Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability. In this paper, we propose a methodology for predicting student performance from their online learning activities that optimizes inference accuracy over different demographic groups such as race and gender. Building upon recent foundations in federated learning, in our approach, personalized models for individual student subgroups are derived from a global model aggregated across all student models via meta-gradient updates that account for subgroup heterogeneity. To learn better representations of student activity, we augment our approach with a self-supervised behavioral pretraining methodology that leverages multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums), and include a neural network attention mechanism in the model aggregation stage. Through experiments on three real-world datasets from online courses, we demonstrate that our approach obtains substantial improvements over existing student modeling baselines in predicting student learning outcomes for all subgroups. Visual analysis of the resulting student embeddings confirm that our personalization methodology indeed identifies different activity patterns within different subgroups, consistent with its stronger inference ability compared with the baselines.

Exploiting the ever-growing corpus of scientific content calls for new ways and means to effectively organize, search, and retrieve scientific formulae. We propose a new data-driven framework for retrieving similar scientific formulae via learned formula representations based on tree embeddings. FORTE (for FOrmula Representation learning via Tree Embeddings) leverages operator tree representations of symbolic scientific formulae (such as math equations) to explicitly capture their inherent structural and semantic properties. FORTE employs i) a tree encoder that encodes the formula’s operator tree into an embedding vector and ii) a tree decoder that directly generates a formula’s operator tree from the embedding vector. We also develop a novel tree beam search algorithm that improves the quality of the decoded operator trees. We demonstrate that FORTE (sometimes significantly) outperforms various baseline methods on formula reconstruction and retrieval using a real-world dataset comprising 770k scientific formulae collected on-line.

We study the problem of predicting student knowledge acquisition in online courses from clickstream behavior. Motivated by the proliferation of eLearning lecture delivery, we specifically focus on student in-video activity in lectures videos, which consist of content and in-video quizzes. Our methodology for predicting in-video quiz performance is based on three key ideas we develop. First, we model students’ clicking behavior via time-series learning architectures operating on raw event data, rather than defining hand-crafted features as in existing approaches that may lose important information embedded within the click sequences. Second, we develop a self-supervised clickstream pre-training to learn informative representations of clickstream events that can initialize the prediction model effectively. Third, we propose a clustering guided meta-learning-based training that optimizes the prediction model to exploit clusters of frequent patterns in student clickstream sequences. Through experiments on three real-world datasets, we demonstrate that our method obtains substantial improvements over two base-line models in predicting students’ in-video quiz performance. Further, we validate the importance of the pre-training and meta-learning components of our framework through ablation studies. Finally, we show how our methodology reveals insights on video-watching behavior associated with knowledge acquisition for useful learning analytics.

The development of new technologies at an unprecedented rate is rapidly changing the landscape of the labor market. Therefore, for workers who want to build a successful career, acquiring new skills required by new jobs through lifelong learning is crucial. In this paper, we propose a novel and interpretable monotonic nonlinear state-space model to analyze online user professional profiles and provide actionable feedback and recommendations to users on how they can reach their career goals. Specifically, we use a series of binary-valued and non-decreasing latent states to represent the expanding skill set of each user throughout their career and propose an efficient inference method under our model. Using a series of experiments on two large real-world datasets, we show that our model (sometimes significantly) outperforms existing methods on the tasks of company, job title, and skill prediction. More importantly, our model is interpretable and can be used for other important tasks including skill gap identification and career path planning. Using a series of case studies, we show that our model can provide i) actionable feedback to users and guide them through their upskilling and reskilling processes and ii) recommendations of feasible paths for users to reach their career goals.

Knowledge tracing (KT) refers to the problem of predicting future learner performance given their past performance in educational applications. Recent developments in KT using flexible deep neural network-based models excel at this task. However, these models often offer limited interpretability, thus making them insufficient for personalized learning, which requires using interpretable feedback and actionable recommendations to help learners achieve better learning outcomes. In this paper, we propose attentive knowledge tracing (AKT), which couples flexible attention-based neural network models with a series of novel, interpretable model components inspired by cognitive and psychometric models. AKT uses a novel monotonic attention mechanism that relates a learner's future responses to assessment questions to their past responses; attention weights are computed using exponential decay and a context-aware relative distance measure, in addition to the similarity between questions. Moreover, we use the Rasch model to regularize the concept and question embeddings; these embeddings are able to capture individual differences among questions on the same concept without using an excessive number of parameters. We conduct experiments on several real-world benchmark datasets and show that AKT outperforms existing KT methods (by up to $6%$ in AUC in some cases) on predicting future learner responses. We also conduct several case studies and show that AKT exhibits excellent interpretability and thus has potential for automated feedback and personalization in real-world educational settings.

The 2016 United States presidential election has been characterized as a period of extreme divisiveness that was exacerbated on social media by the influence of fake news, trolls, and social bots. However, the extent to which the public became more polarized in response to these influences over the course of the election is not well understood. In this paper we propose IdeoTrace, a framework for (i) jointly estimating the ideology of social media users and news websites and (ii) tracing changes in user ideology over time. We apply this framework to the last two months of the election period for a group of 47508 Twitter users and demonstrate that both liberal and conservative users became more polarized over time.

Over the past decade low graduation and retention rates has plagued higher education institutions. To assist students in choosing a sequence of courses, choosing majors and successful academic pathways; many institutions provide several on-site academic advising services supported by data driven educational technologies. Accurate performance prediction can serve as the backbone for degree planning software, personalized advising systems and early warning systems that can identify students at-risk of dropping from their field of study. In this work, we present a deep learning based recommender system approach called Neural Collaborative Filtering (NCF) for predicting the grade a student will earn in a course that he/she plans to take in the next-term. Prior grade prediction methods are based on matrix factorization (MF) where students and courses are represented in a latent "knowledge" space. The deep learning inspired approach provides added flexibility in learning the latent spaces in comparison to MF approaches. The proposed approach also incorporates instructor information besides student and course information. Moreover, for proper analysis of the learned model parameters, we assume the embeddings obtained for students, courses and instructors should be non-negative. This non-negative NCF model referred by NCFnn model adds a rectified linear units (ReLU) on the embedding layer of NCF. The experimental results on datasets from George Mason University, a large, public university in the United States, demonstrate that the proposed NCF approaches significantly outperform competitive baselines across different test sets.

It is of crucial importance to simultaneously protect against sensitive attributes in data while building predictive models. In this paper, we tackle the problem of learning representations from raw data that are i) informative and predictive of desirable variables, and ii) private and protect against adversaries that attempt to recover sensitive variables. We cast this problem under the generative adversarial network (GAN) framework and design three components: an encoder, an ally that predicts the desired variables, and an adversary that predicts the sensitive ones. As a use case, we apply our approach to learn representations of raw student clickstream event data captured as they watch lecture videos in massive open online courses (MOOCs). Through experiments on a real-world dataset collected from a MOOC, we demonstrate that our method can learn a low-dimensional representation of each user that i) excels at classifying whether a user will answer a quiz question correctly, and ii) prevents an adversary from recovering each user's identity. Our results indicate that our approach is effective in learning representations that are both informative and private.

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for educational applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA) that jointly traces learner concept knowledge over time, analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc., or the forgetting effect), and estimates the content organization and difficulty of the questions in assessments. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instant. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, analyzing the quality and content organization of learning resources, and estimating the question--concept associations and the question difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses compared to existing collaborative filtering and knowledge tracing methods.