\individualKnowledgeMapsC. \textbf{B. Average learning maps estimated between each successive pair of quizzes.} The learning maps follow the same general format as the knowledge maps in Panel A, but here - the shading at each coordinate indicates the \textit{difference} between - the corresponding coordinates in the indicated \textit{pair} of knowledge - maps---i.e., how much the estimated knowledge ``changed'' between the two + the shading at each coordinate indicates the difference between + the corresponding coordinates in the indicated pair of knowledge + maps---i.e., how much the estimated knowledge changed between the two quizzes. Each map reflects an average across all participants. For individual participants' maps, see Supplementary Figures~\individualLearningMapsA~and~\individualLearningMapsB. \textbf{C. @@ -849,7 +853,7 @@ \section*{Results} (green) lectures, on average, across all timepoints' topic vectors.} \label{fig:knowledge-maps} - \end{figure} +\end{figure} We projected the resulting 100-dimensional topic vectors (for each second of the lectures and each quiz question) onto a shared 2-dimensional plane (see @@ -858,11 +862,11 @@ \section*{Results} projections of the lectures and questions. We then used Equation~\ref{eqn:rbf-knowledge} to estimate participants' knowledge at each of these 10,000 sampled locations, and averaged these estimates across -participants to obtain an estimated average \textit{knowledge map} +participants to obtain an estimated average ``knowledge map'' (Fig.~\ref{fig:knowledge-maps}A). Intuitively, the knowledge map constructed -from a given quiz's responses provides a visualization of ``how much'' +from a given quiz's responses provides a visualization of how much participants knew about any content expressible by the fitted text embedding -model at the point in time when they completed that quiz. +model at the point in time when they completed that quiz. We note that we used these 2D maps solely for visualization; all relevant comparisons, distance computations, and statistical tests we report above were carried out in the original 15-dimensional space, using the 15-topic model. @@ -879,7 +883,7 @@ \section*{Results} content that is nearby (i.e., related to) the content from the lecture they watched prior to taking Quiz~2. This localization is non-trivial: these knowledge estimates are informed only by the embedded coordinates of the -\textit{quiz questions}, not by the embeddings of either lecture (see +quiz questions, not by the embeddings of either lecture (see Eqn.~\ref{eqn:rbf-knowledge}). Finally, the knowledge map estimated from Quiz~3 responses shows a second increase in knowledge, localized to the region surrounding the embedding of the \textit{Birth of Stars} lecture participants @@ -889,7 +893,7 @@ \section*{Results} participants viewed each lecture is displayed in Figure~\ref{fig:knowledge-maps}B. Taking the point-by-point difference between the knowledge maps estimated from responses to a successive pair of quizzes -yields a \textit{learning map} that describes the \textit{change} in estimated +yields a ``learning map'' that describes the change in estimated knowledge from one quiz to the next. These learning maps highlight that the estimated knowledge increases we observed across maps were specific to the regions around the embeddings of each lecture, in turn. @@ -909,7 +913,7 @@ \section*{Results} Forces} embedding tended to be weighted more heavily by the topics expressed in that lecture. Similarly, the top-weighted words at the example coordinate near the \textit{Birth of Stars} embedding tended to be weighted more heavily by the -topics expressed in \textit{that} lecture. The top-weighted words at the +topics expressed in that lecture. The top-weighted words at the example coordinate between the two lectures' embeddings show a roughly even mix of words most strongly associated with each lecture. @@ -936,88 +940,88 @@ \section*{Discussion} conceptual knowledge. First, from a methodological standpoint, our modeling framework provides a systematic means of mapping out and characterizing knowledge in maps that have infinite (arbitrarily many) numbers of coordinates, -and of ``filling out'' those maps using relatively small numbers of -multiple-choice quiz questions. Our experimental finding that we can use these -maps to predict success on held-out questions has several psychological -implications as well. For example, concepts that are assigned to nearby -coordinates by the text embedding model also appear to be ``known to a similar -extent'' (as reflected by participants' responses to held-out questions; +and of filling out those maps using relatively small numbers of +multiple-choice quiz questions. Our experimental finding that we can use these +maps to predict success on held-out questions has several psychological +implications as well. For example, concepts that are assigned to nearby +coordinates by the text embedding model also appear to be known to a similar +extent (as reflected by participants' responses to held-out questions; Fig.~\ref{fig:predictions}). This suggests that participants also -\textit{conceptualize} similarly the content reflected by nearby embedding -coordinates. How participants' knowledge ``falls off'' with spatial distance is +conceptualize similarly the content reflected by nearby embedding +coordinates. How participants' knowledge falls off with spatial distance is captured by the knowledge maps we infer from their quiz responses (e.g., Figs.