Evaluating the impact of prior required scaffolding items on the improvement of student performance prediction

Asselman, Amal; Khaldi, Mohamed; Souhaib, Aammou

doi:10.1007/s10639-019-10077-3

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…Hence, taking into account scaffolding interactions indicates the willingness to integrate the learners' data with information about how or when they use scaffolding materials during their learning process (e.g. [78]). Finally, considering…”

Section: Knowledge Sourcementioning

confidence: 99%

“…[59], [41], [ [20], [74], [34], [36], [75], [37], [38], [39], [76] [27], [43], [44], [52], [45], [68], [54], [37], [47], [46] [69], [55], [56], [57], [58], [59], [60], [61], [62], [50] [34], [63], [59] [20], [42], [77], [79] Hypothesis set [71], [33], [38], [78], [47], [49] [53], [72], [51] [71], [26], [48], [33], [49], [40] [48], [13], [70], [41], [64],…”

Section: Domain Knowledgementioning

confidence: 99%

See 1 more Smart Citation

Hybrid Models for Knowledge Tracing: A Systematic Literature Review

Zanellati,

Mitri,

Gabbrielli

et al. 2024

IEEE Trans. Learning Technol.

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Knowledge Tracing is a well-known problem in AI for Education, consisting of monitoring how the knowledge state of students changes during the learning process and accurately predicting their performance in future exercises. In recent years, many advances have been made thanks to various machine learning and deep learning techniques. Despite their satisfactory performances, they have some pitfalls, e.g. modeling one skill at a time, ignoring the relationships between different skills, or inconsistency for the predictions, i.e. sudden spikes and falls across time steps. For this reason, hybrid machine-learning techniques have also been explored. With this systematic literature review, we aim to illustrate the state of the art in this field. Specifically, we want to identify the potential and the frontiers in integrating prior knowledge sources in the traditional machine learning pipeline as a supplement to the normally considered data. We applied a qualitative analysis to distill a taxonomy with three dimensions: knowledge source, knowledge representation, and knowledge integration. Exploiting this taxonomy, we also conducted a quantitative analysis to detect the most common approaches.

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Section: Knowledge Sourcementioning

confidence: 99%

Section: Domain Knowledgementioning

confidence: 99%

Hybrid Models for Knowledge Tracing: A Systematic Literature Review

Zanellati,

Mitri,

Gabbrielli

et al. 2024

IEEE Trans. Learning Technol.

View full text Add to dashboard Cite

show abstract

Section: Domain Knowledgementioning

confidence: 99%

Section: Domain Knowledgementioning

confidence: 99%

Hybrid Models for Knowledge Tracing: a Systematic Literature Review

Zanellati¹,

Mitri²,

Gabbrielli³

et al. 2023

Preprint

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<p>Knowledge Tracing is a well-known problem often tackled through purely data-driven approaches. In recent years, many advances have been made in research with various machine learning and deep learning techniques. Despite their satisfactory performance, they have some pitfalls, e.g. modeling one skill at a time, ignoring the relationships between different skills, or inconsistency for the predictions, i.e. sudden spikes and falls across time steps. For this reason, hybrid machine-learning techniques have also been explored. With this systematic literature review, we aim to illustrate this field's state of the art. Specifically, we want to identify the potential and the frontiers in integrating prior knowledge sources in the traditional machine learning pipeline as a supplement to the normally considered data. We applied a qualitative analysis to distill a taxonomy with three dimensions: knowledge source, knowledge representation, and knowledge integration. Exploiting this taxonomy, we also conducted a quantitative analysis to detect the most common approaches.</p>

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“…Set in the context of the traditional classroom teaching scenario, the existing research on course performance prediction is mainly based on students' performance in the target course (the course to be predicted) [20], such as attendance, homework completion, periodical exam scores, etc. Since the data are remarkably dependent on information obtained in the process of the target course, modeling work is often carried out during the course [17,20], or even near its end [21]. Marbouti et al [16] used attendance, tests, and weekly homework five weeks after the beginning of the course to predict whether students were at risk of failing the course.…”

Section: Related Workmentioning

confidence: 99%

Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction

Yang

Wang

et al. 2021

Entropy

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The university curriculum is a systematic and organic study complex with some immediate associated steps; the initial learning of each semester’s course is crucial, and significantly impacts the learning process of subsequent courses and further studies. However, the low teacher–student ratio makes it difficult for teachers to consistently follow up on the detail-oriented learning situation of individual students. The extant learning early warning system is committed to automatically detecting whether students have potential difficulties—or even the risk of failing, or non-pass reports—before starting the course. Previous related research has the following three problems: first of all, it mainly focused on e-learning platforms and relied on online activity data, which was not suitable for traditional teaching scenarios; secondly, most current methods can only proffer predictions when the course is in progress, or even approaching the end; thirdly, few studies have focused on the feature redundancy in these learning data. Aiming at the traditional classroom teaching scenario, this paper transforms the pre-class student performance prediction problem into a multi-label learning model, and uses the attribute reduction method to scientifically streamline the characteristic information of the courses taken and explore the important relationship between the characteristics of the previously learned courses and the attributes of the courses to be taken, in order to detect high-risk students in each course before the course begins. Extensive experiments were conducted on 10 real-world datasets, and the results proved that the proposed approach achieves better performance than most other advanced methods in multi-label classification evaluation metrics.

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Evaluating the impact of prior required scaffolding items on the improvement of student performance prediction

Cited by 8 publications

References 18 publications

Hybrid Models for Knowledge Tracing: A Systematic Literature Review

Hybrid Models for Knowledge Tracing: A Systematic Literature Review

Hybrid Models for Knowledge Tracing: a Systematic Literature Review

Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction

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