Improving early prediction of academic failure using sentiment analysis on self‐evaluated comments

Yu, Liang-Chih; Lee, C.W.; Pan, Hsin-Hung; Chou, Chih-Yueh; Chao, Po Yao; Chen, Z.H.; Tseng, Shu Fen; Chan, Chien-Lung; Lai, K. Robert

doi:10.1111/jcal.12247

Cited by 73 publications

(56 citation statements)

References 31 publications

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“…Many studies have also claimed that ensembled methods, such as AdaBoost and Random Forest, usually perform better and more robust than single classifier [16]. Furthermore, Table 2 shows that deep learning models perform slightly better than AdaBoost under the same data condition, which is line with previous findings [16], [30], [50]. It is concluded that DNN is a promising method for building prediction models than traditional machine learning algorithms in education.…”

Section: Experimental Results and Discussion 1) Prediction Performsupporting

confidence: 85%

“…Specifically, LVAE component mainly aims to learn the latent feature distribution of at-risk students and generate some at-risk samples for the purpose of obtaining a balanced dataset. Due to the outstanding prediction performance of DNN [16], [30], it has been applied to perform final prediction in order to explore whether the latent feature distribution of at-risk students learnt by LVAE component is helpful for accurately identifying and capturing at-risk students. Finally, the effectiveness and robustness of the proposed LVAEPre framework are verified through multiple sets of experiments.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Hung

2020

IEEE Access

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The rapid development of learning technologies has enabled online learning paradigm to gain great popularity in both high education and K-12, which makes the prediction of student performance become one of the most popular research topics in education. However, the traditional prediction algorithms are originally designed for balanced dataset, while the educational dataset typically belongs to highly imbalanced dataset, which makes it more difficult to accurately identify the at-risk students. In order to solve this dilemma, this study proposes an integrated framework (LVAEPre) based on latent variational autoencoder (LVAE) with deep neural network (DNN) to alleviate the imbalanced distribution of educational dataset and further to provide early warning of at-risk students. Specifically, with the characteristics of educational data in mind, LVAE mainly aims to learn latent distribution of at-risk students and to generate at-risk samples for the purpose of obtaining a balanced dataset. DNN is to perform final performance prediction. Extensive experiments based on the collected K-12 dataset show that LVAEPre can effectively handle the imbalanced education dataset and provide much better and more stable prediction results than baseline methods in terms of accuracy and F 1.5 score. The comparison of t-SNE visualization results further confirms the advantage of LVAE in dealing with imbalanced issue in educational dataset. Finally, through the identification of the significant predictors of LVAEPre in the experimental dataset, some suggestions for designing pedagogical interventions are put forward. INDEX TERMS Performance prediction, early warning prediction, latent variational autoencoder, resampling methods, deep neural network, t-SNE.

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Section: Experimental Results and Discussion 1) Prediction Performsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

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

Yang

Hung

2020

IEEE Access

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“…The valence and arousal model, often called the "circumplex model" of affect [40] is widely used in psychological and psycholinguistic studies [41]. In computational linguistics, this model is applied when the interest is in continuous measurements of valence and arousal rather than in the specific discrete emotional categories [42].…”

Section: Plos Onementioning

confidence: 99%

Words describing feelings about death: A comparison of sentiment for self and others and changes over time

et al. 2021

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Understanding public attitudes towards death is needed to inform health policies to foster community death awareness and preparedness. Linguistic sentiment analysis of how people describe their feelings about death can add to knowledge gained from traditional self-reports. This study provided the first description of emotive attitudes expressed towards death utilising textual sentiment analysis for the dimensions of valence, arousal and dominance. A linguistic lexicon of sentiment norms was applied to activities conducted in an online course for the general-public designed to generate discussion about death. We analysed the sentiment of words people chose to describe feelings about death, for themselves, for perceptions of the feelings of ‘others’, and for longitudinal changes over the time-period of exposure to a course about death (n = 1491). The results demonstrated that sadness pervades affective responses to death, and that inevitability, peace, and fear were also frequent reactions. However, words chosen to represent perceptions of others’ feelings towards death suggested that participants perceived others as feeling more negative about death than they do themselves. Analysis of valence, arousal and dominance dimensions of sentiment pre-to-post course participation demonstrated that participants chose significantly happier (more positive) valence words, less arousing (calmer) words, and more dominant (in-control) words to express their feelings about death by the course end. This suggests that the course may have been helpful in participants becoming more emotionally accepting in their feelings and attitude towards death. Furthermore, the change over time appeared greater for younger participants, who showed more increase in the dominance (power/control) and pleasantness (valence) in words chosen at course completion. Sentiment analysis of words to describe death usefully extended our understanding of community death attitudes and emotions. Future application of sentiment analysis to other related areas of health policy interest such as attitudes towards Advance Care Planning and palliative care may prove fruitful.

