Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs

Moreno-Marcos, Pedro Manuel; Muñoz‐Merino, Pedro J.; Maldonado-Mahauad, Jorge; Pérez‐Sanagustín, Mar; Alario-Hoyos, Carlos; Kloos, Carlos Delgado

doi:10.1016/j.compedu.2019.103728

Cited by 108 publications

(73 citation statements)

References 25 publications

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“…In addition, local persistence can slightly improve predictive models. When predicting global persistence/dropout and average grade, the percentage of attempted exercises is a prominent variable (as it also happened in [29]) and can achieve good results by itself. This limits the predictive power of other variables.…”

Section: Resultsmentioning

confidence: 99%

“…However, more variables can be introduced and analyzed to see if they can enhance the predictive power [6]. For example, Moreno-Marcos et al [29] concluded that variables related to self-regulated learning could achieve accurate predictions when predicting dropout. In this line, this paper aims to contribute with the analysis of the predictive power of local persistence when predicting global persistence and students' performance (objective O5).…”

Section: Global Persistencementioning

confidence: 99%

“…In order to analyze this issue, several models have been developed using local persistence with other single variables. These models have been developed using the Random Forest (RF) algorithm, as it the best and most consistent algorithm among the four algorithms when predicting the three dependent variables (according to the previous analysis) and it also outperforms other algorithms in other related papers (e.g., [29,47]). The variables used in each model are presented in Table 5.…”

Section: Prediction Of Global Persistence and Students' Performance Umentioning

confidence: 99%

“…It is directly related with the term dropout, as students who do not complete the course (i.e., they are not persistent in a global sense) can count as a dropout. As dropout can be measured in many different ways and its definition may be sometimes difficult because some students can be inactive for a period and then continue the course [29], two measures are considered and discussed throughout the paper.…”

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confidence: 99%

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Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning

et al. 2020

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In education, several studies have tried to track student persistence (i.e., students’ ability to keep on working on the assigned tasks) using different definitions and self-reported data. However, self-reported metrics may be limited, and currently, online courses allow collecting many low-level events to analyze student behaviors based on logs and using learning analytics. These analyses can be used to provide personalized and adaptative feedback in Smart Learning Environments. In this line, this work proposes the analysis and measurement of two types of persistence based on students’ interactions in online courses: (1) local persistence (based on the attempts used to solve an exercise when the student answers it incorrectly), and (2) global persistence (based on overall course activity/completion). Results show that there are different students’ profiles based on local persistence, although medium local persistence stands out. Moreover, local persistence is highly affected by course context and it can vary throughout the course. Furthermore, local persistence does not necessarily relate to global persistence or engagement with videos, although it is related to students’ average grade. Finally, predictive analysis shows that local persistence is not a strong predictor of global persistence and performance, although it can add some value to the predictive models.

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Section: Resultsmentioning

confidence: 99%

Section: Global Persistencementioning

confidence: 99%

Section: Prediction Of Global Persistence and Students' Performance Umentioning

confidence: 99%

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confidence: 99%

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Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning

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“…Moreno-Marcos et al [53] 2020 Coursera MOOC RF, GLM, SVM & DT Xing and Du [54] 2019 Canvas MOOC DL Liu and Li [55] 2017 XuetangX MOOC K-means Nagrecha et al [31] 2017 edX MOOC DT & LR Chen and Zang [56] 2017 Coursera MOOC RF Xing et al [43] 2016 Canvas MOOC GBN & DT Crossley et al [48] 2016 Coursera MOOC NLP Chaplot et al [46] 2015 [42] for successful predictions. Among ML algorithms, some works also focus on decision tree [43][44][45], sentiment-based artificial neural network [46], deep neural network [47], and natural language processing statistical models [48][49][50].…”

Section: Author Year Dataset Techniquementioning

confidence: 99%

On the Effectiveness of Self-Training in MOOC Dropout Prediction

Goel

Goyal

2020

Open Computer Science

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Massive open online courses (MOOCs) have gained enormous popularity in recent years and have attracted learners worldwide. However, MOOCs face a crucial challenge in the high dropout rate, which varies between 91%-93%. An interplay between different learning analytics strategies and MOOCs have emerged as a research area to reduce dropout rate. Most existing studies use click-stream features as engagement patterns to predict at-risk students. However, this study uses a combination of click-stream features and the influence of the learner’s friends based on their demographics to identify potential dropouts. Existing predictive models are based on supervised learning techniques that require the bulk of hand-labelled data to train models. In practice, however, scarcity of massive labelled data makes training difficult. Therefore, this study uses self-training, a semi-supervised learning model, to develop predictive models. Experimental results on a public data set demonstrate that semi-supervised models attain comparable results to state-ofthe-art approaches, while also having the flexibility of utilizing a small quantity of labelled data. This study deploys seven well-known optimizers to train the self-training classifiers, out of which, Stochastic Gradient Descent (SGD) outperformed others with the value of F1 score at 94.29%, affirming the relevance of this exposition.

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Improving learner engagement in MOOCs using a learning intervention system: A research study in engineering education

Cobos

Ruiz‐Garcia

2020

Comp Applic In Engineering

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The development of web applications is an essential and critical skill for Computer Science Engineers. Nowadays, this is a core subject in the Computer Science Engineering Studies and an increasingly demanded subject in other Engineering Studies. With the aim to facilitate introductory know-how in this area, the University Autónoma of Madrid offers at edX Platform the Massive Open Online Course (MOOC): "Introduction to Development of Web Applications". Several studies have emphasised the positive impact of methods, such as the use of continuous feedback, to improve and promote learning persistence and engagement in online courses. We propose a system for periodically providing MOOC learners information about their progress and suggestions about how can they can improve their learner performance based on their achievements and interactions in MOOCs. This system is called Learning Intervention System for edX MOOCs (edX-LIS). To test the intervention strategy on learners' behaviour supported by edX-LIS, a research study was conducted with the learners of the mentioned MOOC. The results obtained from this study have demonstrated that the intervention strategy supported by the edX-LIS has a positive impact on the motivation, persistence and engagement of the learners in the MOOC.

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Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs

Cited by 108 publications

References 25 publications

Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning

Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning

On the Effectiveness of Self-Training in MOOC Dropout Prediction

Improving learner engagement in MOOCs using a learning intervention system: A research study in engineering education

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