~\ref{fig:smoothness},~\ref{fig:knowledge-maps}). In other words, our study shows that knowledge about a given concept implies knowledge about related concepts, and how far this implication extends in text embedding space. -In our study, we characterize the ``coordinates'' of participants' knowledge +In our study, we characterize the coordinates of participants' knowledge using a relatively simple ``bag-of-words'' text embedding model~\citep[LDA; ][]{BleiEtal03}. More sophisticated text embedding models, such as transformer-based models~\citep{ViswEtal17, DevlEtal18, ChatGPT, TouvEtal23}, -can leverage additional textual information such as complex grammatical and -semantic relationships between words, higher-order syntactic structures, -stylistic features, and more. We considered using transformer-based models in -our study, but we found that the text embeddings derived from these models were -surprisingly uninformative with respect to differentiating or otherwise -characterizing the conceptual content of the lectures and questions we used -(see \suppResults). We suspect that this reflects a broader challenge in -constructing models that are both high-resolution within a given domain (e.g., -the domain of physics lectures and questions) \textit{and} sufficiently broad -as to enable them to cover a wide range of domains. Essentially, -``larger'' language models learn more complex features of language through -training on enormous and diverse text corpora. But as a result, their -embedding spaces also ``span'' an enormous and diverse range of conceptual -content, sacrificing a degree of specificity in their capacities to distinguish -subtle conceptual differences within a more narrow range of content. -In comparing our LDA model (trained specifically on the lectures used in our -study) to a larger transformer-based model (BERT), we found that our LDA model provides -both coverage of the requisite material and specificity at the level of -individual questions, while BERT essentially relegates the contents of both -lectures and all quiz questions (which are all broadly about ``physics'') to a -tiny region of its embedding space, thereby blurring out meaningful distinctions -between different specific concepts covered by the lectures and questions -(Supp.~Fig.~\ldaVsBERT). We note that these are not criticisms of BERT, nor of -other large language models trained on large and diverse corpora. Rather, our -point is that simpler models trained on relatively small but specialized -corpora can outperform much more complex models trained on much larger corpora -when we are specifically interested in capturing subtle conceptual differences -at the level of a single, narrowly focused course lecture or quiz question. On the other hand, if -our goal had been to choose a model that generalized to many different content -areas simultaneously, we would expect our LDA model to perform comparatively poorly to BERT or -other much larger general-purpose models. We suggest that bridging this tradeoff -between achieving high resolution within a single content area and the ability to -generalize to many diverse content areas will be an important challenge for +can leverage additional textual information such as complex grammatical and +semantic relationships between words, higher-order syntactic structures, +stylistic features, and more. We considered using transformer-based models in +our study, but we found that the text embeddings derived from these models were +surprisingly uninformative with respect to differentiating or otherwise +characterizing the conceptual content of the lectures and questions we used +(see \suppDiscussion). We suspect that this reflects a broader challenge in +constructing models that are both high-resolution within a given domain (e.g., +the domain of physics lectures and questions) and sufficiently broad +as to enable them to cover a wide range of domains. Essentially, +``larger'' language models learn more complex features of language through +training on enormous and diverse text corpora. But as a result, their +embedding spaces also ``span'' an enormous and diverse range of conceptual +content, sacrificing a degree of specificity in their capacities to distinguish +subtle conceptual differences within a more narrow range of content. +In comparing our LDA model (trained specifically on the lectures used in our +study) to a larger transformer-based model (BERT), we found that our LDA model provides +both coverage of the requisite material and specificity at the level of +individual questions, while BERT essentially relegates the contents of both +lectures and all quiz questions (which are all broadly about physics) to a +tiny region of its embedding space, thereby blurring out meaningful distinctions +between different specific concepts covered by the lectures and questions +(Supp.~Fig.