scholarly journals Automated analysis of cognitive presence in MOOC discussions

2020 ◽  
Vol 2 (1) ◽  
pp. 46-47
Author(s):  
Yuanyuan Hu ◽  
Claire Donald ◽  
Nasser Giacaman
Keyword(s):  
Machine Learning ◽  
Online Discussion ◽  
Learning Experience ◽  
Computer Conferencing ◽  
Online Discussions ◽  
Cognitive Presence ◽  
Machine Learning Techniques ◽  
Rater Reliability ◽  
Learning Techniques ◽  
Research Bias

The Community of Inquiry (CoI) framework [1] has been broadly used to analyse learning experience in online discussion forums for two decades. Cognitive presence, which is a primary dimension of the CoI framework, manifests the reflection of (re)constructing knowledge and problem-solving processes in the learning experience [2]. Researchers doing text analysis using machine learning techniques are making promising contributions to analysing phases of cognitive presence automatically [3]–[5] in online discussions. However, most studies of automated cognitive analysis focus on improving the accuracy and reliability of the classifiers. They ignored that another purpose of applying machine learning techniques in educational research should be to pinpoint research bias that scholars neither intended to nor can have found without computer support. This session will present the example of ‘research bias’ discovered from both manual and automated classification of cognitive phases, provoking scholars to rethink and improve the conflicting part in the taxonomies of cognitive presence under MOOC context.   The manual-classification rubric that used to label discussion messages of a target MOOC combines Garrison, Anderson and Archer’s [2] scheme with Park’s [6] revised version. The rubric describes four phases of cognitive presence (i.e. triggering event, exploration, integration and resolution), and indicators of each phase in online discussions. We reported the average inter-rater reliability between two human raters achieved 95.4% agreement (N = 1002) with a Cohen’s weighted kappa of 0.96. Interestingly, we found the average inter-rater reliability decreased to 80.1% after increasing the size of data samples (N = 1918) and the number of human raters to three. After training the automated classifiers to predict phases of cognitive presence, the confusion matrix implies that most of the disagreements between computer raters occurred between adjacent phases of cognitive presence. The disagreements between human raters also have the same problems. We assume the additional categories may exist between cognitive phases in such MOOC discussion messages. These details will be discussed during the presentation.   References [1]       D. Garrison, T. Anderson, and W. Archer, “Critical Inquiry in a Text-Based Environment: Computer Conferencing in Higher Education,” Internet High. Educ., vol. 2, no. 2, pp. 87–105, 1999. [2]       D. Garrison, T. Anderson, and W. Archer, “Critical thinking, cognitive presence, and computer conferencing in distance education,” Am. J. Distance Educ., vol. 15, no. 1, pp. 7–23, 2001. [3]       V. Kovanović, S. Joksimović, D. Gašević, and M. Hatala, “Automated cognitive presence detection in online discussion transcripts,” in Automated cognitive presence detection in online discussion transcripts’ CEUR Workshop Proceedings (vol. 1137), 2014. [4]       V. Kovanović et al., “Towards automated content analysis of discussion transcripts,” Proc. Sixth Int. Conf. Learn. Anal. Knowl. - LAK ’16, pp. 15–24, 2016. [5]       E. Farrow, J. Moore, and D. Gasevic, “Analysing discussion forum data: a replication study avoiding data contamination,” 9th Int. Learn. Anal. Knowl. Conf., no. March, 2019. [6]       C. Park, “Replicating the Use of a Cognitive Presence Measurement Tool,” J. Interact. Online Learn., vol. 8, no. 2, pp. 140–155, 2009.

scholarly journals Doc2Vec &Naïve Bayes: Learners’ Cognitive Presence Assessment through Asynchronous Online Discussion TQ Transcripts

2019 ◽  
Vol 14 (08) ◽  
pp. 70 ◽  
Author(s):  
Hind Hayati ◽  
Abdessamad Chanaa ◽  
Mohammed Khalidi Idrissi ◽  
Samir Bennani
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Online Discussions ◽  
Cognitive Presence ◽  
Machine Learning Techniques ◽  
Face To Face ◽  
Learning Techniques ◽  
Bayes Algorithm ◽  
Context Features

Due to the lack of face to face interaction in online learning environment, this article aims essentially to give tutors the opportunity to understand and analyze learners’ cognitive behavior. In this perspective, we propose an automatic system to assess learners’ cognitive presence regarding their social interactions within synchronous online discussions. Combining Natural Language Preprocessing, Doc2Vec document embedding method and machine learning techniques; we first make some transformations and preprocessing to the given transcripts, then we apply Doc2Vec method to represent each message as a vector that will be concatenated with LIWC and context features. The vectors are input data of Naïve Bayes algorithm; a machine learning method; that aims to classify transcripts according to cognitive presence categories.

scholarly journals Python the game changer in the field of Machine Learning, Data Science and IoT: A Review

