An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

Du, Xu; Yang, Juan; Hung, Jui-Long

doi:10.1109/access.2020.2964845

Cited by 21 publications

(15 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Unbalanced data is a challenging issue in machine learning (Du et al , 2020). Unbalanced data means the target cases (at-risk students) represent only a very small portion in the population compared with the non-target cases (successful students).…”

Section: Data Collection and Analysis Methodsmentioning

confidence: 99%

“…The development of technology enriched instructional formats and forms of delivery, such as those used in blended and online learning, has resulted in increased availability of student learning data. Because of the ability of online learning systems to track and store student’s online activities (Du et al , 2019), analytics becomes a possible solution to automate the analysis of student’s learning status, which can then be used to trigger corresponding decision-making processes via machine learning algorithms (Du et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving predictive power through deep learning analysis of K-12 online student behaviors and discussion board content

Hung

Rice

Kepka

et al. 2020

IDD

Self Cite

View full text Add to dashboard Cite

Purpose For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis. Design/methodology/approach This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach. Findings The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students. Originality/value The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.

show abstract

Section: Data Collection and Analysis Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving predictive power through deep learning analysis of K-12 online student behaviors and discussion board content

Hung

Rice

Kepka

et al. 2020

IDD

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since deep learning has had many achievements in various fields, such as image recognition and natural language processing [24], some studies have started to adopt deep learning techniques in the educational field. For example, Du et al [25] proposed a CNN-based model called Channel Learning Image Recognition (CLIR) and provided visualized results to let teachers observe the difference between the at-risk students and other students. In their study, they arranged the learning features each week as a two-dimensional image and applied CNN to make predictions [25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, Du et al [25] proposed a CNN-based model called Channel Learning Image Recognition (CLIR) and provided visualized results to let teachers observe the difference between the at-risk students and other students. In their study, they arranged the learning features each week as a two-dimensional image and applied CNN to make predictions [25]. With the data of 5235 students and 576 absolute features, the recall rate of the CLIR models they proposed was over 77.26%.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, if the provided learning data are accumulated during different learning periods, the performance of the model is drastically reduced even with such operations. To avoid filling in zeros, which may alter the distribution of data, previous studies [21,22,25,26] used the same length of time as the input when training the model. For example, if we want to predict the outcomes of students in the ninth week, we only provide the model with the cumulative nine weeks of data.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks

2021

Sustainability

View full text Add to dashboard Cite

While the use of deep neural networks is popular for predicting students’ learning outcomes, convolutional neural network (CNN)-based methods are used more often. Such methods require numerous features, training data, or multiple models to achieve week-by-week predictions. However, many current learning management systems (LMSs) operated by colleges cannot provide adequate information. To make the system more feasible, this article proposes a recurrent neural network (RNN)-based framework to identify at-risk students who might fail the course using only a few common learning features. RNN-based methods can be more effective than CNN-based methods in identifying at-risk students due to their ability to memorize time-series features. The data used in this study were collected from an online course that teaches artificial intelligence (AI) at a university in northern Taiwan. Common features, such as the number of logins, number of posts and number of homework assignments submitted, are considered to train the model. This study compares the prediction results of the RNN model with the following conventional machine learning models: logistic regression, support vector machines, decision trees and random forests. This work also compares the performance of the RNN model with two neural network-based models: the multi-layer perceptron (MLP) and a CNN-based model. The experimental results demonstrate that the RNN model used in this study is better than conventional machine learning models and the MLP in terms of F-score, while achieving similar performance to the CNN-based model with fewer parameters. Our study shows that the designed RNN model can identify at-risk students once one-third of the semester has passed. Some future directions are also discussed.

show abstract

Explainable Prediction of Student Performance in Online Courses

Capuano,

Rossi,

Ströele

et al. 2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

Cited by 21 publications

References 43 publications

Improving predictive power through deep learning analysis of K-12 online student behaviors and discussion board content

Improving predictive power through deep learning analysis of K-12 online student behaviors and discussion board content

Early Warning System for Online STEM Learning—A Slimmer Approach Using Recurrent Neural Networks

Explainable Prediction of Student Performance in Online Courses

Contact Info

Product

Resources

About