~\ldaVsBERT). We note that these are not criticisms of BERT, nor of +other large language models trained on large and diverse corpora. Rather, our +point is that simpler models trained on relatively small but specialized +corpora can outperform much more complex models trained on much larger corpora +when we are specifically interested in capturing subtle conceptual differences +at the level of a single, narrowly focused course lecture or quiz question. On the other hand, if +our goal had been to choose a model that generalized to many different content +areas simultaneously, we would expect our LDA model to perform comparatively poorly to BERT or +other much larger general-purpose models. We suggest that bridging this tradeoff +between achieving high resolution within a single content area and the ability to +generalize to many diverse content areas will be an important challenge for future work. At the opposite end of the spectrum from large language models, one could also -imagine using an even \textit{simpler} ``model'' than LDA that relates the -contents of course lectures and quiz questions through explicit word-overlap -metrics (rather than similarities in the latent topics they exhibit). In a -supplementary analysis (Supp.~Fig.~\jaccard), we compared the LDA-based +imagine using an even simpler ``model'' than LDA that relates the +contents of course lectures and quiz questions through explicit word-overlap +metrics (rather than similarities in the latent topics they exhibit). In a +supplementary analysis (Supp.~Fig.~\jaccard), we compared the LDA-based question-lecture matches shown in Figure~\ref{fig:question-correlations} with -analogous matches based on the Jaccard similarity between each question's text -and each sliding window from the corresponding lecture's transcript. As for -the embeddings derived from BERT, we found that this word-matching approach also blurred -meaningful distinctions between concepts presented in different parts of each +analogous matches based on the Jaccard similarity between each question's text +and each sliding window from the corresponding lecture's transcript. As for +the embeddings derived from BERT, we found that this word-matching approach also blurred +meaningful distinctions between concepts presented in different parts of each lecture and tested by different quiz questions. But rather than characterizing -their contents at too \textit{broad} a semantic scale, the lack of specificity -in this approach arises from considering too \textit{narrow} a semantic scale: -the sorts of concepts typically conveyed in course lectures and tested by quiz -questions are not defined (and meaningful similarities and distinctions between +their contents at too broad a semantic scale, the lack of specificity +in this approach arises from considering too narrow a semantic scale: +the sorts of concepts typically conveyed in course lectures and tested by quiz +questions are not defined (and meaningful similarities and distinctions between them do not tend to emerge) at the level of individual words. -In other words, while the embedding spaces of more complex large language models -afford low resolution at the scale of individual course lectures and questions -because they ``zoom out'' too far, simpler word-matching measures afford low -resolution because they ``zoom \textit{in}'' too far. In this way, we view our -approach as occupying a sort of ``sweet spot'' between simpler and more complex -alternatives, in that it enables us to characterize the contents of course -materials at the appropriate semantic scale where relevant concepts ``come into -focus.'' Our approach enables us to accurately and consistently identify each -question's content in a way that matches it with specific content from the -lectures and distinguishes it from other questions about similar content. In -turn, this enables us to construct accurate predictions about participants' -knowledge of the conceptual content tested by individual quiz questions +In other words, while the embedding spaces of more complex large language models +afford low resolution at the scale of individual course lectures and questions +because they ``zoom out'' too far, simpler word-matching measures afford low +resolution because they ``zoom in'' too far. In this way, we view our +approach as occupying a sort of sweet spot between simpler and more complex +alternatives, in that it enables us to characterize the contents of course +materials at the appropriate semantic scale where relevant concepts ``come into +focus.'' Our approach enables us to accurately and consistently identify each +question's content in a way that matches it with specific content from the +lectures and distinguishes it from other questions about similar content. In +turn, this enables us to construct accurate predictions about participants' +knowledge of the conceptual content tested by individual quiz questions (Fig.~\ref{fig:predictions}). Another application for large language models that does \textit{not} require @@ -1029,15 +1033,15 @@ \section*{Discussion} these generative text model-based systems do not explicitly model what learners know, or how their knowledge changes over time with training. One could imagine building a hybrid system that combines the best of both worlds: a large -language model that can \textit{generate} text, combined with a smaller model -that can \textit{infer} what learners know and how their knowledge changes over +language model that can generate text, combined with a smaller model +that can infer what learners know and how their knowledge changes over time. Such a hybrid system could potentially be used to build the next generation of interactive tutoring systems that are able to adapt to learners' needs in real time, and provide more nuanced feedback about what learners know and what they do not know. One limitation of our approach is that topic models contain no explicit -internal representations of more complex aspects of ``knowledge,'' like +internal representations of more complex aspects of knowledge, like knowledge graphs, dependencies