Pre-course student performance prediction with multi-instance multi-label learning

Ma, Yuling; Cui, Chaoran; Nie, Xiushan; Yang, Gongping; Shaheed, Kashif; Yin, Yilong

doi:10.1007/s11432-017-9371-y

Cited by 22 publications

(14 citation statements)

References 4 publications

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“…More recent works have focused on novel methods to make timely predictions. For example, Ma et al [16] formulated the task of pre-course student performance prediction as a multi-instance multi-label (MIML) problem. They proved that it was desirable to predict each student's performance on a certain course before its commencement rather than after finishing it.…”

Section: Student Performance Predictionmentioning

confidence: 99%

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Wang

Guo

et al. 2020

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As students’ behaviors are important factors that can reflect their learning styles and living habits on campus, extracting useful features of them plays a helpful role in understanding the students’ learning process, which is an important step towards personalized education. Recently, the task of predicting students’ performance from their campus behaviors has aroused the researchers’ attention. However, existing studies mainly focus on extracting statistical features manually from the pre-stored data, resulting in hysteresis in predicting students’ achievement and finding out their problems. Furthermore, due to the limited representation capability of these manually extracted features, they can only understand the students’ behaviors shallowly. To make the prediction process timely and automatically, we treat the performance prediction task as a short-term sequence prediction problem, and propose a two-stage classification framework, i.e., Sequence-based Performance Classifier (SPC), which consists of a sequence encoder and a classic data mining classifier. More specifically, to deeply discover the sequential features from students’ campus behaviors, we first introduce an attention-based Hybrid Recurrent Neural Network (HRNN) to encode their recent behaviors by giving a higher weight to the ones that are related to the students’ last action. Then, to conduct student performance prediction, we further involve these learned features to the classic Support Vector Machine (SVM) algorithm and finally achieve our SPC model. We conduct extensive experiments in the real-world student card dataset. The experimental results demonstrate the superiority of our proposed method in terms of Accuracy and Recall.

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Section: Student Performance Predictionmentioning

confidence: 99%

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Wang

Guo

et al. 2020

Information

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“…Meier et al [17] used homework, test scores, and course project completion information to predict students' final grades four weeks after the course started. Some studies [22,23] considered students' performance in the midterm exams of the course, leading to the prediction of performance only after halfway through the teaching process.…”

Section: Related Workmentioning

confidence: 99%

“…The work most related to ours is that by Ma et al [22,39], who used a multi-label multiinstance algorithm to predict pre-class student performance, but during data preprocessing, some curriculum features that were considered irrelevant were forcefully removed, and the correlation among features, and between features and labels, was not fully considered, and some possible relevant course feature information was directly ignored. For example, some selective courses may have a certain effect on subsequent professional ones, and the direct deletion of selective course information may lead to some feature effects that weaken the predictive performance.…”

Section: Related Workmentioning

confidence: 99%

Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction

Yang

Wang

et al. 2021

Entropy

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The university curriculum is a systematic and organic study complex with some immediate associated steps; the initial learning of each semester’s course is crucial, and significantly impacts the learning process of subsequent courses and further studies. However, the low teacher–student ratio makes it difficult for teachers to consistently follow up on the detail-oriented learning situation of individual students. The extant learning early warning system is committed to automatically detecting whether students have potential difficulties—or even the risk of failing, or non-pass reports—before starting the course. Previous related research has the following three problems: first of all, it mainly focused on e-learning platforms and relied on online activity data, which was not suitable for traditional teaching scenarios; secondly, most current methods can only proffer predictions when the course is in progress, or even approaching the end; thirdly, few studies have focused on the feature redundancy in these learning data. Aiming at the traditional classroom teaching scenario, this paper transforms the pre-class student performance prediction problem into a multi-label learning model, and uses the attribute reduction method to scientifically streamline the characteristic information of the courses taken and explore the important relationship between the characteristics of the previously learned courses and the attributes of the courses to be taken, in order to detect high-risk students in each course before the course begins. Extensive experiments were conducted on 10 real-world datasets, and the results proved that the proposed approach achieves better performance than most other advanced methods in multi-label classification evaluation metrics.

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“…The study defined two behavioral characteristics of orderliness and diligence and analyzed the correlation between the regularity of campus life and academic performance. Reference [2] proposed a preclass student performance prediction method based on multiexample multilabel learning. The idea of this method is to use students' behavior in completed courses to predict their difficulties in learning new courses.…”

Section: Introductionmentioning

confidence: 99%

A College Student Behavior Analysis and Management Method Based on Machine Learning Technology

Shen

Yuan

2021

Wireless Communications and Mobile Computing

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A digital campus will generate a large amount of student-related data. How to analyze and apply these data has become the key to improving the management level of students. The analysis of student behavior data can not only assist schools in early warning of dangerous events and strengthen school safety but also can use real data to describe student behavior, thereby providing quantitative data support for scholarship and grant evaluation. This paper takes a university student as the research object, collects various data in the digital campus platform, and uses an adaptive K -means algorithm in the machine learning algorithm to cluster the data. Analyze the behavior of college students from the clustering results, so as to provide a basis for the education management and learning ability improvement of college students. Specifically, the student’s study, life, and consumption data are selected as the data to describe the student’s behavior at school. This data is input into the adaptive K -means algorithm to obtain different types of student consumption habits, living habits, and learning habits. Through the analysis results, it can be found that the problem of the group of students with low financial ability, the problem of too long online time for students, and the number of books borrowed are too low. According to the characteristics of these problems, teachers and schools are provided with targeted management suggestions. The analysis of student behavior based on machine learning technology provides a reference for the formulation of students’ school management policies and provides teachers with information on students’ personality characteristics, which is conducive to improving teachers’ teaching effects. In short, the management of the results of student behavior analysis can provide a basis for the school to formulate reasonable management policies, thereby promoting precision management and scientific decision-making.

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Pre-course student performance prediction with multi-instance multi-label learning

Cited by 22 publications

References 4 publications

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction

A College Student Behavior Analysis and Management Method Based on Machine Learning Technology

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