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Predicting student performance in a blended MOOC

R. Conijn, A. Van den Beemt, P. Cuijpers
2018 Journal of Computer Assisted Learning  
Predicting student performance is a major tool in learning analytics.  ...  This study aims to identify how different measures of massive open online course (MOOC) data can be used to identify points of improvement in MOOCs.  ...  | Predicting student performance in MOOCs using general frequencies Most studies predicting student performance in MOOCs focused on general frequencies of MOOC activities (e.g., de Barba, Kennedy,  ... 
doi:10.1111/jcal.12270 fatcat:hrw5zdou65fntdqkj3zk7ysjqm

Student success prediction in MOOCs

Josh Gardner, Christopher Brooks
2018 User modeling and user-adapted interaction  
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.  ...  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  ...  Acknowledgements This work was funded in part by the Michigan Institute for Data Science (MIDAS) Holistic Modeling of Education (HOME) project, and the University of Michigan Third Century Initiative.  ... 
doi:10.1007/s11257-018-9203-z fatcat:pgrusb3jqrc7fmc4g4rutwqcne

A Literature Review of Student Performance Prediction in E-Learning Environment

Ruth Chweya, Siti Mariyam Shamsuddin, Samuel-Soma M. Ajibade, Samuel Moveh
2020 Journal of Science Engineering Technology and Management  
This paper aims at reviewing the student prediction performance of students based on their interactivity with the eLearning activities in MOODLE and MOOCs, this was achieved with the use of the student  ...  Predicting Students Performance in e-learning and MOOCs is a vital angle which helps instructors in enhancing the learning and educating process.  ...  e-learning and MOOCs activities, interactivity, and prediction of performance in the virtual learning environment.  ... 
doi:10.46820/jsetm.2020.1103 fatcat:34acbmwlbzasnp7bgjdk4llcua

Predicting Performance on MOOC Assessments using Multi-Regression Models [article]

Zhiyun Ren, Huzefa Rangwala, Aditya Johri
2016 arXiv   pre-print
learning behaviors of students.  ...  The developed model is real-time and tracks the participation of a student within a MOOC (via click-stream server logs) and predicts the performance of a student on the next as- sessment within the course  ...  In this paper, we present models to predict a student's future performance for a certain assessment activity witin a MOOC.  ... 
arXiv:1605.02269v1 fatcat:4e5b72vguvenrg567mbj6ul4i4

Supporting Group Formation in Ongoing MOOCs Using Actionable Predictive Models

Erkan Er, Eduardo Gomez-Sanchez, Miguel L. Bote-Lorenzo, Juan I. Asensio-Perez, Yannis Dimitriadis
2018 2018 Learning With MOOCS (LWMOOCS)  
In this regard, this research work investigated the use of in-situ learning technique to produce useful and actionable information that could assist instructors in group formation while the course continues  ...  Focusing on a particular MOOC context, a predictive model was created to compute the probability that students would participate in group discussions or not.  ...  Access to the data used in this paper was granted by Canvas Network.  ... 
doi:10.1109/lwmoocs.2018.8534691 dblp:conf/lwmoocs/ErGBAD18 fatcat:rk4zrrwdgbflxkmlzgjq55qlui

Informing the Design of Collaborative Activities in MOOCs using Actionable Predictions

Erkan Er, Eduardo Gómez-Sánchez, Miguel L. Bote-Lorenzo, Juan I. Asensio-Pérez, Yannis Dimitriadis
2019 Zenodo  
With the aim of supporting instructional designers in setting up collaborative learning activities in MOOCs, this paper derives prediction models for student participation in group discussions.  ...  The salient feature of these models is that they are built using only data prior to the learning activity, and can thus provide actionable predictions, as opposed to post-hoc approaches common in the MOOC  ...  First, the predictions were generated using data from a past MOOC.  ... 
doi:10.5281/zenodo.4319700 fatcat:4fdxdffh5jaqllpflwx6i44niu

Teachers' Perception About MOOCs and ICT During the COVID-19 Pandemic

Ricardo-Adán Salas-Rueda, Ricardo Castañeda-Martínez, Ana-Libia Eslava-Cervantes, Clara Alvarado-Zamorano
2022 Contemporary Educational Technology  
This quantitative research analyzes the perception of the teachers about the organization of the school activities in MOOCs and use of ICT considering machine learning and decision tree techniques (data  ...  Data science identifies 3 predictive models about MOOCs and ICT through the decision tree technique.  ...  of the school activities in MOOCs and use of ICT for the participation of the studentsPredictive Model 3 (PM3) about the organization of the school activities in MOOCs and use of ICT for the learning  ... 
doi:10.30935/cedtech/11479 fatcat:526tlpnpzbc6ncf5a3jmj3ppim

System Architecture of Big Data in Massive Open Online Courses (BD-MOOCs System Architecture)

Withamon Khajonmote, Kittipong Chinsook, Sununta Klintawon, Chaiyan Sakulthai, Wicha Leamsakul, Natchanok Jansawang, Thada Jantakoon
2022 Journal of Education and Learning  
The system architecture of big data in massive open online courses (BD-MOOCs System Architecture) is composed of six components.  ...  Finally, MOOCs present educational data science challenges such as analyzing student interactions, estimating dropout risk, grading, and making recommendations.  ...  Finally, in order to predict learning performance, the authors looked at students' learning patterns through a sequence of learning behaviors.  ... 
doi:10.5539/jel.v11n3p105 fatcat:wxstwixnsjhhfkuqbgcpxtkgxa

