Pipeline for expediting learning analytics and student support from data in social learning

Jo, Yohan; Tomar, Gaurav Singh; Ferschke, Oliver; Rosé, Carolyn Penstein; Gašević, Dragan

doi:10.1145/2883851.2883912

Cited by 3 publications

(2 citation statements)

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“…We also provide a data model such that if an experiment can be formally described in terms of this data model, then data from the experiment can be analyzed with our system. While there are software systems that address some of these operations [25,26], they do not take the semantics of social experiments into account and largely focus on providing a generic data schema. It is important to note that our software system, presented in this work, is agnostic to deductive or abductive methodologies because our pipelines (described below) are composable.…”

Section: Background and Motivationmentioning

confidence: 99%

Data analysis and modeling pipelines for controlled networked social science experiments

Cedeno-Mieles

Ren

et al. 2020

PLoS ONE

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There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.

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Section: Background and Motivationmentioning

confidence: 99%

Data analysis and modeling pipelines for controlled networked social science experiments

Cedeno-Mieles

Ren

et al. 2020

PLoS ONE

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“…• Peer Recommender: Jo et al (203) filtered course-related tweets by using hashtags and then removed unrelated tweets. They categorized users according to their goal statement in different categories.…”

Section: Othersmentioning

confidence: 99%

Scalable and Practical Recommender System in Massive Open Online Courses (MOOCs)

Khalid

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Massive Open Online Courses (MOOCs) have witnessed a surge in popularity among learners and providers, prompting researchers to explore the enhancement of MOOC utilisation through recommendation systems. However, while existing efforts primarily focus on designing recommender systems capable of providing recommendations across diverse areas of MOOCs, less attention is given to essential characteristics of a robust recommender system, such as practicality and scalability. This literature gap leads to a lack of practical and scalable recommender systems capable of effectively handling increasing volumes of data. Furthermore, current systems leveraging neural networks and deep learning for user behavioural data-based recommendations face challenges in accommodating new data points without retraining. Consequently, scalable and practical recommendation algorithms for MOOCs remain limited in the current literature.To address these shortcomings, this research aims to contribute to the development of scalable and practical recommender systems for MOOCs by utilising demographic and behavioural data of learners, alongside the unique characteristics of MOOCs, for personalised recommendations. In line with these objectives, the study introduced the Novel online Recommender system for MOOCs (NoR-MOOCs) specifically designed for course recommendation. Preliminary studies on the COCO dataset demonstrate the algorithm's promising performance. NoR-MOOCs constructs user profiles based on course ratings provided by users, and its performance evaluation employs predictive and classification accuracy metrics (RMSE, ROC, precision, recall and coverage). A comparison with traditional K-Means and collaborative Filtering algorithms validates the effectiveness of NoR-MOOCs.Additionally, considering the abundant user behavioural and demographic data available in MOOCs, a Novel online Multi-attribute-based Recommendation algorithm for MOOCs (NoM-MOOCs) is designed. This algorithm creates user profiles based on their behavioural and demographic characteristics. Extensive experiments on the Edx and CAN datasets demonstrate the algorithm's significant out-performance compared to the Kernal Mapping recommender system (KMR) in terms of predictive and classification accuracy metrics. Moreover, the analysis of behavioural data from MOOCs logs contributes to forming valuable learning pathways for learners. Existing literature has extensively explored the design and implementation of learning pathway recommendation systems, with such recommender systems being intricately designed using neural networks and deep learning. To this end, a fast online learning path recommender system that incorporates demographic and behavioural data of learners to recommend learning paths is designed. This system identifies similar learners by leveraging demographics and behavioural data and recommends pats paths taken by successful students to struggling students with similar behavioural and demographic profiles. To evaluate the results of this proposed algorithm and the potential impact of the learning path recommender system on learners, an interview with the MOOC professor is conducted.Overall, this research strives to contribute to the advancement of scalable and practical recommender systems for MOOCs, enriching the learning experiences of users and promoting the effective utilisation of MOOCs platforms.

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Expediting the cycle of data to intervention

Rosé

2017

Learning: Research and Practice

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Pipeline for expediting learning analytics and student support from data in social learning

Cited by 3 publications

References 2 publications

Data analysis and modeling pipelines for controlled networked social science experiments

Data analysis and modeling pipelines for controlled networked social science experiments

Scalable and Practical Recommender System in Massive Open Online Courses (MOOCs)

Expediting the cycle of data to intervention

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