Advances in Educational Data Mining Models and the Application of Its Algorithms

In order to find an effective way to improve the quality of school management,finding valuable information from students' original data and providing feedback for student management are necessary. Firstly,some new and successful educational data mining models were analyzed and compared. These models have better performance than traditional models( such as Knowledge Tracing Model) in efficiency,comprehensiveness,ease of use,stability and so on. Then,the neural network algorithm was conducted to explore the feasibility of the application of educational data mining in student management,and the results show that it has enough predictive accuracy and reliability to be put into practice. In the end,the possibility and prospect of the application of educational data mining in teaching management system for university students was assessed.

Research on the Optimization of Teaching and Learning Path in the Context of Educational Data

With the repeated in-depth development of the technological revolution,the combination of education and technology constantly shows a new form.Modern education and Internet education have become the new direction of the current education development efforts.The development of technology has brought great Gospel for the national education.Based on this,from the traditional and modern perspectives,this paper discusses the form and significance of teaching and learning paths,to show the new value of education brought by the change of teaching and learning paths under the background of educational data.

An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery

Recent advancements in computer technologies for data processing,collection,and storage have offered several chances to improve the abilities in production,services,communication,and researches.Data mining(DM)is an interdisciplinary field commonly used to extract useful patterns from the data.At the same time,educational data mining(EDM)is a kind of DM concept,which finds use in educational sector.Recently,artificial intelligence(AI)techniques can be used for mining a large amount of data.At the same time,in DM,the feature selection process becomes necessary to generate subset of features and can be solved by the use of metaheuristic optimization algorithms.With this motivation,this paper presents an improved evolutionary algorithm based feature subsets election with neuro-fuzzy classification(IEAFSS-NFC)for data mining in the education sector.The presented IEAFSS-NFC model involves data pre-processing,feature selection,and classification.Besides,the Chaotic Whale Optimization Algorithm(CWOA)is used for the selection of the highly related feature subsets to accomplish improved classification results.Then,Neuro-Fuzzy Classification(NFC)technique is employed for the classification of education data.The IEAFSS-NFC model is tested against a benchmark Student Performance DataSet from the UCI repository.The simulation outcome has shown that the IEAFSS-NFC model is superior to other methods.

Short Text Mining for Classifying Educational Objectives and Outcomes

Most of the international accreditation bodies in engineering education(e.g.,ABET)and outcome-based educational systems have based their assess-ments on learning outcomes and program educational objectives.However,map-ping program educational objectives(PEOs)to student outcomes(SOs)is a challenging and time-consuming task,especially for a new program which is applying for ABET-EAC(American Board for Engineering and Technology the American Board for Engineering and Technology—Engineering Accreditation Commission)accreditation.In addition,ABET needs to automatically ensure that the mapping(classification)is reasonable and correct.The classification also plays a vital role in the assessment of students’learning.Since the PEOs are expressed as short text,they do not contain enough semantic meaning and information,and consequently they suffer from high sparseness,multidimensionality and the curse of dimensionality.In this work,a novel associative short text classification tech-nique is proposed to map PEOs to SOs.The datasets are extracted from 152 self-study reports(SSRs)that were produced in operational settings in an engineering program accredited by ABET-EAC.The datasets are processed and transformed into a representational form appropriate for association rule mining.The extracted rules are utilized as delegate classifiers to map PEOs to SOs.The proposed asso-ciative classification of the mapping of PEOs to SOs has shown promising results,which can simplify the classification of short text and avoid many problems caused by enriching short text based on external resources that are not related or relevant to the dataset.

Enhancing personalized exercise recommendation with student and exercise portraits

The exercise recommendation system is emerging as a promising application in online learning scenarios,providing personalized recommendations to assist students with explicit learning directions.Existing solutions generally follow a collaborative filtering paradigm,while the implicit connections between students(exercises)have been largely ignored.In this study,we aim to propose an exercise recommendation paradigm that can reveal the latent connections between student-student(exercise-exercise).Specifically,a new framework was proposed,namely personalized exercise recommendation with student and exercise portraits(PERP).It consists of three sequential and interdependent modules:Collaborative student exercise graph(CSEG)construction,joint random walk,and recommendation list optimization.Technically,CSEG is created as a unified heterogeneous graph with students’response behaviors and student(exercise)relationships.Then,a joint random walk to take full advantage of the spectral properties of nearly uncoupled Markov chains is performed on CSEG,which allows for full exploration of both similar exercises that students have finished and connections between students(exercises)with similar portraits.Finally,we propose to optimize the recommendation list to obtain different exercise suggestions.After analyses of two public datasets,the results demonstrated that PERP can satisfy novelty,accuracy,and diversity.

