A Stacking Machine Learning Model for Student Performance Prediction Based on Class Activities in E-Learning

After the spread of COVID-19,e-learning systems have become crucial tools in educational systems worldwide,spanning all levels of education.This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data,making it an attractive resource for predicting student performance.In this study,we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets.The stacking method was employed for modeling in this research.The proposed model utilized weak learners,including nearest neighbor,decision tree,random forest,enhanced gradient,simple Bayes,and logistic regression algorithms.After a trial-and-error process,the logistic regression algorithm was selected as the final learner for the proposed model.The results of experiments with the above algorithms are reported separately for the pass and fail classes.The findings indicate that the accuracy of the proposed model on the OULAD dataset reached 98%.Overall,the proposed method improved accuracy by 4%on the OULAD dataset.

Impact of Remote Learning on Student Performance and Grade: A Virtual World of Education in the COVID-19 Era

The COVID-19 pandemic has had a profound influence on education around the world, with schools and institutions shifting to remote learning to safeguard the safety of students and faculty. Concerns have been expressed about the impact of virtual learning on student performance and grades. The purpose of this study is to investigate the impact of remote learning on student performance and grades, as well as to investigate the obstacles and benefits of this new educational paradigm. The study will examine current literature on the subject, analyze data from surveys and interviews with students and educators, and investigate potential solutions to improve student performance and participation in virtual classrooms. The study’s findings will provide insights into the effectiveness of remote learning and inform ideas to improve student learning and achievement in an educational virtual world. The purpose of this article is to investigate the influence of remote learning on both students and educational institutions. The project will examine existing literature on the subject and collect data from students, instructors, and administrators through questionnaires and interviews. The paper will look at the challenges and opportunities that remote learning presents, such as the effect on student involvement, motivation, and academic achievement, as well as changes in teaching styles and technology. The outcomes of this study will provide insights into the effectiveness of remote learning and will affect future decisions about the usage of virtual learning environments in education. The research will also investigate potential solutions to improve the quality of remote education and handle any issues that occur.

TELF-PSPOC: Three-layer Ensemble Learning Framework for Predicting Student Performance of Online Courses

Virtual learning environment(VLE)MOOC provides large-scale data of resources,activities,and interactions within a course structure for predicting student performance.But it is challenging to extract and learn efficient features from student behaviors.In this paper,a three-layer ensemble learning framework for predicting student performance of online courses(TELF-PSPOC)at an early phase is proposed to analyze data collected from Open University Learning Analytics Dataset(OULAD).First,feature augmentation of student behavior is proposed to enrich current features of student performance,including pass rate and grades of all staged tests,daily clicks of online resources.Second,three-layer ensemble feature learning with heterogeneous classifiers(TEFL-HC)is proposed to benefit the integration of tree model and neural network.Compared with current two-layer ensemble learning,pretraining of features prevents overfitting while using nonlinear regression.The experiment shows that our TELF-PSPOC performs better than several baseline models.Besides,the relationship of the learning results and student behavior via VLE is further discovered.

A study of student performance under English teaching using a decision tree algorithm

With the popularity of English, more and more attention has been paid to students’ Englishlearning. In order to understand student learning and make accurate predictions about studentperformance, this paper analyzed student performance under English teaching by using adecision tree algorithm, i.e. the C4.5 algorithm. The calculation process of the algorithm was simplifiedby the Taylor series, and an example was analyzed. The results showed that the runningtime of the improved C4.5 algorithm was improved by 22.86% compared with the C4.5 algorithm,the precision rate was above 75%, the recall rate was above 85%, and the F1-measure value wasabove 80%. The experimental results verified the effectiveness of the improved C4.5 method instudying student achievement. This work is beneficial to the further optimization of decision treealgorithms and provides some reference for the application of intelligent algorithms in the fieldof education.

A Feature Learning-Based Model for Analyzing Students’ Performance in Supportive Learning

Supportive learning plays a substantial role in providing a quality edu-cation system.The evaluation of students’performance impacts their deeper insight into the subject knowledge.Specifically,it is essential to maintain the baseline foundation for building a broader understanding of their careers.This research concentrates on establishing the students’knowledge relationship even in reduced samples.Here,Synthetic Minority Oversampling TEchnique(SMOTE)technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy.When the initial processing is not done substantially,it leads to misleading prediction accuracy.This research concentrates on modelling an efficient classifier model to predict students’perfor-mance.Generally,the online available student dataset comprises a lesser amount of sample,and k-fold cross-validation is performed to balance the dataset.Then,the relationship among the students’performance(features)is measured using the auto-encoder.The stacked Long Short Term Memory(s-LSTM)is used to learn the previous feedback connection.The stacked model handles the provided data and the data sequence for understanding the long-term dependencies.The simula-tion is done in the MATLAB 2020a environment,and the proposed model shows a better trade-off than the existing approaches.Some evaluation metrics like pre-diction accuracy,sensitivity,specificity,AUROC,F1-score and recall are evalu-ated using the proposed model.The performance of the s?LSTM model is compared with existing approaches.The proposed model gives 89% accuracy,83% precision,86%recall,and 87%F-score.The proposed model outperforms the existing systems in terms of the earlier metrics.

