scholarly journals Big Data Characterization of Learner Behaviour in a Highly Technical MOOC Engineering Course

2016 ◽  
Vol 3 (3) ◽  
pp. 170-192 ◽  
Author(s):  
Kerrie Anna Douglas ◽  
Peter Bermel ◽  
Md Monzurul Alam ◽  
Krishna Madhavan
Keyword(s):  
Big Data ◽  
Learning Analytics ◽  
Statistical Test ◽  
Future Research ◽  
User Behaviour ◽  
Significant Difference ◽  
Nonparametric Statistical Test ◽  
Nonparametric Statistical

MOOCs attract a large number of users with unknown diversity in terms of motivation, ability, and goals. To understand more about learners in a MOOC, the authors explored clusters of user clickstream patterns in a highly technical MOOC, Nanophotonic Modelling through the algorithm k-means++.  Five clusters of user behaviour emerged: Fully Engaged, Consistent Viewers, One-Week Engaged, Two-Week Engaged, and Sporadic users. Assessment behaviours and scores are then examined within each cluster, and found different between clusters. Nonparametric statistical test, Kruskal-Wallis yielded a significant difference between user behaviour in each cluster. To make accurate inferences about what occurs in a MOOC, a first step is to understand the patterns of user behaviour. The latent characteristics that contribute to user behaviour must be explored in future research. Keywords: MOOCs, Learning Analytics, Assessment

ANOCVA: A Nonparametric Statistical Test to Compare Clustering Structures

2018 ◽  
pp. 113-125
Author(s):  
Alexandre Galvão Patriota ◽  
Maciel Calebe Vidal ◽  
Davi Augusto Caetano de Jesus ◽  
André Fujita
Keyword(s):  
Statistical Test ◽  
Nonparametric Statistical Test ◽  
Nonparametric Statistical

scholarly journals Nonparametric flow cytometry analysis.

1979 ◽  
Vol 27 (1) ◽  
pp. 293-296 ◽  
Author(s):  
C B Bagwell ◽  
J L Hudson ◽  
G L Irvin
Keyword(s):  
Flow Cytometry ◽  
Statistical Method ◽  
Biological Systems ◽  
Bayes Theorem ◽  
Statistical Test ◽  
Flow Cytometry Analysis ◽  
Nonparametric Statistical Test ◽  
The Mean ◽  
Nonparametric Statistical

A nonparametric statistical test for the analysis of flow cytometry derived histograms is presented. The method involves smoothing and translocation of data, area normalization, channel by channel determination of the mean and S.D., and use of Bayes' theorem for unknown histogram classification. With this statistical method, different sets of histograms from numerous biological systems can be compared.

scholarly journals NONPARAMETRIC STATISTICAL TEST APPROACHES IN GENETICS DATA

2015 ◽  
Vol 5 (1) ◽  
pp. 77 ◽  
Author(s):  
RAKESH Kumar Saroj ◽  
K.H.H.V.S.S. Narsimha Murthy ◽  
Mukesh Kumar
Keyword(s):  
Statistical Test ◽  
Nonparametric Statistical Test ◽  
Nonparametric Statistical

scholarly journals Contemporary Privacy Theory Contributions to Learning Analytics

2014 ◽  
Vol 1 (1) ◽  
pp. 140-149 ◽  
Author(s):  
Jennifer Heath
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Information Systems ◽  
Student Voice ◽  
Learning Analytics ◽  
Higher Education Institutions ◽  
Future Research ◽  
Potential Contribution

With the continued adoption of learning analytics in higher education institutions, vast volumes of data are generated and “big data” related issues, including privacy, emerge. Privacy is an ill-defined concept and subject to various interpretations and perspectives, including those of philosophers, lawyers, and information systems specialists. This paper provides an overview of privacy and considers the potential contribution contemporary privacy theories can make to learning analytics. Conclusions reflect on the suitability of these theories towards the advancement of learning analytics and future research considers the importance of hearing the student voice in this space.

scholarly journals A Study on GBW-KNN Using Statistical Testing

2021 ◽  
Vol 12 (5) ◽  
pp. 271-277
Author(s):  
Seowon Song, Young Sang Kwak, Myung-ho Kim, Min Soo Kang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Real Time ◽  
Industrial Revolution ◽  
Statistical Test ◽  
Statistical Testing ◽  
Performance Difference ◽  
Significant Difference ◽  
The Difference ◽  
Unlabeled Instance

In the 4th industrial revolution, big data and artificial intelligence are becoming more and more important. This is because the value can be four by applying artificial intelligence techniques to data generated and accumulated in real-time. Various industries utilize them to provide a variety of services and products to customers and enhance their competitiveness. The KNN algorithm is one of such analysis methods, which predicts the class of an unlabeled instance by using the classes of nearby neighbors. It is used a lot because it is simpler and easier to understand than other methods. In this study, we proposed a GBW-KNN algorithm that finds KNN after assigning weights to each individual based on the KNN graph. In addition, a statistical test was conducted to see if there was a significant difference in the performance difference between the KNN and GBW-KNN methods. As a result of the experiment, it was confirmed that the performance of GBW-KNN was excellent overall, and the difference in performance between the two methods was significant.

