Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning

With the popularity of e-learning,personalization and ubiquity have become important aspects of online learning.To make learning more personalized and ubiquitous,we propose a learner model for a query-based personalized learning recommendation system.Several contextual attributes characterize a learner,but considering all of them is costly for a ubiquitous learning system.In this paper,a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling.A total of 208 students are surveyed.DEMATEL(Decision Making Trial and Evaluation Laboratory)technique is used to establish the validity and importance of the identified contexts and find the interdependency among them.The acquiring methods of these contexts are also defined.On the basis of these contexts,the learner model is designed.A layered architecture is presented for interfacing the learner model with a query-based personalized learning recommendation system.In a ubiquitous learning scenario,the necessary adaptive decisions are identified to make a personalized recommendation to a learner.

The Position of Non-native English Teachers in Globalisation

The traditional prejudice against Non-native English language teachers(NNETs)has been challenged recently.This essay examines features of NNETs and calls for acknowledging their integral position in global English teaching filed thanks to their established bilingual models for learners,mastery of professional knowledge,resourcefulness in teaching practice,and adaptability to local contexts.

Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and time.Web-based learning promotes active and independent learning.Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process.This digital learning improves the quality of teaching and also promotes educational equity.However,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning environment.The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy.It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course.It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level.In this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment.Catering to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm.An adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.

A probabilistic generative model for tracking multi-knowledge concept mastery probability

Knowledge tracing aims to track students’knowledge status over time to predict students’future performance accurately.In a real environment,teachers expect knowledge tracing models to provide the interpretable result of knowledge status.Markov chain-based knowledge tracing(MCKT)models,such as Bayesian Knowledge Tracing,can track knowledge concept mastery probability over time.However,as the number of tracked knowledge concepts increases,the time complexity of MCKT predicting student performance increases exponentially(also called explaining away problem).When the number of tracked knowledge concepts is large,we cannot utilize MCKT to track knowledge concept mastery probability over time.In addition,the existing MCKT models only consider the relationship between students’knowledge status and problems when modeling students’responses but ignore the relationship between knowledge concepts in the same problem.To address these challenges,we propose an inTerpretable pRobAbilistiC gEnerative moDel(TRACED),which can track students’numerous knowledge concepts mastery probabilities over time.To solve explain away problem,we design long and short-term memory(LSTM)-based networks to approximate the posterior distribution,predict students’future performance,and propose a heuristic algorithm to train LSTMs and probabilistic graphical model jointly.To better model students’exercise responses,we proposed a logarithmic linear model with three interactive strategies,which models students’exercise responses by considering the relationship among students’knowledge status,knowledge concept,and problems.We conduct experiments with four real-world datasets in three knowledge-driven tasks.The experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students’future performance and can learn the relationship among students,knowledge concepts,and problems from students’exercise sequences.We also conduct several case studies.The case studies show that TRACED exhibits excellent interpretability and thus has the potential for personalized automatic feedback in the real-world educational environment.

Design and Implementation of Intelligent Textbooks: From a Perspective of Digital Transformation in Education

With the application of the internet,big data,and artificial intelligence(AI)in education,a new form of textbook construction has been developed,promoting digital textbook development.However,problems during digital textbook construction still exist.This study analyzes the necessity of intelligent textbook development and explains the educational characteristics of intelligent textbooks,such as intelligent adaptation learning,intelligent guidance,accompanying evaluation,and in-depth learning interaction from the perspective of instruction theory.Moreover,combining the technical advantages of AI,including knowledge mapping,learning analysis,online tracking,learner digital profiles,and in-depth interaction,and taking existing achievements for reference,it describes the methods and strategies of intelligent textbooks to achieve corresponding educational functions and also makes interpretation with specific cases.Given the educational and engineering characteristics of intelligent textbooks,this study analyzes the strategies for promoting the construction of intelligent textbooks from adhering to the instruction theory,paying attention to the iteration of practice,and adopting technical methods to supervise and regulate.

Adaptive learning path recommendation model for examination-oriented education

Adaptive learning paths provide individual learning objectives that best match a learner’s characteristics.This is especially helpful when learners need to balance limited available learning time and multiple learning objectives.The automatic generation of personalized learning paths to improve learning efficiency has therefore attracted significant interest.However,most current research only focuses on providing learners with adaptive objects and sequences according to their own interests or learning goals given a normal amount of time or ordinary conditions.There is little research that can help learners to obtain the most important knowledge for a test in the shortest time possible,which is a typical scenario in exanimation-oriented education systems.This study aims to solve this problem by introducing a new approach that builds on existing methods.First,the eight properties in Gardner’s multiple intelligence theory are introduced into the present knowledge and learner models to define the relationship between learning objects(LOs)and learners,thereby improving recommendation accuracy rates.Then,a novel adaptive learning path recommendation model is presented where viable knowledge topologies,knowledge bases and the previously-established properties relating to a learner’s ability are combined by Dempster-Shafer(D-S)evidence theory.A series of practical experiments were performed to assess the approach’s adaptability,the appropriateness of the selected evidence and the effectiveness of the recommendations.In the results,it was found that the proposed learning path recommendation model helped learners learn the most important elements and obtain superior test grades when confronted with limited time for learning.