Research on the Construction of a Data Warehouse Model for College Student Performance

Students’grades not only serve as an effective indicator of their learning achievements but also to some extent reflect the completion of teaching tasks by the instructors.Currently,many universities across the country have collected and recorded various information about students and teachers in the school’s information management system,but it is only a simple storage record and has not effectively excavated hidden information,and data have not been fully utilized.Student performance information,enrolment information,course information,teaching plans,and teacher-related information are currently stored in separate databases,which are independent of each other,making it difficult to perform effective data analysis.Data warehousing technology can integrate various information and use data analysis software to excavate more high-value information,which is convenient for teaching evaluation and optimizing teaching strategies.Based on data warehousing technology,the article uses the hierarchical concept of data warehousing to construct the ODS layer,DWD layer,DWS layer and ETL layer.Facing the data warehousing topic,the article designs the data warehousing conceptual model,logical model,and physical model based on student performance,providing a model basis for later data mining.

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%.

PerformanceVis:Visual analytics of student performance data from an introductory chemistry course

We present PerformanceVis,a visual analytics tool for analyzing student admission and course performance data and investigating homework and exam question design.Targeting a university-wide introductory chemistry course with nearly 1000 student enrollment,we consider the requirements and needs of students,instructors,and administrators in the design of PerformanceVis.We study the correlation between question items from assignments and exams,employ machine learning techniques for student grade prediction,and develop an interface for interactive exploration of student course performance data.PerformanceVis includes four main views(overall exam grade pathway,detailed exam grade pathway,detailed exam item analysis,and overall exam&homework analysis)which are dynamically linked together for user interaction and exploration.We demonstrate the effectiveness of PerformanceVis through case studies along with an ad-hoc expert evaluation.Finally,we conclude this work by pointing out future work in this direction of learning analytics research.

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.

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.

Innovative Research on the Application of Flexible Learning in Classroom Teaching at Suan Sunandha Rajabhat University in Thailand

This article aims to introduce an innovative approach to classroom student participation and academic performance in a flexible learning environment at Suan Sunandha Rajabhat University in Thailand.To achieve this goal,a series of theories,concepts,and related research were reviewed and a comprehensive model of relevant factors was constructed.The research design adopted a mixed methods approach,utilizing quantitative research to test the relationships between variables in the model,followed by qualitative research to gain a deeper understanding of how these factors affect students’grades.Combining the results of quantitative and qualitative research can provide guidance for improving the performance of Suan Sunandha Rajabhat University students in flexible learning systems in Thailand.

Building Features in Schools That Influence Academic Performance

School decision makers are faced with a great many decisions when considering a school renovation or new school building. All stakeholders want a building that is safe and provides an optimal learning environment. However, it is often difficult to know which building features will have the greatest effect on student learning. Because of a limited understanding of the relationship between individual building features and student learning, researchers at the University of Oklahoma hope to explore how building components influence student and teacher performance. This paper explores the importance of school building features that can be designed and changed during a renovation project. The hope is to one day determine which features have the greatest impact on student test scores. The research team believes that although it is difficult to find the exact relationship between each building features and student outcomes with one study, if multiple users repeat the same or similar studies, hopefully we will one day know the effect of these building features. In order to develop feature building users survey and physical assessment tools, it was necessary for investigators to develop a list of important building features and their associated definitions in layman terms. This was accomplished through utilization and conducting of a CAB （community advisory board） and subject matter expert materials. In addition, previous research relating to different school building features and their associations with student performance were reviewed. To define and narrow the list the researchers, community educational, and building professionals rated based on their professional experience, how directly related each feature is to student performance. The building feature list serves as a starting point to determine which features should be analyzed in a later phase of the project. It is hoped that resulting tools based on the work of this project can be used by school decision-makers and researchers to access building features that have been identified through research as being important for student and teacher performance.