or associations between concepts, causality, and so on. These representations might (in principle) be added as extensions to our approach to more accurately and precisely capture, characterize, and track @@ -1078,7 +1082,7 @@ \section*{Discussion} evaluating the complex models and data that are a hallmark of naturalistic paradigms. -Beyond specifically predicting what people \textit{know}, the fundamental ideas +Beyond specifically predicting what people know, the fundamental ideas we develop here also relate to the notion of ``theory of mind'' of other individuals~\citep{GoldWinn12, KansEtal15, Melt11}. Considering others' unique perspectives, prior experiences, knowledge, goals, etc., can help us to more @@ -1096,7 +1100,7 @@ \section*{Discussion} be able to communicate about the corresponding conceptual content. Ultimately, our work suggests a rich new line of questions about the geometric -``form'' of knowledge, how knowledge changes over time, and how we might map +form of knowledge, how knowledge changes over time, and how we might map out the full space of what an individual knows. Our finding that detailed estimates about knowledge may be obtained from short quizzes shows one way that traditional approaches to evaluation in education may be extended. We hope that @@ -1104,12 +1108,14 @@ \section*{Discussion} delivering educational content that are tailored to individual students' learning needs and goals. -\section*{Materials and methods} +\section*{Methods} \subsection*{Participants} We enrolled a total of 50 Dartmouth undergraduate students in our study. -Participants received optional course credit for enrolling. We asked each +Participants received optional course credit for enrolling. +No statistical method was used to predetermine sample size and no +participants were excluded from the dataset. We asked each participant to complete a demographic survey that included questions about their age, gender, native spoken language, ethnicity, race, hearing, color vision, sleep, coffee consumption, level of alertness, and several aspects of @@ -1117,7 +1123,10 @@ \subsection*{Participants} Participants' ages ranged from 18 to 22 years (mean: 19.52 years; standard deviation: 1.09 years). A total of 15 participants reported their gender as -male and 35 participants reported their gender as female. A total of 49 +male and 35 participants reported their gender as female. As we had no a +priori hypotheses regarding sex or gender differences in this study, +neither sex nor gender was considered in the study design and we did not +perform any sex- or gender-based analyses. A total of 49 participants reported their native language as ``English'' and 1 reported having another native language. A total of 47 participants reported their ethnicity as ``Not Hispanic or Latino'' and three reported their ethnicity as @@ -1206,14 +1215,14 @@ \subsection*{Experiment}\label{subsec:experiment} questions that tested for general conceptual knowledge about basic physics (covering material that was not presented in either video). To help broaden the set of lecture-specific questions, our team worked through each lecture in -small segments to identify what each segment was ``about'' conceptually, and +small segments to identify what each segment was about conceptually, and then write a question about that concept. The general physics questions were drawn from our team's prior coursework and areas of interest, along with internet searches and brainstorming with the project team and other members of J.R.M.'s lab. Although we attempted to design the questions to test ``conceptual knowledge,'' we note that estimating the specific ``amount'' of conceptual -understanding that each question ``requires'' to answer is somewhat subjective, -and might even come down to the ``strategy'' a given participant used to answer +understanding that each question requires to answer is somewhat subjective, +and might even come down to the strategy a given participant used to answer the question at that particular moment. The full set of questions and answer choices may be found in Supplementary Table~\questions. The final set of questions (and response options) was reviewed and approved by J.R.M. before we @@ -1250,11 +1259,11 @@ \subsubsection*{Constructing text embeddings of multiple lectures and questions} Dirichlet Allocation; ][]{BleiEtal03} trained on a set of documents to discover a set of $k$ ``topics'' or ``themes.'' Formally, each topic is defined as a distribution of weights over words in the model's vocabulary -(i.e., the union of all unique words across all documents, excluding ``stop -words''). Conceptually, each topic is intended to give larger weights to words +(i.e., the union of all unique words across all documents, excluding stop-words). +Conceptually, each topic is intended to give larger weights to words that are semantically related (as inferred from their tendency to co-occur in the same document). After fitting a topic model, each document in the training -set, or any \textit{new} document that contains at least some of the words in +set, or any new document that contains at least some of the words in the model's vocabulary, may be represented as a $k$-dimensional vector describing how much that document (most probably) reflects each topic. To select an appropriate $k$ for our model, as a starting point, we identified the @@ -1301,15 +1310,15 @@ \subsubsection*{Constructing text embeddings of multiple lectures and questions} two