Prediction of student academic performance based on their emotional wellbeing and interaction on various e-learning platforms

Kukkar, Ashima; Mohana, Rajni; Sharma, Aman; Nayyar, Anand

doi:10.1007/s10639-022-11573-9

Cited by 38 publications

(20 citation statements)

References 38 publications

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“…The authors created social circumstances that called for the portrayal of emotions in order to make the game appealing. In [25] suggests a revolutionary Student Academic Performance Predicting (SAPP) system. It has a design a proper and forecasts whether pupils will pass or fail using a 4-layer stacked Long Short Term Memory (LSTM) network, Random Forest (RF), and Gradient Boosting (GB) technique combo.…”

Section: Related Workmentioning

confidence: 99%

“…The experimental result is carried out by using the parameters such as accuracy, precision, Root Mean Square Error (RMSE), Root Absolute Error (RAE). These parameters are compared with three state of art methods such as Student Academic Performance Predicting (SAPP) system [25], Temporal Convolutional Neural Networks (T-CNN) [26], Attentive Interactive Student Performance prediction model (CAISP) [28]with the proposed Interaction students Education Neural network ( Int_Edu_NN) . These are analyzed for two datasets such as AffectNet and ASSIST2009.…”

Section: Experimental Analysismentioning

confidence: 99%

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Exploration of Emotion Analysis and Personalized Emotion Adjustment Algorithm in English Learning Process

Liang

2024

jes

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An important component of human existence and behaviour is emotion. The automatic identification of emotions has gained significant importance in the domains of affective computing and human-machine interaction in recent times. Getting pictures of people's facial expressions is one of the simplest and least expensive methods for identifying emotions among the several physiological and kinematic data that may be utilised. Because of variations in anatomy, culture, and environment, developing a generalised, cross-subject model for emotion identification from facial expression remains difficult.This study presents an intelligent classroom that uses e-learning to help persons with autism spectrum condition strengthen their emotional abilities. Monitoring autism child engagement and participation in class while looking for signs of attention is necessary for effective classroom education. Teaching quality is increasingly being determined by the teachers' capacity to assess and evaluate the behaviour of their pupils in the classroom. Modern classroom systems are enhanced with the most recent technologies in educational institutions to make them more interactive, student-centered, and personalised. Even with these tools, it can be challenging for teachers to gauge their pupils' levels of interest and focus. This study makes use of contemporary technology to establish a real-time, intelligent vision-based classroom that can track students' moods, attendance, and levels of focus even when they are wearing face masks. In order to evaluate students' attention or lack thereof in a classroom, we trained AlexNet models that recognise student behaviour, including recognising facial expressions. Another designed to manage the irregular relationship between pupils and activities is the Interaction Students Education Neural Network ( Int_Edu_NN). The Q-matrix is used to firstly determine the pupil variable and the assignment indicator. This network is analyzed using two datasets in terms of various parameters and hence the proposed Int_Edu_NN achieves 99.% and 98% of accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Experimental Analysismentioning

confidence: 99%

Exploration of Emotion Analysis and Personalized Emotion Adjustment Algorithm in English Learning Process

Liang

2024

jes

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“…However, the highest predictive accuracy for students with grades of C (70-79) or below was only 63%. Ashima Kukkar et al [7] used a combination of a four-layer stacked long short-term memory (LSTM) network, random forest (RF), and gradient boosting (GB) techniques to predict whether students passed or failed, achieving an accuracy rate of 96% by verifying with the OULAD dataset. Wenjing Ban et al [8] designed a multi-algorithm online learning performance prediction framework that integrated neural networks, decision trees, K-nearest neighbors, random forests, and logistic regression algorithms to predict online learning performance.…”

Section: Related Workmentioning

confidence: 99%

Multi-Output Based Hybrid Integrated Models for Student Performance Prediction

Xue

Niu

2023

Applied Sciences

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In higher education, student learning relies increasingly on autonomy. With the rise in blended learning, both online and offline, students need to further improve their online learning effectiveness. Therefore, predicting students’ performance and identifying students who are struggling in real time to intervene is an important way to improve learning outcomes. However, currently, machine learning in grade prediction applications typically only employs a single-output prediction method and has lagging issues. To advance the prediction of time and enhance the predictive attributes, as well as address the aforementioned issues, this study proposes a multi-output hybrid ensemble model that utilizes data from the Superstar Learning Communication Platform (SLCP) to predict grades. Experimental results show that using the first six weeks of SLCP data and the Xgboost model to predict mid-term and final grades meant that accuracy reached 78.37%, which was 3–8% higher than the comparison models. Using the Gdbt model to predict homework and experiment grades, the average mean squared error was 16.76, which is better than the comparison models. This study uses a multi-output hybrid ensemble model to predict how grades can help improve student learning quality and teacher teaching effectiveness.

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“…Input Features and Performance Focus Studies Performance Prediction using preadmission data High school GPA, GAT and Admission test scores (predict performance at the end of first year of undergrad program) (Tan et al, 2022), (Martínez-Navarro et al, 2021), (Alharthi, 2021), (Erika B. Varga, 2021), (Adekitan and N-Osaghae, 2019) Performance Prediction using academic data of undergrad program's initial years GPA of first-second year of undergrad program and grades in a some courses (predict graduating GPA) (Hashim et al, 2020), (Qazdar et al, 2019), (Miguéis et al, 2018), (Asif et al, 2017), (Hoffait and Schyns, 2017), (Jia and Maloney, 2015) Performance Prediction using low-cost variables Class participation, resource availability, heterogeneity, and class strength (predict future academic performance) (Tomasevic et al, 2020), (Yousafzai et al, 2020), (Xu et al, 2019), (Helal et al, 2018), (Sandoval et al, 2018), (Thiele et al, 2016), (Xing et al, 2015) Performance Prediction using non-academic variables in addition to academic data Behavioral and emotional characteristics, social and demographic features (forecast future academic performance) (Wild et al, 2023), (Kukkar et al, 2023), (Yao et al, 2019), (Nti et al, 2022), (Karagiannopoulou et al, 2021), (Keser and Aghalarova, 2021), (Fernandes et al, 2019), (Thiele et al, 2016)…”

Section: Factors For Categorizing Research Studiesmentioning

confidence: 99%

“…According to some studies, only academic factors are not enough for performance pre diction, but socio-demographic factors can be beneficial too (Thiele et al, 2016;Wild et al, 2023;Kukkar et al, 2023;Nti et al, 2022). Yao et al (2019) predicted the performance of undergraduate students based on their behavior in school.…”

Section: Performance Prediction In Coursesmentioning

confidence: 99%

Enhancing Student Performance Prediction via Educational Data Mining on Academic data

Alamgir¹,

Akram²,

Karim³

et al. 2023

Informatics in Education

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Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modeling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students’ performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performance and identify the critical areas that need significant improvement.

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Prediction of student academic performance based on their emotional wellbeing and interaction on various e-learning platforms

Cited by 38 publications

References 38 publications

Exploration of Emotion Analysis and Personalized Emotion Adjustment Algorithm in English Learning Process

Exploration of Emotion Analysis and Personalized Emotion Adjustment Algorithm in English Learning Process

Multi-Output Based Hybrid Integrated Models for Student Performance Prediction

Enhancing Student Performance Prediction via Educational Data Mining on Academic data

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