Adaptive and Personalized Exercise Generation for Online Language Learning (2023)

Abstract: Adaptive learning aims to provide customized educational activities (e.g., exercises) to address individual learning needs. However, manual construction and delivery of such activities is a laborious process. Thus, in this paper, we study a novel task of adaptive and personalized exercise generation for online language learning. To this end, we combine a knowledge tracing model that estimates each student’s evolving knowledge states from their learning history and a controlled text generation model that generates exercise sentences based on the student’s current estimated knowledge state and instructor requirements of desired properties (e.g., domain knowledge and difficulty). We train and evaluate our model on real-world learner interaction data from Duolingo and demonstrate that LMs guided by student states can generate superior exercises. Then, we discuss the potential use of our model in educational applications using various simulations. These simulations show that our model can adapt to students’ individual abilities and can facilitate their learning efficiency by personalizing learning sequences.

## introduction adaptive learning technologies which continuously monitor student progress to dynamically adjust the level or type of learning materials based on the individual's abilities are quite popular @xcite . empirical studies have shown various benefits of adaptive learning, such as improved student learning outcomes @xcite , lower dropout rates @xcite , and increased instructor satisfaction @xcite . despite their effectiveness, designing adaptive systems is challenging as it usually involves planning a series of exercises that is personalized and adaptive to each student, which requires diverse exercise planning as well as an understanding of the student learning process. on the other hand, powered by advances in neural nlp, works have been done for automatically generating text-based exercises or questions for educational purposes in second language learning @xcite , mathematics @xcite @xcite , and computer science @xcite . nevertheless, how to apply these approaches in adaptive systems remains an open question. first, existing methods largely rely on pre-defined question templates or specified information sources (e.g., a passage), thereby resulting in limited knowledge coverage and low question difficulty control, and as a consequence, do not meet each student's individual and nuanced learning needs. besides, they are usually designed to generate standalone exercises, whereas adaptive learning systems usually require a continuous supply of exercises. another related line of research studies exercise recommendation to customize learning content based on individual ca-pabilities and goals @xcite . however, these systems are limited by the diversity of the exercise pool. to address the above limitations, we study the task of exercise generation in the context of adaptive learning, where we hypothesize that a student's dynamic knowledge state holds the key to generating adaptive and personalized exercises. specifically, we ground our study in the domain of language learning to create exercise sentences for translation, of which figure 1 illustrates the overall process. we start with an assumption about the dynamics between exercise difficulty, vocabulary, and a student's knowledge state ( § 3). then, we propose an approach ( § 4) that marries knowledge tracing (kt; @xcite ), a technique for estimating students' mastery states of knowledge components from their learning history, with a controlled text generation model that generates the next exercise based on instructor requirements, such as specified domain knowledge and target difficulty. we further explore various strategies to adapt the generation of exercises based on students' changing knowledge states. in doing this, our model not only supports personalized generation where the instructor (or the system) can express some desired properties of the generated exercises but is also adaptive to each student's learning progress. we conduct extensive experiments on real-world student learning data from duolingo foot_1 , a popular online language learning platform that offers structured and individualized learning content. our results ( § 5) show that pre-trained lms can help kt assess student language knowledge while student states estimated by kt can guide lms to generate adaptive and personalized exercises. we further discuss the potential use of our model in educational applications with simulations. the simulations show that our model can dynamically adjust exercise difficulty to match individual learning progress and facilitate their learning efficiency by customizing exercise sequences. early kt approaches model f kt as variants of logistic regression, such as item response theory (irt) and additive factor model (afm) @xcite , or probabilistic models such as bayesian knowledge tracing @xcite and its variants @xcite . these approaches heavily rely on their assumptions of the learning process which are often incomplete. in recent years, neural networks have become the dominant method in this area. @xcite proposed the first deep knowledge tracing model based on recurrent neural networks. after that, various architectures have been applied to model different characteristics of learning, such as self-attention @xcite , memory networks (abdelrahman and wang, 2019), and graph neural networks @xcite . exercise generation. previous exercise generation approaches for language learning primarily retrieve and manipulate text to create fixed types of exercises, such as gap fill and multiple-choice exercises @xcite @xcite , which are limited by the richness of the corpus. besides them, some question generation (qg) approaches have been proposed for educational purposes @xcite . while some of them allow for user control of certain question properties, they do not consider learners' individual and dynamic learning needs and progress. thus, they cannot achieve the goal of adaptive learning. recently, @xcite proposed an adaptive question generation model that connects question difficulty with student knowledge. however, it neither models students' fine-grained knowledge states nor provides control over domain knowledge. consequently, it is insufficient for practical use. controlled text generation (ctg) methods aim to steer text generation toward certain attributes. existing ctg approaches can be broadly classified into three types: directly training a classconditional language model (cclm) @xcite @xcite , guiding a model via an attribute discriminator @xcite , or manipulating decoder's logits (also referred to as weighted decoding) @xcite . this study explores difficulty and lexical control in generating language learning exercises. additionally, we seek to adapt the model's controllability to different users by building the dependency between control signals and individual states. ## problem formalization let @xmath0, r 1 ), ..., (e n , r n )} be a student's learning history consisting of n exercises and responses. here, e @xmath1,1 , ..., w i,|e i | } is an exercise sentence for translation and r @xmath2, 1} |e i | is the correctness label for each word in e i . we generate the next exercise e n+1 based on: v} is a subset of vocabulary v that should be included in the output. in general, the knowledge components can be user or system defined based on the current learning material. given the above setting, we formalize our task as: our task is distinct from previous ctg works in two aspects: 1) our control is dynamic; student states acting as control are also learnable; 2) there is a strong dependency among control signals (eqs. 3 and 4), which is non-trivial to learn. note that in this work, we measure difficulty via student performance and only consider vocabulary knowledge in defining s for simplicity. other definitions of sentence difficulty (e.g., definitions that incorporate other types of linguistic knowledge such as syntax) can be explored in future work. ## methodology our model is illustrated in figure 2 . we first employ a knowledge tracer t ( § 4.1) to estimate a student's time-varying knowledge states. then, we build an lm-based exercise generator g ( § 4.2) to create exercises based on estimated states and specified difficulty and knowledge components (words). we jointly optimize the two modules with an inconsistency loss ( § 4.3) at training and apply a constrained decoding strategy ( § 4.4) at inference. finally, we discuss how our model can accommodate personalized learning recommendation algorithms on the fly ( § 4.5). ## experimental results and analysis we experiment on the english track of duolingo second language acquisition modeling (slam) dataset @xcite , which contains about 1 million interactions of 2.6k learners over the first 30 days of learning a second language. for each student, we use the first 80% of interactions for training, and the subsequent and the last 10% for validation and testing, respectively. details of the dataset and experimental setup are in appendix b. we first evaluate the ability of the kt model to estimate student knowledge states in § 5.1. then, we analyze the effectiveness of the exercise generator in § 5.2. lastly, we showcase the superiority of our model in two educational scenarios with simulation experiments in § 5.3. ## conclusion we propose an adaptive and personalized exercise generation model combining recent advances in knowledge tracing and controllable generation using pre-trained lms. our approach works by learning the dynamics between exercise difficulty and student vocabulary knowledge in the domain of language learning. experimental results on real-world language learning data from duolingo demonstrate that our model can generate adaptive and personalized exercises needed in an educational setting. we further showcase our model's applicability in education with simulation studies.