Early Prediction of Student Performance in Blended Learning Courses Using Deep Neural Networks

Raga, Rodolfo C.; Raga, Jennifer

doi:10.1109/iset.2019.00018

Cited by 31 publications

(19 citation statements)

References 13 publications

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“…Thirty-eight (61.29%) models provided summative predictions, while 19 (30.64%) models provided formative predictions (e.g., weekly or monthly) of student performance. Only five studies calculated both formative and summative predictions of student performance, i.e., [46,68,76,84,88].…”

Section: Learning Outcomes As Indicators Of Student Performancementioning

confidence: 99%

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

2020

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The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplored. A decade of research work conducted between 2010 and November 2020 was surveyed to present a fundamental understanding of the intelligent techniques used for the prediction of student performance, where academic success is strictly measured using student learning outcomes. The electronic bibliographic databases searched include ACM, IEEE Xplore, Google Scholar, Science Direct, Scopus, Springer, and Web of Science. Eventually, we synthesized and analyzed a total of 62 relevant papers with a focus on three perspectives, (1) the forms in which the learning outcomes are predicted, (2) the predictive analytics models developed to forecast student learning, and (3) the dominant factors impacting student outcomes. The best practices for conducting systematic literature reviews, e.g., PICO and PRISMA, were applied to synthesize and report the main results. The attainment of learning outcomes was measured mainly as performance class standings (i.e., ranks) and achievement scores (i.e., grades). Regression and supervised machine learning models were frequently employed to classify student performance. Finally, student online learning activities, term assessment grades, and student academic emotions were the most evident predictors of learning outcomes. We conclude the survey by highlighting some major research challenges and suggesting a summary of significant recommendations to motivate future works in this field.

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Section: Learning Outcomes As Indicators Of Student Performancementioning

confidence: 99%

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

2020

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“…However, very few prior studies have explored the student performance prediction from a sequence modeling perspective [27,28]. For instance, a recent study [29] experimented on prediction models for student performance in the early stages of blended learning courses, which applied deep neural network architecture and utilized online activity attributes as input patterns extracted from the activity logs stored by Moodle. A recent study [30] transformed the time series click data of students' eBook behaviors into different features to predict whether a student passes the course or not.…”

Section: Deep Learning-based Sequence Modelingmentioning

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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“…This can be applied for different purposes such as student behavior modeling, prediction of performance, prediction of dropout and retention, and resources recommendation (Papamitsiou & Economides, 2014). The task of predicting student performance involves approximating students' future status given a record of the past sequence of behaviors exhibited or activities engaged by a student (Raga & Raga, 2019). A brief overview of the main related previous research on student performance prediction is presented in the following.…”

Section: Learning Analytics and Student Performance Predictionmentioning

confidence: 99%

“…Along the same lines, Vialardi et al (2011) used data mining techniques that employed the students' academic performance records to design a recommender system in support of the enrolment process. Raga and Raga (2019) develop a prediction model for student performance in the early stages of blended learning courses using deep neural network architecture and utilizing online activity attributes as input patterns.…”

Section: Learning Analytics and Student Performance Predictionmentioning

confidence: 99%

A Learning Fuzzy Cognitive Map (LFCM) Approach to Predict Student Performance

2021

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Aim/Purpose: This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background: Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student performance. Although the available sets of common prediction approaches, such as Artificial Neural Networks (ANN) and regression, work well with large datasets, they face challenges dealing with small sample sizes, limiting their practical applications in real practices. Methodology: Six distinct categories of performance antecedents are adopted here as course characteristics, LMS characteristics, student characteristics, student engagement, student support, and institutional factors, along with measurement items within each category. Furthermore, we assessed the student’s overall performance using three items of student satisfaction score, knowledge construction level, and student GPA. We have collected longitudinal data from 30 postgraduates in four subsequent semesters and analyzed data using the Learning Fuzzy Cognitive Map (LFCM) technique. Contribution: This research proposes a brand new approach, Learning Fuzzy Cognitive Map (LFCM), to predict student performance. Using this approach, we identified the most influential determinants of student performance, such as student engagement. Besides, this research depicts a model of interrelations among the student performance determinants. Findings: The results suggest that the model reasonably predicts the incoming sequence when there is a limited sample size. The results also reveal that students’ total online time and the regularity of learning interval in LMS have the largest effect on overall performance. The student engagement category also has the highest direct effect on student’s overall performance. Recommendations for Practitioners: Academic institutions can use the results and approach developed in this paper to identify students’ performance antecedents, predict the performance, and establish action plans to resolve the shortcomings in the long term. Instructors can adjust their learning methods based on the feedback from students in the short run on the operational level. Recommendation for Researchers: Researchers can use the proposed approach in this research to deal with the problems in other domains, such as using LMS for organizational/institutional education. Besides, they can focus on specific dimensions of the proposed model, such as exploring ways to boost student engagement in the learning process. Impact on Society: Our results revealed that students are at the center of the learning process. The degree to which they are dedicated to learning is the most crucial determinant of the learning outcome. Therefore, learners should consider this finding in order the gain value from the learning process. Future Research: As a potential for future works, the proposed approach could be used in other contexts to test its applicability. Future studies could also improve the performance level of the proposed LFMC model by tuning the model’s elements.

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Early Prediction of Student Performance in Blended Learning Courses Using Deep Neural Networks

Cited by 31 publications

References 13 publications

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

Student Performance Prediction with Short-Term Sequential Campus Behaviors

A Learning Fuzzy Cognitive Map (LFCM) Approach to Predict Student Performance

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