Tracking Knowledge Structures and Proficiencies of Students With Learning Transfer

Liu, Hengyu; Zhang, Tiancheng; Fan, Li; Gu, Yu

doi:10.1109/access.2020.3032141

Cited by 13 publications

(13 citation statements)

References 18 publications

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“…Knowledge structure [49], [50], [51] [52], [44], [53], [54], [37], [46], [51] [26], [48], [33], [55], [56], [57], [58], [59], [60], [61], [62], [50], [49] [13], [63], [64], [65], [66] Class context [48] [48], [67] Learning knowledge…”

Section: Domain Knowledgementioning

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%

“…It can be provided by an educational expert (e.g. [59]) or estimated directly in the embedding layer by exploiting Graph Neural Network (GNN) that extends existing neural network methods for processing the data represented in graph domains (e.g. [55]).…”

Section: Knowledge Representationmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Domain Knowledgementioning

confidence: 99%

Section: Domain Knowledgementioning

confidence: 99%

See 1 more Smart Citation

Hybrid Models for Knowledge Tracing: a Systematic Literature Review

Zanellati¹,

Mitri²,

Gabbrielli³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Gan et al [32] incorporated learners' abilities, item difficulty, and learning and forgetting factors together and utilized the factorization machine to trace the evolution of each student's knowledge acquisition. Liu et al [33] combined studentrelated (e.g., learning and forgetting curve) and exerciserelated priors (e.g., knowledge structure), and designed a probabilistic matrix factorization framework to track a student's knowledge mastery levels. Furthermore, Liu et al [34] introduced the relationship between exercises owning the same knowledge concept and further improved the model performance.…”

Section: Factorization Machine-based Kt Modelsmentioning

confidence: 99%

SPAKT: A Self-Supervised Pre-TrAining Method for Knowledge Tracing

Han

Qiao

et al. 2022

IEEE Access

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Knowledge tracing (KT) is the core task of computer-aided education systems, and it aims at predicting whether a student can answer the next exercise (i.e., question) correctly based on his/her historical answer records. In recent years, deep neural network-based approaches have been widely developed in KT and achieved promising results. More recently, several researches further boost these KT models via exploiting plentiful relationships including exercise-skill relations (E-S), the exercise similarity (E-E) as well as skill similarity (S-S). However, these relationship information are frequently absent in many real-world educational applications, and it is a labor-intensive work for human experts to label it. Inspired by recent advances in natural language processing domain, we propose a novel pre-training approach, namely as SPAKT, and utilize self-supervised learning to pre-train exercise embedding representation without the need for expensive human-expert annotations in this paper. Contrary to existing pre-training methods that highly rely on manually labeling knowledge about the E-E, S-S, or E-S relationships, the core idea of the proposed SPAKT is to design three self-attention modules to model the E-S, E-E, and S-S relationships, respectively, and all of these three modules can be trained in the self-supervised setting. As a pre-training approach, our SPAKT can be effortlessly incorporated into existing deep neural network-based KT frameworks. We experimentally show that, even without using expensive annotations about the aforementioned three kinds of relationships, our model achieves competitive performance compared with state-of-the-arts. Our algorithm implementations have been made publicly available at https://github.com/Vinci-hp/pretrainKT.

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“…Settles and Meeder [14] proposed a PFA extended model via the half-life regression, based on the Ebbinghaus forgetting curve by modeling repeated exercises at different time intervals, which partly considers the forgetting factor. Furthermore, many other researchers focus on the forgetting curve and learning curve [26], [35], [27]. In particular, Schmidhuber [26] proposed an interpretable probability matrix factorization framework using the two curves to track students' knowledge proficiency, which is more accurate than other CDMs (i.e., BKT or DINA).…”

Section: Knowledge Diagnosis With Cognitive Featuresmentioning

confidence: 99%

A Dynamic Knowledge Diagnosis Approach Integrating Cognitive Features

Huang

Yang

et al. 2021

IEEE Access

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The rapid development of data analytic technologies has advanced personalized learning and increased its popularity in K-12 education significantly. Specifically, one fundamental step in personalized learning is knowledge proficiency diagnosis which reveals blind spots in students' knowledge. However, existing approaches to diagnosis either exploit data from a one-time assessment for the cognitive diagnosis (ignoring the previous historical interactions) or trace the knowledge state using recurrent neural networks to predict students' future performance (ignoring the cognitive features). To this end, this study proposes a dynamic approach to knowledge diagnosis integrating cognitive features with a key-value memory network to store latent exercise information and capture long-term temporal features based on cognitive psychology. Specifically, given the characteristics of assessment data in China, our approach mainly aims to model sequence data with cognitive features, including forgetting and learning. Two corresponding gates are used to weaken the knowledge memory and strengthen the repeated knowledge memory over time, respectively, in the memory updating process. Finally, to evaluate our approach, we conducted extensive experiments on four real-world datasets collected from K-12 education. The results show that the approach can effectively process the time sequence in education, whose prediction results are better and more stable than other existing baseline models. We also conducted experiments for parameter sensitivity, different feature integration methods, and the effectiveness of cognitive features to ensure that the models achieved the best results. The application visualization further confirms the practicability of our approach in dealing with problems of dynamic knowledge diagnosis.INDEX TERMS Cognitive features, dynamic knowledge diagnosis, key-value memory, performance prediction.

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Tracking Knowledge Structures and Proficiencies of Students With Learning Transfer

Cited by 13 publications

References 18 publications

Hybrid Models for Knowledge Tracing: a Systematic Literature Review

Hybrid Models for Knowledge Tracing: a Systematic Literature Review

SPAKT: A Self-Supervised Pre-TrAining Method for Knowledge Tracing

A Dynamic Knowledge Diagnosis Approach Integrating Cognitive Features

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