Predicting Achievement of Students in Smart Campus

Qu, Shaojie; Li, Kan; Zhang, Shuhui; Wang, Yongchao

doi:10.1109/access.2018.2875742

Cited by 43 publications

(39 citation statements)

References 13 publications

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“…Part (a) is the data pre-processing, part (b) is a multi-layer LSTM neural network, part (c) is a pattern adapter and part (d) is the attention mechanism. Studying different student behavior patterns can also strengthen teaching outcomes [29]. Kahan [22] identified seven types of participant behaviors and termed them tasters (64.8%), downloaders (8.5%), disengagers (11.5%), offline engagers (3.6%), online engagers (7.4%), moderately social engagers (3.7%), and social engagers (0.6%).…”

Section: Framework and Data Pre-processingmentioning

confidence: 99%

Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs

Zhang

et al. 2019

Applied Sciences

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With the development of data mining technology, educational data mining (EDM) has gained increasing amounts of attention. Research on massive open online courses (MOOCs) is an important area of EDM. Previous studies found that assignment-related behaviors in MOOCs (such as the completed number of assignments) can affect student achievement. However, these methods cannot fully reflect students’ learning processes and affect the accuracy of prediction. In the present paper, we consider the temporal learning behaviors of students to propose a student achievement prediction method for MOOCs. First, a multi-layer long short-term memory (LSTM) neural network is employed to reflect students’ learning processes. Second, a discriminative sequential pattern (DSP) mining-based pattern adapter is proposed to obtain the behavior patterns of students and enhance the significance of critical information. Third, a framework is constructed with an attention mechanism that includes data pre-processing, pattern adaptation, and the LSTM neural network to predict student achievement. In the experiments, we collect data from a C programming course from the year 2012 and extract assignment-related features. The experimental results reveal that this method achieves an accuracy rate of 91% and a recall of 94%.

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Section: Framework and Data Pre-processingmentioning

confidence: 99%

Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs

Zhang

et al. 2019

Applied Sciences

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“…The use of MOOCs can improve students' academic performance, and researchers have turned their attention to this field [10][11][12]. Conijn, Van, and Cuijpers [13] investigated the relationship among students' final exam grade, activity frequencies, specific course item frequencies, and the order of activities.…”

Section: Prediction In Moocsmentioning

confidence: 99%

“…The Loss LM(g) can be calculated by Formula (12). q is the actual probability distribution for a sample and p is the output probability distribution from the MLP.…”

Section: Loss Functionmentioning

confidence: 99%

“…Furthermore, w c is the weight of task c, M is the number of LSTM units in one layer, w k c is the weight of kth unit in the last layer of task c for classification, x c is the input of the shared parameters layer in task c, and LL(k, x c , h t ) is the loss of kth unit in the last layer of LSTM. LL can be calculated similarly as Formula (12).…”

Section: Loss Functionmentioning

confidence: 99%

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Predicting Student Performance and Deficiency in Mastering Knowledge Points in MOOCs Using Multi-Task Learning

Xu-ri

et al. 2019

Entropy

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Massive open online courses (MOOCs), which have been deemed a revolutionary teaching mode, are increasingly being used in higher education. However, there remain deficiencies in understanding the relationship between online behavior of students and their performance, and in verifying how well a student comprehends learning material. Therefore, we propose a method for predicting student performance and mastery of knowledge points in MOOCs based on assignment-related online behavior; this allows for those providing academic support to intervene and improve learning outcomes of students facing difficulties. The proposed method was developed while using data from 1528 participants in a C Programming course, from which we extracted assignment-related features. We first applied a multi-task multi-layer long short-term memory-based student performance predicting method with cross-entropy as the loss function to predict students’ overall performance and mastery of each knowledge point. Our method incorporates the attention mechanism, which might better reflect students’ learning behavior and performance. Our method achieves an accuracy of 92.52% for predicting students’ performance and a recall rate of 94.68%. Students’ actions, such as submission times and plagiarism, were related to their performance in the MOOC, and the results demonstrate that our method predicts the overall performance and knowledge points that students cannot master well.

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“…Finally, deep learning-based imputation method has been studied for various fields such as medical data [19], biological data [20], and traffic data [21] using multi-layer perceptron (MLP) [22], K-nearest neighbours (KNN) [23], self organising maps (SOM) [24] and autoencoder (AE) [25]. In the case of smart grid applications, denoising autoencoder is utilized for missing imputation of power system monitoring data [26].…”

Section: Introductionmentioning

confidence: 99%