Logistic Regression Analysis on Learning Behavior and Learning Effect Based on SPOC Data

Ran, Juan; Zhang, Guiyun; Zheng, Tong; Wang, Wenhui

doi:10.1109/iccse.2018.8468834

Cited by 12 publications

(5 citation statements)

References 2 publications

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“…Particularly, EDM technology includes unique data interpretation and processing techniques, such as clustering [62], classification [54], prediction [2, 51], association mining [64], visualization [35], outlier detection [43], and Time‐Series analysis [13]. In addition, many methods and algorithms have been applied, such as an intelligent agent, neural network [11], the decision tree algorithm [8], Bayesian network [2], logistic regression [30], K‐means [10], and fuzzy logic [3].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

SPOC online video learning clustering analysis: Identifying learners' group behavior characteristics

Lü

et al. 2023

Comp Applic In Engineering

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With the widespread of Small Private Online Courses (SPOC) in colleges and universities, course organizers who provide high-quality personalized course activities need to understand learners' learning status and characteristics, and then optimize the course teaching. However, little research has been done on different learners' group behavior characteristics, such as which indicators can reflect learner groups' behavior, and what are the typical behavior characteristics of different learner groups. In this work, we established a Python Language Foundation self-built SPOC course, and 109 undergraduates' learning behavior data were collected and analyzed. From 74-dimensional behavior log data consisting of 72 video-viewing, course video score, and comprehensive score, Principal Component Analysis was performed to reduce dimensionality. Agglomerative hierarchical clustering was used to divide learners into different categories, and the results showed that 15 groups are clustered. According to the analysis of the four indicators for group characteristics, which are the completion and viewing-stability of task-point videos, unit test excellence, and comprehensive score, it is identified into five primary types, including positive type, regular type, negative type, seminegative type, and a fluctuation type. Experiments conducted on a real data set across different academic years and courses show that the proposed approach has better clustering accuracy and practicability. It is expected that this work may be used to strengthen the personalized learning support services system in educational institutions and develop a tool that integrates exploration and analysis work onto the web platform.

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Section: Theoretical Backgroundmentioning

confidence: 99%

SPOC online video learning clustering analysis: Identifying learners' group behavior characteristics

Lü

et al. 2023

Comp Applic In Engineering

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“…Classification Accuracy. This is the ratio of the number of correct predictions to the total number of samples imputed for training and testing phase, Confusion Matrix which gives a vivid description of the performance of models, AUCROC [29,30,31,32,33] which is the probability that a machine learning algorithm will rank a randomly chosen positive example higher than a randomly chosen negative one, F1-Score [30], and Root Mean Squared Error. The F1-Score is normally used to predict since it has the ability to represent both the precision and recall [30].…”

Section: Related Workmentioning

confidence: 99%

Predicting Fraud in Mobile Money Transactions using Machine Learning: The Effects of Sampling Techniques on the Imbalanced Dataset

Botchey

Qin

Hughes-Lartey

et al. 2022

IJCAI

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Mobile Money Fraud is advancing in developing countries. We propose a solution to this problem based on machine learning. Labeled data from financial transactions which includes mobile money transactions are however, skewed towards the legitimate transactions. Machine learning models built with such skewed datasets are unreliable as the prediction algorithms will be biased towards the legitimate transactions. We investigate the performance of different sampling and weighting techniques such as Adaptive Synthetic Sampling (ADASYN) and Synthetic Minority Oversampling Technique (SMOTE). We select Logistic Regression for the experiments due to its simplicity and relatively low computational needs. The performance is evaluated with different metrics. Manually tuning the weights of the classes achieved the best results in our experiments.Povzetek: Opisana je metoda za detekcijo prevar v mobilnih transakcijah s pomočjo strojnega učenja na neuravnoteženih podatkih.

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“…Logistic regression is based on regression analysis and is used to detect the fault in the microgrid for the proposed scheme. It is employed to investigate the relationship between numerous independent variables [43], [44]. It predicts the output of an estimated expected value from a categorical dependent binary variable [45].…”

Section: ) Logistics Regressionmentioning

confidence: 99%

An Intelligent Data Mining-Based Fault Detection and Classification Strategy for Microgrid

Baloch

Muhammad

2021

IEEE Access

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The specific characteristics and operations of microgrid cause protection problems due to high penetration of distributed energy resources. To resolve these issues, the proposed scheme employs the Hilbert transform and data mining approach to protect the microgrid. First, the Hilbert transform is used to preprocess the faulted voltage and current signals to extract the sensitive fault features. Then, the obtained data set of the extracted features is input to the logistic regression classifier for fault detection. Later, fault classification is done by training the AdaBoost classifier. In the proposed scheme, the simulation results for feature extractions are evaluated on a standard International Electrotechnical Commission (IEC) medium voltage microgrid, compatible with MATLAB/SIMULINK software environment, whereas, Python is used for training and testing of data mining model. The results are evaluated under grid-connected and islanded modes for both looped and radial configurations by simulating various fault and no-fault cases. The results show that the accuracy of the proposed logistic regression and AdaBoost classifier is higher when compared to decision tree, support vector machine, and random forest methods. The results further validate the robustness of the proposed method against the measurement noise.

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Logistic Regression Analysis on Learning Behavior and Learning Effect Based on SPOC Data

Cited by 12 publications

References 2 publications

SPOC online video learning clustering analysis: Identifying learners' group behavior characteristics

SPOC online video learning clustering analysis: Identifying learners' group behavior characteristics

Predicting Fraud in Mobile Money Transactions using Machine Learning: The Effects of Sampling Techniques on the Imbalanced Dataset

An Intelligent Data Mining-Based Fault Detection and Classification Strategy for Microgrid

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