A Time Series Classification Method for Behaviour-Based Dropout Prediction

Liu, Haiyang; Wang, Zhihai; Benachour, Phillip; Tubman, Philip

doi:10.1109/icalt.2018.00052

Cited by 36 publications

(22 citation statements)

References 10 publications

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“…We prune all patient trajectories including zero events, which results in a final set of 65k trajectories. For each dataset, as suggested in [9], we perform a daily grouping of events concerning each individual (student or patient). Therefore, we represent each individual's trajectory with a time-matrix T ∈ R ℓ, where ℓ is the length of the adopted time-window in days and is the number of different events types (i.e., features) available in the dataset.…”

Section: Evaluation Methodology 21 Datasetsmentioning

confidence: 99%

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A Reproducibility Study of Deep and Surface Machine Learning Methods for Human-related Trajectory Prediction

Prenkaj

Velardi

Distante

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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In this paper, we compare several deep and surface state-of-the-art machine learning methods for risk prediction in problems that can be modelled as a trajectory of events separated by irregular time intervals. Trajectories are the abstract representation of many reallife data, such as patient records, student e-tivities, online financial transactions, and many others. Given the continuously increasing number of machine learning methods to predict future high-risk events in these contexts, we aim to provide more insight into reproducibility and applicability of these methods when changing datasets, parameters, and evaluation measures. As an additional contribution, we release to the community the implementations of all compared methods.

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Section: Evaluation Methodology 21 Datasetsmentioning

confidence: 99%

“…Ensembles. We use a Random Forest (RF) with bootstrapped samples and the Gini impurity to evaluate the splits [8,9]. Next, Kotsiantis et al [12] construct an ad-hoc ensemble mechanism -hereafter KENS -with majority voting as the consensus function.…”

Section: Methodsmentioning

confidence: 99%

A Reproducibility Study of Deep and Surface Machine Learning Methods for Human-related Trajectory Prediction

Prenkaj

Velardi

Distante

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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“…Selain itu, fitur interaksi juga dapat disajikan dalam bentuk sekuensial, seperti halnya deret waktu, sehingga prediksi kinerja atau kecenderungan mahasiswa melakukan drop out dapat dilakukan secara harian atau mingguan. Fitur sekuensial yang digunakan dalam beberapa literatur sangat bervariasi, yaitu jumlah klik harian dari semua sumber daya VLE [14], jumlah klik pada satu sumber daya VLE tertentu (Forum atau OUcontent atau resource VLE) [8], dan ringkasan dari semua sumber daya VLE (setiap sumber daya VLE memiliki atribut tersendiri) dalam mingguan [7]. Di samping itu, untuk meningkatkan kinerja, fitur sekuensial dengan fitur tabel demografis bisa digabungkan [14].…”

Section: B Fitur Yang Digunakan Pada Oulad (P2)unclassified

“…Metode pembelajaran mesin yang digunakan untuk melakukan analisis pembelajaran dalam pengolahan OULAD sangat bervariasi, antara lain Decision Tree (DT) [5], [9]- [12], [15], [16]; Multilayer Perceptron (MLP)/Artificial Neural Network (ANN) [17], seperti pada [10], [13]; Support Vector Machine (SVM) [18], seperti pada [5], [9], [10]; Logistic Regression (LR) [15], seperti pada [5], [9]; Long Short-Term Memory (LSTM) [19], seperti pada [7], [14]; Gated Recurrent Unit (GRU) [20]; Random Forest (RF) [5], [9]; Adaptive RF (ARF) [21]; Distributed RF (DRF), Gradient Boosting Machine (GBM), dan Generalized Linear Model (GLM) [22]; Gaussian Mixture Models (GMM) [23], seperti pada [6]; dan Time Series Forest (TSF) [24], seperti pada [8]. Selain itu, ada juga yang mengombinasikan dengan metode Principal Component Analysis (PCA) untuk mereduksi fitur sebelum diproses dengan algoritme pembelajaran mesin [5] dan mengombinasikan LSTM dengan Convolutional Neural Network (CNN) [25], seperti pada [14].…”

Section: Model Pembelajaran Mesin Atau Metode Yang Digunakan Pada unclassified

Kajian Penggunaan Data Log Mahasiswa untuk Berbagai Permasalahan Analisis Pembelajaran

