Most cognitive diagnosis research in education has been concentrated on individual assessment, aiming at discovering the latent characteristics of students. However, in many real-world scenarios, group-level assessment is an important and meaningful task, e.g., class assessment in different regions can discover the difference of teaching level in different contexts. In this work, we consider assessing cognitive ability for a group of students, which aims to mine groups’ proficiency on specific knowledge concepts. The significant challenge in this task is the sparsity of group-exercise response data, which seriously affects the assessment performance. Existing works either do not make effective use of additional student-exercise response data or fail to reasonably model the relationship between group ability and individual ability in different learning contexts, resulting in sub-optimal diagnosis results. To this end, we propose a general Multi-Task based Group-Level Cognitive Diagnosis (MGCD) framework, which is featured with three special designs: 1) We jointly model student-exercise responses and group-exercise responses in a multi-task manner to alleviate the sparsity of group-exercise responses; 2) We design a context-aware attention network to model the relationship between student knowledge state and group knowledge state in different contexts; 3) We model an interpretable cognitive layer to obtain student ability, group ability and exercise factors (e.g., difficulty), and then we leverage neural networks to learn complex interaction functions among them. Extensive experiments on real-world datasets demonstrate the generality of MGCD and the effectiveness of our attention design and multi-task learning.

Concept extraction aims to find words or phrases describing a concept from massive texts. Recently, researchers propose many neural network-based methods to automatically extract concepts. Although these methods for this task show promising results, they ignore structured information in the raw textual data (e.g., title, topic, and clue words). In this paper, we propose a novel model, named Guided Attention Concept Extraction Network (GACEN), which uses title, topic, and clue words as additional supervision to provide guidance directly. Specifically, GACEN comprises two attention networks, one of them is to gather the relevant title and topic information for each context word in the document. The other one aims to model the implicit connection between informative words (clue words) and concepts. Finally, we aggregate information from two networks as input to Conditional Random Field (CRF) to model dependencies in the output. We collected clue words for three well-studied datasets. Extensive experiments demonstrate that our model outperforms the baseline models with a large margin, especially when the labeled data is insufficient.