Two in One： The Collaboration Between Science Foundation Lecturers in Imparting Academic Skills to Students

Learning challenges are more common in the 21 st century than many people believe; however, there are more efficient and better methods to develop academic learning skills for students. Identifying learning problems can help both students and lecturers to apply specific learning techniques and work together to be more productive in the process of teaching and learning. This study investigates the collaboration of science foundation lecturers in addressing the students＇ academic performance and challenges in their foundation modules at the University of Venda. The study is also aimed at finding out whether students can be able to apply their information technology （IT） skills in other modules and they make use of group work in projects into different foundation modules to enhance performance and develop academic learning skills. In this study, students were given projects in their respective modules （Foundation Biology and Foundation IT） and it was required to them to make use of their IT skills during the research report and also during the presentation of their project. These projects were done in groups wherein students undertook some mini researches. Their findings were then reported in writing and presentations were held. The Foundation English lecturer helped in assessing the use of the academic language used in writing and during oral presentations. The findings from these projects were that students＇ academic performance and skills improved and their specific knowledge in those modules also improved radically. It was found that group work plays a very imperative role in enhancing the students＇ performance and collaboration in various foundation modules.

THE RELATIONSHIP BETWEEN COMFORT PERCEPTIONS AND ACADEMIC PERFORMANCE IN UNIVERSITY CLASSROOM BUILDINGS

This paper presents preliminary data on a series of building comfort experiments conducted in the field.We performed physical in-situ measurements and solicited responses from 409(184 female;225 male)university students in six different classrooms at the University of Massachusetts-Amherst during three seasons(fall,winter and spring).Our questions focused on student perception of comfort in varied environmental(temperature and humidity,and air speed)conditions.We collected records of student academic performance in the classes,correlating their comfort perceptions to their test scores.Statistical analysis of classroom environ-mental variables,thermal satisfaction,and student scores suggest that by enhancing thermal comfort,we can improve academic performance.

Application of BP Neural Network in Classification and Prediction of Blended Learning Achievements

Analyzing and predicting the learning behavior data of students in blended teaching can provide reference basis for teaching.Aiming at weak generalization ability of existing algorithm models in performance prediction,a BP neural network is introduced to classify and predict the grades of students in the blended teaching.L2 regularization term is added to construct the BP neural network model in order to reduce the risk of overfitting.Combined with Pearson coefficient,effective feature data are selected as the validation dataset of the model by mining the data of Chao-Xing platform.The performance of common machine learning algorithms and the BP neural network are compared on the dataset.Experiments show that BP neural network model has stronger generalizability than common machine learning models.The BP neural network with L2 regularization has better fitting ability than the original BP neural network model.It achieves better performance with improved accuracy.

“Screening”or“Meritocracy”:Does the Choice of a Private School Help Students Achieve Higher Academic Performance?-Based on the PISA 2018 Analysis of Four Provinces and Municipalities in China

The introduced policy of“synchronized enrollment of public and private schools”has once again triggered the debate on the effectiveness of public and private schools.Based on the data of the Program for International Student Assessment(PISA)2018 from four Chinese provinces and municipalities,this paper explores whether private schools gain a relative advantage in student academic performance through student“screening”or academic“meritocracy,”through a hierarchical linear model(HLM)and an empirical test of the propensity score matching(PSM).It has been found that the academic performance of students in private schools is significantly better than that in public schools.But with background,metacognitive ability,and learning hours of students in private schools controlled for,such academic performance is not significantly superior,suggesting that private schools rely heavily on student“screening”to achieve a relative advantage in student academic performance.This finding has also verified the scientific nature of the above policy.

THE NECESSITY AND FEASIBILITY OF INTRODUCING AN ORAL PERFORMANCE TEST FOR CHINESE COLLEGE STUDENTS AT BAND FOUR

The introduction of an oral performance test for Chinese college students at Band Four is proposed. The current CET-4 test and the qualitative problems associated with it are analysed, and the possible positive backwash of such an innovation is explored.

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.

Methodology,Fake Learning,and Emotional Performativity

Purpose:Academics are at the forefront of criticisms about so-called“fake news”considered to undermine evidence-based approaches to the understanding of complex social,political,and economic issues.However,universities contribute to the production of fake news through the legitimization of measures that promote student performativity rewarding their academic nonachievements.This conceptual article will seek to illustrate how this can occur via the writing of methodology chapters by postgraduate students.Design/Approach/Methods:This article provides a critical analysis of the writing of methodology chapters in dissertations and theses in postgraduate education in the social sciences.In so doing,it applies the concept of performativity to student learning.Findings:It is argued that the pressures on students to comply with the requirements of emotional performativity in respect to ideology and method in close-up,qualitative research can lead to fake learning.This phenomenon may be exemplified by reference to a number of practices,namely,phony positionality,methodolatry,ethical cleansing,participatory posturing,and symbolic citation.Originality/Value:This article provides an illustration of the concept of student performativity.It demonstrates that emotional performativity plays a significant role in the way in which students are required to comply with expectations that give rise to inauthenticity in learning.