2021 ◽  
Vol 9 (8) ◽  
pp. 1827-1837
Author(s):  
Prithwish Parial
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Learning Experience ◽  
Object Oriented Programming ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
High Level ◽  
Learning Data ◽  
Game Changer ◽  
Oriented Programming Language

Abstract: Python is the finest, easily adoptable object-oriented programming language developed by Guido van Rossum, and first released on February 20, 1991 It is a powerful high-level language in the recent software world. In this paper, our discussion will be an introduction to the various Python tools applicable for Machine learning techniques, Data Science and IoT. Then describe the packages that are in demand of Data science and Machine learning communities, for example- Pandas, SciPy, TensorFlow, Theano, Matplotlib, etc. After that, we will move to show the significance of python for building IoT applications. We will share different codes throughout an example. To assistance, the learning experience, execute the following examples contained in this paper interactively using the Jupiter notebooks. Keywords: Machine learning, Real world programming, Data Science, IOT, Tools, Different packages, Languages- Python.

scholarly journals ESTIMATING UNDERDIAGNOSIS OF COVID-19 WITH NOWCASTING AND MACHINE LEARNING – EXPERIENCE FROM BRAZIL

2020 ◽  
Author(s):  
Leandro Pereira Garcia ◽  
André Vinícius Gonçalves ◽  
Matheus Pacheco Andrade ◽  
Lucas Alexandre Pedebôs ◽  
Ana Cristina Vidor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Experience ◽  
Data Extraction ◽  
Entire Period ◽  
Machine Learning Techniques ◽  
Ecological Design ◽  
Learning Techniques ◽  
The Difference ◽  
South Of Brazil

ABSTRACTBackgroundBrazil has the second largest COVID-19 number of cases, worldly. Even so, underdiagnosis in the country is massive. Nowcasting techniques have helped to overcome the underdiagnosis. Recent advances in machine learning techniques offer opportunities to refine the nowcasting. This study aimed to analyze the underdiagnosis of COVID-19, through nowcasting with machine learning, in a South of Brazil capital.MethodsThe study has an observational ecological design. It used data from 3916 notified cases of COVID-19, from April 14th to June 02nd, 2020, in Florianópolis, Santa Catarina, Brazil. We used machine-learning algorithm to classify cases which had no diagnosis yet, producing the nowcast. To analyze the underdiagnosis, we compared the difference between the data without nowcasting and the median of the nowcasted projections for the entire period and for the six days from the date of onset of symptoms to diagnosis at the moment of data extraction.ResultsThe number of new cases throughout the entire period, without nowcasting, was 389. With nowcasting, it was 694 (UI95 496-897,025). At the six days period, the number without nowcasting was 19 and 104 (95% UI 60-142) with. The underdiagnosis was 37.29% in the entire period and 81.73% at the six days period.ConclusionsThe underdiagnosis was more critical in six days from the date of onset of symptoms to diagnosis before the data collection than in the entire period. The use of nowcasting with machine learning techniques can help to estimate the number of new cases of the disease.

Text Classification and Topic Modeling for Online Discussion Forums

2020 ◽  
pp. 151-186
Author(s):  
Xin Zhao ◽  
Zhe Jiang ◽  
Jeff Gray
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Online Discussion ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Discussion Forums ◽  
Online Forums ◽  
Unsupervised Machine Learning ◽  
Online Discussion Forums ◽  
Learning Techniques

Online discussion forums play an important role in building and sharing domain knowledge. An extensive amount of information can be found in online forums, covering every aspect of life and professional discourse. This chapter introduces the application of supervised and unsupervised machine learning techniques to analyze forum questions. This chapter starts with supervised machine learning techniques to classify forum posts into pre-defined topic categories. As a supporting technique, web scraping is also discussed to gather data from an online forum. After this, this chapter introduces unsupervised learning techniques to identify latent topics in documents. The combination of supervised and unsupervised machine learning approaches offers us deeper insights of the data obtained from online forums. This chapter demonstrates these techniques through a case study on a very large online discussion forum called LabVIEW from the systems modeling community. In the end, the authors list future trends in applying machine learning to understand the expertise captured in online expert communities.

scholarly journals On predicting academic performance with process mining in learning analytics

2017 ◽  
Vol 10 (2) ◽  
pp. 160-176 ◽  
Author(s):  
Rahila Umer ◽  
Teo Susnjak ◽  
Anuradha Mathrani ◽  
Suriadi Suriadi
Keyword(s):  
Machine Learning ◽  
Process Mining ◽  
Learning Experience ◽  
Dropout Rate ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Content Type ◽  
Machine Learning Classification ◽  
Learning Techniques ◽  
The Impact

Purpose The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques. Design/methodology/approach Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of students’ performance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used. Findings The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way. Practical implications Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate. Social implications Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course. Originality/value This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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