A Hybrid Feature Selection Framework for Predicting Students Performance

Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of society.The selection of features for student’s performance prediction not only plays significant role in increasing prediction accuracy,but also helps in building the strategic plans for the improvement of students’academic performance.There are different feature selection algorithms for predicting the performance of students,however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal features.In this paper,a hybrid feature selection framework(using feature-fusion)is designed to identify the significant features and associated features with target class,to predict the performance of students.The main goal of the proposed hybrid feature selection is not only to improve the prediction accuracy,but also to identify optimal features for building productive strategies for the improvement in students’academic performance.The key difference between proposed hybrid feature selection framework and existing hybrid feature selection framework,is two level feature fusion technique,with the utilization of cosine-based fusion.Whereas,according to the results reported in existing literature,cosine similarity is considered as the best similarity measure among existing similarity measures.The proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of instances.The validated results confirm that the proposed hybrid feature selection framework performs better than the existing hybrid feature selection framework,existing feature selection algorithms in terms of accuracy,f-measure,recall,and precision.Results reported in presented paper show that the proposed approach gives more than 90%accuracy on benchmark dataset that is better than the results of existing approach.

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Educational Data Mining(EDM)is an emergent discipline that concen-trates on the design of self-learning and adaptive approaches.Higher education institutions have started to utilize analytical tools to improve students’grades and retention.Prediction of students’performance is a difficult process owing to the massive quantity of educational data.Therefore,Artificial Intelligence(AI)techniques can be used for educational data mining in a big data environ-ment.At the same time,in EDM,the feature selection process becomes necessary in creation of feature subsets.Since the feature selection performance affects the predictive performance of any model,it is important to elaborately investigate the outcome of students’performance model related to the feature selection techni-ques.With this motivation,this paper presents a new Metaheuristic Optimiza-tion-based Feature Subset Selection with an Optimal Deep Learning model(MOFSS-ODL)for predicting students’performance.In addition,the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers.Besides,the Chaotic Monarch Butterfly Optimization Algo-rithm(CBOA)is used for the selection of highly related features with low com-plexity and high performance.Then,a sailfish optimizer with stacked sparse autoencoder(SFO-SSAE)approach is utilized for the classification of educational data.The MOFSS-ODL model is tested against a benchmark student’s perfor-mance data set from the UCI repository.A wide-ranging simulation analysis por-trayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures.Compared to other methods,experimental results prove that the proposed(MOFSS-ODL)classification model does a great job of predicting students’academic progress,with an accuracy of 96.49%.

Network Modelling and Visualisation Analysis of the Undergraduate Dental Curriculum System in China

Objectives: This study aims to present the characteristics of the undergraduate dental curriculum system using network modelling and visualisation analysis based on complex network theory, thus providing a theoretical foundation for the course development and curriculum reform. Methods: The correlation coefficient was used to quantify the intensity of the correlation between courses, and a visualisation complex network of the dental curriculum was built to explore the curriculum pattern from a dynamic perspective. Further, the statistical measurements of curriculum network were adopted to express the most relevant topological features. Subsequently, the minimum spanning tree and parallel coordinates plot were drawn to explore the curriculum community structure, quantify the key courses, and present different courses in time and space relationships. Results: The correlation analysis results show that the courses are closely related to each other. The main courses focus on pathology, pathophysiology, oral anatomy and physiology, closely connecting almost all medicine-related courses. The whole course network has an average degree value of 41.53, and a clustering coefficient of 0.78, indicating an obvious small-world characteristic. From the perspective of curriculum attributes, the number of public and theoretical courses was more than that of skills and practical courses. Moreover, the academic performance of skills and practical courses was lower than that of public and theoretical courses. Conclusion: The undergraduate dental courses have a progressive structure from basic professional knowledge to professional skills, which is reasonable for the dental education in China. However, some efforts towards curriculum reform based on this study are needed.

Student Performance Prediction Using A Cascaded Bi-level Feature Selection Approach

Features in educational data are ambiguous which leads to noisy features and curse of dimensionality problems.These problems are solved via feature selection.There are existing models for features selection.These models were created using either a single-level embedded,wrapper-based or filter-based methods.However single-level filter-based methods ignore feature dependencies and ignore the interaction with the classifier.The embedded and wrapper based feature selection methods interact with the classifier,but they can only select the optimal subset for a particular classifier.So their selected features may be worse for other classifiers.Hence this research proposes a robust Cascade Bi-Level(CBL)feature selection technique for student performance prediction that will minimize the limitations of using a single-level technique.The proposed CBL feature selection technique consists of the Relief technique at first-level and the Particle Swarm Optimization(PSO)at the second-level.The proposed technique was evaluated using the UCI student performance dataset.In comparison with the performance of the single-level feature selection technique the proposed technique achieved an accuracy of 94.94%which was better than the values achieved by the single-level PSO with an accuracy of 93.67%for the binary classification task.These results show that CBL can effectively predict student performance.

