Behavior-Based Grade Prediction for MOOCs via Time Series Neural Networks

Yang, Tsung-Yen; Brinton, Christopher G.; Joe‐Wong, Carlee; Chiang, Mung

doi:10.1109/jstsp.2017.2700227

Cited by 108 publications

(79 citation statements)

References 21 publications

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“…A recent study [31] explored the factor for improving the performance of prediction of students' quiz scores by using a RNN from learning logs. Yang et al [32] incorporated the data that was collected from the watching click-streams of lecture video into the machine learning feature set, and trained a time series neural network that learns from both prior performance and click-streaming data. Nevertheless, the above studies haven't utilized sequential behavioral data from a smart campus card to address the student performance prediction problem.…”

Section: Deep Learning-based Sequence Modelingmentioning

confidence: 99%

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Wang

Guo

et al. 2020

Information

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As students’ behaviors are important factors that can reflect their learning styles and living habits on campus, extracting useful features of them plays a helpful role in understanding the students’ learning process, which is an important step towards personalized education. Recently, the task of predicting students’ performance from their campus behaviors has aroused the researchers’ attention. However, existing studies mainly focus on extracting statistical features manually from the pre-stored data, resulting in hysteresis in predicting students’ achievement and finding out their problems. Furthermore, due to the limited representation capability of these manually extracted features, they can only understand the students’ behaviors shallowly. To make the prediction process timely and automatically, we treat the performance prediction task as a short-term sequence prediction problem, and propose a two-stage classification framework, i.e., Sequence-based Performance Classifier (SPC), which consists of a sequence encoder and a classic data mining classifier. More specifically, to deeply discover the sequential features from students’ campus behaviors, we first introduce an attention-based Hybrid Recurrent Neural Network (HRNN) to encode their recent behaviors by giving a higher weight to the ones that are related to the students’ last action. Then, to conduct student performance prediction, we further involve these learned features to the classic Support Vector Machine (SVM) algorithm and finally achieve our SPC model. We conduct extensive experiments in the real-world student card dataset. The experimental results demonstrate the superiority of our proposed method in terms of Accuracy and Recall.

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Section: Deep Learning-based Sequence Modelingmentioning

confidence: 99%

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Wang

Guo

et al. 2020

Information

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“…In [10], Yang et al presented a novel technique to predict evolution of a student's grade in MOOCs via time series networks. The technique used here incorporated studentlecture video-watching clickstream data into their machinelearning feature set enabling the training of a time series neural network.…”

Section: B Time Series Data Miningmentioning

confidence: 99%

A Time Series Classification Method for Behaviour-Based Dropout Prediction

Liu¹,

Wang

Benachour

et al. 2018

2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT)

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Students' dropout rate is a key metric in online distance learning courses such as MOOCs. We propose a timeseries classification method to construct data based on students' behavior and activities on a number of online distance learning modules. Further, we propose a dropout prediction model based on the time series forest (TSF) classification algorithm. The proposed predictive model is based on interaction data and is independent of learning objectives and subject domains. The model enables prediction of dropout rates without the requirement for pedagogical experts. Results show that the prediction accuracy on two selected datasets increases as the portion of data used in the model grows. However, a reasonable prediction accuracy of 0.84 is possible with only 5% of the dataset processed. As a result, early prediction can help instructors design interventions to encourage course completion before a student falls too far behind.

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“…(2) Regression problem. By taking the grade as the response variable, SGP is rewritten into assigning scores following the features of student or course, such as linear regression [5,11,12], neural network [13][14][15] and random forest [9]. (3) Matrix completion.…”

Section: Introductionmentioning

confidence: 99%

Graphs Regularized Robust Matrix Factorization and Its Application on Student Grade Prediction

Zhang

Dai

et al. 2020

Applied Sciences

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Student grade prediction (SGP) is an important educational problem for designing personalized strategies of teaching and learning. Many studies adopt the technique of matrix factorization (MF). However, their methods often focus on the grade records regardless of the side information, such as backgrounds and relationships. To this end, in this paper, we propose a new MF method, called graph regularized robust matrix factorization (GRMF), based on the recent robust MF version. GRMF integrates two side graphs built on the side data of students and courses into the objective of robust low-rank MF. As a result, the learned features of students and courses can grasp more priors from educational situations to achieve higher grade prediction results. The resulting objective problem can be effectively optimized by the Majorization Minimization (MM) algorithm. In addition, GRMF not only can yield the specific features for the education domain but can also deal with the case of missing, noisy, and corruptive data. To verify our method, we test GRMF on two public data sets for rating prediction and image recovery. Finally, we apply GRMF to educational data from our university, which is composed of 1325 students and 832 courses. The extensive experimental results manifestly show that GRMF is robust to various data problem and achieves more effective features in comparison with other methods. Moreover, GRMF also delivers higher prediction accuracy than other methods on our educational data set. This technique can facilitate personalized teaching and learning in higher education.

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Behavior-Based Grade Prediction for MOOCs via Time Series Neural Networks

Cited by 108 publications

References 21 publications

Student Performance Prediction with Short-Term Sequential Campus Behaviors

Student Performance Prediction with Short-Term Sequential Campus Behaviors

A Time Series Classification Method for Behaviour-Based Dropout Prediction

Graphs Regularized Robust Matrix Factorization and Its Application on Student Grade Prediction

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