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

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Frontiers of Computer Science 2020年第5期14卷 113-121页

作者： Yuling Ma Chaoran Cui Jun Yu Jie Guo Gongping Yang Yilong Yin School of Software Shandong UniversityJinan250100China School of Information Engineering Shandong Yingcai CollegeJinan250104China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan250014China

In higher education,the initial studying period of each course plays a crucial role for students,and seriously influences the subsequent learning ***,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 *** 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 ***,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 *** 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 *** better represent a student sample and utilize the correlations among courses,we cast the problem as a multi-instance multi-label(MIML)***,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 *** experiments are conducted on five real-world datasets,and the results demonstrate the superiority of our approach over the state-of-the-art methods.

关键词： educational data mining academic early warning system student performance prediction multi-instance multi-label learning multi-task learning

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

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Journal of Computer Science & Technology 2018年第4期33卷 654-667页

作者： De-Fu Lian Qi Liu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China School of Computer Science and Technology University of Science and Technology of China Hefei 230022 China

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.

关键词： book-borrowing record educational data mining matrix factorization multi-task learning student performance prediction transfer learning

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

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Tsinghua Science and Technology 2020年第3期25卷 336-347页

作者： Yimin Wen Ye Tian Boxi Wen Qing Zhou Guoyong Cai Shaozhong Liu Guangxi Key Laboratory of Trusted Software and the School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China. the School of Business Guilin University of Electronic TechnologyGuilin 541004China. the College of Computer Science Chongqing UniversityChongqing 400044China. the College of Foreign Studies Guilin University of Electronic TechnologyGuilin 541004China.

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.

关键词： Massive Open Online Courses(MOOCs) dropout prediction local correlation of learning behaviors Convolutional Neural Network(CNN) educational data mining

An Improved Evolutionary Algorithm for data mining and Knowledge Discovery

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Computers, Materials & Continua 2022年第4期71卷 1233-1247页

作者： Mesfer Al Duhayyim Radwa Marzouk Fahd N.Al-Wesabi Maram Alrajhi Manar Ahmed Hamza Abu Sarwar Zamani Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza 12613Egypt Department of Computer Science Faculty of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems Faculty of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Recent advancements in computer technologies for data processing,collection,and storage have offered several chances to improve the abilities in production,services,communication,and *** mining(DM)is an interdisciplinary field commonly used to extract useful patterns from the *** the same time,educational data mining(EDM)is a kind of DM concept,which finds use in educational ***,artificial intelligence(AI)techniques can be used for mining a large amount of *** 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 *** 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 *** presented IEAFSS-NFC model involves data pre-processing,feature selection,and ***,the Chaotic Whale Optimization Algorithm(CWOA)is used for the selection of the highly related feature subsets to accomplish improved classification ***,Neuro-Fuzzy Classification(NFC)technique is employed for the classification of education *** IEAFSS-NFC model is tested against a benchmark Student Performance dataSet from the UCI *** simulation outcome has shown that the IEAFSS-NFC model is superior to other methods.

关键词： educational data mining feature selection whale optimization classification

A Hybrid Feature Selection Framework for Predicting Students Performance

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Computers, Materials & Continua 2022年第1期70卷 1893-1920页

作者： Maryam Zaffar Manzoor Ahmed Hashmani Raja Habib KS Quraishi Muhammad Irfan Samar Alqhtani Mohammed Hamdi High Performance Cloud Computing(HPC3) Department of Computer and Information SciencesUniversiti Teknologi PETRONAS32610SeriIskandarMalaysia Department of Computer Science and Information Technology University of LahorePakistan Department of Process Engineering Pakistan Institute of Engineering&Applied SciencesNiloreIslamabadPakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia College of Computer Science and Information Systems Najran University61441NajranSaudi Arabia

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 *** 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 *** 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 *** 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 *** 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 *** 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 ***,according to the results reported in existing literature,cosine similarity is considered as the best similarity measure among existing similarity *** proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of *** 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 *** reported in presented paper show that the proposed approach gives more than 90%accuracy on benchmark dataset that is better tha

关键词： educational data mining feature selection hybrid feature selection

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

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International Journal of Intelligent Computing and Cybernetics 2017年第2期10卷 111-129页

作者： Ali Hasan Alsaffar Department of Computer Science University of BahrainSakhirBahrain

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 *** 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 ***/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 *** a research paradigm,the paper follows Cross-Industry Standard Process for data mining using RapidMiner Studio software *** classification algorithms are experimented:C4.5 and CART Decision Trees,Naive Bayes,k-neighboring,rule-based induction and support vector ***-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 ***/value-This paper proposes two synthetic attributes that are integrated into real data *** key motivation is to improve the quality of the data and make classification algorithms perform *** paper also presents empirical results showing the effect of these attributes on selected classification algorithms.

关键词： Classification educational data mining Performance prediction RapidMiner Synthetic attributes

Short Text mining for Classifying educational Objectives and Outcomes

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Computer Systems Science & Engineering 2022年第4期41卷 35-50页

作者： Yousef Asiri College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

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 ***,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)*** addition,ABET needs to automatically ensure that the mapping(classiﬁcation)is reasonable and *** classiﬁcation also plays a vital role in the assessment of students’*** 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 *** this work,a novel associative short text classiﬁcation tech-nique is proposed to map PEOs to *** datasets are extracted from 152 self-study reports(SSRs)that were produced in operational settings in an engineering program accredited by *** datasets are processed and transformed into a representational form appropriate for association rule *** extracted rules are utilized as delegate classiﬁers to map PEOs to *** 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.

关键词： ABET accreditation association rule mining educational data mining engineering education program educational objectives student outcomes associative classiﬁcation

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

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Intelligent Automation & Soft Computing 2023年第6期36卷 3621-3636页

作者： Ierin Babu R.MathuSoothana S.Kumar Department of Computer Science and Engineering Noorul Islam Centre for Higher EducationKanyakumari629180India Department of Information Technology Noorul Islam Centre for Higher EducationKanyakumari629180India

educational data mining(EDM)is an emergent discipline that concen-trates on the design of self-learning and adaptive *** education institutions have started to utilize analytical tools to improve students’grades and *** of students’performance is a difficult process owing to the massive quantity of educational ***,Artificial Intelligence(AI)techniques can be used for educational data mining in a big data *** the same time,in EDM,the feature selection process becomes necessary in creation of feature *** 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 *** 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’*** addition,the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of ***,the Chaotic Monarch Butterfly Optimization Algo-rithm(CBOA)is used for the selection of highly related features with low com-plexity and high ***,a sailfish optimizer with stacked sparse autoencoder(SFO-SSAE)approach is utilized for the classification of educational *** 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 *** 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%.

关键词： Students’performance analysis educational data mining feature selection deep learning metaheuristics outlier detection

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

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International Journal of Innovation Studies 2020年第4期4卷 134-147页

作者： Chong Guan Jian Mou Zhiying Jiang School of Business Singapore University of Social Sciences(SUSS)Singapore School of Business Pusan National UniversityBusan46241South Korea

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 *** doing so,we seek to uncover the prominent keywords associated with AI-enabled pedagogical adaptation research in each decade,due to the discipline’s *** 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 *** importantly,our analysis highlights the paradigm shifts and emergent trends that are gaining prominence in the field of educational *** instance,the results suggest the decline in conventional tech-enabled instructional design research and the flourishing of student profiling models and learning ***,this paper serves to raise awareness on the opportunities and challenges behind AI and DL for pedagogical adaptation and initiate a dialogue.

关键词： Artificial intelligence Systematic review Intelligent tutoring systems Virtual reality educational data mining