Fuzzy cognitive diagnosis for modelling examinee performance

© 2018 ACM. Recent decades have witnessed the rapid growth of educational data mining (EDM), which aims at automatically extracting valuable information from large repositories of data generated by or related to people's learning activities in educational settings. One of the key EDM tasks is cognitive modelling with examination data, and cognitive modelling tries to profile examinees by discovering their latent knowledge state and cognitive level (e.g. the proficiency of specific skills). However, to the best of our knowledge, the problem of extracting information from both objective and subjective examination problems to achieve more precise and interpretable cognitive analysis remains underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees' cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then we combine fuzzy set theory and educational hypotheses to model the examinees' mastery on the problems based on their skill proficiency. Finally, we simulate the generation of examination score on each problem by considering slip and guess factors. In this way, the whole diagnosis framework is built. For further comprehensive verification, we apply our FuzzyCDF to three classical cognitive assessment tasks, i.e., predicting examinee performance, slip and guess detection, and cognitive diagnosis visualization. Extensive experiments on three real-world datasets for these assessment tasks prove that FuzzyCDF can reveal the knowledge states and cognitive level of the examinees effectively and interpretatively

Identifiable Cognitive Diagnosis with Encoder-decoder for Modelling Students' Performance

Cognitive diagnosis aims to diagnose students' knowledge proficiencies based on their response scores on exam questions, which is the basis of many domains such as computerized adaptive testing. Existing cognitive diagnosis models (CDMs) follow a proficiency-response paradigm, which views diagnostic results as learnable embeddings that are the cause of students' responses and learns the diagnostic results through optimization. However, such a paradigm can easily lead to unidentifiable diagnostic results and the explainability overfitting problem, which is harmful to the quantification of students' learning performance. To address these problems, we propose a novel identifiable cognitive diagnosis framework. Specifically, we first propose a flexible diagnostic module which directly diagnose identifiable and explainable examinee traits and question features from response logs. Next, we leverage a general predictive module to reconstruct response logs from the diagnostic results to ensure the preciseness of the latter. We furthermore propose an implementation of the framework, i.e., ID-CDM, to demonstrate the availability of the former. Finally, we demonstrate the identifiability, explainability and preciseness of diagnostic results of ID-CDM through experiments on four public real-world datasets

Exploiting Cognitive Structure for Adaptive Learning

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19

Integrating Timing Considerations to Improve Testing Practices

Integrating Timing Considerations to Improve Testing Practices synthesizes a wealth of theory and research on time issues in assessment into actionable advice for test development, administration, and scoring. One of the major advantages of computer-based testing is the capability to passively record test-taking metadata—including how examinees use time and how time affects testing outcomes. This has opened many questions for testing administrators. Is there a trade-off between speed and accuracy in test taking? What considerations should influence equitable decisions about extended-time accommodations? How can test administrators use timing data to balance the costs and resulting validity of tests administered at commercial testing centers? In this comprehensive volume, experts in the field discuss the impact of timing considerations, constraints, and policies on valid score interpretations; administrative accommodations, test construction, and examinees’ experiences and behaviors; and how to implement the findings into practice. These 12 chapters provide invaluable resources for testing professionals to better understand the inextricable links between effective time allocation and the purposes of high-stakes testing

Attention deficit hyperactivity disorder assessment based on patient behavior exhibited in a car video game: A pilot study

This article belongs to the Special Issue Advances in ADHD.Symptoms of Attention Deficit Hyperactivity Disorder (ADHD) include excessive activity, difficulty sustaining attention, and inability to act in a reflective manner. Early diagnosis and treatment of ADHD is key but may be influenced by the observation and communication skills of caregivers, and the experience of the medical professional. Attempts to obtain additional measures to support the medical diagnosis, such as reaction time when performing a task, can be found in the literature. We propose an information recording system that allows to study in detail the behavior shown by children already diagnosed with ADHD during a car driving video game. We continuously record the participants" activity throughout the task and calculate the error committed. Studying the trajectory graphs, some children showed uniform patterns, others lost attention from one point onwards, and others alternated attention/inattention intervals. Results show a dependence between the age of the children and their performance. Moreover, by analyzing the positions by age over time using clustering, we show that it is possible to classify children according to their performance. Future studies will examine whether this detailed information about each child"s performance pattern can be used to fine-tune treatment.This research was partially funded by: Ministerio de Ciencia, Innovación y Universidades, Spanish National Project, grant number RTI2018-101857-B-I00, Ministerio de Universidades (Grant for the requalification of permanent lectures, David Delgado-Gómez), Instituto Salud Carlos III, grant number DTS21/00091