lines, and so on. This ensured that each line from the transcripts appeared in the same number ($w$) of sliding windows. We next performed a series of standard text preprocessing steps: normalizing case, lemmatizing, removing -punctuation and removing stop-words. We constructed our corpus of stop words by +punctuation and removing stop-words. We constructed our corpus of stop-words by augmenting the Natural Language Toolkit~\citep[NLTK; ][]{BirdEtal09} English -stop word list with the following additional words, selected using one of the +stop-word list with the following additional words, selected using one of the approaches suggested by~\citet{BoydEtal14}: ``actual,'' ``actually,'' ``also,'' ``bit,'' ``could,'' ``e,'' ``even,'' ``first,'' ``follow,'' ``following,'' ``four,'' ``let,'' ``like,'' ``mc,'' ``really,'', ``saw,'' ``see,'' ``seen,'' ``thing,'' and ``two.'' This yielded sliding windows containing an average of 73.8 remaining words, and spanning an average of 62.22~seconds. We treated the -text from each sliding window as a single ``document'' and combined these +text from each sliding window as a single document and combined these documents across the two lectures' windows to create a single training corpus for the topic model. @@ -1327,7 +1336,7 @@ \subsubsection*{Constructing text embeddings of multiple lectures and questions} linear interpolation (independently for each topic dimension) to resample the resulting time series to one vector per second. We also used the fitted model to obtain topic vectors for each quiz question in our pool (see Supp.~Tab.~\questions). -Taken together, we obtained a \textit{trajectory} for each lecture video, describing +Taken together, we obtained a trajectory for each lecture video, describing its path through topic space, and a single coordinate for each question (Fig.~\ref{fig:sliding-windows}C). Embedding both lectures and all of the questions using a common model enables us to compare the content from different @@ -1350,7 +1359,7 @@ \subsubsection*{Estimating dynamic knowledge traces}\label{subsec:traces} \end{equation} and where $\mathrm{mincorr}$ and $\mathrm{maxcorr}$ are the minimum and maximum correlations between the topic vectors for any lecture timepoint and quiz question, taken over all -timepoints in the given lecture and all questions \textit{about} that +timepoints in the given lecture and all questions about that lecture appearing on the given quiz. We also define $f(s, \Omega)$ as the $s$\textsuperscript{th} topic vector from the set of topic vectors $\Omega$. Here $t$ indexes the time series of lecture topic vectors $L$, and $i$ and $j$ index @@ -1376,14 +1385,14 @@ \subsubsection*{Generalized linear mixed models}\label{subsec:glmm} whether estimates of participants' knowledge at the embedding coordinates of individual quiz questions could be used to reliably predict their abilities to correctly answer those questions. In essence, we treated each question a given -participant answered on a given quiz as a ``lecture'' consisting of a single +participant answered on a given quiz as a lecture consisting of a single timepoint, and used Equation~\ref{eqn:prop} to estimate the participant's knowledge for its embedding coordinate based on their performance on all -\textit{other} questions they answered on that same quiz (``All questions''; +other questions they answered on that same quiz (``All questions''; Fig.~\ref{fig:predictions}, top row). Additionally, for each lecture-related question (i.e., excluding questions about general physics knowledge), we computed analogous knowledge estimates based on two different subsets of -questions the participant answered on the same quiz: (1) all \textit{other} +questions the participant answered on the same quiz: (1) all other questions about the same lecture as the target question (``Within-lecture''; Fig.~\ref{fig:predictions}, middle rows), and (2) all questions about the other of the two lectures (``Across-lecture''; Fig.~\ref{fig:predictions}, @@ -1391,7 +1400,7 @@ \subsubsection*{Generalized linear mixed models}\label{subsec:glmm} In performing these analyses, our null hypothesis is that the knowledge estimates we compute based on the quiz questions' embedding coordinates do -\textit{not} provide useful information about participants' abilities to correctly answer +not provide useful information about participants' abilities to correctly answer those questions---in other words, that there is no meaningful difference (on average) between the knowledge estimates we compute for questions participants answered correctly versus incorrectly. Specifically, since we @@ -1400,37 +1409,37 @@ \subsubsection*{Generalized linear mixed models}\label{subsec:glmm} embedding-space distance from the target coordinate; see Eqn.