## 1 Introduction  
With the rapid development of information technology, online learning systems such as Learning Management Systems (LMSs) and Massive Open Online Courses (MOOCs) have been widely used to support learners' self-regulated and hybrid learning. These systems not only provide learners with learning resources such as videos, documents, questionnaires, and quizzes, but also trace learners' learning activities and generate records of their learning behavior. They can help instructors identify characteristics of learning behavior and predict students' performance through an in-depth analysis of these records. At present, many researchers use methods such as data mining and machine learning to extract valuable information from these records, leading to the emergence of educational data mining [1], an interdisciplinary research field. Predicting students' performance is one of the most important issues in educational data mining, and researchers have made remarkable achievements regarding this issue. By predicting students' performance, instructors can understand the situation of learners effectively and guide learners who may be at risk of academic failure as soon as possible. Researchers collect records including learners' demographic features, historical academic performance and learning behavior, and then establish prediction models using decision trees, Naive Bayes, logistic regression, K-Nearest Neighbor (KNN) and other algorithms to predict students' performance [2]. Due to these systems' capacity to automatically track learners' online learning activities, increasingly, researchers have started to extract valuable information from large-scale, high-quality learning behavior records. In recent years, deep neural networks have achieved great success in many fields [3], such as image recognition, natural language processing, and anomaly detection. Increasingly, researchers have begun to use deep neural networks to solve problems in educational data mining [4]. [PERSON] et al. created 20 variables from the logs collected in an LMS to describe learners' online learning behavior, including login times, total course access and discussion posts, and they established a prediction model for students' performance using the Random Forest (RF) method [5]. [PERSON] et al. collected the records of learners watching videos from an MOOC and established a regression model regarding the scores obtained on courses using a Feedforward Neural Network (FNN) [6]. [PERSON] et al. gathered logs from three courses from SPOC, established dozens of variables to describe online learning behavior, and used ECOC-based algorithms to establish a prediction model of students' performance [7]. Although these predictions achieved high accuracy, there are two flaws. Firstly, the variables/features of learning behavior used to predict students' performance can only be created after the learners finish all their learning activities; therefore, it is impossible to predict performance in advance. Secondly, the established prediction models cannot extract more representative latent features from the raw records, which affects the accuracy of the predictions. In this study, we proposed a representation of an online learning activity sequence, which can be used as the input for a deep neural network, so that the students' performance can be predicted at the beginning of and during online learning. In addition, we designed an autoencoder based on a deep neural network, which can extract latent features from the original representation to improve the accuracy of the predictions. Based on the representation and the autoencoder, we designed an end-to-end prediction model of a student's performance based on their individual online learning activity sequence. Specifically, the contributions of this paper are as follows:  
1. We propose a representation of an online learning activity sequence that can be used as the input for a deep neural network to predict students' academic performance. 2.