Engaging Learning Analytics in MOOCS: the good, the bad, and the ugly [article]

Mohammad Khalil, Behnam Taraghi, Martin Ebner
2016 arXiv   pre-print
In this publication, we discuss the principles of engaging Learning Analytics in MOOCs learning environments and review its potential and capabilities (the good), constraints (the bad), and fallacy analytics  ...  Massive Open Online Courses (MOOCs) are considered to be a very active and an innovative form of bringing educational content to a broad community.  ...  Additionally, Learning Analytics is used in predicting performance and motivation (Edtstadler, Ebner & Ebner, 2015) .  ... 
arXiv:1606.03776v1 fatcat:5ekr6523ircpbbtso32yflg6cq

Early Dropout Prediction in MOOCs through Supervised Learning and Hyperparameter Optimization

Theodor Panagiotakopoulos, Sotiris Kotsiantis, Georgios Kostopoulos, Omiros Iatrellis, Achilles Kameas
2021 Electronics  
As a result, large amounts of data regarding students' demographic characteristics, activity patterns, and learning performances are generated and stored in institutional repositories on a daily basis.  ...  In this context, the main purpose of the present study is to employ a plethora of state-of-the-art supervised machine learning algorithms for predicting student dropout in a MOOC for smart city professionals  ...  Guo and Reinecke (2014) analyzed student activity in four edX MOOCs [6] . To this end, data comprising 140,546 students were evaluated using multiple linear regression.  ... 
doi:10.3390/electronics10141701 fatcat:fb52f4ictrdobpbe34s657lsh4

Predicting Student Outcomes in Online Courses Using Machine Learning Techniques: A Review

Areej Alhothali, Maram Albsisi, Hussein Assalahi, Tahani Aldosemani
2022 Sustainability  
This paper presents a comprehensive review of state-of-the-art studies that examine online learners' data to predict their outcomes using machine and deep learning techniques.  ...  With the increased demands and challenges in online education, several researchers have investigated ways to predict student outcomes, such as performance and dropout in online courses.  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/su14106199 fatcat:uprot74fkrclnlwelhlxeycjwa

MOOC Evaluation System Based on Deep Learning

Jian-Wei Tzeng, Chia-An Lee, Nen-Fu Huang, Hao-Hsuan Huang, Chin-Feng Lai
2022 International Review of Research in Open and Distance Learning  
Questionnaire response rates are also too low for MOOCs to be credibly evaluated. This study explored the use of deep learning techniques to assess MOOC student experiences.  ...  In conclusion, our system can accurately predict student satisfaction even when questionnaire response rates are low.  ...  Some studies of MOOC performance have analyzed the language used in discussion forums to make predictions.  ... 
doi:10.19173/irrodl.v22i4.5417 doaj:b0472e4d558a4c7e887032585a87e861 fatcat:r2254iviubcibch5kg45z3yqja

Academic Management and Analysis Method of MOOC

Fei Lang, Siwen Zhang, Shaobo Li, Guanglu Sun
2016 International Journal of Hybrid Information Technology  
To assess and predict students' learning trends and achievements, an academic analysis model was established based on learning and interaction data analysis.  ...  To supervise and trace students' learning process, an academic management system of MOOC was constructed.  ...  This system not only supports teacher to manage and participate in students learning activities in MOOC courses, but also can model, analyze and evaluate students' academic performance by applying the  ... 
doi:10.14257/ijhit.2016.9.11.20 fatcat:5yezn6xpvfg4pby3zh24qlwita

Malaysia MOOC: Improving Low Student Retention with Predictive Analytics

Nadirah Mohamad, Nor Bahiah Ahmad, Dayang Norhayati Abang Jawawi
2018 International Journal of Engineering & Technology  
The strategies include the usage of machine learning and data mining techniques in analysing students' online interactions to predict student retention rates.  ...  Therefore, this paper discusses the issue of student retention in MOOCs, explores possible intervention plans using data mining and its suitability with the current platforms used for MOOCs.  ...  Data Centre, Soft Computing Research Group SCRG for the inspiration in making this study a success.  ... 
doi:10.14419/ijet.v7i2.29.13662 fatcat:x7hwzoxeknhtbc7zc2daguozdu

Malaysia MOOC: Improving Low Student Retention with Predictive Analytics

Nadirah Mohamad, Nor Bahiah Ahmad, Dayang Norhayati Abang Jawawi
2018 International Journal of Engineering & Technology  
The strategies include the usage of machine learning and data mining techniques in analysing students' online interactions to predict student retention rates.  ...  Therefore, this paper discusses the issue of student retention in MOOCs, explores possible intervention plans using data mining and its suitability with the current platforms used for MOOCs.  ...  Data Centre, Soft Computing Research Group SCRG for the inspiration in making this study a success.  ... 
doi:10.14419/ijet.v7i2.29.15139 fatcat:m22peuzk2rftbdsyr7zq4isuua
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