~\ref{eqn:prop}), if these weights are uninformative (e.g., randomly distributed), then our estimates of participants' knowledge should be equivalent (on average) to the -\textit{unweighted} proportion of correctly answered questions used to compute +unweighted proportion of correctly answered questions used to compute them. In general, for a given participant and quiz, this expected null value (i.e., that participant's proportion-correct score on that quiz) is the same for any coordinate in the embedding space (e.g., any lecture timepoint, quiz question, etc.). However, in the ``All questions'' and ``Within-lecture'' versions of the analyses shown in Figure~\ref{fig:predictions}, we estimate each participant's -knowledge for each target question using all \textit{other} questions (or all -\textit{other} questions about the same lecture) they answered on the same quiz. +knowledge for each target question using all other questions (or all +other questions about the same lecture) they answered on the same quiz. This introduces a systematic dependency between a participant's success on a target question and their proportion-correct score on the remaining questions available to estimate their knowledge for it. For example, suppose a participant correctly answered $n$ out of $q$ questions on a given quiz. If we hold out a -single \textit{correctly} answered question as the target, the proportion of +single correctly answered question as the target, the proportion of remaining questions answered correctly would be $\frac{n - 1}{q - 1}$, whereas -if we hold out a single \textit{incorrectly} answered question, the proportion +if we hold out a single incorrectly answered question, the proportion of remaining questions answered correctly would be $\frac{n}{q - 1}$. Thus, the proportion of correctly answered remaining questions (and therefore the null-hypothesized value of a knowledge estimate computed from them) is always -\textit{lower} for target questions a participant answered correctly than for +lower for target questions a participant answered correctly than for those they answered incorrectly. To correct for this baseline difference under our null hypothesis, we used a rebalancing procedure that ensured our knowledge estimates for questions each participant answered correctly and incorrectly were computed from the -\textit{same} proportion of correctly answered questions. For each target +same proportion of correctly answered questions. For each target question on a given participant's quiz, we first identified all remaining questions with the opposite ``correctness'' label (i.e., if the target question was answered correctly, we identified all remaining incorrectly answered questions, and vice versa). We then held out each of these opposite-label questions, in turn, along with the target question, and estimated the participant's knowledge -for the target question using all \textit{other} remaining questions. Since each +for the target question using all other remaining questions. Since each of these subsets of remaining questions was constructed by holding out one correctly answered question and one incorrectly answered question from the participant's quiz responses, if the participant correctly answered $n$ out of $q$ @@ -1472,15 +1481,15 @@ \subsubsection*{Generalized linear mixed models}\label{subsec:glmm} \[ \mathtt{accuracy \sim knowledge + (knowledge\ \vert\ participant) + (knowledge\ \vert\ question)} \] -where ``\texttt{accuracy}'' is a binary value indicating whether each target -question was answered correctly or incorrectly, ``\texttt{knowledge}'' is +where \texttt{accuracy} is a binary value indicating whether each target +question was answered correctly or incorrectly, \texttt{knowledge} is estimated knowledge at each target question's embedding coordinate, -``\texttt{participant}'' is a unique identifier assigned to each participant, -and ``\texttt{question}'' is a unique identifier assigned to each quiz +\texttt{participant} is a unique identifier assigned to each participant, +and \texttt{question} is a unique identifier assigned to each quiz question. For models we fit using knowledge estimates for target questions about multiple content areas (i.e., in the ``All questions'' version of the analysis), we also included an additional random effect term, -$\mathtt{(knowledge\ \vert\ lecture)}$, where ``\texttt{lecture}'' is a +$\mathtt{(knowledge\ \vert\ lecture)}$, where \texttt{lecture} is a categorical value denoting whether the target question was about \textit{Four Fundamental Forces}, \textit{Birth of Stars}, or general physics knowledge. Note that with our coding scheme, identifiers for each \texttt{question} are @@ -1489,7 +1498,7 @@ \subsubsection*{Generalized linear mixed models}\label{subsec:glmm} effects from the maximal model until it successfully converged with a full-rank random effects variance-covariance matrix. We obtained the odds ratios reported in Figure~\ref{fig:predictions} by exponentiating the estimated coefficient for -``\texttt{knowledge}'' from each fitted model. Conceptually, these odds ratios +\texttt{knowledge} from each fitted model. Conceptually, these odds ratios represent how many times greater the odds are that a given participant will answer a given question correctly if their estimated knowledge for its embedding coordinate is 1, compared to if it is 0. We estimated 95\% confidence intervals @@ -1501,10 +1510,10 @@ \subsubsection*{Generalized linear mixed models}\label{subsec:glmm} GLMM's ability to explain participants' success on individual quiz questions to that of an analogous model which assumed (as we assume under our null hypothesis) that knowledge estimates for correctly and incorrectly answered -questions did \textit{not} systematically differ, on average. Specifically, we +questions did not systematically differ, on average. Specifically, we used the same sets of observations to which we fit each ``full'' model to fit a second ``null'' model with the same random effects structure, but with -the coefficient for the fixed effect of ''\texttt{knowledge}'' constrained to zero +the coefficient for the fixed effect of \texttt{knowledge} constrained to zero (i.e., we removed