Can We Predict Student Performance Based on Tabular and Textual Data?

Qu, Yubin; Li, Fang; Li, Long; Dou, Xianzhen; Wang, Hongmei

doi:10.1109/access.2022.3198682

Cited by 9 publications

(8 citation statements)

References 20 publications

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“…No Approach Paper 1 Academic Performance Prediction [11], [12], [17], [18], [23], [24], [35], [37], [38], [41], [44], [45], [51], [56], [57], [64], [71], [74], [75], [78], [80], [81], [84] 2 At-Risk Student Prediction [1], [9], [10], [13], [19], [36], [50], [55], [59], [62], [65], [66], [73], [77], [79], [82] 3 Dropout/Graduation Prediction [14], [15], [20], [21], [47], [48], [54], [60], [61], [63], [72],…”

Section: Table 4 Mapping Of Approachesmentioning

confidence: 99%

“…The prompt identification of students' academic performance is widely recognized as a vital asset for enhancing the quality of education [71]. Researchers are showing interest in predicting academic performance, this can be seen from increasing numbers of research on predicting academic performance as well as selected primary publications stating that it is beneficial to students [23], [24], [35], [44], [45], [51], [71], [74], [78], [80], [84], instructors [11], [12], [17], [18], [23], [24], [37], [38], [41], [44], [45], [57], [64], [75], [78], educational institutions [17], [23], [24], [37], [78]. From the discussion above, it can be concluded that there are several objectives behind predicting academic performance:…”

Section: Table 4 Mapping Of Approachesmentioning

confidence: 99%

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Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

Pelima,

Sukmana,

Rosmansyah

2024

IEEE Access

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Predicting university student graduation is a beneficial tool for both students and institutions. With the help of this predictive capacity, students may make well-informed decisions about their academic and career paths, and institutions can proactively identify students who may not graduate and offer tailored support to ensure their success. The use of machine learning for predicting university student graduation has drawn more attention in recent years. Large datasets of student academic performance data can be used to train machine learning algorithms to identify patterns that are applicable in predicting future outcomes. In accordance with some studies, this approach predicts student graduation with an accuracy rate as high as 90%. Many SLRs have been conducted in this field, but there are still limitations, including not discussing the predictive models and algorithms used, a lack of coverage of the machine learning algorithms applied, small database coverage, keyword selection that does not cover all synonyms relevant to the investigation, and less specific data collection transparency. By delving into the limitations of existing SLRs on this topic, this research not only enhances the understanding of machine learning applications in forecasting student graduation but also fills a crucial gap in the literature. The inclusion of weaknesses in current SLRs provides a foundation for justifying the need for this study, emphasizing the necessity of a more nuanced and comprehensive review to advance the field and guide future research efforts in smart learning environments. This research conducts a thorough systematic review of the existing literature on machine learning-based student graduation prediction models from 70 journal articles from 2018 through 2023 that are pertinent. This review includes the various machine learning algorithms that have been implemented, the various academic performance data that was obtained from students, and the effectiveness of the models that have been developed. It also discusses the difficulties and potential advantages of utilizing machine learning to predict student graduation. The review indicates that the most common approach employed is the prediction of students' academic performance, which relies on data obtained from the Learning Management System and Student Information System. The primary data utilized for prediction purposes consists Student retention and time of academic and behavioral information. Among the various algorithms employed, Support Vector Machine and Random Forest are the most commonly utilized. This study makes a significant contribution to the advancement of learner modules within the smart learning environment.

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Section: Table 4 Mapping Of Approachesmentioning

confidence: 99%

Section: Table 4 Mapping Of Approachesmentioning

confidence: 99%

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

Pelima,

Sukmana,

Rosmansyah

2024

IEEE Access

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“…The most common datasets are the ones with both textual and visual features (MM-IMDB (Ovalle et al, 2017), V-SNLI (Vu et al, 2018), MultiOFF (Suryawanshi et al, 2020, WIT (Srinivasan et al, 2021), MELINDA , etc.). Meanwhile, (Shi et al, 2021;Xu et al, 2019;Qu et al, 2022) provide benchmark datasets for table and text modalities, For the combination of table and image modalities, there are a bunch of datasets from the medical domain (Lin et al, 2020;Lee et al, 2019;Kautzky et al, 2020;Gupta et al, 2020). Other than the mentioned table, text, and image modalities, multimodal learning has also been conducted in time-series, speech, and video modalities (Zhang et al, 2022.…”

