Research on Deep Knowledge Tracking Incorporating Rich Features and Forgetting Behaviors

The individualization of education and teaching through the computer⁃aided education system provides students with personalized learning,so that each student can obtain the knowledge they need.At this stage,there are a lot of intelligent tutoring systems.In these systems,students􀆳learning actions are tracked in real⁃time,and there are a lot of available data.From these data,personalized education that suits each student can be mined.To improve the quality of education,some models for predicting students􀆳next practice have been produced,such as Bayesian Knowledge Tracing(BKT),Performance Factor Analysis(PFA),and Deep Knowledge Tracing(DKT)with the development of deep learning.However,the model only considers the knowledge component and correctness of the problem,ignoring the breadth of other characteristics of the information collected by the intelligent tutoring system,the lag time of the previous interaction,the number of past attempts to a problem,and situations that students have forgotten the knowledge.Although some studies consider forgetting and rich information when modeling student knowledge,they often ignore student learning sequences.The main contribution of this paper is in two aspects.One is to transform the input into a position feature vector by introducing an auto⁃encoding network layer and to carry out multiple sets of bad political combinations.The other is to consider repeated time intervals,sequence time intervals,and the number of attempts to simulate forgetting behavior.This paper proposes an adaptive algorithm for the original DKT model.By using the stacked auto⁃encoder network,the input dimension is reduced to half of the original and the original features are retained and consider the forgetting memory behavior according to the time sequence of students􀆳learning.The model proposed in this paper has been experimented on two public data sets to improve the original accuracy.

Exercise Recommendation with Preferences and Expectations Based on Ability Computation

In the era of artificial intelligence,cognitive computing,based on cognitive science;and supported by machine learning and big data,brings personalization into every corner of our social life.Recommendation systems are essential applications of cognitive computing in educational scenarios.They help learners personalize their learning better by computing student and exercise characteristics using data generated from relevant learning progress.The paper introduces a Learning and Forgetting Convolutional Knowledge Tracking Exercise Recommendation model(LFCKT-ER).First,the model computes students’ability to understand each knowledge concept,and the learning progress of each knowledge concept,and the model consider their forgetting behavior during learning progress.Then,students’learning stage preferences are combined with filtering the exercises that meet their learning progress and preferences.Then students’ability is used to evaluate whether their expectations of the difficulty of the exercises are reasonable.Then,the model filters the exercises that best match students’expectations again by students’expectations.Finally,we use a simulated annealing optimization algorithm to assemble a set of exercises with the highest diversity.From the experimental results,the LFCKT-ER model can better meet students’personalized learning needs and is more accurate than other exercise recommendation systems under various metrics on real online education public datasets.