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“…Except from predicting affective states and performance and explaining engagement, other studies employed multimodal data for other research objectives, as well, including modeling dialogue Grafsgaard, Lester, & Boyer, 2015;Worsley, 2018), idea creation (Furuichi & Worsley, 2018) or motivational intentions (Yu et al, 2018), assessing presentation skills (Chen et al, 2016;Ochoa et al, 2018) and predicting collaborative coordination/synchrony between the collaborating peers (eg, Grafsgaard, Duran, Randall, Tao, & D'Mello, 2018;Schneider & Blikstein, 2015;Stewart, Keirn, & D'Mello, 2018;Worsley, 2014). Furthermore, substantial work has been done in the area of providing feedback using data in one or more modalities (Pardo, Poquet, Martínez-Maldonado, & Dawson, 2017).…”

Section: Related Work: Utilizing Multimodal Data To Predict Learning mentioning

confidence: 99%

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Sharma¹,

Papamitsiou²,

Giannakos³

2019

Brit J Educational Tech

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Students' on‐task engagement during adaptive learning activities has a significant effect on their performance, and at the same time, how these activities influence students' behavior is reflected in their effort exertion. Capturing and explaining effortful (or effortless) behavior and aligning it with learning performance within contemporary adaptive learning environments, holds the promise to timely provide proactive and actionable feedback to students. Using sophisticated machine learning (ML) algorithms and rich learner data, facilitates inference‐making about several behavioral aspects (including effortful behavior) and about predicting learning performance, in any learning context. Researchers have been using ML methods in a “black‐box” approach, ie, as a tool where the input data is the learner data and the output is a given class from the chosen construct. This work proposes a methodological shift from the “black‐box” approach to a “grey‐box” approach that bridges the hypothesis/literature‐driven (feature extraction) “white‐box” approach with the computation/data‐driven (feature fusion) “black‐box” approach. This will allow us to utilize data features that are educationally and contextually meaningful. This paper aims to extend current methodological paradigms, and puts into practice the proposed approach in an adaptive self‐assessment case study taking advantage of new, cutting‐edge, interdisciplinary work on building pipelines for educational data, using innovative tools and techniques. What is already known about this topic Capturing and measuring learners' engagement and behavior using physiological data has been explored during the last years and exhibits great potential. Effortless behavioral patterns commonly exhibited by learners, such as “cheating,” “guessing” or “gaming the system” counterfeit the learning outcome. Multimodal data can accurately predict learning engagement, performance and processes. What this paper adds Generalizes a methodology for building machine learning pipelines for multimodal educational data, using a modularized approach, namely the “grey‐box” approach. Showcases that fusion of eye‐tracking, facial expressions and arousal data provide the best prediction of effort and performance in adaptive learning settings. Highlights the importance of fusing data from different channels to obtain the most suited combinations from the different multimodal data streams, to predict and explain effort and performance in terms of pervasiveness, mobility and ubiquity. Implications for practice and/or policy Learning analytics researchers shall be able to use an innovative methodological approach, namely the “grey‐box,” to build machine learning pipelines from multimodal data, taking advantage of artificial intelligence capabilities in any educational context. Learning design professionals shall have the opportunity to fuse specific features of the multimodal data to drive the interpretation of learning outcomes in terms of physiological learner states. The constraints from th...

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Improving early prediction of academic failure using sentiment analysis on self‐evaluated comments

Cited by 73 publications

References 31 publications

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

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

Words describing feelings about death: A comparison of sentiment for self and others and changes over time

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

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