We present a novel method for obtaining high-quality, domain-targeted multiple choice
questions from crowd workers. Generating these questions can be difficult without trading …

Recent advances in AI have resulted in large pre-trained language models with superior
performance on text generation tasks, prompting the question of whether we can use them to …

Session-based recommender systems have attracted much attention recently. To capture
the sequential dependencies, existing methods resort either to data augmentation …

To assess the knowledge proficiency of a learner, multiple choice question is an efficient
and widespread form in standard tests. However, the composition of the multiple choice …

This study investigates the behavioral intentions of a younger generation of digital natives to
improve individual capabilities through digital transformation. In this regard, 193 high school …

We investigate the task of distractor generation for multiple choice reading comprehension
questions from examinations. In contrast to all previous works, we do not aim at preparing …

We investigate how machine learning models, specifically ranking models, can be used to
select useful distractors for multiple choice questions. Our proposed models can learn to …

We present a novel approach to automated question generation that improves upon prior
work both from a technology perspective and from an assessment perspective. Our system is …

Generating high-quality complete question sets (for example, the question, answer and
distractors) in reading comprehension tasks is challenging and rewarding. This paper …

We address the problem of automatically generating quiz-style knowledge questions from a
knowledge graph such as DBpedia. Questions of this kind have ample applications, for …