Multi-Attention Fusion Modeling for Sentiment Analysis of Educational Big Data

As an important branch of natural language processing,sentiment analysis has received increasing attention.In teaching evaluation,sentiment analysis can help educators discover the true feelings of students about the course in a timely manner and adjust the teaching plan accurately and timely to improve the quality of education and teaching.Aiming at the inefficiency and heavy workload of college curriculum evaluation methods,a Multi-Attention Fusion Modeling(Multi-AFM)is proposed,which integrates global attention and local attention through gating unit control to generate a reasonable contextual representation and achieve improved classification results.Experimental results show that the Multi-AFM model performs better than the existing methods in the application of education and other fields.

Student Performance Prediction via Attention-Based Multi-Layer Long-Short Term Memory

Online education has attracted a large number of students in recent years, because it breaks through the limitations of time and space and makes high-quality education at your fingertips. The method of predicting student performance is to analyze and predict the student’s final performance by collecting demographic data such as the student’s gender, age, and highest education level, and clickstream data generated when students interact with VLE in different types of specific courses, which are widely used in online education platforms. This article proposes a model to predict student performance via Attention-based Multi-layer LSTM (AML), which combines student demographic data and clickstream data for comprehensive analysis. We hope that we can obtain a higher prediction accuracy as soon as possible to provide timely intervention. The results show that the proposed model can improve the accuracy of 0.52% - 0.85% and the F1 score of 0.89% - 2.30% on the four-class classification task as well as the accuracy of 0.15% - 0.97% and the F1 score of 0.21% - 2.77% on the binary classification task from week 5 to week 25.

ChatGPT:The New Trend of Smart Education

In response to the limitations of the traditional education and teaching model,this article proposes a smart education model based on ChatGPT.The model actively breaks the constraint of time and space and the design patterns of traditional education,providing smart education services including personalized learning,smart tutoring and evaluation,educational content creation support,and education big data analysis.Through constructing an open and inclusive learning space and creating flexible and diverse educational models,ChatGPT can help to meet students’individuality and overall development,as well as assist teachers in keeping up with the students’learning performance and developmental requirements in real-time.This provides an important basis for optimizing teaching content,offering personalized and accurate cultivation,and planning the development path of students.

Artificial intelligence innovation in education: A twenty-year data-driven historical analysis

Reflecting on twenty years of educational research,we retrieved over 400 research article on the application of artificial intelligence(AI)and deep learning(DL)techniques in teaching and learning.A computerised content analysis was conducted to examine how AI and DL research themes have evolved in major educational journals.By doing so,we seek to uncover the prominent keywords associated with AI-enabled pedagogical adaptation research in each decade,due to the discipline’s dynamism.By examining the major research themes and historical trends from 2000 to 2019,we demonstrate that,as advanced technologies in education evolve over time,some areas of research topics seem have stood the test of time,while some others have experienced peaks and valleys.More importantly,our analysis highlights the paradigm shifts and emergent trends that are gaining prominence in the field of educational research.For instance,the results suggest the decline in conventional tech-enabled instructional design research and the flourishing of student profiling models and learning analytics.Furthermore,this paper serves to raise awareness on the opportunities and challenges behind AI and DL for pedagogical adaptation and initiate a dialogue.

Data Augmentation and Deep Neuro-fuzzy Network for Student Performance Prediction with MapReduce Framework

The main aim of an educational institute is to offer high-quality education to students. The system to achieve better quality in the educational system is to find the knowledge from educational data and to discover the attributes that manipulate the performance of students. Student performance prediction is a major issue in education and training, specifically in the educational data mining system. This research presents the student performance prediction approach with the MapReduce framework based on the proposed fractional competitive multi-verse optimization-based deep neuro-fuzzy network. The proposed fractional competitive multi-verse optimization-based deep neuro-fuzzy network is derived by integrating fractional calculus with competitive multi-verse optimization. The MapReduce framework is designed with the mapper and the reducer phase to perform the student performance prediction mechanism with the deep learning classifier. The input data is partitioned at the mapper phase to perform the data transformation process, and thereby the features are selected using the distance measure. The selected unique features are employed for the data segmentation process, and thereafter the prediction strategy is accomplished at the reducer phase by the deep neuro-fuzzy network classifier. The proposed method obtained the performance in terms of mean square error, root mean square error and mean absolute error with the values of 0.338 3, 0.581 7, and 0.391 5, respectively.

Toward Training and Assessing Reproducible Data Analysis in Data Science Education

Reproducibility is a cornerstone of scientific research.Data science is not an exception.In recent years scientists were concerned about a large number of irreproducible studies.Such reproducibility crisis in science could severely undermine public trust in science and science-based public policy.Recent efforts to promote reproducible research mainly focused on matured scientists and much less on student training.In this study,we conducted action research on students in data science to evaluate to what extent students are ready for communicating reproducible data analysis.The results show that although two-thirds of the students claimed they were able to reproduce results in peer reports,only one-third of reports provided all necessary information for replication.The actual replication results also include conflicting claims;some lacked comparisons of original and replication results,indicating that some students did not share a consistent understanding of what reproducibility means and how to report replication results.The findings suggest that more training is needed to help data science students communicating reproducible data analysis.