this term from the null model). We then compared each full model to its reduced (null) equivalent using a likelihood-ratio test (LRT). Because the standard asymptotic $\chi^2_d$ approximation of the null @@ -1547,8 +1556,8 @@ \subsubsection*{Estimating the ``smoothness'' of knowledge}\label{subsec:smoothn Next, for each participant in the current subsample, and for each of the three quizzes they completed (separately), we iteratively treated each of the 15 -questions appearing on the given quiz as the ``reference'' question. We -constructed a series of concentric 15-dimensional ``spheres'' centered on the +questions appearing on the given quiz as the reference question. We +constructed a series of concentric 15-dimensional spheres centered on the reference question's embedding-space coordinate, where each successive sphere's radius increased by 0.01 (correlation distance) between 0 and 2, inclusive (i.e., tiling the range of possible correlation distances with 201 spheres in @@ -1570,7 +1579,7 @@ \subsubsection*{Estimating the ``smoothness'' of knowledge}\label{subsec:smoothn interval for the overall proportion correct (i.e., analogous to the black error bands in Fig.~\ref{fig:smoothness}A). -This resulted in two ``intersection'' distances for each quiz (for correctly +This resulted in two intersection distances for each quiz (for correctly answered and incorrectly answered reference questions). Repeating this full process for each of the 10,000 bootstrap iterations output two distributions of intersection distances for each of the three quizzes. The means and 95\% @@ -1581,7 +1590,7 @@ \subsubsection*{Creating knowledge and learning map visualizations}\label{subsec An important feature of our approach is that, given a trained text embedding model and participants' performance on each quiz question, we can estimate -their knowledge about \textit{any} content expressible by the embedding +their knowledge about any content expressible by the embedding model---not solely the content explicitly probed by the quiz questions, or even appearing in the lectures. To visualize these estimates (Fig.~\ref{fig:knowledge-maps}, Supp. @@ -1592,10 +1601,10 @@ \subsubsection*{Creating knowledge and learning map visualizations}\label{subsec 15-topic embedding space, we used a 100-topic embedding space for these visualizations. This change in the number of topics overcame an undesirable behavior in the UMAP embedding procedure, whereby embedding coordinates for the -15-topic model tended to be ``clumped'' into separated clusters, rather than +15-topic model tended to be grouped into separated clusters, rather than forming a smooth trajectory through the 2D space. When we increased the number of topics to 100, the embedding coordinates in the 2D space formed a smooth -trajectory through the space, with substantially less clumping +trajectory through the space, with substantially less aggressive grouping (Fig.~\ref{fig:knowledge-maps}). Creating a ``map'' by sampling this 100-dimensional space at high resolution to obtain an adequate set of topic vectors spanning the embedding space would be computationally intractable. @@ -1615,7 +1624,7 @@ \subsubsection*{Creating knowledge and learning map visualizations}\label{subsec whose elements are always non-negative and sum to one. Although UMAP is an invertible transformation at the embedding locations of the original data, other locations in the embedding space will not necessarily follow the same -implicit ``rules'' as the original high-dimensional data. For example, +implicit rules as the original high-dimensional data. For example, inverting an arbitrary coordinate in the embedding space might result in negative-valued vectors, which are incompatible with the topic modeling framework. To protect against this issue, we log-transformed the topic vectors @@ -1629,7 +1638,7 @@ \subsubsection*{Creating knowledge and learning map visualizations}\label{subsec every question, we defined a rectangle enclosing the 2D projections of the lectures' and quizzes' embeddings. We then sampled points from a regular $100 \times 100$ grid of coordinates that evenly tiled this enclosing rectangle. We -sought to estimate participants' knowledge (and learning, i.e., changes in +sought to estimate participants' knowledge (and learning; i.e., changes in knowledge) at each of the resulting 10,000 coordinates. To generate our estimates, we placed a set of 39 radial basis functions (RBFs) @@ -1640,9 +1649,9 @@ \subsubsection*{Creating knowledge and learning map visualizations}\label{subsec \mathrm{RBF}(x, \mu, \lambda) = \exp\left\{-\frac{||x - \mu||^2}{\lambda}\right\}. \label{eqn:rbf} \end{equation} -The $\lambda$ term in the RBF equation controls the ``smoothness'' of the +The $\lambda$ term in the RBF equation controls the smoothness of the function, where larger values of $\lambda$ result in smoother maps. In our -implementation we used $\lambda = 50$. Next, we estimated the ``knowledge'' +implementation, we used $\lambda = 50$. Next, we estimated the ``knowledge'' at each coordinate, $x$, using: \begin{equation} \hat{k}(x) = \frac{\sum_{i \in \mathrm{correct}} \mathrm{RBF}(x, q_i, \lambda)}{\sum_{j = 1}^N \mathrm{RBF}(x, q_j, \lambda)}. @@ -1650,30 +1659,480 @@ \subsubsection*{Creating knowledge and learning map visualizations}\label{subsec \end{equation} Equation~\ref{eqn:rbf-knowledge} computes the weighted proportion of correctly answered questions, where the weights are given by how nearby (in the 2D