A Process for Predicting MOOC Attrition

Sharkey, Mike; Sanders, Robert

doi:10.3115/v1/w14-4109

Cited by 30 publications

(11 citation statements)

References 2 publications

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“…For example, Li et al (2017), citing Zhou et al (2015), notes that "data preprocessing should be considered with more attention than learning algorithms". Sharkey and Sanders (2014) claims that feature extraction is "arguably the most important step in the process of developing a predictive model." Taylor et al (2014) state that "[w]e attribute success of our models to these variables (more than the models themselves)...any vague assumptions, quick and dirty data conditioning or preparation will create weak foundations for one's modeling and analyses," emphasizing their feature extraction methods over their modeling techniques despite fitting over 70,000 models in this experiment.…”

Section: Feature Extraction As Critical To Predictive Modeling In Moocsmentioning

confidence: 99%

Student success prediction in MOOCs

Gardner

Brooks

2018

User Model User-Adap Inter

153

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Predictive models of student success in Massive Open Online Courses (MOOCs) are a critical component of effective content personalization and adaptive interventions. In this article we review the state of the art in predictive models of student success in MOOCs and present a categorization of MOOC research according to the predictors (features), prediction (outcomes), and underlying theoretical model. We critically survey work across each category, providing data on the raw data source, feature engineering, statistical model, evaluation method, prediction architecture, and other aspects of these experiments. Such a review is particularly useful given the rapid expansion of predictive modeling research in MOOCs since the emergence of major MOOC platforms in 2012. This survey reveals several key methodological gaps, which include extensive filtering of experimental subpopulations, ineffective student model evaluation, and the use of experimental data which would be unavailable for real-world student success prediction and intervention, which is the ultimate goal of such models. Finally, we highlight opportunities for future research, which include temporal modeling, research bridging predictive and explanatory student models, work which contributes to learning theory, and evaluating long-term learner success in MOOCs.

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Section: Feature Extraction As Critical To Predictive Modeling In Moocsmentioning

confidence: 99%

Student success prediction in MOOCs

Gardner

Brooks

2018

User Model User-Adap Inter

153

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“…The first is whether a learner will still participate in the last week of the course [33][34][35]. The second is whether the current week is the last week a learner has activities [17,19,36]. Those two definitions are similar because they are related to the final state of a learner, and the dropout label cannot be determined until the end of the course.…”

Section: Problem Statementmentioning

confidence: 99%

MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine

Jing

Feng

Sun

et al. 2019

Mathematical Problems in Engineering

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Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. High dropout rate is a universal but unsolved problem in MOOCs. Dropout prediction has received much attention recently. A previous study reported the problem of learning behavior discrepancy leading to a wide range of fluctuation of prediction results. Besides, previous methods require iterative training which is time intensive. To address these problems, we propose DT-ELM, a novel hybrid algorithm combining decision tree and extreme learning machine (ELM), which requires no iterative training. The decision tree selects features with good classification ability. Further, it determines enhanced weights of the selected features to strengthen their classification ability. To achieve accurate prediction results, we optimize ELM structure by mapping the decision tree to ELM based on the entropy theory. Experimental results on the benchmark KDD 2015 dataset demonstrate the effectiveness of DT-ELM, which is 12.78%, 22.19%, and 6.87% higher than baseline algorithms in terms of accuracy, AUC, and F1-score, respectively.

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“…The prediction task was performed by the integrated predictor and evaluated by precision and recall as indicators. Similar researches are also done [5][6][7][8].…”

Section: Related Workmentioning

confidence: 79%

“…7 Total time of watching videos The length of time spent on watching course videos on website. 8 Frequency of accomplishing assignments…”

Section: Data Preprocessingmentioning

confidence: 99%

Finding out Reasons for Low Completion in MOOC Environment: An Explicable Approach Using Hybrid Data Mining Methods

Liu¹,

Li²

2017

dtssehs

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Abstract. Because of the worldwide rapid development of MOOC, academic researches and industrial applications of MOOC have become a branch of the major concerns in modern education and information technology fields. This paper focuses on low completion phenomenon in MOOC environment and proposes an explicable approach to find out hidden reasons convincingly. Different from existing works, this approach utilizes data mining methods to make quantitative analysis. It employs learners clustering basing on their study features at first, aiming at discovering inactive learners automatically. These learners are representative of low completion in course study on MOOC platform. Their study behaviors and interactions with website are analyzed with association rules mining in order to explore potential patterns and rules. The extracted rules are used to find out and explain the reasons for low completion in MOOC environment. The experimental result on practical XuetangX platform reveals several strong rules with high support, confidence and lift, which can be regarded as evidence and reference for further explanations of reasons for low completion in MOOC environment.

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A Process for Predicting MOOC Attrition

Cited by 30 publications

References 2 publications

Student success prediction in MOOCs

Student success prediction in MOOCs

MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine

Finding out Reasons for Low Completion in MOOC Environment: An Explicable Approach Using Hybrid Data Mining Methods

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