Jointly Recommending Library Books and Predicting Academic Performance： A Mutual Reinforcement Perspective

The prediction of academic performance is one of the most important tasks in educational data mining, and has been widely studied in massive open online courses （MOOCs） and intelligent tutoring systems. Academic performance can be affected by factors like personality, skills, social environment, and the use of library books. However, it is still less investigated about how the use of library books can affect the academic performance of college students and even leverage book-loan history for predicting academic performance. To this end, we propose a supervised content-aware matrix factorization for mutual reinforcement of academic performance prediction and library book recommendation. This model not only addresses the sparsity challenge by explainable dimension reduction techniques, but also quantifies the importance of library books in predicting academic performance. Finally, we evaluate the proposed model on three consecutive years of book-loan history and cumulative grade point average of 13 047 undergraduate students in one university. The results show that the proposed model outperforms the competing baselines on both tasks, and that academic performance not only is predictable from the book-loan history but also improves the recommendation of library books for students.

Consideration of the Local Correlation of Learning Behaviors to Predict Dropouts from MOOCs

Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning,this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network(CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected.

Multi-task MIML learning for pre-course student performance prediction

In higher education,the initial studying period of each course plays a crucial role for students,and seriously influences the subsequent learning activities.However,given the large size of a course’s students at universities,it has become impossible for teachers to keep track of the performance of individual students.In this circumstance,an academic early warning system is desirable,which automatically detects students with difficulties in learning(i.e.,at-risk students)prior to a course starting.However,previous studies are not well suited to this purpose for two reasons:1)they have mainly concentrated on e-learning platforms,e.g.,massive open online courses(MOOCs),and relied on the data about students’online activities,which is hardly accessed in traditional teaching scenarios;and 2)they have only made performance prediction when a course is in progress or even close to the end.In this paper,for traditional classroom-teaching scenarios,we investigate the task of pre-course student performance prediction,which refers to detecting at-risk students for each course before its commencement.To better represent a student sample and utilize the correlations among courses,we cast the problem as a multi-instance multi-label(MIML)problem.Besides,given the problem of data scarcity,we propose a novel multi-task learning method,i.e.,MIML-Circle,to predict the performance of students from different specialties in a unified framework.Extensive experiments are conducted on five real-world datasets,and the results demonstrate the superiority of our approach over the state-of-the-art methods.

Empirical study on the effect of using synthetic attributes on classification algorithms

Purpose-The purpose of this paper is to present an empirical study on the effect of two synthetic attributes to popular classification algorithms on data originating from student transcripts.The attributes represent past performance achievements in a course,which are defined as global performance(GP)and local performance(LP).GP of a course is an aggregated performance achieved by all students who have taken this course,and LP of a course is an aggregated performance achieved in the prerequisite courses by the student taking the course.Design/methodology/approach-The paper uses Educational Data Mining techniques to predict student performance in courses,where it identifies the relevant attributes that are the most key influencers for predicting the final grade(performance)and reports the effect of the two suggested attributes on the classification algorithms.As a research paradigm,the paper follows Cross-Industry Standard Process for Data Mining using RapidMiner Studio software tool.Six classification algorithms are experimented:C4.5 and CART Decision Trees,Naive Bayes,k-neighboring,rule-based induction and support vector machines.Findings-The outcomes of the paper show that the synthetic attributes have positively improved the performance of the classification algorithms,and also they have been highly ranked according to their influence to the target variable.Originality/value-This paper proposes two synthetic attributes that are integrated into real data set.The key motivation is to improve the quality of the data and make classification algorithms perform better.The paper also presents empirical results showing the effect of these attributes on selected classification algorithms.

Research and Application of AI-Enabled Education

Artificial intelligence technology has developed rapidly in variousfields and has been widely used.Education and teaching are also areas in which artificial intelligence is applied.Research on artificial intelligence-enabled(AI-enabled)education and teaching is emerging,such as educational data mining and intelligent assisted teaching systems.First,research on AI-enabled education is introduced,and then the differences between AI-enabled education and tradi-tional education and cases of educational data mining,learning prediction,learn-ing resource recommendation,and various intelligent-assisted teaching systems are analysed.Our existing research results and future development are proposed,such as research on online learning session dropout prediction and the design and implementation of the zhixin teaching assistance system.Finally,this paper concludes that artificial intelligence has been well integrated into education and teaching activities in various ways and has improved students’learning experience and teachers’teaching quality.AI-enabled education and teaching is efficient and will play an increasingly important role.