Big data academic and learning analytics

2018 ◽  
Vol 32 (6) ◽  
pp. 1099-1117 ◽  
Author(s):  
Sushil S. Chaurasia ◽  
Devendra Kodwani ◽  
Hitendra Lachhwani ◽  
Manisha Avadhut Ketkar
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Learning Analytics ◽  
Predictive Analytics ◽  
Business Value ◽  
Sufficient Evidence ◽  
Future Research ◽  
Content Type ◽  
Potential Benefits ◽  
Value Model

Purpose Although big data analytics (BDA) have great benefits for higher education institutions (HEIs), due to lack of sufficient evidence on how BDA investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The purpose of this paper is to propose a big data academic and learning analytics enabled business value model to explain BDA potential benefits and business value which can be obtained by developing such analytics capabilities in HEIs. Design/methodology/approach The study examined 47 case descriptions from 26 HEIs to investigate the causal association between the BDA current and potential benefits and business value creation path for big data academic and learning analytics success in HEIs. Findings The pressure of compliance with all legal and regulatory requirements and competition had pushed HEIs hard to adopt BDA tools. However, the study found out that application of risk and security and predictive analytics to higher education fields is still in its infancy. Using this theoretical model, the results provide new insights to higher education administrators on ways to create BDA capabilities for HEIs transformation and suggest an empirical foundation that can lead to more thorough analysis of BDA implementation. Originality/value A distinctive theoretical contribution of this study is its conceptualization of understanding business value from BDA in the typical setting of higher education. The study provides HEIs with an all-inclusive understanding of BDA and gives insights on how it helps to transform HEIs. The new perspectives associated with the big data academic and learning analytics enabled business value model will contribute to future research in this area.

Agenda for Future Research and Concluding Comments

2019 ◽  
pp. 810-818
Author(s):  
Sandra A. Brown ◽  
Robert A. Zucker
Keyword(s):  
Substance Abuse ◽  
Big Data ◽  
Drugs Of Abuse ◽  
Causal Structure ◽  
Frame Of Reference ◽  
Future Research ◽  
Clinical Symptomatology ◽  
Cross Domain ◽  
Collection Programs

This concluding chapter highlights issues we see as especially important next-step agendas for the field. The issues we have highlighted concern (a) the implications that a developmental frame of reference provides in characterizing and parsing the etiology and course of addictive behavior; (b) the relevance of event-level predictors occurring in microtime and the extent to which they will supercede the more summative indicators that currently dominate the substance abuse field; (c) the increasing awareness, and characterization of drug-specific influences, and the degree to which these influences are useful in evaluating the vulnerability potential of drugs of abuse; (d) the differences in characterization of clinical symptomatology and course that have the potential to occur when evaluation of psychopathology and the details of intervention methods are unpacked with a specifically developmental lens; (e) the insights that new big data collection programs will create in understanding the cross-domain causal structure of substance abuse.

scholarly journals A Pilot Study for the Statistical Treatment of Nutritional Parameters in the Quality Control of Canned Fishing Goods

1999 ◽  
Vol 82 (5) ◽  
pp. 1247-1256
Author(s):  
Marta Bartos-Lorenzo ◽  
Alberto Calviño-López ◽  
Fernando Dalama-Iglesias ◽  
Maria de la Torre-Lamosa ◽  
Guadalupe Martín-Pardo ◽  
...  
Keyword(s):  
Pilot Study ◽  
Statistical Treatment ◽  
Statistical Test ◽  
Nutritional Composition ◽  
Data Sets ◽  
Total Proteins ◽  
European Directives ◽  
Nonparametric Statistical Test ◽  
Chemometric Techniques ◽  
Nonparametric Statistical

Abstract This pilot study was derived as a consequence of European Directives 496/90 and 493/91 in which a regulation on the labeling of canned fishing goods was established. The study was intended primarily to assess whether different Spanish canned fishing goods might be differentiated by their basic nutritional composition (i.e., ash, chlorine as NaCl, fat, humidity, total proteins, and dry residue) and, second, to study each particular type of good. Accordingly, a univariate nonparametric statistical test and 2 multivariate chemometric techniques (factor and cluster analyses) were used. The pilot study revealed that (1) the basic nutritional variables did not allow a clear distinction among canned goods when different commodities were considered, but they seemed useful for obtaining information for only one type of good; and (2) the variables that gave the most useful information to visualize the appearance of groups in the data sets were humidity, dry residue, fat, and proteins, although their particular usefulness was found to be different when different species were considered.

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