Question Generation for Adaptive Education (2021)

Abstract: Intelligent and adaptive online education systems aim to make high-quality education available for a diverse range of students. However, existing systems usually depend on a pool of hand-made questions, limiting how fine-grained and open-ended they can be in adapting to individual students. We explore targeted question generation as a controllable sequence generation task. We first show how to fine-tune pre-trained language models for deep knowledge tracing (LM-KT). This model accurately predicts the probability of a student answering a question correctly, and generalizes to questions not seen in training. We then use LM-KT to specify the objective and data for training a model to generate questions conditioned on the student and target difficulty. Our results show we succeed at generating novel, well-calibrated language translation questions for second language learners from a real online education platform.

## introduction online education platforms can increase the accessibility of educational resources around the world. however, achieving equitable outcomes across diverse learning needs benefits from systems that are adaptive and individualized to each student @xcite . traditionally, adaptive education methods involve planning over a pool of pre-made questions @xcite . these are naturally limited by the diversity and coverage of the pool, as well as the scaling capacity of curriculum planning algorithms. recent approaches, such as procedural generation for personalized programming games @xcite , are limited to well-specified small domains. we address these limitations by leveraging recent success in deep generative models, in particular language models (lms). target @xmath0.86 .14 0.80 <@xmath1 ## background & related works there exists a rich body of work on precisely modeling student "ability" and learning. for example, item response theory (irt) seeks to model individual student ability based on their responses to different questions, creating a strong factorization between students and test items @xcite . meanwhile, computer adaptive testing (cat) techniques are used to determine a fixed student ability as quickly as possible by selecting test items based on information utility @xcite @xcite . however, these methods, which have been used to develop efficient standardized tests, do not necessarily optimize a student's learning experience @xcite . we instead focus on tracking each student's evolving knowledge, choosing questions to target difficulty. knowledge tracing (kt) seeks to model a student's knowledge state from their answer history in order to help individualize exercise sequences @xcite . this draws inspiration from traditional education curriculum practices, such as distributed spacing of vocabulary @xcite and mixed review in mathematics @xcite . to address simplifying assumptions in earlier kt approaches, such as discrete knowledge representations, @xcite introduced deep knowledge tracing (dkt), which uses rnns to enable more complex knowledge representations for students. recently, saint+ @xcite showed state-of-the-art performance on the popular ednet kt task using a transformer model to capture temporal information across activities, motivating our use of transformer lms. controllable text generation aims to steer lms towards desired attributes. examples include using reinforcement learning to control quality metrics @xcite , adjusting sampling weights to control for poetry style @xcite , and learning to condition on valence or domain-specific codes @xcite . to the best of our knowledge, we are 3 method given any autoregressive language model (e.g. gpt-2 @xcite , we can fine-tune a lm-kt model (p θ kt ) to predict whether an individual student will correctly answer the next question. if this model has well-calibrated uncertainty, we can use its predicted probability of a correct answer as a proxy for the difficulty of a question to a student. we then train a question generation model (p θ qg ) to generate a new question conditioned on a student and desired target difficulty. question representation unlike standard dkt, which treats questions as ids or simple handcrafted features, we represent questions fully in text (e.g. "she eats" in figure 1 ). this is a key contribution of our work, required by our eventual goal of generating questions in text, and allows the model to leverage similarity across linguistic features. we thus represent a question q as a sequence of words, with prefix and suffix tokens: we represent a student as a temporally-evolving sequence of questions and their responses. as in much previous kt work, we represent the student response as simply correct/incorrect, with special @xmath2. a student's current state is thus represented as a sequence of all past question and response pairs: question generation we frame question generation as finetuning a new autoregressive lm. given random samples of students and questions from a held-out set not used to train lm-kt, we can construct a new dataset d consisting of s i d @xmath3, @xmath4, q i ) is the continuous difficulty value assigned by lm-kt. we learn a linear layer to map the continuous input difficulty into a difficulty control vector c d of dimension matching the lm word-embeddings, which we append to the token embeddings. unlike lm-kt, we train our question generation model p θ qg to minimize the loss only on the question text, which only appears after @xmath5. if t g is the token index @xmath6, then our modified loss is: ## experiments our method generalizes to any education activity that can be represented with text sequences. due to the availability of real student learning data, we focus on a reverse language translation task, where a student translates phrases from their native language (e.g. english, "she eats") to the second language they are learning (e.g. spanish, "ella come"). ## conclusion our work is a first step toward showing that sequence-based models combined with domain knowledge, such as pre-trained lms, can be leveraged for adaptive learning tasks. we show how to use modern lms to generate novel reversetranslation questions that achieve a target difficulty, allowing adaptive education methods to expand beyond limited question pools. limitations of our approach include the compute constraints of large lms and training data availability. more detailed student data will be crucial to future model development. for instance, while most publicly available education datasets do not include the full student responses (e.g. full translation response in duolingo), such information could significantly improve the performance of our lm-kt model. other future directions include exploring non-language domains, such as math or logic exercises, and controlling for auxiliary objectives such as question topic. finally, designing appropriate user studies to evaluate our method is a complex yet critical next step to determine its suitability in a real-world education setting. our techniques allows control for individual student difficulty, but it leaves open the question of optimal curriculum design using difficulty-directed question generation.