Neural Information Processing Systems http://nips.cc/

Cognitive diagnosis models (CDMs) are designed to learn students' mastery levels using their response logs. CDMs play a fundamental role in online education systems since they significantly influence downstream applications such as teachers' guidance and computerized adaptive testing. Despite the success achieved by existing CDMs, we find that they suffer from a thorny issue that the learned students' mastery levels are too similar. This issue, which we refer to as oversmoothing, could diminish the CDMs' effectiveness in downstream tasks. CDMs comprise two core parts: learning students' mastery levels and assessing mastery levels by fitting the response logs. This paper contends that the oversmoothing issue arises from that existing CDMs seldom utilize response signals on exercises in the learning part but only use them as labels in the assessing part. To this end, this paper proposes an oversmoothing-resistant cognitive diagnosis framework (ORCDF) to enhance existing CDMs by utilizing response signals in the learning part. Specifically, ORCDF introduces a novel response graph to inherently incorporate response signals as types of edges. Then, ORCDF designs a tailored response-aware graph convolution network (RGC) that effectively captures the crucial response signals within the response graph. Via ORCDF, existing CDMs are enhanced by replacing the input embeddings with the outcome of RGC, allowing for the consideration of response signals on exercises in the learning part. Extensive experiments on real-world datasets show that ORCDF not only helps existing CDMs alleviate the oversmoothing issue but also significantly enhances the models' prediction and interpretability performance. Moreover, the effectiveness of ORCDF is validated in the downstream task of computerized adaptive testing.

Knowledge tracing is a method used in education to assess and track the acquisition of knowledge by individual learners. It involves using a variety of techniques, such as quizzes, tests, and other forms of assessment, to determine what a learner knows and does not know about a particular subject. The goal of knowledge tracing is to identify gaps in understanding and provide targeted instruction to help learners improve their understanding and retention of material. This can be particularly useful in situations where learners are working at their own pace, such as in online learning environments. By providing regular feedback and adjusting instruction based on individual needs, knowledge tracing can help learners make more efficient progress and achieve better outcomes. Effectively solving the KT problem would unlock the potential of computer-aided education applications such as intelligent tutoring systems, curriculum learning, and learning materials recommendations. In this paper, we will present the results of the implementation of two Knowledge Tracing algorithms on a newly released dataset as part of the AAAI2023 Global Knowledge Tracing Challenge.

With the concept of teaching being introduced to the machine learning community, a teacher model start using dynamic loss functions to teach the training of a student model. The dynamic intends to set adaptive loss functions to different phases of student model learning. In existing works, the teacher model 1) merely determines the loss function based on the present states of the student model, i.e., disregards the experience of the teacher; 2) only utilizes the states of the student model, e.g., training iteration number and loss/accuracy from training/validation sets, while ignoring the states of the loss function. In this paper, we first formulate the loss adjustment as a temporal task by designing a teacher model with memory units, and, therefore, enables the student learning to be guided by the experience of the teacher model. Then, with a dynamic loss network, we can additionally use the states of the loss to assist the teacher learning in enhancing the interactions between the teacher and the student model. Extensive experiments demonstrate our approach can enhance student learning and improve the performance of various deep models on real-world tasks, including classification, objective detection, and semantic segmentation scenarios.

Ready or not, chatGPT (the newest version of OpenAI's impressive AI technologies) is now in your classroom. It can write papers, essays, and poems. It can create art and write computer code in many languages. This is not however the time to panic; it is the time to focus on the value you offer students as their instructor. Below are some easy to implement suggestions that will help you prepare for the upcoming semester.

The COVID-19 crisis has forced millions of teachers around the world to rapidly learn how to use technology to effectively support student learning and assessment, stay connected with their students, experiment with teaching models, and reduce the workload so they can focus on teaching. There are many promising solutions that are helping teachers become more effective, including new technologies such as machine learning (ML), artificial intelligence (AI) and optimised workflows. For example, Revisely is an education company that helps teachers give better feedback on students' writing assignments, such as essays and papers. It saves teachers time by offering built-in comment sets and doing a plagiarism check on student work, among other features. In addition, teachers can track the performance of students on all assignments throughout their learning journey.

To master a discipline such as algebra or physics, students must acquire a set of cognitive skills. Traditionally, educators and domain experts manually determine what these skills are and then select practice exercises to hone a particular skill. We propose a technique that uses student performance data to automatically discover the skills needed in a discipline. The technique assigns a latent skill to each exercise such that a student's expected accuracy on a sequence of same-skill exercises improves monotonically with practice. Rather than discarding the skills identified by experts, our technique incorporates a nonparametric prior over the exercise-skill assignments that is based on the expert-provided skills and a weighted Chinese restaurant process.

The teaching profession is under siege. Working hours for teachers are increasing as student needs become more complex and administrative and paperwork burdens increase. According to a recent McKinsey survey, conducted in a research partnership with Microsoft, teachers are working an average of 50 hours a week 1 1. While most teachers report enjoying their work, they do not report enjoying the late nights marking papers, preparing lesson plans, or filling out endless paperwork. Burnout and high attrition rates are testaments to the very real pressures on teachers.