space) -each question is to the $x$. We also defined \textit{learning maps} as the +each question is to the $x$. We also defined ``learning maps'' as the coordinate-by-coordinate differences between any pair of knowledge maps. -Intuitively, learning maps reflect the \textit{change} in knowledge +Intuitively, learning maps reflect the change in knowledge across two maps. -\section*{Author contributions} - -Conceptualization: P.C.F., A.C.H., and J.R.M. Methodology: P.C.F., A.C.H., and -J.R.M. Software: P.C.F. Validation: P.C.F. Formal analysis: P.C.F. Resources: P.C.F., -A.C.H., and J.R.M. Data curation: P.C.F. Writing (original draft): J.R.M. Writing -(review and editing): P.C.F., A.C.H., and J.R.M. Visualization: P.C.F. and J.R.M. -Supervision: J.R.M. Project administration: P.C.F. Funding acquisition: J.R.M. - - \section*{Data availability} All of the data analyzed in this manuscript may be found at https://github.com/Con\-text\-Lab/eff\-ic\-ient-learn\-ing-khan. +All Khan Academy content is available for free at https://www.khan\-academy.org. \section*{Code availability} All of the code for running our experiment and carrying out the analyses may be found at https://github.com/Con\-text\-Lab/eff\-ic\-ient-learn\-ing-khan. + +%\bibliographystyle{apa} +%\bibliography{CDL-bibliography/cdl} + +\begin{thebibliography}{99} + +\bibitem[\protect\astroncite{Ashby and Maddox}{2005}]{AshbMadd05} +Ashby, F.~G. and Maddox, W.~T. 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Writing +(review and editing): P.C.F., A.C.H., and J.R.M. Visualization: P.C.F. and J.R.M. +Supervision: J.R.M. Project administration: P.C.F. Funding acquisition: J.R.M. + + +\section*{Competing interests} + +The authors declare no competing interests. + -\bibliographystyle{apa} -\bibliography{CDL-bibliography/cdl} \end{document} diff --git a/paper/supplement.pdf b/paper/supplement.pdf index 5a8426e..a868bcd 100644 Binary files a/paper/supplement.pdf and b/paper/supplement.pdf differ diff --git a/paper/supplement.tex b/paper/supplement.tex index ef11942..037ec0a 100644 --- a/paper/supplement.tex +++ b/paper/supplement.tex @@ -32,8 +32,8 @@ \newcommand{\knowledgeMaps}{7} \newcommand{\topicModelMethods}{\textit{Constructing text embeddings of multiple lectures and questions}} -\title{\textit{Supplementary materials for}: Text embedding models yield -high-resolution insights into conceptual knowledge from short multiple-choice +\title{\textit{Supplementary information for}: Text embedding models yield +detailed conceptual knowledge maps derived from short multiple-choice quizzes} \author{Paxton C. Fitzpatrick\textsuperscript{1}, Andrew C. @@ -326,7 +326,7 @@ \section*{Supplementary figures} \caption{\textbf{Individual participants' knowledge maps estimated from Quiz 1 responses.} Each panel is in the same format as the knowledge map displayed in the left panel of Figure~\knowledgeMaps A in the main text, - but here the maps are shown separately for each participant.} + but here each participant's map shows only the questions they answered on Quiz 1.} \label{fig:knowledge-maps-q1} \end{figure} @@ -339,7 +339,7 @@ \section*{Supplementary figures} \caption{\textbf{Individual participants' knowledge maps estimated from Quiz 2 responses.} Each panel is in the same format as the knowledge map displayed in the middle panel of Figure~\knowledgeMaps A in the main text, - but here the maps are shown separately for each participant.} + but here each participant's map shows only the questions they answered on Quiz 2.} \label{fig:knowledge-maps-q2} \end{figure} @@ -352,7 +352,7 @@ \section*{Supplementary figures} \caption{\textbf{Individual participants' knowledge maps estimated from Quiz 3 responses.} Each panel is in the same format as the knowledge map displayed in the right panel of Figure~\knowledgeMaps A in the main text, - but here the maps are shown separately for each participant.} + but here each participant's map shows only the questions they answered on Quiz 3.} \label{fig:knowledge-maps-q3} \end{figure} @@ -365,7 +365,7 @@ \section*{Supplementary figures} \caption{\textbf{Individual participants' learning maps estimated from Quiz 1 and 2 responses.} Each panel is in the same format as the learning map displayed in the left panel of Figure~\knowledgeMaps B in the main text, - but here the maps are shown separately for each participant.} + but here each participant's map shows only the questions they answered on Quizzes 1 \& 2.} \label{fig:learning-maps-q1_2} \end{figure} @@ -378,7 +378,7 @@ \section*{Supplementary figures} \caption{\textbf{Individual participants' learning maps estimated from Quiz 2 and 3 responses.} Each panel is in the same format as the learning map displayed in the right panel of Figure~\knowledgeMaps B in the main text, - but here the maps have been computed separately for each participant.} + but here each participant's map shows only the questions they answered on Quizzes 2 \& 3.} \label{fig:learning-maps-q2_3} \end{figure} @@ -400,7 +400,7 @@ \section*{Supplementary figures} \begin{figure}[tp] \centering - \includegraphics[width=0.57\textwidth]{figs/word-overlap-comparison} + \includegraphics[width=0.52\textwidth]{figs/word-overlap-comparison} \caption{\textbf{Topic correlations versus word overlap.} \textbf{A. Single-timepoint comparisons for \textit{Four Fundamental Forces}.} Each @@ -428,7 +428,7 @@ \section*{Supplementary figures} \FloatBarrier -\section*{Supplementary results} +\section*{Supplementary discussion} \doublespacing