Section: Multimodal Classification Datasets Withmentioning

confidence: 99%

“…This progress has been made with large-scale datasets provided to train the data-eager models. So far, there exist many datasets (Ovalle et al, 2017;Shi et al, 2021;Qu et al, 2022;Lin et al, 2020;Lee et al, 2019;Kautzky et al, 2020;Srinivasan et al, 2021) that cover one or two modalities. However, the progress in tabular-text-image multimodal learning lags due to the lack of available resources.…”

Section: Introductionmentioning

confidence: 99%

MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields

Lu,

Qian,

Zhao

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Previous research has demonstrated the advantages of integrating data from multiple sources over traditional unimodal data, leading to the emergence of numerous novel multimodal applications. We propose a multimodal classification benchmark MUG with eight datasets that allows researchers to evaluate and improve their models. These datasets are collected from four various genres of games that cover tabular, textual, and visual modalities. We conduct multi-aspect data analysis to provide insights into the benchmark, including label balance ratios, percentages of missing features, distributions of data within each modality, and the correlations between labels and input modalities. We further present experimental results obtained by several state-of-theart unimodal classifiers and multimodal classifiers, which demonstrate the challenging and multimodal-dependent properties of the benchmark. MUG is released at https://github. com/lujiaying/MUG-Bench with the data, tutorials, and implemented baselines.

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“…Abdelkader et al, 2022;Prabowo et al, 2021;Feng et al, 2022;Nabil et al, 2021;Priyambada et al, 2021;Du et al, 2020a;Ghorbani and Ghousi, 2020;Qu et al, 2022;Zeng et al, 2020;Wen et al, 2020;Li et al, 2020). ‫ان،‬ ‫إير‬ ‫بنجالدش،‬ ‫إيطاليا،‬ ‫إستونيا،‬ ‫و‬ ‫أنه‬ ‫باملالحظة،‬ ‫الجدير‬ ‫من‬ ‫قد‬ ‫أمريكية،‬ ‫كانت‬ ‫النشر‬ ‫أوعية‬ ‫أغلب‬ ‫أن‬ ‫إال‬ ‫األبحاث‬ ‫اء‬ ‫إجر‬ ‫أماكن‬ ‫تنوع‬ ‫رغم‬ ‫إ‬ ‫ذلك‬ ‫يشير‬ ‫بهذا‬ ‫األمريكية‬ ‫العلمية‬ ‫واملجالت‬ ‫البيانات‬ ‫قواعد‬ ‫اهتمام‬ ‫لى‬ ‫البيانات‬ ‫عن‬ ‫التنقيب‬ ‫ملجال‬ ‫النشر‬ ‫خطوط‬ ‫توجه‬ ‫إلى‬ ‫أدى‬ ‫مما‬ ‫املجال،‬ ‫التعليمية‬ Mengash, 2020; Prabowo et al, 2021; Nabil et al, 2021; Wen et al, 2020; Iqbal et al, 2020).…”

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Methods of Overcoming Data Waste in Education: A Systematic Review

Aldossary¹,

Alfarani²

2023

HMS-SJKFU

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This research aimed to perform a systematic review of recent scientific studies that addressed the topic of educational data mining by highlighting recent research trends and reviewing the best practices of smart technology in this field. The current research was restricted to scientific studies and conferences in English that were published through the IEEE database between 2020 and October 2022. After applying the PRISMA form and reviewing and documenting the method, 25 papers were identified that matched the criteria. The outcomes of the current research concluded that the trend of prediction was one of the most common research trends in the domain of educational data mining. Moreover, this trend varied remarkably in its coverage of educational scientific aspects. On the other hand, the outcomes demonstrated that the trend of recommending the most appropriate specialised tracks for learners was one of the least common research trends. With regard to intelligent technical practices, the outcomes of the research revealed that the most used and mature intelligent technologies in practical frameworks and algorithms were intended for prediction purposes, and these were shown to greatly enhance researchers’ ability to reach accurate and logical results that are applicable in real educational contexts. KEYWORDS Artificial intelligence, machine learning, algorithms, neural networks, educational data, educational institutions

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Can We Predict Student Performance Based on Tabular and Textual Data?

Cited by 9 publications

References 20 publications

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields

Methods of Overcoming Data Waste in Education: A Systematic Review

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