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The inevitable shift towards online learning due to the emergence of the COVID-19 pandemic triggered a strong need to assess students using shorter exams whilst ensuring reliability. This study explores a data-centric approach that utilizes feature importance to select a discriminative subset of questions from the original exam. Furthermore, the discriminative question subset’s ability to approximate the students exam scores is evaluated by measuring the prediction accuracy and by quantifying the error interval of the prediction. The approach was evaluated using two real-world exam datasets of the Scholastic Aptitude Test (SAT) and Exame Nacional do Ensino Medio (ENEM) exams, which consist of student response data and the corresponding the exam scores. The evaluation was conducted against randomized question subsets of sizes 10, 20, 30 and 50. The results show that our method estimates the full scores more accurately than a baseline model in most question sizes while maintaining a reasonable error interval. The encouraging evidence found in this paper provides support for the strong potential of the on-going study to provide a data-centric approach for exam size reduction.

Advances in Artificial Intelligence in Education (AIEd) and the ever-growing scale of Interactive Educational Systems (IESs) have led to the rise of data-driven approaches for knowledge tracing and learning path recommendation. Unfortunately, collecting student interaction data is challenging and costly. As a result, there is no public large-scale benchmark dataset reflecting the wide variety of student behaviors observed in modern IESs. Although several datasets, such as ASSISTments, Junyi Academy, Synthetic and STATICS are publicly available and widely used, they are not large enough to leverage the full potential of state-of-the-art data-driven models. Furthermore, the recorded behavior is limited to question-solving activities. To this end, we introduce EdNet, a large-scale hierarchical dataset of diverse student activities collected by Santa, a multi-platform self-study solution equipped with an artificial intelligence tutoring system. EdNet contains 131,417,236 interactions from 784,309 students collected over more than 2 years, making it the largest public IES dataset released to date. Unlike existing datasets, EdNet records a wide variety of student actions ranging from question-solving to lecture consumption to item purchasing. Also, EdNet has a hierarchical structure which divides the student actions into 4 different levels of abstractions. The features of EdNet are domain-agnostic, allowing EdNet to be easily extended to different domains. The dataset is publicly released for research purposes. We plan to host challenges in multiple AIEd tasks with EdNet to provide a common ground for the fair comparison between different state-of-the-art models and to encourage the development of practical and effective methods.