Cognitive diagnosis aims to gauge students' mastery levels based on their response logs. Serving as a pivotal module in web-based online intelligent education systems (WOIESs), it plays an upstream and fundamental role in downstream tasks like learning item recommendation and computerized adaptive testing. WOIESs are open learning environment where numerous new students constantly register and complete exercises. In WOIESs, efficient cognitive diagnosis is crucial to fast feedback and accelerating student learning. However, the existing cognitive diagnosis methods always employ intrinsically transductive student-specific embeddings, which become slow and costly due to retraining when dealing with new students who are unseen during training. To this end, this paper proposes an inductive cognitive diagnosis model (ICDM) for fast new students' mastery levels inference in WOIESs. Specifically, in ICDM, we propose a novel student-centered graph (SCG). Rather than inferring mastery levels through updating student-specific embedding, we derive the inductive mastery levels as the aggregated outcomes of students' neighbors in SCG. Namely, SCG enables to shift the task from finding the most suitable student-specific embedding that fits the response logs to finding the most suitable representations for different node types in SCG, and the latter is more efficient since it no longer requires retraining. To obtain this representation, ICDM consists of a construction-aggregation-generation-transformation process to learn the final representation of students, exercises and concepts. Extensive experiments across real-world datasets show that, compared with the existing cognitive diagnosis methods that are always transductive, ICDM is much more faster while maintains the competitive inference performance for new students.

I am going to speak with you using an AI voice because I have a sore throat. I had built a text to speech voice synthesizer as my college project in my Computer Science Engineering undergrads decades back. I am using Google WaveNet for text to speech. This is #SudhaLive my weekly livestream where I share opportunities in AI space -- jobs, fellowship, courses and my analysis of one topic from the AI world. Let's hear a female voice from India now.

Coronavirus Diaries is a series of dispatches exploring how the coronavirus is affecting people's lives. This as-told-to essay is based on a conversation with An Luu, a 21-year-old senior at Rice University in Houston, who got a false positive due to a COVID-19 test glitch earlier this month. Luu was one of many Rice students whose positive (later discovered to be false positive) test results caused the university to move classes online. Ninety-five percent of the student population of Rice is vaccinated, including Luu. Slate reached out to Rice University's Crisis Management Team for comment on Luu's experience.

A major challenge in the Deep RL (DRL) community is to train agents able to generalize their control policy over situations never seen in training. Training on diverse tasks has been identified as a key ingredient for good generalization, which pushed researchers towards using rich procedural task generation systems controlled through complex continuous parameter spaces. In such complex task spaces, it is essential to rely on some form of Automatic Curriculum Learning (ACL) to adapt the task sampling distribution to a given learning agent, instead of randomly sampling tasks, as many could end up being either trivial or unfeasible. Since it is hard to get prior knowledge on such task spaces, many ACL algorithms explore the task space to detect progress niches over time, a costly tabula-rasa process that needs to be performed for each new learning agents, although they might have similarities in their capabilities profiles. To address this limitation, we introduce the concept of Meta-ACL, and formalize it in the context of black-box RL learners, i.e. algorithms seeking to generalize curriculum generation to an (unknown) distribution of learners. In this work, we present AGAIN, a first instantiation of Meta-ACL, and showcase its benefits for curriculum generation over classical ACL in multiple simulated environments including procedurally generated parkour environments with learners of varying morphologies. Videos and code are available at https://sites.google.com/view/meta-acl .

With the coronavirus pandemic choking social interaction and global travel, many of the nation's approximately 800 Japanese-language schools are struggling because new students have not been able to enter Japan. Since private schools basically rely on tuition fees, they are facing an existential crisis, people familiar with the matter say. Enrollment at Japanese-language schools halved to around 50,000 from about 100,000 in March due to graduation and other reasons, including coronavirus restrictions, they said. Japan has banned the entry of people from 100 countries and regions as part of efforts to curb the pandemic. According to the Justice Ministry, students can enroll in a Japanese school for up to two years.

We revisit a framework for designing peer-based intelligent tutoring systems motivated by McCalla's ecological approach, where learning is facilitated by the previous experiences of peers with a corpus of learning objects. Prior research demonstrated the value of a proposed algorithm for modeling student learning and for selecting the most beneficial learning objects to present to new students. In this paper, we first adjust the validation of this approach to demonstrate its ability to cope with errors in assessing the learning of student peers. We then deepen the representation of learning objects to reflect the expected time to completion and demonstrate how this may lead to more effective selection of learning objects for students, and thus more effective learning. As part of our exploration of these new adjustments, we offer insights into how the size of learning object repositories may affect student learning, suggesting future extensions for the model and its validation.