Data science foundry for MOOCs

Boyer, Sébastien; Gelman, Ben; Schreck, Benjamin; Veeramachaneni, Kalyan

doi:10.1109/dsaa.2015.7344825

Cited by 12 publications

(8 citation statements)

References 5 publications

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“…Nevertheless, there are not many studies that have investigated the potentials of transfer across MOOCs in comparison to post-hoc prediction approaches. Authors in [31] and [32] have tested the transferability of a dropout prediction model across different MOOCs. The results were quite promising, showing that different courses could be used to train a model to make predictions in another course.…”

Section: Transfer Across Courses and In-situ Learning In Mooc Researchmentioning

confidence: 99%

Generating actionable predictions regarding MOOC learners’ engagement in peer reviews

Gómez‐Sánchez

Bote‐Lorenzo

et al. 2019

Behaviour & Information Technology

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Peer review is one approach to facilitate formative feedback exchange in MOOCs; however, it is often undermined by low participation. To support effective implementation of peer reviews in MOOCs, this research work proposes several predictive models to accurately classify learners according to their expected engagement levels in an upcoming peer-review activity, which offers various pedagogical utilities (e.g., improving peer reviews and collaborative learning activities). Two approaches were used for training the models: in situ learning (in which an engagement indicator available at the time of the predictions is used as a proxy label to train a model within the same course) and transfer across courses (in which a model is trained using labels obtained from past course data). These techniques allowed producing predictions that are actionable by the instructor while the course still continues, which is not possible with post-hoc approaches requiring the use of true labels. According to the results, both transfer across courses and in situ learning approaches have produced predictions that were actionable yet as accurate as those obtained with cross validation, suggesting that they deserve further attention to create impact in MOOCs with real-world interventions. Potential pedagogical uses of the predictions were illustrated with several examples.

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Section: Transfer Across Courses and In-situ Learning In Mooc Researchmentioning

confidence: 99%

Generating actionable predictions regarding MOOC learners’ engagement in peer reviews

Gómez‐Sánchez

Bote‐Lorenzo

et al. 2019

Behaviour & Information Technology

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“…Educational activities datasets, such as web-log files traced from Leaning Management Systems (LMS) or Massive Open Online Couse (MOOC's) are increasingly being used to analyze students' learning behavior. Interesting examples come from various universities around the world including Massachusetts Institute of Technology [32], [33], University of Vigo [34], University of Liége [35], Open University of China [36], University of Alagoas [37] along with others, which used information from LMS and other repositories and applied different Machine Learning algorithms to predict students' performances. Table 1 highlights some successful ML works at some of the mentioned Higher Educational Institutions.…”

Section: B Machine Learning Algorithms In the Educational Fieldmentioning

confidence: 99%

Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions

et al. 2019

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Decisions made at the strategic level of Higher Educational Institutions (HEIs) affect policies, strategies, and actions that the institutions make as a whole. Decision's structures at HEIs are depicted in this paper and their effectiveness in supporting the institutions' governance. The disengagement of the stakeholders and the lack of using efficient computational algorithms lead to 1) the decision process takes longer; 2) the ''whole picture'' is not involved along with all data necessary; and 3) small academic impact is produced by the decision, among others. Machine learning is an emerging field of artificial intelligence that using various algorithms analyzes information and provides a richer understanding of the data contained in a specific context. Based on the author's previous works, we focus on supporting decision-making at a strategic level, being deans' concerns the preeminent mission to bolster. In this paper, three supervised classification algorithms are deployed to predict graduation rates from real data about undergraduate engineering students in South America. The analysis of receiver operating characteristic (ROC) curve and accuracy are executed as measures of effectiveness to compare and evaluate decision tree, logistic regression, and random forest, where this last one demonstrates the best outcomes.

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“…Today, an increasing number of courses are offered in the MOOC mode [Fox et al 2015;Boyer et al 2015], such as through Class Central [Classcentral 2016;Coursera 2016], edX [Edx 2016;Udacity 2016] and Udemy [Udemy 2016]. The MOOC model is fundamentally changing the way courses are offered by utilizing online, distributed, and open data, curriculum development resources and expertise, and delivery channels and services.…”

Section: Data Science Disciplinary Developmentmentioning

confidence: 99%

Data Science

Cao

2017

ACM Comput. Surv.

295

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The 21st century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights, and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics. Although it is widely debated whether big data is only hype and buzz, and data science is still in a very early phase, significant challenges and opportunities are emerging or have been inspired by the research, innovation, business, profession, and education of data science. This article provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons, and thinking about data science and analytics.

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Data science foundry for MOOCs

Cited by 12 publications

References 5 publications

Generating actionable predictions regarding MOOC learners’ engagement in peer reviews

Generating actionable predictions regarding MOOC learners’ engagement in peer reviews

Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions

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