Kusumawardani

Alfarozi

2020

JNTETI

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Pada saat ini, penyelenggaraan sistem pembelajaran daring menjadi hal yang penting di tengah pandemi untuk menekan persebaran virus COVID-19. Namun, sistem ini sangat sulit menjaga motivasi dan tingkat keterlibatan mahasiswa karena tidak ada interaksi langsung antara pengajar dengan mahasiswa. Makalah ini meninjau penggunaan data log mahasiswa untuk kebutuhan analisis pembelajaran guna memprediksi kinerja atau kecenderungan drop-out mahasiswa dari suatu mata kuliah dengan melihat pada data log interaksi mahasiswa dengan sistem dan data demografis mahasiswa menggunakan suatu data terbuka, yaitu Open University Learning Analytics Dataset (OULAD). Dari tinjauan beberapa artikel penelitian yang merujuk pada dataset tersebut, ada beberapa hal yang perlu ditinjau: 1) permasalahan yang sering diangkat, yaitu prediksi kecenderungan gagal dari mata kuliah tertentu, prediksi kinerja, dan prediksi keterlibatan mahasiswa; 2) fitur yang digunakan pada saat pemodelan, yaitu fitur demografis dan interaksi, baik yang diringkas secara harian atau mingguan dengan berbagai representasi fitur; 3) metode analisis pembelajaran yang secara khusus menggunakan metode pembelajaran mesin yang sering digunakan, yaitu Decision Tree (DT), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), dan Long Short-Term Memory (LSTM). Makalah ini juga mendiskusikan proses mitigasi dari mahasiswa yang berisiko, perancangan sistem data yang mendukung analisis pembelajaran, dan permasalahan yang sering ditemui pada saat proses pemodelan.

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“…Behaviour analysis and prediction could be used in many fields of real life, for example, in the educational domain: predicting students' progress, trends in a particular subject, evaluation of a course/teacher [27][28][29][30], and dropout rate in an institute [31]. Researchers also study classification and prediction of customer shopping trends concerning a particular product [32], which is of prime importance in e-business and e-commerce.…”

Section: Literature Reviewmentioning

confidence: 99%

A Neuro-fuzzy approach for user behaviour classification and prediction

et al. 2019

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Big data and cloud computing technology appeared on the scene as new trends due to the rapid growth of social media usage over the last decade. Big data represent the immense volume of complex data that show more details about behaviours, activities, and events that occur around the world. As a result, big data analytics needs to access diverse types of resources within a decreased response time to produce accurate and stable business experimentation that could help make brilliant decisions for organizations in real-time. These developments have spurred a revolutionary transformation in research, inventions, and business marketing. User behaviour analysis for classification and prediction is one of the hottest topics in data science. This type of analysis is performed for several purposes, such as finding users' interests about a product (for marketing, e-commerce, etc.) or toward an event (elections, championships, etc.) and observing suspicious activities (security and privacy) based on their traits over the Internet. In this paper, a neuro-fuzzy approach for the classification and prediction of user behaviour is proposed. A dataset, composed of users' temporal logs containing three types of information, namely, local machine, network and web usage logs, is targeted. To complement the analysis, each user's 360-degree feedback is also utilized. Various rules have been implemented to address the company's policy for determining the precise behaviour of a user, which could be helpful in managerial decisions. For prediction, a Gaussian Radial Basis Function Neural Network (GRBF-NN) is trained based on the example set generated by a Fuzzy Rule Based System (FRBS) and the 360-degree feedback of the user. The results are obtained and compared with other state-of-the-art schemes in the literature, and the scheme is found to be promising in terms of classification as well as prediction accuracy.

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A Time Series Classification Method for Behaviour-Based Dropout Prediction

Cited by 36 publications

References 10 publications

A Reproducibility Study of Deep and Surface Machine Learning Methods for Human-related Trajectory Prediction

A Reproducibility Study of Deep and Surface Machine Learning Methods for Human-related Trajectory Prediction

Kajian Penggunaan Data Log Mahasiswa untuk Berbagai Permasalahan Analisis Pembelajaran

A Neuro-fuzzy approach for user behaviour classification and prediction

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