Learning Analytics to Identify Students At-risk in MOOCs

Srilekshmi, M; Sindhumol, S.; Chatterjee, Shiffon; Bijlani, Kamal

doi:10.1109/t4e.2016.048

Cited by 13 publications

(4 citation statements)

References 1 publication

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“…For example, Lacave et al (2018) use enrolment age, prior choice of subject and information on scholarships in order to predict student dropouts. In addition to demographic variables (Aulck et al 2017;Sarker 2014), the student's academic self-concept, academic history and workrelated data are used to predict student performance (Mitra and Goldstein 2015), while others use GPA, academic load, and access to counselling (Rogers et al 2014), the student's financial background (Thammasiri et al 2014), or academic performance history (Bydzovska and Popelinsky 2014;Sales et al 2016;Srilekshmi et al 2016) as predictors of students at risk.…”

Section: Predictors For Study Successmentioning

confidence: 99%

Utilising learning analytics to support study success in higher education: a systematic review

Ifenthaler

Yau

2020

Education Tech Research Dev

168

101

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Study success includes the successful completion of a first degree in higher education to the largest extent, and the successful completion of individual learning tasks to the smallest extent. Factors affecting study success range from individual dispositions (e.g., motivation, prior academic performance) to characteristics of the educational environment (e.g., attendance, active learning, social embeddedness). Recent developments in learning analytics, which are a socio-technical data mining and analytic practice in educational contexts, show promise in enhancing study success in higher education, through the collection and analysis of data from learners, learning processes, and learning environments in order to provide meaningful feedback and scaffolds when needed. This research reports a systematic review focusing on empirical evidence, demonstrating how learning analytics have been successful in facilitating study success in continuation and completion of students' university courses. Using standardised steps of conducting a systematic review, an initial set of 6220 articles was identified. The final sample includes 46 key publications. The findings obtained in this systematic review suggest that there are a considerable number of learning analytics approaches which utilise effective techniques in supporting study success and students at risk of dropping out. However, rigorous, large-scale evidence of the effectiveness of learning analytics in supporting study success is still lacking. The tested variables, algorithms, and methods collected in this systematic review can be used as a guide in helping researchers and educators to further improve the design and implementation of learning analytics systems.

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Section: Predictors For Study Successmentioning

confidence: 99%

Utilising learning analytics to support study success in higher education: a systematic review

Ifenthaler

Yau

2020

Education Tech Research Dev

168

101

View full text Add to dashboard Cite

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“…Based on the predictive models mentioned in the previous section, some examples of EWS are students' dropout detection on face-to-face environments [35], students' dropout on online settings [36][37][38], or early identification of at-risk students, which may allow some type of intervention to increase retention and success rate [16,25,26,39].…”

Section: Early Warning Systemsmentioning

confidence: 99%

An Early Warning System to Detect At-Risk Students in Online Higher Education

2020

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Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.

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“…Srilekshmi et al proposed a prediction model based on association analysis by using previous student data. The model proposes a real-time feedback to the students at-risk of getting dropped out [10].…”

Section: Literature Surveymentioning

confidence: 99%

To evaluate the performance of machine learning algorithms in predicting student dropout on MOOC platforms

Kumar

Singh

Sharma

2022

J. Phys.: Conf. Ser.

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Online learning using Massive Open Online courses(MOOCs) has gained a lot of hype in recent years due to its great potential in having the widest reach in delivering the state-of-the-art resources to the unlimited number of online learners without limiting itself to any geographical boundary. Along with gaining popularity, MOOCs have been facing challenges like high attrition or dropout rate since its birth. The main motivating factor behind the study is to fill the gap which has been there because of very limited literature available there to find the real cause behind these challenges. The current study tries to find the solution of the said challenges by finding the significant contributing factors which highly affect the target variable in the study which is number of certified students in this case. The dataset used in this paper is publicly available in dataverse repository of Harvard university. The dataset is a compilation of student clickstream log data consisting of 641138 instances of enrolled students in various MOOC courses of Harvard and MIT. The study evaluates machine learning models like logistic regression, decision tree, random forest, K-Nearest Neighbor to determine their efficiency in predicting the student dropout. The results of this study can be used to create a framework for recommending necessary actions to the at-risk students to reduce the dropout rate.

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Learning Analytics to Identify Students At-risk in MOOCs

Cited by 13 publications

References 1 publication

Utilising learning analytics to support study success in higher education: a systematic review

Utilising learning analytics to support study success in higher education: a systematic review

An Early Warning System to Detect At-Risk Students in Online Higher Education

To evaluate the performance of machine learning algorithms in predicting student dropout on MOOC platforms

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