Digital learning environments generate a precise record of the actions learners take as they interact with learning materials and complete exercises towards comprehension. With this high quantity of sequential data comes the potential to apply time series models to learn about underlying behavioral patterns and trends that characterize successful learning based on the granular record of student actions. There exist several methods for looking at longitudinal, sequential data like those recorded from learning environments. In the field of language modelling, traditional n-gram techniques and modern recurrent neural network (RNN) approaches have been applied to algorithmically find structure in language and predict the next word given the previous words in the sentence or paragraph as input. In this paper, we draw an analogy to this work by treating student sequences of resource views and interactions in a MOOC as the inputs and predicting students' next interaction as outputs. In this study, we train only on students who received a certificate of completion. In doing so, the model could potentially be used for recommendation of sequences eventually leading to success, as opposed to perpetuating unproductive behavior. Given that the MOOC used in our study had over 3,500 unique resources, predicting the exact resource that a student will interact with next might appear to be a difficult classification problem. We find that simply following the syllabus (built-in structure of the course) gives on average 23 accuracy in making this prediction, followed by the n-gram method with 70.4 , and RNN based methods with 72.2 . This research lays the ground work for recommendation in a MOOC and other digital learning environments where high volumes of sequential data exist.
Other work has been done to map assignments, student ability, lesson gains and pre-requisites all onto the same dimensional embedding space, where dimensions represent latent skills @cite_11 . This mapping enables the model to produce potential lesson and assignment pathways so that students can have an optimal path for learning. The work in this paper differs by attempting to utilize all sources of student actions, such as forum posts or specific video viewings, and as such requires the use of a different approach.
@cite_11
2288180544 2806098286 2320007652 2334493732
Students in online courses generate large amounts of data that can be used to personalize the learning process and improve quality of education. In this paper, we present the Latent Skill Embedding (LSE), a probabilistic model of students and educational content that can be used to recommend personalized sequences of lessons with the goal of helping students prepare for specic assessments. Akin to collaborative ltering for recommender systems, the algorithm does not require students or content to be described by features, but it learns a representation using access traces. We formulate this problem as a regularized maximum-likelihood embedding of students, lessons, and assessments from historical student-content interactions. An empirical evaluation on large-scale data from Knewton, an adaptive learning technology company, shows that this approach predicts assessment results competitively with benchmark models and is able to discriminate between lesson sequences that lead to mastery and failure. In this work, we take a representation learning perspective on hierarchical reinforcement learning, where the problem of learning lower layers in a hierarchy is transformed into the problem of learning trajectory-level generative models. We show that we can learn continuous latent representations of trajectories, which are effective in solving temporally extended and multi-stage problems. Our proposed model, SeCTAR, draws inspiration from variational autoencoders, and learns latent representations of trajectories. A key component of this method is to learn both a latent-conditioned policy and a latent-conditioned model which are consistent with each other. Given the same latent, the policy generates a trajectory which should match the trajectory predicted by the model. This model provides a built-in prediction mechanism, by predicting the outcome of closed loop policy behavior. We propose a novel algorithm for performing hierarchical RL with this model, combining model-based planning in the learned latent space with an unsupervised exploration objective. We show that our model is effective at reasoning over long horizons with sparse rewards for several simulated tasks, outperforming standard reinforcement learning methods and prior methods for hierarchical reasoning, model-based planning, and exploration. A latent graphical model integrating multi-target tracking, group discovery, and activity recognition is proposed.Performance of activity recognition improves when multi-target tracking and group clustering are incorporated.Group activities are better recognized based on the structured relations within the group and group-group compatibilities.Increasing the connectivity of different groups improves the overall performance.Incorporating activity information leads to robust group localization in the video. Beyond recognizing actions of individuals, activity group localization in videos aims to localize groups of persons in spatiotemporal spaces and recognize what activity the group performs. In this paper, we propose a latent graph model to simultaneously address the problem of multi-target tracking, group discovery and activity recognition. Our key insight is to exploit the contextual relations among people. We present them as a latent relational graph, which hierarchically encodes the association potentials between tracklets, intra-group interactions, correlations, and inter-group compatibilities. Our model is capable of propagating multiple evidences among different layers of the latent graph. Particularly, associated tracklets assist accurate group discovery, activity recognition can benefit from knowing the whole structured groups, and the group and activity information in turn provides strong cues for establishing coherent associations between tracklets. Experiments on five datasets demonstrate that our model achieves both significant improvements in activity group localization and competitive performance on activity recognition. We present a novel latent embedding model for learning a compatibility function between image and class embeddings, in the context of zero-shot classification. The proposed method augments the state-of-the-art bilinear compatibility model by incorporating latent variables. Instead of learning a single bilinear map, it learns a collection of maps with the selection, of which map to use, being a latent variable for the current image-class pair. We train the model with a ranking based objective function which penalizes incorrect rankings of the true class for a given image. We empirically demonstrate that our model improves the state-of-the-art for various class embeddings consistently on three challenging publicly available datasets for the zero-shot setting. Moreover, our method leads to visually highly interpretable results with clear clusters of different fine-grained object properties that correspond to different latent variable maps.